text:
\section{One-dimensional Verification Tests}
\label{1d_testing}

The SPARSE model improves upon the First-Order model in two ways: Firstly, SPARSE
accounts for the subscale particle fluctuations through stress terms. Secondly,
SPARSE averages the carrier-phase velocity at the particle locations correctly.
In this section, we verify the impact of these two model improvements with
one-dimensional test cases.

In the tests,  the carrier field is analytically
specified to prevent errors in the carrier-phase solver from polluting  the model error
investigation.
Ten thousand particles initialized in a cloud are individually traced  according to
(\ref{gov_eqns_part_model}). We refer to this solution as the ``exact'' solution.
SPARSE and the First-Order model trace the averaged location and velocity of this cloud
with a single computational parcel.
The stress terms and carrier-phase velocity at the particle location are \textit{a priori} obtained
from the ``exact'' solution.

\subsection{Effect of the Reynolds Stress Equivalent Terms}
\label{uniform_velocity}

To isolate the effect of the stress closure from the effect of the carrier-phase velocity closure,
we first consider a constant carrier-phase velocity,
\begin{align}
u(x,t) = 10,
\end{align}
in which case $\overline{u(x_p)} = u(\bar{x}_p)$, i.e. the carrier-phase
velocity modeled by the First-Order model and the SPARSE model are the same in this case. The
difference between the two models can hence only be caused by the stress terms in SPARSE.

For the exact reference case ten thousand  particles are initialized at the following locations and velocities,
\begin{align}
x_p &= \sigma_x\cdot d_{cloud},\nonumber\\
v_p &= 5.0 + \sigma_v\cdot\gamma,
\label{eq:ic_1d}
\end{align}
where the cloud diameter, $d_{cloud}$=1, and $\sigma_x, \sigma_y$ are uniformly distributed random numbers
between -1 and 1.  The maximum amplitude of the initial velocity perturbations is $\gamma$=10.
The modeled parcel's location and velocity is initialized with the mean of the exact locations and velocities, respectively.
Time integration is performed using a first order explicit time stepping routine with a time step of $\Delta t = 10^{-5}$, which
ensures that numerical time integration errors are smaller than the model errors.

The particle correction factor, $f(a)$, is taken to  be a
linear function of $a$,
\begin{align}
f(a) = \frac{a}{10},
\label{eq:linearf}
\end{align}
similiar to low Reynolds number correction factors \cite{schiller_naumann}.
Since derivatives of order higher than one are zero,
the second-order expansion in (\ref{taylor_expansion})  and hence the SPARSE model is exact.
With exact \textit{a priori} closure
the SPARSE model is the same as the exact model as confirmed in Figure \ref{case_1_magnitudes}.
Figures \ref{case_1_errors_vp} and \ref{case_1_errors_xp} show
the errors in  the location and velocity in time determined with
\begin{align}
\epsilon_{x_p} = \frac{|\overline{x_{p,exact}} - x_{p,model}|}{\overline{x_{p,exact}}},\nonumber\\
\epsilon_{v_p} = \frac{|\overline{v_{p,exact}} - v_{p,model}|}{\overline{v_{p,exact}}}.
\label{SPARSE_errors}
\end{align}
The figures highlight the importance of inclusion of the second order stress term in SPARSE.
The truncation of the expansion at $O(a')$ in the First-Order model leads to errors in the averaged particle velocity
on the order of tens of percentage (a maximum of 17\%) at time $t=1$ whereas the SPARSE model is exact.
The First-Order model does not account for the subscale kinetic energy in the particle phase, causing the modeled particle to lag behind the average particle position of the exact cloud.

\begin{figure}[h]
	\centering
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Particle_Velocity_Test1}
		\caption{}
		\label{case_1_magnitudes_vp}
	\end{subfigure}
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Particle_Position_Test1}
		\caption{}
		\label{case_1_magnitudes_xp}
	\end{subfigure}
	\caption{Particle (a) velocities and (b) locations using the mean of the exact particle locations as well as the First-Order and SPARSE models with a uniform background fluid velocity and a linear correction factor.}
	\label{case_1_magnitudes}
\end{figure}

\begin{figure}[h]
	\centering
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Velocity_Error_Test1}
		\caption{}
		\label{case_1_errors_vp}
	\end{subfigure}
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Position_Error_Test1}
		\caption{}
		\label{case_1_errors_xp}
	\end{subfigure}
	\caption{Modeling error of the mean particle (a) velocity and (b) location using the First-Order and SPARSE models with a uniform background fluid velocity and a linear correction factor.}
	\label{case_1_errors}
\end{figure}

For the initial conditions in (\ref{eq:ic_1d}),
the interphase velocity difference can be negative and hence the correction factor in (\ref{eq:linearf})
can also have negative values.
For a negative correction factor particles are nonphysically propelled.

Using the following correction factor of
\begin{align}
f(a) = \frac{|a|}{10},
\label{eq:fa_1dabs}
\end{align}
based on the absolute velocity, $|a|$, is more physical since $f(a)$ is always positive.
Note that $f(a)$ in (\ref{eq:fa_1dabs}) is non-linear
because of discontinuous derivatives at $a=0$.

The non-linearity leads to errors in the modeling of the average particle velocity with the SPARSE model (Figures \ref{case_2_magnitudes_xp} and \ref{case_2_errors}).
Because the SPARSE model accounts for the perturbations in the particle phase,
it  is still significantly  more accurate than the First-Order model.

\begin{figure}[h]
	\centering
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Particle_Velocity_Case2}
		\caption{}
		\label{case_2_magnitudes_vp}
	\end{subfigure}
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Particle_Position_Case2}
		\caption{}
		\label{case_2_magnitudes_xp}
	\end{subfigure}

	\caption{Particle (a) velocities and (b) locations using the mean of the exact particle locations as well as the First-Order and SPARSE models with a uniform background fluid velocity and an absolute value correction factor.}
	\label{case_2_magnitudes}
\end{figure}

\begin{figure}[h]
	\centering
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Velocity_Error_Case2}
		\caption{}
		\label{case_2_errors_vp}
	\end{subfigure}
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Position_Error_Case2}
		\caption{}
		\label{case_2_errors_xp}
	\end{subfigure}

	\caption{Modeling error of the mean particle (a) velocity and (b) location using the First-Order and SPARSE models with a uniform background fluid velocity and an absolute value correction factor.}
	\label{case_2_errors}
\end{figure}

\subsection{Effect of Modeling the Average Cloud Velocity}
\label{uniform_correction_factor}

To test the erroneous assumption that $\bar{u}({x}_{p})$ = $\bar{u}(\bar{x}_{p})$
in the First-Order model,  a constant correction factor comparable to the Stokes' drag \cite{stokes} is used,
\begin{align}
f(a) = \frac{24}{St}.
\end{align}
The combination of the constant correction factor, for which the Taylor expansions terms are zero, and a spatially varying carrier-phase velocity,
\begin{align}
u(x) = \cos(\pi x) + x,
\end{align}
enables an investigation into the errors caused by the incorrect sampling of the carrier-phase velocity at a single point in the
First-Order model.

\begin{figure}[ht]
	\centering
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Particle_Velocity_Test3}
		\caption{}
		\label{case_3_magnitudes_vp}
	\end{subfigure}
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Particle_Position_Test3}
		\caption{}
		\label{case_3_magnitudes_xp}
	\end{subfigure}
	\caption{Particle (a) velocities and (b) locations using the mean of the exact particle locations as well as the First-Order and SPARSE models with a spatially varying background fluid velocity.}
	\label{case_3_magnitudes}
\end{figure}

\begin{figure}[ht]
	\centering
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Velocity_Error_Test3}
		\caption{}
		\label{case_3_errors_vp}
	\end{subfigure}
	\begin{subfigure}[b]{0.48\textwidth}
		\includegraphics[width=\textwidth]{Position_Error_Test3}
		\caption{}
		\label{case_3_errors_xp}
	\end{subfigure}
	\caption{Modeling error of the mean particle (a) velocity and (b) location using the First-Order and SPARSE models with a spatially varying background fluid velocity.}
	\label{case_3_errors}
\end{figure}

Figures \ref{case_3_magnitudes_vp} and \ref{case_3_errors_vp} shows that First-Order modeling of the fluid velocity can
lead to errors as high as 17\% whereas the averaged velocity and location of the cloud is
\textit{a priori} closed in an exact manner with the SPARSE model.
Because the perturbations in the fluid velocity are periodic, the errors in the modeled particle velocity are oscillatory and only cause a maximum 1\% error in the particle location in Figures
\ref{case_3_magnitudes_xp} and \ref{case_3_errors_xp}.

\section*{Acknowledgements}
We gratefully acknowledge the financial support by the Air Force Office of Scientific Research under grant number FA9550-16-1-0008.

\bibliographystyle{elsarticle-num}

\section{Validation SPARSE:  Decaying Isotropic Turbulence}
\label{3d_isotropic}

To more rigorously test SPARSE a group of particles traced in a
decaying isotropic turbulence according to the ``exact'' model is compared with the First-Order
model and \textit{a priori} closed SPARSE.

The isotropic turbulence simulation is performed in a cube with periodic boundary conditions on all sides.
Following \cite{blaisdell_isotropic,JKM05}
an  initial  correlated flow field is determined based on specified energy spectra.
Computations are performed
with a compressible Navier-Stokes solver based on a 4$^{th}$ order central difference method for the fluxes
(as described \cite{JD09}).
To verify the Navier-Stokes solver, we compare computations with this code
with  128 cubed number of grid points
with the case referred to as "iga96" in \cite{blaisdell_isotropic} computed with
a $N=96$ Fourier spectral method.
The average initial fluctuating Mach number,
\begin{align}
M_0 = \frac{\sqrt{\overline{u_i'u_i'}}}{c(\overline{T_0})},
\end{align}
where $u_i'$ is the fluctuating fluid velocity is set to $M_0 = 0.05$.
The reference fluid Reynolds number is set to $Re_f = 2357$.
The decay of turbulent kinetic energy, TKE = $\frac{1}{2}\overline{ {{u_i}^\prime}^2}$, in Figure \ref{initial_TKE}
compares well with the results from Blaisdell et. al. \cite{blaisdell_isotropic}.
The oscillatory trend in TKE is well-known and documented and is caused by pressure-dilation \cite{kida}.

\begin{figure}[h]
	\centering
	\includegraphics[width=\textwidth]{TKE_Decay}
	\caption{Comparison of the turbulent kinetic energy (TKE) versus time in isotropic turbulence using a high-order EL code from Jacobs et. al. \cite{JD09} and a Fourier spectral method performed by Blaisdell et. al. \cite{blaisdell_isotropic}.}
	\label{initial_TKE}
\end{figure}

To test the particle models an exact case that initializes $30^3=27,000$ particles uniformly over
3x3x3 grid cells,  serves as a reference against which
the First-Order model and SPARSE are tested.
As compared to iga96 the initial velocity field is scaled by  a factor five. This ensures that
the carrier phase disperses the particle cloud more significantly than the iga96 velocity field
and hence the effects of the cloud modeling are more visible.
The initial particle velocity is  set according to a uniform random
number around zero with an absolute maximum of 2.5.
We use the following drag correction function
\be
C_D = \left(0.38+\frac{24}{Re_p}+\frac{4}{Re_p{\nicefrac{1}{2}}}\right)\left(1+e^{-\frac{0.43}{M_p^{4.67}}}\right),
\label{eq:f1_2}
\ee
that corrects the Stokes drag for high relative particle Reynolds number, $Re_{p}=|v_{f}-v_{p}|d_{p}/\nu$, and
Mach number, $M_{p}=|v_{f}-v_{p}|/\sqrt{T_{f}}$, according to Boiko et. al. \cite{BKKPPF97, JD09, Davis1, Davis2, Davis_carbuncle}.
The particles' response time is set to $\tau_p = 0.01$.
Matlab was used to compute the partial derivatives of the drag coefficient equation, which are needed for the SPARSE model in
(\ref{eq:SPARSE}).

The time lapse in Figure \ref{3D_Particles}
shows that the  cloud modeled with SPARSE (red sphere)
closely follows the average location of the exact cloud (green sphere), while
the First-Order model (red sphere) deviates significantly from the exact cloud.
The temporal dispersion of the exact cloud is visualized by the large  diameter of the
exact model (green) sphere as compared to initial diameter of the (red and blue) cloud approaches.
Dispersion, as measured by for example the rate of change of the cloud radius, is not modeled in the  SPARSE  and First-Order model and
it is the subject of ongoing investigation.
\begin{figure}[ht]
	\centering
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{3D_Image_Time_0}
		\caption{t = 0}

	\end{subfigure}
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{3D_Image_Time_16}
		\caption{t = 2}

	\end{subfigure}
		\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{3D_Image_Time_32}
		\caption{t = 4}

	\end{subfigure}
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{3D_Image_Time_48}
		\caption{t = 6}

	\end{subfigure}
		\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{3D_Image_Time_64}
		\caption{t = 8}

	\end{subfigure}
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{3D_Image_Time_80}
		\caption{t = 10}

	\end{subfigure}
	\caption{Helicity projected onto the cubical computational domain faces
and particle cloud locations for the 3D isotropic decaying turbulence case at
times t = (a) 0.0, (b) 1.6, (c) 3.2, (d) 4.8, (e) 6.4 and (f) 8.0. The First-Order
 modeled particle is visualized by  a blue sphere, the SPARSE modeled by a
red (sphere) and the exact model by a  green sphere. The root mean square
dispersion of the the exact location is visualized by the diameter of the
green sphere. The  diameter of the red and blue sphere visualize the initial
rms dispersion.} \label{3D_Particles}
\end{figure}

A comparison of the average particle cloud distance from the origin
\begin{align}
|{\bf x_p}| = \sqrt{x_{p}^2+y_{p}^2+z_{p}^2},\nonumber
\end{align}
and  averaged velocity,
\begin{align}
|{\bf v_p}| = \sqrt{u_{p}^2+v_{p}^2+w_{p}^2},
\end{align}
in Figure \ref{avg_stats_3d} confirms the improved modeling by SPARSE as compared to the First-Order model.
While both the exact cloud and the computational clouds are initially entrained
 in the same turbulent eddy, the average velocity over the cloud used in SPARSE is near zero, while the local velocity used
in the First-Order model is large. The latter cloud is hence displaced more.
With increasing time, the exact cloud disperses  and the fluid velocity is sampled over a larger area.
Because the turbulence is isotropic and decaying the averaged  fluid velocity goes to zero over a larger
area and increasing time, respectively. The local velocity decays in time also, but deviates from the near zero, averaged
velocity leading to the deviation of the First-Order model in time.

Errors in the magnitude of the distance from the origin and velocity plotted
in Figure \ref{avg_errors_3d} shows that SPARSE modeling errors are non-zero, because the drag correction in
(\ref{eq:f1_2})is
non-linear and hence the Taylor truncation error in (\ref{taylor_expansion}) used for SPARSE is non-zero.
The error however is small, within 0.5\% of the exact model.
\begin{figure}[ht]
	\centering
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{Magnitude_Velocity}
		\caption{}

	\end{subfigure}
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{Magnitude_Location}
		\caption{}

	\end{subfigure}
	\caption{The magnitude of the average (a) velocity and (b) particle distance from the origin is shown when computed using the
First-Order model, SPARSE model and average over the physical particles.}
	\label{avg_stats_3d}
\end{figure}
\begin{figure}[ht]
	\centering
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{Magnitude_Velocity_Error}
		\caption{}

	\end{subfigure}
	\begin{subfigure}[b]{0.45\textwidth}
		\includegraphics[width=\textwidth]{Magnitude_Location_Error}
		\caption{}

	\end{subfigure}
ma	\caption{The error in computing the magnitude of the average (a) velocity and (b) particle distance from the origin is shown when computed using the First-Order model, SPARSE model and average over the physical particles.}
	\label{avg_errors_3d}
\end{figure}

The impact of  two modeling components of the SPARSE model, including the averaging of the fluid velocity of the cloud  and the stress modeling are compared
in Figure \ref{RHS_comp}. Plotted are the  two terms on the right-hand side of
(\ref{eq:SPARSE}). The first term that is affected by the cloud velocity averaging
is significantly larger than the second term that is proportional to the sub-cloud stresses. The second term is on the order of $10^{-5}$. In general, the effect of the  sub-cloud stresses is not necesarilly smaller than the effect of velocity averaging as was seen in the 1D tests.

\begin{figure}[ht]
	\centering
		\includegraphics[width=0.45\textwidth]{Combined_Forcing_Terms}
		\caption{}
		\label{}
	\caption{Comparison of the two terms on the right hand side of (\ref{eq:SPARSE},
$f(\bar{\bf a})\bar{\bf a}$ and $\frac{df(\bar{\bf a})}{d {\bf a}}\overline{{\bf a}^\prime {\bf a}^\prime}$. The first term (blue line) is dependend on the SPARSE fluid velocity modeling and the second term (red line) is proportional to the interphase stresses.
}
	\label{RHS_comp}
\end{figure}

\clearpage

\section{Conclusions and Future Work}
\label{conclusion}

SPARSE provides a model that traces a group of point particles through a single computational point parcel.
The model improves upon Cloud-In-Cell methods by, firstly, accounting for fluid and particle  stress
(i.e. the Reynolds stress equivalent) terms
and, secondly, averaging of the fluid velocity at the Lagrangian computational particle.
One-dimensional and three-dimensional \textit{a priori} tests show that both improvements yield
an excellent comparison of the  averaged trace of a cloud of particles and the computational parcel.

This paper is only  a first step in the development of a closed SPARSE model. Current investigation
focuses on closure based on a multi-scale approach as reported in \cite{Sen15}.

\section{Introduction}
\label{introduction}

The Eulerian-Lagrangian (EL) model, introduced by Crowe at al. \cite{CSS77, crowe_book}, is one of the major approaches used to for computing the interaction of a large number of particles with a turbulent flow.
In the EL model each particle is traced in its Lagrangian frame, i.e. the frame moving with the particle.
Because particles are treated as volumeless mathematical points in the EL ``point-particle'' approach, the tracing of many particles becomes much more computationally efficient.
With a point-particle assumption, the simulation of large number of particles in process-scale environments becomes feasible.

If the number of particles in a computation is prohibitively large, groups of physical particles are amalgamated into a single computational particle to further economize the computational cost.
This type of method is also known as Cloud-In-Cell (CIC) \cite{birdsall:JCP}.
In CIC methods, groups of particles are modeled as points and their motion is forced by the drag exerted on them by the fluid \cite{JD09, TD09, Davis1,
Davis2}.
The CIC method as conventionally implemented does not account for sub-particle cloud dynamics
resulting from turbulent fluctuations or particle-particle interactions.

For the modeling of subgrid and/or sub-particle-cloud scales of turbulent carrier-phase flows, Large-Eddy Simulation
is commonly employed.
In LES, filtered Navier-Stokes equations resolve large turbulent structures, while dissipation of energy from the subgrid scales (SGS) is modeled.
Commonly used SGS models include the classic Smagorinsky \cite{smagorinsky1} and Dynamic Smagorinsky \cite{dynamicsmagorinsky} models.

Similar to the modeling of SGS with eddy viscosity models in the carrier phase, SGS models are required for an accurate particle tracer \cite{Launder, Menter:1994, Sarkar90, Sutanu:1991, Sutanu:JFM}.
To this end, models
have been formulated with either a deterministic approach or stochastic modeling of subgrid scales.
In the deterministic approach, the instantaneous velocities are reconstructed for use in particle equations through defiltering \cite{Shotorban:2005, kuerten2005can, Shotorban:2005a, kuerten2006subgrid, shotorban2007improvement}.
Shotorban and co-workers \cite{Shotorban:2005, Shotorban:2005a, shotorban2007improvement} employed Approximate Deconvolution (AD) \cite{stolz2001approximate} for defiltering.
Deconvolution is a mathematical method to approximately reconstruct the instantaneous velocity through consecutively applying a filtering operation on the filtered velocities.
The consecutive application of the filtering operator is a result of a series expansion for the deconvolution.
Filtering, itself, is a convolution product of the instantaneous velocity and the filter kernel.
Kuerten and Verman \cite{kuerten2005can} employed a defiltering technique in which the filtering inversion is carried out in the Fourier space for the streamwise and spanwise directions while the inversion is approximated by a Taylor series for the cross-stream direction.
Although defiltering is efficient to implement, it can only be carried out for the represented modes.

A Langevin type stochastic differential equation can be used to compute the evolution of particles when the carrier phase is simulated by LES \cite{shotorban2004particle, Shotorban:2005, shotorban2006particle}.
A similar model was previously proposed for particles by Pozorski and Minier \cite{pozorski1998lagrangian, pozorski1999probability} and Minier and Peirano \cite{minier2001pdf} when the carrier phase is simulated by RANS.
Shotorban and Mashayek \cite{shotorban2006particle} extended  the application of the Langevin equation from a simple RANS framework \cite{pozorski1998lagrangian, pozorski1999probability, minier2001pdf} to a higher-resolution LES model by using the stochastic differential equation employed to solve LES equations through a Filtered Density Function approach \cite{gicquel2002velocity}.
A modified version of this model was proposed by Berrouk et al. \cite{berrouk2007stochastic} to account for crossing-trajectory effects.

While the modeling the effect of subgrid stresses on the dispersion of particles has been studied, the effect of sub-scale particle fluctuations under the computational particle assumptions of CIC has received much less attention.
Several articles \cite{chen1985, mostafa1988, tu1994} have studied the inclusion of small-scale particle-fluid energy transfer in their turbulence models.
However, these studies focus on the effect that the particles have on the fluid
rather than the influence of individual modeled particles on the averaged
computational particle dynamics.

In recent work, we have undertaken a multi-scaling modeling
effort \cite{Sen15} in which macro-scale models such as the LES and CIC models are closed using results
from full resolution meso-scale simulations that solve for the turbulence and flow over particles
with moving boundaries. The general framework enables direct closure of averaging
terms in a wide parameter space that otherwise would take a great range of challenging and meticolous
experimentation to obtain emperical formulas. Within this framework, we have more freedom to develop
macro-models, for example, for particle cloud dynamics.

This paper presents a model that accounts for sub-scale interphase velocity perturbations on
computational particle dispersion patterns in a CIC framework. The model is general and is a good
fit for a multi-scale framework.
The drag forcing term in the Lagrangian governing equation for the particle momentum employs a correction factor for high Reynolds and Mach numbers.
The particle drag correction factor is expanded using a Taylor expansion, which enables a simple Reynolds averaging of the governing equations to yield a second order perturbation term.
These higher order terms are deemed "Reynolds Stress Equivalent" terms, which capture the effects of meso-scale interphase velocity perturbations.
We therefore refer to the model as Subgrid Particle Averaged Reynolds Stress Equivalent, which leads to the appropriate acorynm SPARSE for this model
which requires a small number of computational particles to simulate
a larger number of real particles.
The objective of this paper is to derive the model and demonstrate that SPARSE improves upon the tracing of the average location of a cloud of particles in CIC.
One-dimensional verification tests as well as a three-dimensional homogeneous turbulence flow case
demonstrate the efficacy of the model.

The derivation of the SPARSE model is provided in the next section.
In Section \ref{1d_testing}, we present \emph{a priori} testing of the SPARSE model on a one-dimensional analytic
 carrier-phase field followed by testing in a three-dimensional periodic box with decaying isotropic turbulence in Section \ref{3d_isotropic}.
Conclusions and recommendations are reserved for the final section.

\section{Subgrid Particle Averaged Reynolds Stress Equivalent Model}

\section{Derivation of the Model}
\label{derivation}
The Lagrangian equations that govern the particle motion under the point particle assumption in general form are,
\begin{eqnarray}
\frac{d{\bf {x_{p}}}}{dt} &= & {\bf {v_{p}}},\\
\label{eq:xp}
\frac{d{\bf {v_{p}}}}{dt} &= & \nicefrac{C_{D_s}}{\tau_p}({\bf u} - {\bf {v_{p}}}),
\label{eq:velp}
\end{eqnarray}
where ${\bf {v_{p}}}$ is the particle velocity vector,  ${\bf u}$ is the carrier-phase velocity vector.
and $\tau_p$ is the particle time constant \cite{crowe_book}.  The drag correction factor, $C_{D_s}$, is necessarily an empirical  function
that modifies the Stokes drag for a number of non-linear physical effects, such as Reynolds
number \cite{crowe_book}, Mach number \cite{BKKPPF97}, or particle number density \cite{Tong}.
Since  many of the physical corrections such as Reynolds number and Mach number correction
depend on the relative interphase velocity, ${\bf a}={\bf u} - {\bf {v_{p}}}$, we rewrite (\ref{eq:velp}) as
\begin{align}
\frac{d{\bf {x_{p}}}}{dt} &= {\bf {v_{p}}},\nonumber\\
\frac{d{\bf {v_{p}}}}{dt} &= f({\bf a})\cdot {\bf a}.
\label{gov_eqns_part_model}
\end{align}
to derive the SPARSE model.
We have taken the correction function, $f({\bf a})=C_{D_s}/\tau_p$ that depends on the relative interphase velocity only.
The derivation of the SPARSE model, however, is easily extended
for a correction function that is  dependent on more variables (see also Remarks below).

Cloud-In-Cell (CIC) models a group of particles with a single computational parcel.
The properties of  the group of particles are ensemble averaged as
\begin{align}
\overline{\eta} = \frac{1}{N}\sum_{i=1}^N \eta_i.
\end{align}
To derive a governing equation for the averaged single parcel motion
a Reynolds decomposition, $ \eta = \bar {\eta} + \eta^\prime$, is performed to split particle properties
into an averaged,   $\bar{\eta}$, and fluctuating, $ \eta^\prime$, component. Reynolds
averaging  (\ref{gov_eqns_part_model}) leads to
\begin{align}
\frac{d \overline{(\bar{\bf x}_{p} + {\bf x}_{p}^\prime)}}{dt} &= \overline{\bar{\bf v}_{p} + {\bf v}_{p}^\prime},\nonumber\\
\frac{d \overline{(\bar{\bf v}_{p} + {\bf v}_{p}^\prime)}}{dt} &= \overline{f(\bar{\bf a} + {\bf a}^\prime})\cdot (\bar{\bf a} + {\bf a^\prime}).
\end{align}
Using
\begin{align}
\overline{\frac{d \eta}{dt}} = \frac{d \bar{\eta}}{dt},
\end{align}
 for linear  derivative operators,
the averaged computational particle velocity and position can be written as
\begin{align}
\frac{d{ \bar{\bf x}_{p}}}{dt} &= {\bar{\bf v}_{p}},\nonumber\\
\frac{d{ \bar{\bf v}_{p}}}{dt} &= \overline{f(\bar{\bf a} + {\bf a}^\prime)\cdot (\bar{\bf a} + \bar{\bf a}^\prime)}.
\end{align}
Here, $\bar{\bf a} = \bar{\bf u} -   \bar{\bf {v}}_{p}$ where $\bar{\bf u}$
 is the average of the carrier-phase velocity at all particle locations and
${\bf a}^\prime = {\bf u}^\prime - {\bf  v}_{p}^\prime$ where ${\bf u}^\prime$ are the carrier-phase velocity
fluctuations at the particle positions and ${\bf v}_p^\prime$ are the fluctuations in
the particle phase.

In the tradtional CIC approach \cite{birdsall:JCP} the average of the correction factor term is simply set as the correction factor for the average interphase velocity,
\begin{equation}
\overline{f(\bar{\bf a} + \bf{a}^\prime}) = f(\bar{\bf a}),
\label{eq:firstorder}
\end{equation}
leading to
\begin{align}
\frac{d \bar{\bf x}_{p}}{dt} &=   \bar{\bf v}_{p},\nonumber\\
\frac{d \bar{\bf v}_{p}}{dt} &= f(\bar{\bf a})\cdot (\bar{\bf a}),
\label{first_order_model}
\end{align}
where the averaged carrier-phase velocity is taken as the carrier-phase velocity
 at the average particle position, $\bar{\bf u} = {\bf u}(\bar{\bf x}_{p})$.

Instead of the assumption in (\ref{eq:firstorder}), we propose to Taylor
expand, $f(\bar{\bf a} + {\bf a} ^\prime)$ around  ${\bar{\bf a}}$. With the three-dimensional vector components,
$\bar{\bf a}=(a_1,a_2,a_3) = (\bar{a}_1+a_1^\prime,\bar{a}_2+a_2^\prime,\bar{a}_3+a_3^\prime)$, this leads to
\begin{align}
f({\bf a}) = &f( \bar{\bf  a}) + \frac{df(\bar{\bf a})}{da_1}((\bar{a}_1+a_1^\prime)-\bar{a}_1)+ \frac{df(\bar{\bf a})}{da_2}((\bar{a}_2+a_2^\prime)
-\bar{a}_2) ...\nonumber\\
&+ \frac{df(\bar{\bf a})}{da_3}((\bar{a}_3+a_3^\prime) -\bar{a}_3) + O({a^\prime}^2),\nonumber\\
= &f(\bar{\bf a}) + \frac{df(\bar{\bf a})}{da_1}a_1^\prime + \frac{df(\bar{\bf a})}{da_2}a_2^\prime+ \frac{df(\bar{\bf a})}{da_3}a_3^\prime+ O({a^\prime}^2).
\label{taylor_expansion}
\end{align}
The Taylor series is truncated at second order terms $O({a^\prime}^2)$ for interphase velocity fluctuations that
are small as compared to the averaged interphase velocity.

Substituting (\ref{taylor_expansion}) in the particle momentum equation (\ref{gov_eqns_part_model}) yields,
\begin{align}
\frac{d{\bf v}_{p}}{dt} = (f(\bar{\bf a}) + \frac{d f({\bf \overline a})}{d {\bf a}}{\bf a}^\prime)(\bar{\bf a}+{\bf a}^\prime).
\end{align}
After expanding and averaging, we obtain
\begin{align}
\frac{d\bar{\bf v}_{p}}{dt} = &f(\bar{\bf a})\bar{\bf a}
+ \frac{df(\bar{\bf a})}{d {\bf a}}\overline{{\bf a}^\prime {\bf a}^\prime}
\label{eq:SPARSE}
\end{align}
In (\ref{eq:SPARSE}) the terms, $\overline{{\bf a}^\prime {\bf a}^\prime}$, are recognized as stresses that typically arise in Reynolds
averaging and that require closure.
In the present study these terms will be closed using an \emph{a priori} approach where the
results of Eulerian-Lagrangian simulations are averaged to find the stresses  in the SPARSE model.

\vspace{0.7cm}

{\large \textit{Remarks:}}

\renewcommand{\labelenumii}{\Roman{enumii}}
\begin{enumerate}
\item The average carrier-phase velocity in the cloud is not equal to the carrier-phase
      velocity at the averaged particle location, $\overline{{\bf  u}({\bf x}_p)} \neq
      {\bf u}(\bar{\bf x}_p)$. A model is required to close the averaged
      carrier-phase velocity of the particles.
      In this paper we use an \textit{a priori} closure.
\item Equation (\ref{first_order_model}) results if in the SPARSE derivation
      the first-order term in the Taylor expansion  in (\ref{taylor_expansion}) is truncated.
      The CIC is hence a first-order model, whereas  SPARSE is a second-order model.
      Depending on the truncation of the Taylor series any order model may be derived.
      In the remainder of this paper, we refer to the combination of the
      CIC model and the carrier-phase velocity at averaged particle location taken
      (erroneously) to be equal to  ${\bf u}(\bar{\bf x}_p)$ as a First-Order Model.
      The combination of a second-order model and $\overline{u(\bf x)}$ as the carrier-phase velocity, we shall
      refer to as the SPARSE model.
\item Taylor expansion of the correction function leads to terms in the particle momentum equation that have integer powers only.
      Reynolds averaging on terms with integer powers is straightforward, whereas Reynolds averaging on terms with real and fractional powers (typical for the empirical functions such as $C_{Ds}$ ) is very  challenging.
\item It is assumed that the interphase velocity perturbations are small. This is not always the case. For example,
      when the particle response time, $\tau_p$, is large (heavy particles) and the initial particle velocities are large and have
      a large variation, then the fluctuations of the particles are large compared to the computational particle velocity.
      In this case the validity of CIC and/or SPARSE will only hold for short times.
\item If $f({\bf a})$ depends on variables other then the interphase velocity, for example, particle diameter or number density,
      then a Taylor expansion has to be performed around those variables also. Averaging leads to additional second-order correlations
      that require closure.
\item For a \textit{posteriori} tests, the interphase stress terms require modeling and extensive knowledge of
      $f({\bf a})$. In \cite{Sen15} we present a multi-scale framework in which we use full-scale simulations
      to obtain detailed information for $f({\bf a})$.
\end{enumerate}

redpajama_set_name: RedPajamaArXiv


text:
Porsche News
Watch a Lifted Porsche Cayenne Go Three-Wheeling Up Hell's Gate in Moab
The visuals are too good to pass up here.
byPeter Holderith| UPDATED May 14, 2021 11:35 AM
Peter HolderithView peter holderith's Articles
_baldtires
Hell's Gate in Moab is considered a pretty tricky obstacle for any 4x4 but as of late, a bunch of unassuming vehicles have been making their way up the tight rocky trail. First it was a Kia Sorento, then a nearly-stock Ford Super Duty pickup—even the Baby Bronco made a successful pass a few weeks ago.
Now, a Porsche Cayenne on big all-terrain tires has been filmed making an attempt at the climb. Admittedly, crawling up Hell's Gate has a lot to do with wheel placement as opposed to out-and-out off-road performance, but seeing this Porsche in action is impressive nonetheless. It's not every day a suburbia-bound luxury SUV makes moves like this.
Harrison Schoen via YouTube
The Cayenne in question is a facelifted first-generation model, built in 2008. This particular rock-crawler is an S trim, meaning it gets several upgrades not limited to a 4.5-liter V8 producing 340 horsepower. The stick shift is only available on the GTS, so this Cayenne is definitely an automatic, which makes it easier for crawling, anyway.
If you watch the entire video embedded below, you can see the SUV doesn't just make it up Hell's Gate; it conquers several other serious obstacles around Moab as well. That's thanks in part to the new wheels and big tires, but also because the Cayenne has a relatively advanced AWD system. Like other SUVs, it senses wheel slip and applies power where it can find grip. It does this with a system of multi-plate clutches and for being a 2008 model, it's pretty nifty.
The tires are 33-inch Nitto Trail Grapplers, and the Cayenne has been lifted three inches thanks to a EuroWise kit in order to accommodate them. The front upper control arms are also new tubular units for increased strength and durability. Other mods include a roof rack and new LED lights—crucial to completing any and every four-wheeling obstacle.
All of the SUV's adventures can be found on its dedicated Instagram, which is run by its owner Harrison Schoen.
Got a tip or question for the author? You can reach them here: peter@thedrive.com
News by BrandPorsche NewsWatch This Video

redpajama_set_name: RedPajamaCommonCrawl


If you would like your album or book to be considered by The Irish Music Review, please first have a look at the reviews that are published on this site and ascertain whether your material is suitable for consideration.
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text:
POV Winner Spoiler: Week 7
The following contains spoilers from the "Power of Veto" competition
POV Players: Christine, Donny, Zach, Caleb, Derrick, & Nicole
POV Winner:
Follow @TheBBinsider for all things BB16
Follow @BBLF_TBBI for live tweet updates
Donny cannot get evicted hes my favorite!
Nicholas Avellino August 9, 2014 at 10:01 PM
I hope he doesn't leave we love Donny!
Angela Thomason August 9, 2014 at 10:13 PM
i hope Donny don't leave big brother I want him to win the game
Raphael August 10, 2014 at 11:22 AM
I think Donny is boring, but to me it makes no difference whether he stays or not. Nicole is the troublemaker and the one who creates intrigue, she has to leave ASAP!!
You can't honestly sit there and say that Nicole is a troublemaker out of EVERYONE in this house.. Are you kidding me? Do you even watch live feeds? Or even the show for that matter?? Frankie has played this game disgusting and dirty.. Throwing Zach under the bus? Seriously? Zach had Frankie's back to the END!!!!! That's stupid. Zach.. Well, there's just nothing to say about Zach. He tells his game to everyone so there's no denying he's attempted to lie, he's just bad at it. Even though I like Derrick, he's also lied to many people.. I think the only person he hasn't lied to is Cody.. Derrick is very good at damage control, which is why people don't target him.. I said all that to prove my point, Nicole is definitely not the biggest troublemaker & you're blind to the TRUTH if you think that..
Raphael August 10, 2014 at 4:13 PM
I'm sorry but I will not argue with you. You have every right to have an opinion and think whatever you want, just like me.


The residential enclave, Shapoorji Pallonji ParkWest features the very best in Shapoorji Pallonji luxury segment. The project offers spacious Apartments with luxurious features. Beautiful landscapes all around Shapoorji Pallonji ParkWest Phase 2 makes it more special & Elite.The builder is guaranteed to bring a quality living experience to the community of Binnypete with brilliant architecture and equivalent lifestyle in Shapoorji Pallonji ParkWest.
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No need for an explanation.
We wanted chocolate. We wanted muffins. We made Chocolate Muffins! And they were good. They are sweet—yet not too sweet and are fine for a nice breakfast treat.
1. Melt butter and chopped chocolate in the microwave (or using a double boiler). Let cool.
2. In a bowl, mix together the flour, sugar, cocoa, baking powder, baking soda and salt.
3. In a separate bowl, whisk together the eggs, buttermilk and vanilla.
4. Add the liquid ingredients to the dry. Add the chocolate mixture.
5. Using a wooden spoon or spatula, mix it up.
6. Divide the batter evenly into the muffin tin.
7. Bake at 375 degrees for about 20 minutes.
8. Let cool and eat for breakfast, snack, lunch or dinner!


text:
2015 AUGUST ISSUE
Gateway Commerce Center: 5,000 jobs and counting
Credit card changes bring new costs, risks to retailers
Illinois faces medical wakeup call, as nurse retirements loom
New freight district head aims to move bi-state forward
POINT: Should the federal government raise the overtime salary threshold?
COUNTERPOINT: Should the federal government raise the overtime salary threshold?
Q&A with Lisa Peck, community and economic development director, city of Highland
Engineering Roundup
IBJ Business News
New Catholic high school opening is historic for diocese
State assessment test being shortened for coming year
Southwestern Illinois Commercial Real Estate Transactions
Businesses can boost profits in face of minimum wage increases
Anti-worker system not the answer
Hoffman committed to working families
Make sure you're getting exemptions
Maybe electing a non-politician wasn't a good idea after all
A lesson in compromise: Illinois could learn from South Carolina
Spotlight on Southwestern Illinois Engineering and Architecture
Kaskaskia Engineering Group, LLC, Restaurant, Sports and Hotel/Convention Complex, Belleville
Rhutasel and Associates Inc., Fayetteville Terminal Access Road Project, St. Clair County
Lawrence Group, Belleville City Hall/Police Department Renovation Project
Hurst-Roche Engineers, Inc., Collinsville Water Treatment Plant Project
FGM Architects, Edwardsville Spray & Play Park, Edwardsville
EWR Associates, Inc., new Flora Elementary School
Quality Testing and Engineering, Inc., new St. Elizabeth's Hospital, O'Fallon
You are here: Home Archives 2015 Issues August 2015 COUNTERPOINT: Should the federal government raise the overtime salary threshold?
No, overtime proposal would erode pathway to careers
By MATTHEW SHAY
The Obama administration's proposal to expand overtime isn't law yet, but it certainly resembles one – the law of unintended consequences.
Shay The administration has been selling the proposal under the guise of increasing income for store-level managers and expanding the middle class. The truth is that the plan would severely hamper retailers' ability to provide a ladder to success for those looking to build a career. In fact, the proposal could force thousands of full-time career positions to be downgraded to part-time while doing little to increase most workers' take-home pay.
Under current regulations, workers making up to $455 a week are guaranteed overtime pay when they exceed 40 hours a week. Managers making more can be declared exempt but only if they meet conditions such as having supervision of other workers as their primary duty. Under the proposal, the salary threshold would be more than doubled, to $970 a week. And the administration is asking for comments on whether new restrictions can be placed on how much time managers can spend on non-supervisory duties and still be exempt.
The proposal seems expressly targeted at retail. It would cost many store managers and assistant managers their exemption from complicated overtime rules and keep them from doing what it takes to make their stores successful.
For career-oriented managers, staying late isn't a punishment – they think big-picture and know it's an opportunity to wrap up loose ends or get a jump on the next day. Opening an extra cash register or helping a customer find an item increases store sales. Cleaning a bathroom, if it comes to that, boosts the store's image. All of these actions improve a store's performance, and managers whose stores have good performance receive bonuses or get promoted. So, even if an extra hour of work doesn't bring an immediate extra hour of pay, it pays off far more in the end.
Under the plan, more than half a million career-path managers could become hourly workers. Retail companies would rely more on technology, which allows fewer managers to exert greater control, with the opportunity to move up diminished. And with mid-level professional ranks hollowed out, the retail industry's pipeline of talent to be promoted from within would dry up.
It's not surprising then that the majority of retail managers oppose overtime expansion. A survey conducted by research firm GfK found 81 percent said it would impact customer service and three-quarters said it would diminish the effectiveness of their training and undermine their ability to lead employees by example.
Another study from Oxford Economics found the proposal could add as much as $9.5 billion to payroll costs if fully implemented. But with the pool of payroll money limited in the still-tight economy, the study said most employers would be forced to lower base wages and reduce hours or benefits, with few workers seeing any net increase in bottom-line income. In addition, the proposal could result in nearly $1 billion in administrative costs – money that otherwise could go to job creation.
The Oxford study found overtime expansion could create more than 100,000 new jobs – but this is one time when job creation is not good news. Rather than paying time-and-a-half, retailers could be forced to split good full-time jobs in two, so the new jobs would all be part-time positions.
The administration seems to be under the impression that they can expand the middle class by government mandate, but there simply isn't any magic pot of money that lets employers pay more just because the government says so. Retailers also want to grow the middle class. But they believe careers are the path to success, not time clocks.
Matthew Shay is president and CEO of the National Retail Federation in Washington, D.C.
Congressmen applaud military confirmations
WASHINGTON, D.C. – The ascension of a former Scott Air Force Base commander into a top Pentagon post could bode well for Southern Illinois.
U.S. Reps. Rodney Davis, R-Taylorville, Mike Bost, R-Murphysboro, and John Shimkus, R-Collinsville, all applauded the Senate's recent confirmation of Gen. Paul Selva as the vice chair of the Joint Chiefs of Staff and Gen. Darren McDew as the commander of the U.S. Transportation Command at Scott Air Force Base:
"Both have exhibited exemplary leadership while at Scott Air Force Base and we look forward to working with them as they assume their new leadership positions," they said in a joint statement.
Selva was the head of TRANSCOM. That could be meaningful in any discussions on the future of Scott AFB.


The working paper How territorial are today's innovation systems? A case study on the soil decontamination innovation system can be accessed here.
For further example analyses please consult the second interim Green Horizons report. Click here.


This image just calls to me. They are so at ease even though bodies are not meant to do that naturally. You get to see a lot in NYC. Original 32" x 40". Available. Price on request. Frame and Matte not included.


Duncan won't hold a town hall so let's hold one instead! We'll invite to speak anyone that intends to challenge Duncan Hunter for his congressional seat in 2018. Let's discuss the issues that matter most to voters: health care, immigration, fair trade, income inequality, civil rights and equal pay.


text:
Expand View {{?:_cloudinary}} Expand View {{?}} Expand View {{/}}
Milford Reservoir, State Park and Wildlife Area
3612 State Park Rd, Milford, KS 66514
Located near Junction City on the shores of the state's largest lake, 16,000-acre Milford Reservoir, Milford State Park is a favorite getaway for outdoors loving visitors. It is a popular destination for anglers eager for walleye, crappie and largemouth bass, white bass, catfish and smallmouth bass. There are many opportunities to observe or photograph wildlife on the 19,000-acre wildlife area located on the west side and upper end of the reservoir. The Steve Lloyd refuge contains an additional 1100 acres. The diverse habitats support many species of nongame birds, mammals, reptiles and aquatic life. Nearby Milford Nature Center and Milford Fish Hatchery (both are located below the dam) offer a variety of interpretive exhibits and displays. The lake hosts a large influx of bald eagles in the winter. Hunters will find a variety of game including quail, pheasant, prairie chicken, duck, goose, rabbit, turkey, deer and squirrel. Trappers are also active throughout the season in pursuit of raccoon, muskrat, beaver and other furbearers. Milford State Park's Eagle Ridge Trail Over six miles of hiking, biking or horse-back riding through some of the prettiest areas in Milford State Park, with some good view of the lake. The trail is easily handled by beginners. Milford Reservoir is operated by the U.S. Army Corps of Engineers. Campsites and RV hookups, Cabins, Swimming, Boating, Skiing, Marina, Fishing, Hunting (in the wildlife area), Wildlife-watching, Trails (including equestrian trails)
Drinking Water:
Dump Stations:
Full Utility:
Playgrounds:
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Showers:
Swimming Beach:
Bathhouse:
Boat Ramp Lanes:
Boating:
Camping Cabins:
Courtesy Docks:
DumpStations:
Equestrian Trails:
Fishing:
Hunting:
Lodging Nearby:
Picnic Areas:
Primitive Camping:
Trails:
Electric And Water:
Kansas highlights


ARS sent personal that were part of a 10 man relief team to Haiti. The team offered a wide variety of skill sets, including medical, logistics, security, heavy equipment operations, and urban search/rescue.
ARS participated in National Level Exercise 2009 (NLE 09). NLE 09 was the first major exercise conducted by the United States government that focused exclusively on terrorism prevention and protection.
November 2 - 13, 2009 ARS supported the U.S. Department of Homeland Security with the Terrorism Prevention Exercise Program, Silver Rogue.
ARS preformed a 12 week 68W medical training POI, FTX and certification for the Armenian Peacekeeping Brigade in Yerevan, Armenia.
ARS is an agile and innovative, woman-owned company offering products, services and training. We have experience in industrial health & safety and hazardous materials.
Our staff has a long history of working together with responders, both in training and operations.
We have a diverse background including extensive experience with military and civilian emergency response.
ARS also offers an extensive selection of CBRNE/WMD products, training and cutting edge technologies.
ARS provides a wide range of training services CONUS or OCONUS:, , .
ARS offers a wide variety of products for industrial Health and Safety, First Responders, Law Enforcement /Tactical, Military and CBRNE equipment and custom kits.
Check out our online store for the largest selection and contact us for the lowest pricing.


Family Life Broadcasting's 88.1 KJTY Topeka, KS is attempting to rimshot Kansas City a little more as it is applying to move its transmitter site eastward to near Lawrence where it will operate with 88kW at 176.9 meters (Coverage Map). Concurrently, Templo Apostoles y Prophetas Bethel's 88.1 KZGC-FM Garden City, KS applies to upgrade to 1kW at 125 meters (Coverage Map).
Liberman Broadcasting is reapplying for two of its expiring CP's. In the Dallas suburbs, "FunAsia 104.9" KZMP-FM Pilot Point seeks 42kW at 606 meters (Coverage Map). In suburban Houston, "La Raza 103.3" KJOJ-FM Freeport wants 100kW at 595.8 meters (Coverage Map).
In between Houston and San Antonio, Roy Henderson's 92.3 KNRG New Ulm, TX seeks to increase from a Class A to C3 with 12kW at 145 meters (Coverage Map).
Charles Crawford's CP for 92.7 KFON Mullin, TX seeks a move to 103.3 with 100 watts at 47 meters (Coverage Map).
Abilene Christian University's Public News/Talk 89.7 KACU Abliene proposes an upgrade from a Class C2 to C1 with a move to 89.5. It will operate with 100kW at 189 meters (Coverage Map).
Finally in Texas, with its CP set to expire on November 2, Panther Communications seeks a modification of 102.9 KXON Ozona, TX to 200 watts at -16 meters to get the station on the air before losing its permit (Coverage Map).
Marble City Media's CP for 105.1 WRFS Rockford, AL seeks a modification to 6kW at 100 meters (Coverage Map).
Super Waves LLC has applied and been granted a modification of its CP for 94.1 WLZT Worthington, IN to 700 watts at 48 meters (Coverage Map).
Lendsi Radio's Classic Hits "V105.5" WVBG-FM Redwood/Vickburg, MS seeks a modification to 2.6kW at 154 meters (Coverage Map).
Calvary Chapel of Finger Lakes' silent 91.1 WZXJ Bronson, FL applies to move to a new tower with 1kW at 99 meters (Coverage Map).
Intermountain Public Radio's 91.9 KTYN Thyne, WY applies to increase from a Class A to a Class C3 with 370 watts at 708 meters (Coverage Map).
VLN Broadcasting's CP for 101.1 in West Salem, IL applies to relocate to 93.7 with 3.5kW at 126 meters (Coverage Map).
Montrose Christian Broadcasting seeks to lower the antenna height of its CP for 107.5 KSYF Olathe, CO from 541 meters to 531 meters while remaining with 20kW (Coverage Map).
Circuitwerkes' 94.1 W231CT Orlando applies to relocate to a new location north of the city with 250 watts at 149 meters rebroadcasting "CBS Sports 810" WRSO Orlovista (Coverage Map).
Clyde Scott Jr.'s 103.5 W278BV Macon, GA seeks to move to 92.7 with 120 watts at 30 meters rebroadcasting Cox Media's "Kiss 104.1" WALR-FM Palmetto/Atlanta (Coverage Map).
iHeartMedia's 106.5 W293AH Normal AL proposes a modification to its CP to move to Huntsville to operate with 250 watts at 422 meters rebroadcasting 100.3 WQRV-HD2 Meridianville (Coverage Map).
Educational Media Foundation's 97.7 W249CT Williamsburg, VA seeks to drop to 13 watts at 116 meters rebroadcasting 90.7 WZLV Cape Charles (Coverage Map).
EMF's 98.3 K252FE Columbia, MO proposes a modification to 140 watts at 100 meters rebroadcasting 92.1 KMFC Centralia (Coverage Map).
In Kansas, EMF's 93.5 K228DW Hutchinson applies for a displacement move to 93.9 with 20 watts at 52 meters (Coverage Map). Bible Broadcasting's 93.5 K228DY Newton seeks to shift to 104.1 with its current 250 watts at 67.9 meter facilities rebroadcasting 88.3 KYFW Wichita (Coverage Map). Both moves are due to Entercom's 93.9 KDGS Andover/Wichita moving to 93.5.
Great Plains Christian Radio's 100.9 K265DY Ness City, KS applies to move to 101.1 with 170 watts at 74 meters (Coverage Map) rebroadcasting 99.1 KJIL Copeland.
Eastern Airwaves' 94.3 W232AV Lenoir, NC holds a CP to move to 93.7, but now seeks to shift to 104.5 with 250 watts at 290 meters rebroadcasting Bluegrass 1080 WKGX Lenoir (Coverage Map).
Radio Acquisition Corp's. 105.7 W289BQ Morristown, TN applies to move to 250 watts at 181 meters while continuing to rebroadcast Classic Hits 1150 WCRK Morristown (Coverage Map).
Educational Media Foundation's 105.5 W288CM Connersville, IN applies for new 27 watts at 73 meters rebroadcasting 89.3 WKRT Richmond, IN (Coverage Map).
Scott Communication's 99.9 W260CE Livington, AL seeks to shift to 100.1 with 250 watts at 15 meters rebroadcasting 104.9 WSLY York (Coverage Map).


text:
Slider News
Art Culture & Yoga Ayurvedic
Budget Rs 7.8 million allocated for conserving endangered Red Panda
28th Dec, 2020 | Tourism Mail Crew
ILAM, Dec 28 : Sandakpur rural municipality has signed a contract of Rs 7.8 million for the conservation of endangered wildlife Red Panda. The wildlife is to be managed in around 200 ropani land at Maipatal of Sulubung in Sandakpur.
Photo Courtesy: Munmun Lama
Maipatal is a popular area for Red Panda. The rural municipality is carrying out the rare wildlife conservation in collaboration with the Red Panda Network. The rural municipality has engaged the contractor for creating environment for the management and protection of Red Panda in the area.
Spokesperson of the rural municipality Giriraj Khanal, arrangement of caretaker will also be made for the management and protection of the wildlife. Big trees will also be cut down for enabling protective environment for the Red Panda. "Saplings of local species however will be planted", he added.
The rural municipality has also prepared necessary structures for the management of the endangered species.
Meanwhile, the rural municipality has developed a plan to plant saplings of various species in 700 ropani land at Jauwari for the management of the Red Panda. The plantation drive has been put in place for the conservation of the wildlife as well as the protection of wildlife route.
© 2021 Med Insight Pvt. Ltd. Reg No: 21/073-74 / Publisher: Prashant Pradhan / Editor: Tek Bahadur Mahat
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text:
Home :: Military :: Library :: Policy :: Army :: Accp :: Is3003 ::
SOVIET ARMED FORCES ORGANIZATION
MQS MANUAL TASK: 01-3353.01-0010
TASK DESCRIPTION:
You will become familiar with the threat posed to US armed forces by the growth and power of Soviet armed forces.
You will be able to explain the composition, organization and doctrine of the Soviet armed forces.
You will be given narrative information and illustrations for FM 100-2-1, 100-2-2, and 100-2-3.
It is your responsibility as an intelligence officer to know where and what the enemy is doing in accordance with FM 100-2-1, 100-2-2, and 100-2-3.
The material contained in this lesson was derived from the following publications:
FM 1-402.
FM 100-2-1.
Supr 03497.
The Soviet Union has one of the largest and most modern armed forces in the world today with more than 4.8 million members. For the past 25 years, it has been growing in size and modernizing.
The threat posed to US armed forces around the world by growth and power of the Soviet Armed Forces are explained in detail, the composition, organization, and doctrine, with the capabilities and limitations of their weapons and equipment.
COMPONENTS OF SOVIET ARMED FORCES
The Soviet Armed Forces have retained the Tsarist "classic" European model, conscription of all enlisted personnel are led by a cadre of professionals and have an extensive program of modernization and expansion. The Soviets have twice as many men under arms as the US does, backed up by a trained reserve force of equilavant size. Approximately 25,000,000 men are in the Soviet military reserves. This is 30 times the size of the US military reserves.
Soviet military forces are divided into five major components:
* Strategic rocket forces.
* Air defense troops.
* Air forces.
* Naval forces.
* Ground forces.
The Strategic Rocket Forces (SRF). The Soviet Union's main instrument for intercontinental thermonuclear war is the SRF. Their mission is to destroy enemy means of nuclear attack and key military, political, economic, and communication centers. The SRF intercontinental ballistic missiles (ICBMs) deployed in missile fields along the Trans-Siberian Railway, and medium-range ballistic missiles are located in the western and southern regions of the USSR. The Intermediate Nuclear Forces (INF) Treaty will eliminate intermediate and medium range system, they will remain a threat to European targets until 1991.
The Air Defense Troops (Voyska PVO). These forces defend against enemy air attack and aerial reconnaissance by diverting or destroying enemy aircraft, missiles, or space objects while in the air. They have early warning and air defense, fighter-interceptors, surface-to-air missiles (SAM), and antiballistic missile (ABM) launchers around Moscow, with a civil defense capability. Moscow is the only city in the world with an ABM system.
The Air Forces. The Soviet air forces are the largest in the world and are divided into three components, each with a separate mission:
* Long-range aviation.
* Frontal aviation.
* Military transport aviation.
Long-Range Aviation (LRA). The LRA is comparable to the US Air Force (USAF) Strategic Air Command (SAC), minus SACs ICBM force. It has several hundred aircraft, including BEAR and BISON long-range bombers, BADGER, BLINDER, and BACKFIRE medium-range bombers and tankers (Figure 1-1). It has three air armies--two in the western USSR and one in the Far East along the border with China. Seventy-five percent of its aircraft are stationed in the European USSR; most of the remainder are on the Chinese border to bomb strategic targets--enemy missile sites, nuclear arsenals, naval bases, strategic bomber bases, and war industries.
Figure 1-1. Long-Range Strike and Support Aircraft.
Aviation of the Front (AOF). AOF is comparable to the USAF Tactical Air Comand (TAC). It provides air support to ground forces. Its forces are assigned to the military districts (MDs) of the USSB and groups of forces in Eastern Europe. The AOF is composed of fighters, bombers, fighterbombers, and regiments of assault helicopters, transport helicopters, and reconnaissance aircraft.
AOFs modernization include the entire counter-air and 75 percent of the ground attack force. Soviet ground attack fighters have laser range finders, terrain avoidance radar, and television or laser-guided bombs on newer generation aircraft to enhance versatility and combat capabilities. They can carry payloads of 4,000 pounds over 300 nautical miles, and, over shorter ranges, 10,000 pounds of bombs, rockets, and guided missiles.
Military Transport Aviation (VTA). VTA is the Soviet equivalent of the USAF Military Airlift Command (MAC). It provides airlift support to all branches of the Soviet armed forces. It contains more than 600 transport aircraft in three categories:
* Strategic airlift (IL-78 CANDID, AN-22 COCK, and AN-124 CONDOR).
* Operational-tactical transports (AN-12 CUB).
* Tactical transports which include both fixed-wing aircraft and about 3,000 transport helicopters like the Mi-6 HOOK and the Mi-8 HIP.
Figure 1-2. IL-76 (1) and AN-22 Cock (2)
Figure 1-3. AN-12 CUB.
Figure 1-4. Mi-6 HOOK.
Figure 1-5. Mi-8 HIP.
During the 1973 Middle East War, MOSCOW flew 930 sorties to supply Egypt 15,000,000 tons of supplies. Over a three-month period in 1977-78, the Soviet Union airlifted 600 armored vehicles, numerous tanks, and 400 artillery pieces to Ethiopia. The Soviet airline Aeroflot can augment military airlift with 1,300 aircraft; during the Angolian crisis, Aeroflot flew 14 missions and airlifted 25,000 Cubans to Angola. The VTA can airlift two airborne divisions (14,000 men) a range of 300 miles or one airborne division 1,000 miles.
The Navy. At the end of World War II, the Soviet Navy had a mission and capability which had remained unchanged since the time of Peter the Great--that of coastal defense. Soviet global aspirations and crises of the late 1950s and early 1960s, such as in Lebanon, the Dominican Republic, and the Cuban missile crisis, demonstrated the need for a broadly-based, general purpose navy. Since then, the Soviets have developed a modern navy capable of projecting military, political, and economic influence throughout the world. It operates from the ports in the Mediterranean Sea, Atlantic, Pacific, and Indian Oceans, and Caribbean waters from bases in Cuba. The Soviet Navy is composed of the Northern, Pacific, Baltic, and Black Sea Fleets, and the Caspian Sea Flotilla.
The Northern Fleet. Based at Murmansk and Pachenga, the Northern Fleet is the most powerful of the Soviet fleets, poses the greatest threat to the US and the Atlantic Ocean. Armed with surface combatants, submarines, aircraft and ballistic missiles which can strike and interdict North Atlantic sea lanes.
The Pacific Fleet. Including the Indian Ocean Squadron, the Pacific Fleet is slightly larger than the Northern Fleet. It is based in Vladivostok and Petropavlovsk on the Kamchatka Peninsula.
The Baltic and the Black Sea Fleets. These fleets support operations and seizure of critical water passages like the Danish Straits, the Bosphorous, and the Daradenelies.
The Caspian Sea Flotilla. A force of about 40 combatants and auxiliaries, this Flotilla supports ground operations and provides coastal patrolling. The Caspian Sea Flotilla may reinforce other naval units by traveling through the Volga waterway system.
The Soviet Navy has 276 principle combatants, 308 submarines, 311 auxiliaries, and 4 aircraft carriers, and keeps over two-thirds of its fleet in port at any one time.
The Soviet Navy also has a force of naval infantry, similar to the US Marine Corps. There is now a brigade of Soviet Naval Infantry (SNI) stationed with the Pacific Fleet. Further, the Northern, Baltic, and Black Sea Fleets has a brigade of SNI assigned to it. See lesson four for more details concerning the SNI.
Ground Forces. The largest of the Soviet military services. They are highly modernized, well equipped, and have great firepower and mobility. Manpower and material combined make the present Soviet ground forces the most powerful land Army in the world. The main combat power of the ground forces is in tank divisions (TDs) and motorized rifle divisions (MRDS). Airborne troops and special troops, and also part of the Soviet Ground Forces, which includes engineers, signal, chemical, radiotechnical, motor transport, railroad, and highway troops.
Following World War II, Stalin maintained massive ground forces to offset the threat of American nuclear power. As the Soviets developed their own strategic nuclear capability and forces, their emphasis shifted away from the ground forces. Under Krushchev, in the late 1950s and early 1960s, the size of the ground forces was reduced, while strategic rocket forces increased in size. During 1980s, the Soviets are concerned about the growing threat from China and the wars in the Middle East and Far East.
They are increasingly aware a war may be fought without the use of strategic nuclear weapons. For these reasons, the Brezhnev regime emphasized the importance of the ground forces. More than 30 divisions have been added since 1967, and the weapons and equipment of all types have been introduced. Officer and conscript training has been improved. New tactics, strategy, and doctrine have also been developed.
The Soviets believe any war could involve nuclear weapons, therefore, nuclear delivery means have been assigned at all levels from division up. The Soviets have the largest and most effective array of chemical weapons and equipment in the world and are capable of using chemical agents from battalion up. Reports from Afghanistan indicate they use chemical weapons in any conflicts of violent, sustained, and deep offensive actions. Mechanized and armored formations, supported by aviation and artillery, seize the initiative, penetrate defenses, and drive deeply and rapidly into enemy area.
World War II has taught the Soviets the necessity of having a fully operational strategic command structure. Thus, there is a unified system of command, capable of exerting centralized direction, but designed to permit decentralization of functions to lower levels. The Soviet national military command authority is composed of three major bodies: Council of Defense, the Main Military Council, and the General Staff.
Council of Defense. Planning and preparing the country for war, is the responsibility of the council of defense headed by the General Secretary of the Communist Party. The council is made up of Politiburo members, the Minister of Defense and is the highest military-economic planning agency; it deliberates interrelated issues concerning the nation's defenses, economic plans, and government branches; the mobilization of industry, transportation, and manpower for war, and the peacetime structure of the armed forces. In wartime, it would be the State Committee of Defense, a war cabinet with oversight of political, diplomatic, and economic matters concerned with military operations. (See Figure 1-6.)
Main Military Council. The Council of Defense for leadership and status of the Soviet Armed forces in peacetime. The Minister of Defense heads this council. The chairman of the Council of Defense is a member, as are the First Deputy Ministers of Defense. The ministers include the Chief of the General Staff and the Commander in Chief of the Warsaw Pact Forces. Other members might include the commanders of the five military services, the Chief of the Main Political Administration, the Chief of the Rear Services, and the Chief of Civil Defenses. In wartime, the Main Military Council would be transferred into Headquarters of the Supreme High Command (STAVKA), representing the top echelon of Soviet wartime military control. The General Secretary of the Communist Party, as Chairman of the State Committee of Defense, would become the Supreme Commander in Chief of the Soviet Armed Forces. The STAVKA would plan and direct strategic operations on a global scale within Theaters of Military Operations (TVD), the number of Fronts, their composition, missions, and conduct or operations would be established, it would also monitor the individual Front and fleet actions and supervise the coordination between them.
The General Staff. The major link in the centralization of the Soviet National Military Command Authority, is the General Staff. The General Staff is the executive agency for the Main Military Council in peacetime, and in wartime for the STAVKA. The Soviet General Staff is charged with the basic military planning in the Soviet Armed Forces, both in peace and war. The military services, military districts, and the Groups of Forces outside of the USSR report to Minister of Defense through the General Staff in peacetime.
Figure 1-6. The Soviet National Military Command Authority.
In wartime, field forces in a TVD (Fronts and fleets) would report to the Supreme Commander in Chief and the STAVKA through the General Staff. (See Figure 1-6).
Soviet Ground Forces Strength. Soviet ground forces contain 1,825,000 personnel, 2,000,000 trained Army reservists, and 191 ground force divisions with 134 MRDs, 50 TDs, and 7 Airborne divisions. In addition, there are 20 divisions of artillery, which are armed with conventional tubes and multiple rocket launchers (MRLs).
FIELD FORCES ORGANIZATION
The Combined Arms Commands, in peacetime, are the MDs within the Soviet Union and Groups of Forces in Eastern Europe and are subordinate to the Ministry of Defense.
The Military District (MD). A territorial administration command of military areas, recruiting districts, military schools, garrisons and training areas of command. MDs are organized as a Front or other field command for wartime operations.
The 16 MDs. The Soviet Union is divided into 16 MDs. District boundaries coincide with administrative subdivisions comprising the district. An MD may include portions of two adjacent republics or even two or more entire republics. The names of the districts and the location of their headquarters are shown in Figure 1-7.
Groups of Forces. Outside the Soviet Union four major Army units with subordinate AOFs in Soviet bloc nations form the armed forces of the Warsaw Pact under command of a Soviet Marshal. These Soviet forces abroad are the Group of Soviet Forces/Germany (GSFG---11 TDs and 18 MRDs) in East Germany; the Northern Group of Forces (NGF--2 TDs) in Poland; the Central Group of Forces (CGF--2 TDs and 3 MRDs) in Czechoslovakia; and the Southern Group of Forces (SGF--2 TDs and 2 MRDs) in Hungary. The GSFG could constitute a Front organization in wartime, consisting of three to five armies with organic artillery, missile, air defense, engineer, signal, intelligence, reconnaissance, and rear service units; plus aviation, air assault, and special purpose forces. The other groups are smaller, but could be rapidly expanded to large, highly capable Front organizations. Those divisions outside the USSR and along the borders have the highest degree of readiness. (See Figure 1-7).
The Front. The largest field formation in wartime, the Front is an operational and administrative unit; its size and composition can vary widely depending on the mission and situation. Roughly equivalent to a US/NATO Army group, a Front has three to five armies with organic artillery, missile, air defense, engineer, signal, intelligence, reconnaissance, and rear service units, plus aviation, air assault, and special purpose forces. (See Figure 1-8).
Airborne Division. Assigned on a contingency basis to a Front with special purpose units, whose employment is carefully controlled by the Ministry of Defense. The first unit deployed in Afghanistan was an airborne division.
Artillery Division. Allocated to each Front to provide fire support, its brigades, regiments, and battalions are attached forward to armies or divisions. An artillery division has four artillery brigades, an MRL brigade, an anti-tank brigade, a motor transport battalion, and a target acquisition battalion. (See Figure 1-9.) Heavy artillery brigades have capable guns and heavy mortars and are assigned to the Front, but these are NOT subordinate to the artillery division.
SCUD and SCALEBOARD Surface-to-Surface Missile (SSM) Brigades. Used in a general fire support role for Front operations. These are the Front commander's most readily available chemical and nuclear weapon systems.
The SCALEBOARD and SS-23 SPIDER are being eliminated due to the INF Treaty. Shorter-range SCUDs are not affected by the treaty, and will remain in the forces structure.
SAM Brigades. An integrated Front air defense envelope, these units provide air defense support for the Front and Army rear areas.
Figure 1-7. Soviet Military Districts and Groups of Forces.
Figure 1-8. Front army Composition.
Engineer Units. These would include combat engineer units (general construction, assault crossing, and pontoon) and logistical support units (pipeline construction and topography).
Figure 1-9. Artillery Division, Front.
Radio-Electronic Combat (REC). Capable of radio and radar intercept, direction finding, and communications jamming. Their mission is to target or jam at least 60 percent of the enemy's key electronic emitters.
Signal Brigades. Provide the Front-to-Army communications via land line, radio, radio/relay, and courier.
The Aviation of the Front (AOF). The largest operational unit of tactical aviation provides reconnaissance, counter-air, support of ground troops, and helicopter lift for airmobile operations. In peacetime, AOFs are subordinate to military districts or groups of Soviet forces; in wartime, they are subordinate to Air Force Frontal Aviation Directorate. (See Figure 1-10).
Figure 1-10. Tactical Air Army, Front.
Combined Arms and Tank Armies. The organization of a Soviet Army is flexible, depending upon its mission. The Combined Arms Army (CAA) is an operational and administrative organization having more MRDs than TDs. There are typically two to four MRDs present, and one or two TDs. The numbers and types of combat service support units are tailored to the mission. But a fairly typical structure is shown in Figure 1-11.
CAA Mission in front's first echelon is to destroy enemy resistance on the forward edge of the battle area (FEBA) and create gaps large enough to permit the deployment of front exploitation forces--usually a tank force of the front second echelon--or to penetrate and exploit immediate successes themselves. The army is expected to advance far enough in the first few days to destroy the continuity of enemy tactical defenses, including the corps reserves. The CAA is motorized rifle heavy and thus would be the most likely force to attack well-prepared defensive positions, consisting of antitank defenses. The Soviets studied the 1973 Middle East War very carefully and concluded pure tank forces were very susceptible to modern antitank guided missile systems.
The Tank Army varies from the CAA only in the mix of MRDs and TDs. Specifically, a Tank Army has more tank divisions than motor-rifle divisions. The support forces are identical, both in size and type of units. Of course, just as in a CAA, support forces are tailored to the army's mission. (See Figure 1-12).
The Tank Army has two primary missions: in the offense, exploitation of a penetration; in the defense, counterattack. The tank army may be used as the first echelon (penetration) force under certain special conditions: when enemy anti-tank guided missile (ATGM) defenses are weak, when the enemy has had no time to prepare a defense, when a pursuit is being conducted, or when gaps in the enemy's defense have been created by massive conventional or nuclear fires. The Soviet Army has massive amounts of artillery, some of which is allocated to subordinate divisions, and some of which may be retained for use by the army, especially in the counter-fire role. Besides subordinate divisional artillery, the army has a brigade of guns and howitzers and a regiment of multiple rocket launchers. (See Figure 1-13). The army may allocate these assets to divisions, but will not allocate any of its SSMs. Doctrinally, nuclear release is never allowed lower than army in the Soviet system; further, a division commander does not have the assets to exploit the 300 km range of these weapons. The army commander also has extension river-crossing capabilities and sufficient motor transport to sustain him in both the offense and defense.
Figure 1-11. Combined Arms Army.
Figure 1-12. Tank Army
Figure 1-13. Army Artillery Brigade, Tank and combined Arms Army.
*	All battalions are equipped with 24 weapons, 8 per battery.
**	The 2S5, 152mm self-propelled gun has replaced the M-46, 130mm towed gun in most first-line units of the Soviet Ground Forces. However, some units within the Soviet Union and the Non-Soviet Warsaw Pact will probably continue to use the M-46 for several years to come.
Principal Items of Equipment
Gun self-propelled, 152mm, 255 or towed, 130mm, M-46.........................
Howitzer, towed, 152mm, D-20.................................................................
ACRV, M-1974, Mod 1/2/2A/3................................................................
ACRV, M-1979, BTR-60PA.....................................................................
ASV, BMP M-1975 Small Fred.................................................................
ASV, MTLB M-1975 Big Fred..................................................................
ACV, BTR-60PA w/R-145BM..................................................................
Tractor, Artillery, AT-S..............................................................................
Radar, Masterological, End Tray/Leg Drive.................................................
Motorized Rifle Division has four maneuver regiments: three motorized rifle regiments (MRRs), and one tank regiment. Combat support is provided by one artillery regiment, and one D4SAM regiment, an anti-tank battalion, a reconnaissance battalion, a helicopter squadron, engineer, signal, and chemical defense battalions, and an artillery command battery. The tank regiment of the MRD has a howitzer battalion, the MRL battalion is part of the artillery regiment, the reconnaissance battalion has six medium tanks, and the helicopter squadron is in the division organization for the first time. Combat service support is provided by motor transport, maintenance, and medical battalions as well as a mobile field bakery. (See Figure 1-14).
Figure 1-14. Motorized Rifle Division.
The Motorized Rifle Regiment (MRR). The MRR is the basic combined arms organization and most common maneuver element of the Soviet ground forces. Motorized rifle, armor, artillery, anti-aircraft artillery, anti-tank, engineer, signal, and combat service assets are organic to the MRR. The MRR is the smallest organization in which all of these elements are represented and resembles division organization, with three motorized rifle battalions (MRBs) and one tank battalion, a 122mm howitzer battalion, and three 120mm mortar batteries, one battery organic to each MRB. The MRR is equipped with the BMP or BTR series of troop-carrying vehicles. (See Figure 1-15 and 1-16.)
Figure 1-15. BMP-2.
Figure 1-16. BTR 60 PA/70/80.
Both BMP and BTR regiments are equipped with the AC4GS-17 automatic grenade launcher within MRBs. The BMP regiments have a battalion of 122mm self-propelled howitzers, the BTR regiments have 122mm towed howitzers (D-30) and anti-tank platoons within the MRBs, a feature not found in the BMP regiments. (See Figures 1-19 and 21.)
Tank battalions of both BMP- and BTR-equipped MRR are composed of 40 medium tanks; the T-64, T-72, or T-80. (See Figure 1-17 and 1-18.)
Figure 1-17. T-64.
Figure 1-18. Motorized Rifle Regiment (BMP), MRD and TD.
Figure 1-19. MOTORIZED RIFLE REGIMENT (BTR). MRD.
Figure 1-20. Tank Battalion. Motorized Rifle Regiment. MRD and TD.
The Tank Division has three medium tank regiments, an MRR, an artillery regiment, a SAM regiment, a reconnaissance battalion, a signal battalion, a chemical defense battalion, an engineer battalion, and service support elements. The subordinate elements of the TD and the MRD are identical or similar, but has more combat equipment and personnel than MRD. (See Figure 1-21.)
The Tank Regiment. Tank regiments of the TD consist of five combat and service support battalions: three tank battalions of 31 tanks each, one motorized rifle battalion, and one howitzer battalion. The howitzer battalion is an addition to the regimental structure, and is composed of 122mm self-propelled howitzers. The tank regiment is an upgrade in motorized rifle assets from company to battalion strength, equipped with the BMP. These battalions are identical to the BMP battalions found in the motorized rifle regiments with BMPs. Tank regiments of TDs resemble MRRs by having five combat and service support battalions, and tank-heavy in terms of combat power. (See Figure 1-22.)
The Airborne Division. Being fully motorized, increases its combat power, mobility and airdrop capability. The BMD amphibious airborne combat vehicle is in all three of its airborne (infantry) regiments. Combat support is provided by an artillery regiment, an assault gun (ASU-85) battalion, and an anti-aircraft battalion. (See Figure 1-23.)
The artillery regiment is a unique structure. It has only two subordinate battalions: one battalion of 18 D-30 122mm howitzers and one battalion of 12 D-30's and 6 GAZ-66 mounted multiple rocket launchers. The D-30 is the same weapon found in BTR regiments. The GAZ-66 MRL is unique to airborne/ air assault forces. It is a light system with 12 launcher tubes, firing the same rocket as the BM-21.
The Airborne Regiment has three airborne battalions and one mortar battery, one ATGM battery, and one anti-aircraft battery. (See Figure 1-24.)
Each regiment is equipped with approximately 100 BMDs in 4 different configurations. The basic BMD is the standard squad vehicle. The BMD M-1979/1 is used by weapon squads within companies, and the BMD M-1979/3 is used as a command vehicle at battalion and regimental headquarters. A fourth variant, the BMD M-1981/1, has been identified, although its role and deployment pattern have not yet been determined. (See Figure 1-25.)
Figure 1-21. Tank Division.
Figure 1-22. Tank Regiment.
Figure 1-23. Airborne Division.
Figure 1-24. Airborne Regiment.
Figure 1-25. BMD M1981/1.
Practice Exercise: Lesson 1


text:
Arhopalus is a genus of beetles in the family Cerambycidae, the longhorn beetles, in the tribe Asemini.

Species
BioLib lists:
 Arhopalus asperatus (LeConte, 1859)
 Arhopalus biarcuatus Pu, 1981
 Arhopalus brunneus (Gardner, 1942)
 Arhopalus cavatus Pu, 1981
 Arhopalus coreanus (Sharp, 1905)
 Arhopalus cubensis (Mutchler, 1914)
 Arhopalus deceptor (Sharp, 1905)
 Arhopalus exoticus (Sharp, 1905)
 Arhopalus ferus (Mulsant, 1839)
 Arhopalus foveatus Chiang, 1963
 Arhopalus foveicollis (Haldeman, 1847)
 Arhopalus hispaniolae (Fisher, 1942)
 †Arhopalus pavitus (Cockerell, 1927)
 Arhopalus pinetorum (Wollaston, 1863)
 Arhopalus productus (LeConte, 1850)
 Arhopalus rusticus (Linnaeus, 1758)
 Arhopalus syriacus (Reitter, 1895)
 Arhopalus tibetanus (Sharp, 1905)
 Arhopalus tobirensis Hayashi, 1968

External links

 Arhopalus in Fauna Europaea

Spondylidinae
Cerambycidae genera

redpajama_set_name: RedPajamaWikipedia


text:
WORLD Colombia, Venezuela to reopen border on Sept. 26
Colombia, Venezuela to reopen border on Sept. 26
Venezuelan President Nicolas Maduro and Colombian President Gustavo Petro announced on Friday that the two countries decided to reopen the border on September 26 and resume flights.
Venezuelan President Nicolas Maduro (R) shakes hands with Colombia's new ambassador Armando Benedetti after a meeting at Miraflores Palace, Caracas, Venezuela, August 29, 2022. /CFP
"I am very happy to announce that as of #26Sep we will jointly open the borders between Venezuela and Colombia. The exchange and cooperation between our peoples restart on the right foot," Maduro tweeted.
Petro also confirmed the border reopening on the same day, noting "On September 26 we will open the border between Colombia and Venezuela. We confirm the Government's commitment to restore brotherly relations."
Flights from Venezuela's capital Caracas and the northern city of Valencia to Colombia's capital Bogota will also resume, Maduro added.
Petro also confirmed the resumption of the air connection and cargo transport on Twitter.
A sign with a message that reads in Spanish: "Don't Generalize", on the Simon Bolivar International Bridge to return to San Antonio, Venezuela, in Cucuta, Colombia, August 5, 2022. /CFP
The relationship between the two countries deteriorated due to disputes caused by suspicious illegal border crossings, resulting in the closure of the border in 2019.
Since Petro took office in Colombia, Caracas and Bogota have progressively advanced in normalizing ties severed in 2019 by his predecessor Ivan Duque.
Colombian ambassador to Venezuela, appointed by Petro, submitted the credential to Maduro, marking the diplomatic ties resume after a three-year break. The two sides are sending ambassadors to each other, seeking to restore military cooperation.
(With input from Xinhua)


The telecoms industry is beginning to take this reality onboard in the face of its dizzying customer churn rates. At the centre of the churn rates lies an apparent heightened interest in customer acquisition over retention.
As captured by this statistic, organizations that take customer experience seriously will stand out and win customer loyalty.
Although, customers do not merely judge an experience they have with a brand based on the industry the outfit is from.
Instead, they benchmark the encounter against all of the "wow" moments they have ever received from any business.
This is pushing telecoms providers to look at the retailers, tech start-ups and digital disruptors that are dramatically reshaping customer expectations. It presents a real challenge for some as telecoms providers don't necessarily have the agility or resources to replicate a model similar to digitally born, data-led start-ups that are hardwired with a customerfirst culture. In order to keep up with customers' wants and needs, many will have to undergo a transformation of some kind.
In the race to gain more visibility on customers, the 'Big Data' movement has seen businesses push to gather as much information as possible. But as many are discovering, data is a game of quality and not necessarily quantity. In CX Network's 2018 Big Book Of Customer insight, Data And Analytics, finding relevant insights in a sea of big data was highlighted yet again as one of the top challenges faced by CX practitioners.
Many are now looking towards the power of 'small data' – smaller datasets that are valuable to a business for driving intelligent decision-making.


Cycling could save the NHS £17 billion within 20 years and increase the mobility of the poorest families by 25%, according to a report released today by British Cycling.
The report, compiled by Dr Rachel Aldred of Westminster University, and released ahead of today's debate on cycling in Parliament, highlights the benefits of cycling for the whole of society, and reiterates the need for a minimum £10 per head per year spend on cycling, rising to £20.
It says Danish levels of cycling would reduce road death deaths by 30%, and cycle casualties by two thirds, while shifting 10% of urban car journeys to bike would reduce air pollution and save up to 400 productive life years.
Dr Rachel Aldred said: "The fantastic thing about investing in cycling is that it can generate benefits in a range of policy areas. Whether the goal is quicker urban journeys, improving life chances for low income people, revitalising town centres or reducing the growing burden of non-communicable diseases – cycling can be part of the solution.
The report quotes the 2011 Census figures, which show transport is a major barrier for jobseekers in England and Wales, who are three times more likely to live in a no-car household than employed people, and yet do not consider cycling an option.
In Denmark, it says, cycling is most important for those on low incomes. In the Netherlands cycling helps older people maintain independent mobility after they can no longer drive, with more than 20% of 80-84 year olds preferring to cycle.
"This paper shows that more cycling would touch the lives of all Brits in a positive way – whether they themselves choose to use a bike or not. Investing in cycling would make a massive difference to all of society. We only have to look to Denmark and the Netherlands – countries that regularly top surveys on being the happiest and healthiest nations in the world – to see what a transformative effect cycling can have. This is about creating better places to live.
The report says in the Netherlands cycling is statistically safer than driving, a level of safety which, if achieved in Britain, could equate to 80 fewer cycling deaths per year, or an increase from 3% to 12% of people commuting by bike without any increase in cycling deaths. As well as saying cycling could be also highlights psychological benefits of commuting by bike, quoting a study where car commuters were at least 13% more likely to report being constantly under strain compared to those travelling actively.