commit d51e1b4d5da7dbf388feada1d5f96b1cfd77a53a
parent be3767cbc4de68ec8bb8b527e014da0df10182bc
Author: Vincent Forest <vincent.forest@meso-star.com>
Date: Wed, 6 Jan 2021 17:48:39 +0100
Merge remote-tracking branch 'nastar/feature_stardis' into feature_lfs2fat
Diffstat:
1 file changed, 21 insertions(+), 22 deletions(-)
diff --git a/stardis/stardis.html.in b/stardis/stardis.html.in
@@ -19,7 +19,7 @@
</div>
<p>Stardis is a <b>thermal simulation</b> framework for <b>complex 2D and 3D
-environment</b>, based on new <b>Monte-Carlo</b> algorithms built from
+environments</b>, based on new <b>Monte-Carlo</b> algorithms built from
reformulations of the main heat transfer phenomena: conduction, convection and
radiation. A set of cross-recursive algorithms have then been derived, and
result in the simulation of "<b>thermal paths</b>" that explore space and time
@@ -54,10 +54,10 @@ purposes:<p>
temperature (or flux) computed by Stardis must agree with the values computed
by other tools, within the uncertainty range (and also within the limits of
validity of the various <a href="#solver">assumptions</a> used to derive
- Monte-Carlo algorithms). This can prove very useful in order to validate any
- result obtained by any thermal solver in a case when no analytic solution is
- available or when a physical intuition is impossible to achieve due to the
- complexity of the problem.</li>
+ the Monte-Carlo algorithms used in Stardis). This can prove very useful in
+ order to validate any result obtained by any thermal solver in a case when no
+ analytic solution is available or when a physical intuition is impossible to
+ achieve due to the complexity of the problem.</li>
<li><b>Educational purposes</b>: since the various probability sets used by
the underlying Monte-Carlo algorithms solely rely on the physics, thermal
@@ -72,13 +72,12 @@ purposes:<p>
<li><b>Sensitivity analysis</b>: the <a href="#green">Green functions</a> of
the system (estimated and stored during an initial Monte-Carlo computation)
can be reused for subsequent (very fast) post-processing computations. This
- makes possible to explore the sensitivity of any given result to the
+ makes it possible to explore the sensitivity of any given result to the
variations of a boundary or initial condition, or internal power source. This
technique is only a small part of a family of so-called "symbolic"
- Monte-Carlo algorithms that make possible to achieve the same sensitivity
- analysis, but for any thermal parameter (for instance: the conductivity of a
- given solid, a convective exchange coefficient or the emissivity of a
- solid).</li>
+ Monte-Carlo algorithms that extend the scope of sensitivity analysis to any
+ thermal parameter (for instance: the conductivity of a given solid, a
+ convective exchange coefficient or the emissivity of a solid).</li>
</ul>
<p>The Stardis framework is structured around <b>two main components</b>. The
@@ -104,7 +103,7 @@ post-processing. See below for more information on each of these components.<p>
<p><a href=https://gitlab.com/meso-star/stardis-solver.git>Stardis-Solver</a>
is the core library of Stardis: it simulates coupled convecto - conducto -
radiative heat transfers by sampling thermal paths that explore space and time
-until a boundary condition or an initial condition is found. Note that this
+until a boundary condition or an initial condition is met. Note that this
path formulation does not require <b>any volumetric mesh</b>: in addition of
the thermal properties and the limit/boundary conditions, only the geometry
defining the contours of the objects is necessary.</p>
@@ -187,7 +186,7 @@ solid element where an internal source of power must be taken into account.</p>
<h3 id="green">Green function</h3>
<p>The value of temperature computed at a probe position is no more than the
-average of the Monte-Carlo weight for every thermal path. In practice: when no
+mean of the Monte-Carlo weights of a set of thermal paths. In practice: when no
internal power source has to be considered, the weight of any given thermal
path is the temperature of the boundary or initial condition that has been
reached; when internal power sources or imposed fluxes are taken into account,
@@ -197,8 +196,8 @@ local dissipated power/imposed flux.</p>
<p>In any case, the position and date at the end of each thermal path (and also
accumulation coefficients) can be stored during a first complete Monte-Carlo
-simulation. This information, known as the Green function, can then be used in
-(very fast) post-processing to compute all required results for different
+simulation. This information, known as the Green function, can then be used in a
+(very fast) post-processing step to compute all required results for different
boundary and initial conditions (and also different internal power
sources/imposed flux).</p>
@@ -213,9 +212,9 @@ contributions.</p>
<h3 id="visu">Thermal path visualization</h3>
<p>Stardis-Solver can store the complete history of a set of thermal paths for
-later visualization. In addition to positions and dates, physical data is stored
-along thermal paths, such as the type of heat transfer phenomenon involved locally,
-the accumulated power/flux, etc.</p>
+later visualization. In addition to positions and dates, physics data is stored
+along thermal paths, such as the type of heat transfer phenomenon involved at
+each step, the accumulated power/flux, etc.</p>
<h2 id="cli">Stardis CLI tools</h2>
@@ -239,8 +238,8 @@ functions.</p>
<p>The main limitation that the <b>stardis</b> CLI adds to those of
Stardis-Solver is that property descriptions cannot be time or space dependent:
each region of the system, delimited by a boundary, can have its own set of
-properties, but these properties must be constant when stardis-solver allows
-properties varying in time and space.</p>
+properties, but these properties must be constant (whereas stardis-solver allows
+properties varying in time and space).</p>
<h3 id="sgreen-cli">The sgreen CLI</h3>
@@ -289,7 +288,7 @@ $ man sgreen-output
<p>Refer to the <a href=starter-pack.html>Stardis: Starter Pack</a> to quickly
run a thermal simulation through the <code>stardis</code> CLI; this archive
-provides input data and scripts that are good starting points to begin with the
+provides input data and scripts and is a good starting point to begin with the
Stardis framework.</p>
<h2 id=build>Build from sources</h2>
@@ -297,7 +296,7 @@ Stardis framework.</p>
<p>The Stardis framework can be built directly from its source trees. Note
that the whole Stardis framework was successfully built on Windows 10 with
Visual Studio Community 2019. However, we only officially support GNU/Linux 64
-bits and the build procedure is thus given for this system only. The simplest
+bits and the build procedure is thus only given for this system. The simplest
way to build Stardis from its sources is to rely on the <code>stardis</code>
branch of the <a
href="https://gitlab.com/meso-star/star-engine/tree/stardis">Star-Engine</a>
@@ -306,7 +305,7 @@ and the installation of Stardis. This build procedure assumes the following
prerequisites:</p>
<ul>
- <li><a href=https://git-scm.com>git</a> source control as well.</li>
+ <li><a href=https://git-scm.com>git</a> source control.</li>
<li><a href=https://gcc.gnu.org>GNU Compiler Collection</a> in version 4.9.2
or higher.</li>
<li><a href="https://cmake.org">CMake</a> in version 3 or higher.</li>
