commit 3caf8cba7ece9503a13f30dfefa9990199fd0823
parent 000be6987d49ff4e636bc5fab616a11a768899c6
Author: Christophe Coustet <christophe.coustet@meso-star.com>
Date: Wed, 6 Jan 2021 16:29:15 +0100
Fix a few typos
Diffstat:
1 file changed, 19 insertions(+), 19 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
@@ -76,7 +76,7 @@ purposes:<p>
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
+ 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>
@@ -104,7 +104,7 @@ 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>
@@ -162,7 +162,7 @@ Stardis-Solver upon the aforementonned hypothesis.</p>
<h3>Probe computation</h3>
-<p>Stardis-Solvers compute the temperature at any given position (spatial and
+<p>Stardis-Solver computes the temperature at any given position (spatial and
temporal). The main idea is that thermal paths start from this probe position,
and scatter in space while going back in time, until a (spatial) boundary
condition or a (temporal) initial condition is met. In addition to the value of
@@ -179,7 +179,7 @@ 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,
@@ -189,8 +189,8 @@ 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>
@@ -206,8 +206,8 @@ contributions.</p>
<p>Stardis-Solver can store the complete history of a set of thermal paths for
later visualization. In addition to positions and dates, physics data is stored
-along thermal paths, such as the type heat transfer phenomeon involved locally,
-the accumulated power/flux, etc.</p>
+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>
@@ -231,8 +231,8 @@ 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>
@@ -281,7 +281,7 @@ $ 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>
@@ -289,7 +289,7 @@ 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>
@@ -298,7 +298,7 @@ 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>
@@ -327,7 +327,7 @@ procedure is summed up to:</p>
shell.</p>
<pre class="code">
-$ source ~/Stardis-${VERSION}/etc/stardis.profile
+$ source ~/Stardis-${VERSION}/local/etc/stardis.profile
$ stardis -h
</pre>
