commit 508d9b67eafaf1739e400f73fd9e3af3aade7124
parent 803447d9f352de24ee2418d6cda4b85d4209181f
Author: christophe coustet <christophe.coustet@meso-star.com>
Date: Fri, 20 Nov 2020 16:40:56 +0100
Small changes and fixes to existing text
Diffstat:
1 file changed, 14 insertions(+), 13 deletions(-)
diff --git a/stardis/stardis.html.in b/stardis/stardis.html.in
@@ -72,7 +72,8 @@ 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) simulations. This makes possible to
+ can be reused for subsequent (very fast) post-processing computations.
+ This 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
@@ -104,7 +105,7 @@ defining the contours of the objects is necessary.</p>
projects:<p>
<ul>
- <li><a href="#cli">Stardis</a> application is the reference implementation of a
+ <li><a href="#cli">Stardis application</a> is the reference implementation of a
complete workflow using Stardis-Solver.</li>
<li><a
href="https://www.edf.fr/en/the-edf-group/world-s-largest-power-company/activities/research-and-development/scientific-communities/simulation-softwares?logiciel=10818">
@@ -120,8 +121,8 @@ on the following hypothesis:</p>
introduces the notion of a conductive <b>path length</b> within the
Monte-Carlo algorithm. Solutions obtained using this algorithm are formally
exact at the limit of a null path length. In practice, this path length has
- to be adapted for a given geometric configuration so that its value is small
- compared to the smallest typical length of a solid.</li>
+ to be adapted, at the individual solid level, so that its value is small
+ compared to the typical length of the solid.</li>
<li><b>Convection</b>: fluid media are supposed to be <b>isothermal</b>, even if
their temperature may vary with time. This hypothesis relies on the
@@ -133,7 +134,7 @@ on the following hypothesis:</p>
flux (as a difference of temperatures, multiplied by a radiative exchange
coefficient). In order to be valid, this representation of radiative
transfer exchanges requires that the temperature at any position and time is
- close to a known reference temperature.</li>
+ close enough to a known reference temperature.</li>
</ul>
<p>The remaining of this section describes the main functionalities provided by
@@ -145,7 +146,7 @@ Stardis-Solver upon the aforementonned hypothesis.</p>
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
-temperature, using a Monte-Carlo method makes possible to compute a <b>numerical
+temperature, using a Monte-Carlo method makes it possible to compute a <b>numerical
uncertainty</b> (standard deviation of the weight distribution) over each
result.</p>
@@ -153,13 +154,13 @@ result.</p>
<p>Stardis-Solver can compute the flux over any surface (or group of surfaces)
at any time. Alternatively, it can also compute the total energy output from a
-solid element where a internal source of power must be taken into account.</p>
+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
-internal power sources have to be considered, the weight of any given thermal
+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,
additional contributions to the weight must be continuously evaluated by the
@@ -175,12 +176,12 @@ sources/imposed flux).</p>
<h3 id="visu">Thermal path visualization</h3>
-<p>Stardis-Solver can store the complete spatial and temporal position along a
-set of thermal paths, for latter visualization. In addition of their position
-and, each thermal path vertex register additional data as the type of thermal
-phenomena it simulates, the accumulated power/flux along the path, etc.</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>
-<h2 id="cli">Stardis CLI</h2>
+<h2 id="cli">Stardis CLI tools</h2>
<p>TODO</p>
