Stardis

Stardis is a thermal simulation framework for complex 2D and 3D environments, based on new Monte Carlo 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 "thermal paths" that explore space and time until a boundary condition or an initial condition is found. The key concept here is that heat transfer phenomena are not considered separately but are naturally coupled via the cross-recursion of the various Monte Carlo algorithms.

Example of conductive path sampled by Stardis in a foam geometry: it starts from the probe position (green point) and after several diffusive steps reaches a boundary condition (red point).

Beyond its use as a regular thermal simulation tool, the Stardis framework explicitly targets engineers, researcher, teachers or students wishing to fully appropriate the statistical formulation of heat transfer, from theoretical concepts to practical implementation. The complete Stardis framework is thus released under free license that guarantee the users the ability to freely use, study, modify or extend the complete source code according to their needs.

Despite its specific advantages, Stardis is not meant to fully replace already well established and highly validated thermal simulation tools. Instead, it can be seen as an additional tool that can be useful for various purposes:

• Probe computation: Stardis will not compute the full temperature field of a system; instead, it can be used to focus on a specific spatial/temporal zone of the system.

• Reference method: the numerical uncertainty is a unique feature of stochastic methods. It can be reduced using a reformulation of the underlying integral for specific problems, and possibly using better suited sampling techniques. Ultimately, the numerical uncertainty can also be reduced by increasing the number of statistical samples. Any value of 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 assumptions used to derive 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.

• Educational purposes: since the various probability sets used by the underlying Monte Carlo algorithms solely rely on the physics, thermal paths naturally explore the spatial and temporal zones of interest in the system. This can be a drawback in some situations (the paths will have a hard time exiting a highly conductive solid surrounded by non-conductive media) but is a major asset for the direct visualization of what contributes to a given result: the main sources of heat, what transfer modes are dominant, what are the main paths of heat transfer, etc.

• Sensitivity analysis: the Green functions 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 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 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).

~ $ git clone https://gitlab.com/meso-star/star-build.git
~ $ cd star-build
~/star-build $ make \
  PREFIX=/path/to/stardis/ \
  BUILD=src/therm-apps/stardis_0.11.1.sh

~/star-build $ make \
  PREFIX=/path/to/stardis/ \
  BUILD=src/therm-apps/stardis_0.11.1.sh \
  DISTRIB_PARALLELISM=NONE

. /path/to/stardis/etc/profile
stardis -h
man stardis