The Jet Propulsion Laboratory has a keen interest in exploring icy moons in the solar system, particularly Jupiter's Europa. Successful exploration of the moon's surface includes planetary protection initiatives to prevent the introduction of viable organisms from Earth to Europa. To that end, the Europa lander requires a Terminal Sterilization Subsystem (TSS) to rid the lander of viable organisms that would potentially contaminate the moon's environment. Sandia National Laboratories has been developing a TSS architecture, relying heavily on computational models to support TSS development. Sandia's TSS design approach involves using energetic material to thermally sterilize lander components at the end of the mission. A hierarchical modeling approach was used for system development and analysis, where simplified systems were constructed to perform empirical tests for evaluating energetic material formulation development and assist in developing computational models with multiple tiers of physics fidelity. Computational models have been developed using multiple Sandia-native computational tools. Three experimental systems and corresponding computational models have been developed: Tube, Sub-Box Small, and Sub-Box Large systems. This paper presents an explanation of the application context of the TSS along with an overview description of a small portion of the TSS development from a modeling and simulation perspective, specifically highlighting verification, validation, and uncertainty quantification (VVUQ) aspects of the modeling and simulation work. Multiple VVUQ approaches were implemented during TSS development, including solution verification, calibration, uncertainty quantification, global sensitivity analysis, and validation. This paper is not intended to express the design results or parameter values used to model the TSS but to communicate the approaches used and how the results of the VVUQ efforts were used and interpreted to assist system development.
The evaluation of effective material properties in heterogeneous materials (e.g., composites or multicomponent structures) is critically relevant to a wide spectrum of applications, including nuclear power, electronic packaging, flame retardants, hypersonics, and gas turbine power. The work described in this paper is centered around the numerical assessment of the thermal behavior of porous materials obtained from finite element thermal modeling and simulation. Two-dimensional, steady-state analyses were performed on unit cells with centered, circular pores using a second-order accurate Galerkin finite element method (FEM). The effective thermal conductivities of the porous systems were examined, encompassing a range of porosities from 4.9% to 60.1%. The geometries of the models were generated based on ordered circular pores for each modeled porosity level. The system response quantity (SRQ) under investigation was the dimensionless effective thermal conductivity across the unit cell. The dimensionless effective thermal conductivity was compared across all simulated cases, producing a trend between porosity and effective thermal conductivity. In the presented investigation, the method of manufactured solutions (MMS) was used to perform code verification, and the grid convergence index (GCI) was employed to estimate discretization uncertainty as solution verification. Code verification concluded an approximately second order accurate Galerkin FEM solver. It was found that the introduction of porosity to the unit cell material structure reduces effective thermal conductivity, as anticipated. Numerical results obtained in this study are compared to an analytical solution and to a sample of empirical data. This approach can be readily generalized to study a wide variety of porous solids from ranging from structures at the nanoscale—such as nanocarbon tubes—to structures at macrolevel scales—such as geological features.
Physics models-such as thermal, structural, and fluid models-of engineering systems often incorporate a geometric aspect such that the model resembles the shape of the true system that it represents. However, the physical domain of the model is only a geometric representation of the true system, where geometric features are often simplified for convenience in model construction and to avoid added computational expense to running simulations. The process of simplifying or neglecting different aspects of the system geometry is sometimes referred to as "defeaturing."Typically, modelers will choose to remove small features from the system model, such as fillets, holes, and fasteners. This simplification process can introduce inherent error into the computational model. A similar event can even take place when a computational mesh is generated, where smooth, curved features are represented by jagged, sharp geometries. The geometric representation and feature fidelity in a model can play a significant role in a corresponding simulation's computational solution. In this paper, a porous material system-represented by a single porous unit cell-is considered. The system of interest is a two-dimensional square cell with a centered circular pore, ranging in porosity from 1% to 78%. However, the circular pore was represented geometrically by a series of regular polygons with number of sides ranging from 3 to 100. The system response quantity under investigation was the dimensionless effective thermal conductivity, k∗, of the porous unit cell. The results show significant change in the resulting k∗ value depending on the number of polygon sides used to represent the circular pore. In order to mitigate the convolution of discretization error with this type of model form error, a series of five systematically refined meshes was used for each pore representation. Using the finite element method (FEM), the heat equation was solved numerically across the porous unit cell domain. Code verification was performed using the Method of Manufactured Solutions (MMS) to assess the order of accuracy of the implemented FEM. Likewise, solution verification was performed to estimate the numerical uncertainty due to discretization in the problem of interest. Specifically, a modern grid convergence index (GCI) approach was employed to estimate the numerical uncertainty on the systematically refined meshes. The results of the analyses presented in this paper illustrate the importance of understanding the effects of geometric representation in engineering models and can help to predict some model form error introduced by the model geometry.