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SERPENT validation and optimization with mesh adaptive search on stereolithography geometry models
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A data-driven framework for error estimation and mesh-model optimization in system-level thermal-hydraulic simulation
We report over the past decades, several computer codes have been developed for simulation and analysis of thermal-hydraulics and system response in nuclear reactors under operating, abnormal transient, and accident conditions. However, simulation errors and uncertainties still inevitably exist even while these codes have been extensively assessed and used. In this work, a data-driven framework (Optimal Mesh/Model Information System, OMIS) is formulated and demonstrated to estimate simulation error and suggest optimal selection of computational mesh size (i.e., nodalization) and constitutive correlations (e.g., wall functions and turbulence models) for low-fidelity, coarse-mesh thermal-hydraulic simulation, in order to achieve accuracy comparable to that of high-fidelity simulation. Using results from high-fidelity simulations and experimental data with many fast-running low-fidelity simulations, an error database is built and used to train a machine learning model that can determine the relationship between local simulation error and local physical features. This machine learning model is then used to generate insight and help correct low-fidelity simulations for similar physical conditions. The OMIS framework is designed as a modularized six-step procedure and accomplished with state-of-the-art methods and algorithms. A mixed-convection case study was performed to illustrate the entire framework.
Study of Data-Driven Mesh-Model Optimization in System Thermal-Hydraulic Simulation
Quantification of nuclear power plant safety risk requires a systematic and yet practical approach to identification of accident scenarios, assessment of their likelihood and consequences. Instrumental to this goal is risk-informed safety margin characterization (RISMC) framework, whose realization requires computationally robust and affordable methods for sufficiently accurate simulation of complex multi-dimensional physical phenomena, such as turbulent and multi-phase flow. The CFD-like correlation-based codes with 3D simulation capability (e.g., GOTHIC) ensure computational efficiency using coarse mesh size and the sub-grid phenomena in the boundary layer that can be captured by adequate constitutive correlations. However, the error sources and user effects on the selection of mesh size and models lead to unpredictable simulation error, while rich High-Fidelity (HF) data from experiments and numerical simulation using validated code or DNS are not fully explored. It would be useful to have a “smart” data-driven multi-scale framework in which the low-resolution models can be “taught” to emulate high-resolution models. The objective of this work is to develop and evaluate a physical-based data-driven mesh-model optimization approach (Optimized Mesh/Model Information System, OMIS) to estimate the simulation error and give advice on the optimized selection of coarse mesh size and Low-Fidelity (LF) models for System Thermal Hydraulic (STH) simulation to achieve accuracy comparable to that of HF models. This approach takes advantages of computational efficiency of coarse-mesh simulation and application of Machine Learning (ML) algorithms.
Optimization-based mesh correction with volume and convexity constraints
Here, we consider the problem of finding a mesh such that 1) it is the closest, with respect to a suitable metric, to a given source mesh having the same connectivity, and 2) the volumes of its cells match a set of prescribed positive values that are not necessarily equal to the cell volumes in the source mesh. Also, this volume correction problem arises in important simulation contexts, such as satisfying a discrete geometric conservation law and solving transport equations by incremental remapping or similar semi-Lagrangian transport schemes. In this paper we formulate volume correction as a constrained optimization problem in which the distance to the source mesh defines an optimization objective, while the prescribed cell volumes, mesh validity and/or cell convexity specify the constraints. We solve this problem numerically using a sequential quadratic programming (SQP) method whose performance scales with the mesh size. To achieve scalable performance we develop a specialized multigrid-based preconditioner for optimality systems that arise in the application of the SQP method to the volume correction problem. Numerical examples illustrate the importance of volume correction, and showcase the accuracy, robustness and scalability of our approach.
Local element operations for curved simplex meshes
Summary Mesh optimization procedures are generally a combination of node smoothing and discrete operations which affect a small number of elements to improve the quality of the overall mesh. These procedures are useful as a post‐processing step in mesh generation procedures and in applications such as fluid simulations with severely deforming domains. In order to perform high‐order mesh optimization, these ingredients must also be extended to high‐order (curved) meshes. In this work, we present a method to perform local element operations on curved meshes. The mesh operations discussed in this work are edge/face swaps, edge collapses, and edge splitting (more generally refinement) for triangular and tetrahedral meshes. These local operations are performed by first identifying the patch of elements which contain the edge/face being acted on, performing the operation as a “straight‐sided one" by placing the high‐order nodes via an isoparametric mapping from the master element, and smoothing the high‐order nodes on the elements in the patch by minimizing a Jacobian‐based high‐order mesh distortion measure. Since the initial “straight‐sided guess” from the placement of the nodes via the isoparametric mapping frequently results in invalid elements, the distortion measure must be regularized which allows for mesh untangling for the optimization to succeed. We present several examples in 2D and 3D to demonstrate these local operations and how they can be combined with a high‐order node smoothing procedure to maintain mesh quality when faced with severe deformations.
Multi-material ALE remap with interface sharpening using high-order matrix-free finite element methods
The arbitrary Lagrangian-Eulerian (ALE) technique involves remapping field quantities from a Lagrangian mesh to an optimized mesh in a conservative, accurate and bounds-preserving manner. For methods based on arbitrary order finite elements, as described in a reference, material volume fractions are advected in pseudo-time using flux-corrected transport (FCT) without any form of interface reconstruction. In practice, this can lead to excessive propagation of small volume fractions throughout the domain. In addition, this method requires assembly of a global advection matrix to compute the bounds-preserving low-order FCT solution. In this work, we introduce a new approach for ALE remap using a high-order matrix-free technique which incorporates a flux modification to sharpen material interfaces in a conservative manner. Our approach begins with computing a bounds-preserving low-order solution to the ALE remap equations at the element level. We then compute a sharp interface solution (not guaranteed to be bounds-preserving) which comes from solving an augmented version of the ALE remap equations with a conservative flux modification which acts to sharpen material volume fractions based on their gradients and transport directions. Using the sharp interface solution, we make global corrections to the bounds-preserving solution while maintaining preservation of bounds. By blending with the sharpened solution at the global level we are able to globally conserve mass without hindering the remap pseudo-time step. This new interface-aware ALE remap method is based entirely on partial assembly techniques where globally assembled matrix operators are no longer needed, resulting in a globally matrix-free FCT method for multi-material, multi-field ALE remap with high performance on GPU architectures. We present results of our new remap method on 1D, 2D and 3D benchmarks and describe the algorithmic tailoring for GPU architectures that was developed.
Parallel performance optimizations on unstructured mesh-based simulations
This paper addresses two key parallelization challenges the unstructured mesh-based ocean modeling code, MPAS-Ocean, which uses a mesh based on Voronoi tessellations: (1) load imbalance across processes, and (2) unstructured data access patterns, that inhibit intra- and inter-node performance. Our work analyzes the load imbalance due to naive partitioning of the mesh, and develops methods to generate mesh partitioning with better load balance and reduced communication. Furthermore, we present methods that minimize both inter- and intranode data movement and maximize data reuse. Our techniques include predictive ordering of data elements for higher cache efficiency, as well as communication reduction approaches. We present detailed performance data when running on thousands of cores using the Cray XC30 supercomputer and show that our optimization strategies can exceed the original performance by over 2×. Additionally, many of these solutions can be broadly applied to a wide variety of unstructured grid-based computations.
Optimally-Transported Meshes for Model Reduction and Uncertainty Quantification of High-Speed Flow
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Optimal Unstructured Mesh Data Compression and Reconstruction Using Compressed Sensing.
Abstract not provided.
Compressed Sensing and Reconstruction of Unstructured Mesh Datasets: Optimal Compression.
Abstract not provided.
Removing Low-Frequency Artefacts from Topology-Optimized Surface Meshes.
Abstract not provided.
On modeling subgrid-scale macro-structures in narrow twisted channels
Porosity-based subgrid topography models often fail to capture the effects of subgrid-scale topographic features in the interior of coarse grid cells. Existing approaches that modify bottom roughness or a drag coefficient are inadequate for macro-structures (large emergent or submerged obstacles) in subgrid-scale narrow twisted channels. Such structures partially block the cross-sectional area and provide enhanced topographic dissipation – effects that are not well represented by a drag coefficient that scales on a coarse-grid cell-averaged velocity and the cell volume. The relative alignment between mesh and flow further complicates this problem as it makes the subgrid model sensitive to mesh design. In the present study, three new approaches for simulating subgrid-scale macro-structures in narrow channels are proposed. The interior partial-blocking effect of structures is modeled as reduction of grid face-area. The sheltering of flow volumes around obstacles, which leads to topographic dissipation, is modeled by reducing the cell volume in the momentum equation (only). A mesh-shift procedure is designed to optimize mesh alignment for identifiable subgrid features. Combining the three subgrid methods improves the approximation of surface elevation and in-channel flow rate with a coarse-grid model. Tests are conducted for channelized flow using both synthetic domains and real marsh topography. Finally, the new methods reduce the overall mesh dependency of the subgrid model and provides stronger physical connection between effects of macro-structures and their geometry at coarse grid scales.
Not so HOT Triangulations
Here, we propose primal–dual mesh optimization algorithms that overcome shortcomings of the standard algorithm while retaining some of its desirable features. “Hodge-Optimized Triangulations” defines the “HOT energy” as a bound on the discretization error of the diagonalized Delaunay Hodge star operator. HOT energy is a natural choice for an objective function, but unstable for both mathematical and algorithmic reasons: it has minima for collapsed edges, and its extrapolation to non-regular triangulations is inaccurate and has unbounded minima. We propose a different extrapolation with a stronger theoretical foundation, and avoid extrapolation by recalculating the objective just beyond the flip threshold. We propose new objectives, based on normalizations of the HOT energy, with barriers to edge collapses and other undesirable configurations. We propose mesh improvement algorithms coupling these. When HOT optimization nearly collapses an edge, we actually collapse the edge. Otherwise, we use the barrier objective to update positions and weights and remove vertices. By combining discrete connectivity changes with continuous optimization, we more fully explore the space of possible meshes and obtain higher quality solutions.
Comparison of ADVANTG Simulations to Simple Shielding Measurements - Paper 36
Monte Carlo (MC) radiation transport simulations, like those performed using Monte Carlo N-Particle (MCNP), can be highly accurate but require time to reduce the statistical uncertainties in the calculated detector response(s). Many different 'variance reduction' techniques devised over the years increase the efficiency of MC calculations, but these methods can be more art than science. Many of these methods are problem specific or require careful tuning of parameters for good performance. One of the best variance reduction methods, in terms of both reliable performance increases and ease of use, is the use of weight windows. Weight windows are derived from an importance map in space and energy that allows the transport code to focus more simulation time on particles that have a higher likelihood of being important to the final detector response. Less time is spent simulating particles that have little chance of making a contribution to the detector response. MCNP already includes the abilities to generate and use weight windows. Unfortunately, generating weight windows can take a very long time, perhaps longer than the original problem. Oak Ridge National Laboratory has led the development of hybrid methods using a discrete ordinates adjoint calculation to quickly develop an importance map (weight windows) for use in a Monte Carlo code. The Consistent Adjoint Driven Importance Sampling (CADIS) method uses an adjoint calculation to develop both an importance map and a biased source distribution that work together. This method is reasonably automatic, is easy to use, and has been implemented in the Automated Variance reduction Generator (ADVANTG) package that works with MCNP5. ADVANTG reads an MCNP input file and a small amount of extra user information and then runs an adjoint deterministic calculation using Denovo. The importance map and biased source distribution derived from the CADIS method can then be incorporated back into the original MCNP input file to enhance the figure-of-merit (FOM) of the calculation. The FOM is a measure of the efficiency of the calculation using the amount of run time and corresponding level of statistical uncertainty in the detector result. ADVANTG-enhanced MCNP input files have been shown to increase the FOM of many types of problems, obtaining the same level of statistical uncertainty in a shorter time compared to standard MCNP. ADVANTG can also generate variance reduction parameters (importance map and biased source) using the Forward-Weighted CADIS (FW-CADIS) method for optimizing mesh tallies. The CADIS method creates variance reduction parameters that optimize a tally's total quantity (flux, dose rate, interaction rate, etc.) and will not generally increase performance for all parts of a segmented tally. To optimize a segmented tally, the FW-CADIS method uses two discrete ordinates calculations to make variance reduction parameters that help compute a segmented tally with uniform relative uncertainties in each bin. This report will demonstrate that using ADVANTG for neutron and photon radiation shielding problems will (1) preserve the standard MCNP results and (2) obtain those results either with less statistical uncertainty or in shorter times than the standard MCNP calculation. The four demonstration problems in this report are based on simple neutron or photon transmission measurements through different materials. All of the problems here used the CADIS method and still saw improvement across the flux tallies segmented by particle energy. (authors)
Exploring the Feasibility of INCONEL® ALLOY 740H® for Power Plant Headers: Integrating Machine Learning with Computational Fluid Dynamics (CFD)
This keynote presentation explores the behavior of headers—essential components of pipeline systems—using ANSYS simulation software and machine learning techniques. The study aims to predict the thermal and mechanical performance of headers under diverse conditions through both steady-state and transient simulations. We investigate critical parameters such as heat transfer coefficient, fluid velocity, and temperature to optimize header design. Conducted as part of a DOE project led by NCAT in collaboration with UNC Charlotte, this research encompasses multiple key topics. The initial section focuses on the behavior of header systems under steady-state conditions using ANSYS simulation. It underscores the importance of headers in industrial infrastructure, especially in the energy sector, and examines the implications of material selection and flow direction on heat transfer dynamics. Methodologically, we employ Computational Fluid Dynamics (CFD) analysis through ANSYS, detailing the development of models, material properties, geometry specifications, boundary conditions, and meshing strategies. Our simulations explore various operational parameters, including temperature and mass flow rates, crucial for predicting heat transfer coefficients and enhancing header design. Results from the study include parametric investigations into mesh sensitivity, viscosity model evaluations, and the effects of heat transfer locations, all validated against theoretical calculations. We conclude with insights on mesh optimization, the suitability of viscosity models, and recommendations for future research aimed at improving header system efficiency and sustainability in industrial applications.
Electron beam simulation from gun to collector: Towards a complete solution
An electron-beam simulation technique for high-resolution complete EBIS/T modelling is presented. The technique was benchmarked on the high compression HEC{sup 2} test-stand with an electron beam current, current density and energy of 10 A, 10 kA/cm{sup 2} and 49.2 keV, and on the immersed electron beam at REXEBIS for electron beam characteristics of 0.4 A, 200 A/cm{sup 2} and 4.5 keV. In both Brillouin-like and immersed beams the electron-beam radius varies from several millimeters at the gun, through some hundreds of micrometers in the ionization region to a few centimeters at the collector over a total length of several meters. We report on our approach for finding optimal meshing parameters, based on the local beam properties such as magnetic field-strength, electron energy and beam radius. This approach combined with dividing the problem domain into sub-domains, and subsequent splicing of the local solutions allowed us to simulate the beam propagation in EBISes from the gun to the collector using a conventional PC in about 24–36 h. Brillouin-like electron beams propagated through the complete EBIS were used to analyze the beam behavior within the collector region. We checked whether elastically reflected paraxial electrons from a Brillouin-like beam will escape from the collector region and add to the loss current. We have also studied the power deposition profiles as function of applied potentials using two electrode geometries for a Brillouin-like beam including the effects of backscattered electrons.