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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Numerical and experimental investigation of the flame kernel growth in a methane/air mixture near the lean flammability limit

Lean combustion has the potential to improve the thermal efficiency of spark-ignition engines, but it faces the significant challenge of increased cycle-to-cycle variation due to low mixture reactivity and unstable flame dynamics. Computational fluid dynamics (CFD) employing predictive models can guide engine design and optimize operating strategies for lean combustion. However, ignition and combustion models have rarely been validated at fuel-lean conditions, and a fundamental understanding of the early flame kernel growth process is also lacking for a successful sub-model development. Here, the present study develops a numerical simulation framework used to investigate early flame kernel growth in methane/air mixtures. A nanosecond-pulsed discharge (NPD) approach is employed to effectively decouple the flame kernel growth from the electrical discharge due to their difference in timescales, and equivalence ratios near the experimentally measured lean flammability limit (LFL) are selected to focus on challenging mixture conditions. Three numerical investigations, such as the choice of turbulence modeling, grid size, and grid control strategies, are examined to match both LFL and flame kernel structure measured from experiments. It is demonstrated that a quasi-direct numerical simulation (QDNS) with a fixed grid embedding of 10 μm can predict the LFL as φ CFD =0.61 and match the displacement speed of the kernel’s boundary marked in schlieren images. To predict the LFL and flame kernel shape, a fine grid (Δ≤12.5 μm) is needed to capture the consumption of formaldehyde (CH 2 O) in kernel’s reaction branches attached to the anode, and adaptive mesh refinement is replaced with the fixed embedding due to loss of simulation accuracy. Also, it is found that a large-eddy simulation (LES) using the Dynamic Structure model is not suitable for the NPD-induced flame kernel simulation because artificial sub-grid turbulent kinetic energy induced by shock dynamics alters the flow velocity calculation, resulting in divergence of LES from QDNS. Lastly, the simulation well matches the experimental data for the flame kernel evolution in three mixture conditions (φ = 0.7, 0.61, 0.55), showing toroidal flame kernel expansion and flame kernel growth/extinction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Bias-Variance Trade-Off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are provided for selecting biased, unbiased, or hybrid versions, depending on the dimensionality and nonlinearity of the specific PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A New Semistructured Algebraic Multigrid Method

Multigrid methods are well suited to large massively parallel computer architectures because they are mathematically optimal and display good parallelization properties. Since current architecture trends are favoring regular compute patterns to achieve high performance, the ability to express structure has become much more important. The hypre software library provides high-performance multigrid preconditioners and solvers through conceptual interfaces, including a semistructured interface that describes matrices primarily in terms of stencils and logically structured grids. This paper presents a new semistructured algebraic multigrid (SSAMG) method built on this interface. The numerical convergence and performance of a CPU implementation of this method are evaluated for a set of semistructured problems. In conclusion, SSAMG achieves significantly better setup times than hypre’s unstructured AMG solvers and comparable convergence. In addition, the new method is capable of solving more complex problems than hypre’s structured solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

TiO{sub 2}-coated foams as a medium for solar catalysis

Graphical abstract: Photographs taken at the Scanning Electron Microscope of (a) a surface coating of TiO{sub 2}, (b) a mesh of a foam (mesh diameter of 2 mm) and (c) a foam. Research highlights: {yields} Assess the efficiency of the foams as a photocatalytic media. {yields} Foam to improve the apparent quantum yield. {yields} Foam makes good use of the UV rays to break down molecules. -- Abstract: Sunlight irradiating the surface of the Earth represents a maximum input available for a solar catalytic process of 50 W{sub UV} m{sup -2}. We propose using high-porosity, metallic, reticulated foams as the support medium for the photocatalyst in order to improve the apparent quantum yield. The layer of TiO{sub 2} was applied by dip-coating. The measurement of the degradation kinetics was carried out on a model target molecule, 2,4 dichlorophenol, at an initial concentration of 10 mg l{sup -1}. The aim was to assess the efficiency of the foams as a photocatalytic media compared to that of a suspension of catalytic powder (Degussa P25) and the flat 2D support (Ahlstrom cellulose media). The apparent quantum yield of the foam scaffold carrying the TiO{sub 2} was high, showing that, as with the powder suspension, foam makes good use of the UV rays to break down molecules. It is noteworthy that the apparent quantum yield of the foam tended towards that observed for suspensions which form the ideal support thanks to their optimal ability to harness the light.

36 MATERIALS SCIENCE↗
DOE OSTI2016

CFD and X-ray Analysis of Gaseous Direct Injection from an Outward Opening Injector

Using natural gas in an internal combustion engine (ICE) is emerging as a promising way to improve thermal efficiency and reduce exhaust emissions. In the development of such engine platforms, computational fluid dynamics (CFD) plays a fundamental role in the optimization of geometries and operating parameters. One of the most relevant issues in the simulation of direct injection (DI) gaseous processes is the accurate prediction of the gas jet evolution. The simulation of the injection process for a gaseous fuel does not require complex modeling, nevertheless properly describing high-pressure gas jets remains a challenging task. At the exit of the nozzle, the injected gas is under-expanded, the flow becomes supersonic and shocks occur due to compressibility effects. These phenomena lead to challenging computational requirements resulting from high grid resolution and low computational time-steps. In this paper, the simulation of gaseous injection from an outward opening injector was carried out with the commercial CFD software CONVERGE. Argon was injected into a quiescent chamber filled with Nitrogen. Two free-jet cases with injection pressure of 15 and 10 bar, and ambient pressure of 1 and 2 bar respectively, were tested. For the 15 bar injection pressure and 1 bar ambient pressure, a jet impinging on a barrier case was performed as well. The numerical results were validated against experimental mass measurements using X-ray radiography. The results, presented in terms of 2D maps and transverse sections of equivalent path length of argon, showed good agreement between modeling and experiments in terms of jet penetration, shape and gas mass distribution. These emphasized the significant impact of mesh size on the results for the simulation of this type of injector. Due to the small geometrical dimensions a fine mesh is required in order to deliver an accurate description of the mixture formation. Numerical results were discussed with a particular emphasis on the application to engine simulations, where a trade-off between accuracy and computational time is a necessity.

Bartolucci, Lorenzo↗
NASA NTRS2010

NASA's Soil Moisture Active and Passive (SMAP) Mission

The Soil Moisture Active-Passive (SMAP) Mission is one of the first Earth observation satellites being formulated by NASA in response to the 2007 National Research Council s Decadal Survey. SMAP will make global measurements of soil moisture at the Earth's land surface and its freeze-thaw state. These measurements will allow significantly improved estimates of water, energy and carbon transfers between the land and atmosphere. Soil moisture measurements are also of great importance in assessing flooding and monitoring drought. Knowledge gained from SMAP observations can help mitigate these natural hazards, resulting in potentially great economic and social benefits. SMAP observations of soil moisture and freeze/thaw timing over the boreal latitudes will also reduce a major uncertainty in quantifying the global carbon balance and help to resolve an apparent missing carbon sink over land. The SMAP mission concept will utilize an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna flying in a 680 km polar orbit with an 8-day exact ground track repeat aboard a 3-axis stabilized spacecraft to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days. In addition, the SMAP project will use these surface observations with advanced modeling and data assimilation to provide estimates of deeper root-zone soil moisture and net ecosystem exchange of carbon. SMAP recently completed its Phase A Mission Concept Study Phase for NASA and transitioned into Phase B (Formulation and Detailed Design). A number of significant accomplishments occurred during this initial phase of mission development. The SMAP project held several open meetings to solicit community feedback on possible science algorithms, prepared preliminary draft Algorithm Theoretical Basis Documents (ATBDs) for each mission science product, and established a prototype algorithm testbed to enable testing and evaluation of the performance of candidate algorithms. SMAP conducted an Applications Workshop in September 2009 to coordinate with potential application users interested in the mission data. A draft Applications Plan describing the Project s planned outreach to potential applications users has been prepared and will be updated during Phase B. SMAP made a significant evaluation of the potential terrestrial radio frequency interference (RFI) source environment and established radiometer and radar flight hardware and ground processing mitigation approaches. SMAP finalized its science orbit and orbit injection approach to optimize launch mass and prepared launch and commissioning scenarios and timeline. A science data communications approach was developed to maximize available science data volume to improve science margins while maintaining moderately short data product latencies to support many potential applications using existing ground assets and with minimum impact to the flight system. SMAP developed rigid multi-body and flexible body dynamics and control models and system designs for the 6-meter rotating instrument reflector-boom assembly (RBA) and flight system to confirm pointing and control performance, and devised strategies to efficiently implement on-orbit balancing if needed. Industry partners were selected for the spin mechanism assembly (SMA) and RBA. Preliminary designs for the radar and radiometer were initiated, including constructing breadboards of key assemblies.

Kellogg, Kent↗
DOE OSTI2020

Compare linear-system solver and preconditioner stacks with emphasis on GPU performance and propose phase-2 NGP solver development pathway

The goal of the ExaWind project is to enable predictive simulations of wind farms comprised of many megawatt-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary physics codes in the ExaWind project are Nalu-Wind, which is an unstructured-grid solver for the acoustically incompressible Navier-Stokes equations, and OpenFAST, which is a whole-turbine simulation code. The Nalu-Wind model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as reinitialization of matrices and recomputation of preconditioners is required at every time step. In this report we evaluated GPU-performance baselines for the linear solvers in the Trilinos and hypre solver stacks using two representative Nalu-Wind simulations: an atmospheric boundary layer precursor simulation on a structured mesh, and a fixed-wing simulation using unstructured overset meshes. Both strong-scaling and weak-scaling experiments were conducted on the OLCF supercomputer Summit and similar proxy clusters. We focused on the performance of multi-threaded Gauss-Seidel and two-stage Gauss-Seidel that are extensions of classical Gauss-Seidel; of one-reduce GMRES, a communication-reducing variant of the Krylov GMRES; and algebraic multigrid methods that incorporate the afore-mentioned methods. The team has established that AMG methods are capable of solving linear systems arising from the fixed-wing overset meshes on CPU, a critical intermediate result for ExaWind FY20 Q3 and Q4 milestones. For the fixed-wing strong-scaling study (model with 3M grid-points), the team identified that Nalu-Wind simulations with the new Trilinos and hypre solvers scale to modest GPU counts, maintaining above 70% efficiency up to 6 GPUs. However, there still remain significant bottlenecks to performance: matrix assembly (hypre), AMG setup (hypre and Trilinos) In the weak-scaling experiments (going from 0.4M to 211M gridpoints), it's shown that the solver apply phases are faster on GPUs, but that Nalu-Wind simulation times grow, primarily due to the multigrid-setup process. Finally, based on the report outcomes, we propose a linear solver path-forward for the remainder of the ExaWind project. Near term, the NREL team will continue their work on GPU-based linear-system assembly. They will also investigate how the use of alternatives to the NVIDIA UVM (unified virtual memory) paradigm affects performance. Longer term, the NREL team will evaluate algorithmic performance on other types of accelerators and merge their improvements back to the main hypre repository branch. Near term, the Trilinos team will address performance bottlenecks identified in this milestone, such as implementing a GPU-based segregated momentum solve and reusing matrix graphs across linear-system assembly phases. Longer term, the Trilinos team will do detailed analysis and optimization of multigrid setup.

17 WIND ENERGY↗
NASA NTRSDate not supplied

Simplified Applied Loads of a Transonic Truss-Braced Wing Derived from Computational Fluid Dynamics

The transonic truss-braced wing (TTBW) concept is under consideration by the NASA Advanced Air Transport Technology (AATT) project due to its potential to reduce fuel consumption of transport aircraft. To properly evaluate the TTBW concept, detailed structural modeling and subsequent test planning is underway, which requires a simplified set of discrete applied loads that simulate the distributed aerodynamic pressures. Given the unique architecture of the TTBW and its intended operation in the transonic regime, computational fluid dynamics (CFD) is the ideal approach to employ to ensure the aerodynamics of this configuration are sufficiently captured. To convert aerodynamic results into input for structural models, application of a data transfer tool, Discrete Data Transfer Between Dissimilar Meshes (DDTBDM), is presented to resolve aerodynamic pressures from CFD into single load vectors at hardpoints along both the wing and the strut. Evaluation of mapping the aerodynamic forces to hardpoints at rib and spar intersections in a structural model is performed, and the results are also compared with a linear static aeroelastic analysis. The structural responses indicate that distributing forces along only the wing and strut forward spars is insufficient for simulating the distributed aerodynamic loads. Adding moments along the forward spars improved the global response of the wing, but the point moments created unrealistic local rotations within the structure. When distributing loads along both the forward and aft spars of the wing and strut, the global response was less sensitive to the inclusion of point moments, which indicated the arrangement of point forces alone was sufficient input for structural design work and testing and provided a more continuous loading profile.

Finite Element↗
NASA NTRSDate not supplied

Simplified Applied Loads of a Transonic Truss-Braced Wing Derived from Computational Fluid Dynamic

The transonic truss-braced wing (TTBW) concept is under consideration by the NASA Advanced Air Transport Technology (AATT) project due to its potential to reduce fuel consumption of transport aircraft. To properly evaluate the TTBW concept, detailed structural modeling and subsequent test planning is underway, which requires a simplified set of discrete applied loads that simulate the distributed aerodynamic pressures. Given the unique architecture of the TTBW and its intended operation in the transonic regime, computational fluid dynamics (CFD) is the ideal approach to employ to ensure the aerodynamics of this configuration are sufficiently captured. To convert aerodynamic results into input for structural models, application of a data transfer tool, Discrete Data Transfer Between Dissimilar Meshes (DDTBDM), is presented to resolve aerodynamic pressures from CFD into single load vectors at hardpoints along both the wing and the strut. Evaluation of mapping the aerodynamic forces to hardpoints at rib and spar intersections in a structural model is performed, and the results are also compared with a linear static aeroelastic analysis. The structural responses indicate that distributing forces along only the wing and strut forward spars is insufficient for simulating the distributed aerodynamic loads. Adding moments along the forward spars improved the global response of the wing, but the point moments created unrealistic local rotations within the structure. When distributing loads along both the forward and aft spars of the wing and strut, the global response was less sensitive to the inclusion of point moments, which indicated the arrangement of point forces alone was sufficient input for structural design work and testing and provided a more continuous loading profile.

Finite Element↗
DOE OSTI2016

Optimization of an enclosed gas analyzer sampling system for measuring eddy covariance fluxes of H 2 O and CO 2

Several initiatives are currently emerging to observe the exchange of energy and matter between the earth's surface and atmosphere standardized over larger space and time domains. For example, the National Ecological Observatory Network (NEON) and the Integrated Carbon Observing System (ICOS) are set to provide the ability of unbiased ecological inference across ecoclimatic zones and decades by deploying highly scalable and robust instruments and data processing. In the construction of these observatories, enclosed infrared gas analyzers are widely employed for eddy covariance applications. While these sensors represent a substantial improvement compared to their open- and closed-path predecessors, remaining high-frequency attenuation varies with site properties and gas sampling systems, and requires correction. Here, we show that components of the gas sampling system can substantially contribute to such high-frequency attenuation, but their effects can be significantly reduced by careful system design. From laboratory tests we determine the frequency at which signal attenuation reaches 50 % for individual parts of the gas sampling system. For different models of rain caps and particulate filters, this frequency falls into ranges of 2.5–16.5 Hz for CO 2 , 2.4–14.3 Hz for H 2 O, and 8.3–21.8 Hz for CO 2 , 1.4–19.9 Hz for H 2 O, respectively. A short and thin stainless steel intake tube was found to not limit frequency response, with 50 % attenuation occurring at frequencies well above 10 Hz for both H 2 O and CO 2 . From field tests we found that heating the intake tube and particulate filter continuously with 4 W was effective, and reduced the occurrence of problematic relative humidity levels (RH > 60 %) by 50 % in the infrared gas analyzer cell. No further improvement of H 2 O frequency response was found for heating in excess of 4 W. These laboratory and field tests were reconciled using resistor–capacitor theory, and NEON's final gas sampling system was developed on this basis. The design consists of the stainless steel intake tube, a pleated mesh particulate filter and a low-volume rain cap in combination with 4 W of heating and insulation. In comparison to the original design, this reduced the high-frequency attenuation for H 2 O by ≈ 3/4, and the remaining cospectral correction did not exceed 3 %, even at high relative humidity (95 %). The standardized design can be used across a wide range of ecoclimates and site layouts, and maximizes practicability due to minimal flow resistance and maintenance needs. Lastly, due to minimal high-frequency spectral loss, it supports the routine application of adaptive correction procedures, and enables largely automated data processing across sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

miniGMG

miniGMG is a compact geometric multigrid (MG benchmark designed to proxy the performance characteristics of the solves found in adaptive mesh refinement multigrid (AMR MG) applications. It solves the equation a*alpha*u - b div beta grad u = f on a large cubical domain using cell-centered values and periodic boundaries. a and b are scalar constants, alpha and beta are space varying constants, f is the right hand side, and u is the solution. The cubical domain is divided into cubical subdomains which are distributed across the supercomputer. The righthand side f is generated from a continuous function for u. Thus, one can solve this linear system of equations and verify the error properties of the discrete solution u by comparing to a sampling of the continuous u. miniGMG implements both v- and f- cycles, as well as two constant coeffficient discretications of the equation above (one 27pt 2nd order, and one 13pt 4th order). In both cases, the (embedded) v-cycles are truncated when a subdomain reaces 4^3, 2^3, or 1^3 cells (a u-cycle). The multigrd solver then swtiches to one of seven coarse grid (bottom) solves. relaxation, BiCGStab, CABiCGStab, TelescopingCABiCGStab, CG, or CACG. The code exploits two forms of communication-avoiding. The first is exemplified by the referenced SC'12 paper in which DRAM data movment is avoided when smoothing. The second, described in the IPDPS'14 paper avoids collectives in a bottom solve (the CA bottom solves). There are a number of implementations of the operators for both CPUs and NVIDIA GPUs. The CPUs have been optimized to exploit a wavefront approach to communication-avoiding. The GPU versions include a number of optimized and tunable implementations. The code has been demonstrated to scale to 46K nodes on the BGQ Mira (750K cores), 9K nodes on Edison (111K cores), and 14K GPUs on Titan (37M cuda cores). It thus acts as a scalable testbed for a wide range of computer science and applied math research. Nominally, one invokes the benchmark as ./run b x y z alpha beta gamma Each subdomain is (2^b)^3 cells Each MPI process receives a x-by-y-by-z collection of subdomains. There are alpha*beta*gamma total MPI processes For correctness (a cubical domain), *alpha==y*beta==z*gamma.

Williams, Sameul↗
DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗
NASA NTRS2016

Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA)

The proposed paper will cover ongoing effort named HESTIA (Human Exploration Spacecraft Testbed for Integration and Advancement), led at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC) to promote a cross-subsystem approach to developing Mars-enabling technologies with the ultimate goal of integrated system optimization. HESTIA also aims to develop the infrastructure required to rapidly test these highly integrated systems at a low cost. The initial focus is on the common fluids architecture required to enable human exploration of mars, specifically between life support and in-situ resource utilization (ISRU) subsystems. An overview of the advancements in both integrated technologies, in infrastructure, in simulation, and in modeling capabilities will be presented, as well as the results and findings of integrated testing,. Due to the enormous mass gear-ratio required for human exploration beyond low-earth orbit, (for every 1 kg of payload landed on Mars, 226 kg will be required on Earth), minimization of surface hardware and commodities is paramount. Hardware requirements can be minimized by reduction of equipment performing similar functions though for different subsystems. If hardware could be developed which meets the requirements of both life support and ISRU it could result in the reduction of primary hardware and/or reduction in spares. Minimization of commodities to the surface of mars can be achieved through the creation of higher efficiency systems producing little to no undesired waste, such as a closed-loop life support subsystem. Where complete efficiency is impossible or impractical, makeup commodities could be manufactured via ISRU. Although, utilization of ISRU products (oxygen and water) for crew consumption holds great promise of reducing demands on life support hardware, there exist concerns as to the purity and transportation of commodities. To date, ISRU has been focused on production rates and purities for propulsion needs. The meshing of requirements between all potential users, producers, and cleaners of oxygen and water is crucial to guiding the development of technologies which will be used to perform these functions. Various new capabilities are being developed as part of HESTIA, which will enable the integrated testing of these technologies. This includes the upgrading of a 20' diameter habitat chamber to eventually support long duration (90+ day) human-in-the-loop testing of advanced life support systems. Additionally, a 20' diameter vacuum chamber is being modified to create Mars atmospheric pressures and compositions. This chamber, designated the Mars Environment Chamber (MEC), will eventually be upgraded to include a dusty environment and thermal shroud to simulate conditions on the surface of Mars. In view that individual technologies will be in geographically diverse locations across NASA facilities and elsewhere in the world, schedule and funding constraints will likely limit the frequency of physical integration. When this is the case, absent subsystems can be either digitally or physically simulated. Using the Integrated Power Avionics and Software (iPAS) environment, HESTIA is able to bring together data from various subsystems in simulated surroundings, insert faults, errors, time delays, etc., and feed data into computer models or physical systems capable of reproducing the output of the absent subsystems for the consumption of a local subsystems. Although imperfect, this capability provides opportunities to test subsystem integration and interactions at a fraction of the cost. When a subsystem technology is too immature for integrated testing, models can be produced using the General-Use Nodal Network Solver (GUNNS) capability to simulate the overall system performance. In doing so, even technologies not yet on the drawing board can be integrated and overall system performance estimated. Through the integrated development of technologies, as well as of the infrastructure to rapidly and at a low cost, model, simulate, and test subsystem technologies early in their development, HESTIA is pioneering a new way of developing the future of human space exploration.

Banker, Brian F.↗
DOE OSTI2026

Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation

This Final Scientific and Technical Report summarizes work performed under the Phase IIA SBIR project “Enabling the Broader Use of MOOSE for Nuclear Energy and Other Simulation” (DE-SC0020906) from August 2023 through August 2025. The objective of the Phase IIA effort was to mature and harden capabilities developed during Phase II, with the goal of enabling practical interoperability between Coreform’s isogeometric analysis (IGA) technologies and the Multiphysics Object-Oriented Simulation Environment (MOOSE), while improving robustness, performance, and scalability for complex, nuclear-relevant geometries. Over the course of Phase IIA, the project established and validated an extraction-based interoperability pathway between Coreform tools and MOOSE. A combined mesh and matrix format was defined collaboratively with MOOSE developers and integrated into the solver, enabling standard MOOSE workflows to operate on data exported from Coreform’s IGA and Flex Representation Method (FRM) pipelines. Early demonstrations validated architectural compatibility using linear solid mechanics problems, while later efforts focused on benchmark testing and external use. By the end of the project period, engineers at BWXT were able to independently set up and execute a simulation using the Coreform–MOOSE workflow and provide direct feedback that informed further refinement. In parallel, substantial effort was devoted to improving the robustness of trimmed U-spline construction for complex CAD geometries. A growing test suite of nuclear-relevant models was compiled through collaboration with multiple stakeholders and used to drive extensive bug fixing and reliability improvements. These efforts resulted in improved robustness and performance, including the addition of fallback capabilities that enhance reliability when the underlying commercial CAD kernel fails. Performance-oriented work progressed later in the project, with the development and demonstration of methods to decompose complex geometries into structured subregions and updated data representations to support more efficient solver processing. Additionally, extensive enhancements to threadsafe parallel data structures and trimming operations established a foundation for scalable processing of large assemblies. Collaboration with Sandia National Laboratories on the SGM geometric modeling kernel advanced to a functioning interface test case, positioning the workflow for future kernel integration. Overall, the Phase IIA effort successfully transitioned the project from architectural proof-of-concept to externally exercised, solver-integrated capability, while clarifying remaining technical challenges related to standardization, performance optimization, and kernel integration.

42 ENGINEERING↗
DOE OSTI2021

A Systematic Interpretation of Subsurface Proppant Concentration from Drilling Mud Returns: Case Study from Hydraulic Fracturing Test Site (HFTS-2) in Delaware Basin

The aim of this study is generation and validation of a proppant log using analysis of drilling mud returns for child wells. Proppant log provides qualitative as well as quantitative insights into spatial distribution of proppant sand particles from prior stimulation of parent wells. While the basic methodology was developed and formalized during analysis of material collected from through fracture cores at Hydraulic Fracturing Test Site in Midland Basin (HFTS – 1), the test wells at HFTS – 2 in the neighboring Delaware Basin allowed the opportunity to validate the workflow on actual mud return samples from subsurface. As a child well is being drilled, periodic mud return samples are collected at the rig site and preserved for analysis. The workflow involves systematic cleaning of the samples including various steps such as washing, drying and segregation of samples into relevant size fractions of interest (< Mesh 20) based on specifications of pumped sand during stimulation of the parent well. Clean samples are imaged using high resolution transparency scanning. Scan images are then systematically analyzed for particles of interest using computer vision techniques. Sample counts are further validated using elemental analysis of smaller sub-samples at various depths of interest. This step is necessary to isolate proppant versus other naturally occurring minerals such as sulphates and carbonates which show similar optical properties. We successfully correlated proppant distribution against the existing parent well and validated propped versus relatively un-propped zones for a child well at the test site. The advantage of testing the proppant log concept at the HFTS – 2 site is the plethora of additional diagnostic data that is available to validate our primary observations. We can correlate spatial proppant distribution against variability in stimulation response based on independent observations such as image logs, microseismic attributes as well as DAS response, all of which tend to corroborate one another. One of our significant successes was being able to describe varying degrees of impact of the parent well along the lateral length of a stimulated child well. Our workflow represents a systematic and one-of-a-kind interpretation of spatial proppant distribution while drilling child wells. This provides unique opportunities to better understand the current state of the Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/2-21URTC/D021S031R003/2477415/urtec-2021-5189-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5189 2 reservoir being targeted including zones which are likely more drained relative to others and how the planned completion of the child well can be improved. Lastly, this log can be useful is validating optimal well spacing in relatively new fields under development.

58 GEOSCIENCES↗
DOE OSTI2016

Enhanced Shuffling and Fuel Management Capability in PHISICS code

PHISICS (Parallel and Highly Innovative Simulation for the INL Code System) code is a reactor physics package, under development at the Idaho National Laboratory since few years ago. This package is aimed to provide a modern analysis and design tool for reactor physics investigation. It has been designed with the idea to maximize accuracy for a given availability of computational resources and to give state of the art tools and advanced solutions to the nuclear engineer. This is obtained through the implementation of several different solution algorithms and meshing approaches among which the user will be able to choose, in order to balance his computational resources and accuracy needs. PHISICS has been coded following an object-oriented mindset, evolving in a complete modular design; this determines remarkable simplifications in the development of modules by different teams and future maintenance. The different modules currently available in the PHISICS package are a nodal and semi-structured transport core solver (INSTANT), a depletion and burn-up module (MRTAU), a time-dependent solver (Time Integrator), a cross section interpolation and manipulation framework (MIXER), a criticality search module (Criticality) and a fuel management and shuffling component (Shuffle), which is the subject of this paper, having been recently redesigned and improved. PHISICS is fully parallelized to take advantage of multi-core workstations and High Performance Computing systems (4 to 500 cores). In addition, the package is coupled with the system safety analysis code RELAP5-3D. Using the coupling between PHISICS and RELAP5-3D is possible to drive a accurate dynamic analysis switching between a steady state and time-dependent calculations. The main purpose of this paper is to present the redesigned module Shuffle, within the PHISICS tool-kit. All the functionality limitations of the old design have been faced and resolved. The functionality of the module has been fully tested and represented a key development in order to face a challenging problems, such as the revision of the requirements in 10 CFR 50.46C rules, focused on the ECCS rule in LOCA scenarios. The renovated Shuffle module is now completed and represents a key feature of the PHISICS for fuel cycle studies and optimization. Currently it is possible to perform multi-cycle full core analysis including accounting for the thermal-hydraulic feedback by the coupling with RELAP-5. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Influence of Vegetation Canopy Architecture on Dynamics of Spatiotemporal Light Distribution and its Effect on Land-Biosphere-Atmosphere Fluxes of Energy, Carbon, and Water

The dynamic spatiotemporal distribution of light and micro climatic variables (wind speed, atmospheric CO 2 concentration, humidity, pressure, and air temperature) within the plant canopy as determined by the canopy architecture play a critical role in land-biosphere-atmosphere (LBA) photosynthetic carbon uptake, transpiratory water loss and energy partitioning. Vegetation models that include effects of sun and shade flecks, leaf temperature and energy balance partitioning between sensible and latent heat fluxes, and reduced turbulent mixing in dense vegetation canopies can accurately predict canopy performance, particularly its shaded leaves, which can constitute 80% of leaf area and contribute up to 50% of carbon update and 65% of water loss. However, current vegetation models with their bulk characterization of shaded leaves poorly represent the complex three dimensional (3D) canopy architecture are insufficient to capture the spatiotemporal dynamics of shaded leaves. Here, we propose to employ an x-ray computed tomography (XCT) imaging technique in combination with an automated, high throughput, image processing pipeline (3DArch, to be developed) to obtain an explicit 3D triangulated surface mesh of a plant canopy (Fig 1). 3DArch would provide a much needed technological jump in 3D LBA modeling where computer generated artificial 3D models suffer from lack of high resolution detailed canopy architecture parameters. The 3D canopy surface mesh output from 3DArch is coupled with an explicit 3D forward ray tracing model (fastTracer) to obtain a high resolution representation of the dynamic spatiotemporal light environment within the canopy over the course of a day (Fig 1). The results from the light attenuation model are then coupled with a process based multi-scale bio-physical canopy and leaf model of photosynthesis (PlantGro) to get fluxes of carbon, water and energy at the leaf and canopy scale, in addition to estimating whole plant carbon uptake, and water loss. Our proposed model framework would be the first of its kind to provide a breakthrough capability in the field of process-based plant modeling that bridges the key technological gap in shaded leaf modeling through explicit representation of 3D canopy architecture. Computer generated artificial 3D model canopy simulations on grasses show that the canopy light distribution does not follow an exponential behavior as assumed in most 1D vegetation models, but rather exhibits a heterogeneous behavior, with a small but significant portion of interior canopy leaves experiencing peak light levels due to sun flecks while the rest of the canopy receives substantially lower light levels. These results indicate a bias in traditional models that overestimates shaded leaf contribution towards whole canopy photosynthesis by up to 15% and underestimate transpiration by up to 12% with an associated increase in sensible heat fluxes since photosynthesis, transpiration and energy partitioning are tightly coupled processes. However, these test results need to be validated using the 3D architecture obtained from a real plant imaged in 3D using 3DArch, rather than an artificial model plant. Our proposed model framework would allow us to identify inefficiencies in canopy light distribution and prescribe optimal canopy architectures (leaf optical properties, leaf area, leaf angles, row spacing, planting density, etc.) to traditional and genetic engineering based crop breeders, for maximizing target functions such as light use efficiency, water use efficiency, drought tolerance, crop yield, crop resilience etc., with important applications in biofuel crops such as sorghum, and switchgrass. Our model can also be applied for designing optimal canopy architecture that increase surface albedo as a mitigation strategy for global warming and projections of crop yield for future food and water security under climate change.

59 BASIC BIOLOGICAL SCIENCES↗