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At least 1,459 records · Page 81

Convolutional Neural Network Surrogate Models for the Mechanical Properties of Periodic Structures

The work in this paper describes neural network surrogate models for calculating the effective mechanical properties of a periodic composites. The models achieve good accuracy even when only provided with training data sampling a small portion of the design space. As an example, the surrogate models are applied to solving the inverse design problem of finding structures with optimal mechanical properties. The surrogate models are sufficiently accurate to recover optimal solutions in general agreement with established topology optimization methods. However, improvements will be required to develop robust, efficient neural network-based surrogate models and several directions for future research are highlighted here.

42 ENGINEERING↗

Pillars of the Mantle: Imaging the Interior of the Earth with Adjoint Tomography. In: PEARC17 Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, Article No. 75

In this work, we investigate global seismic tomographic models obtained by spectral-element simulations of seismic wave propagation and adjoint methods. Global crustal and mantle models are obtained based on an iterative conjugate-gradient type of optimization scheme. Forward and adjoint seismic wave propagation simulations, which result in synthetic seismic data to make measurements and data sensitivity kernels to compute gradient for model updates, respectively, are performed by the SPECFEM3D_GLOBE package [1] [2] at the Oak Ridge Leadership Computing Facility (OLCF) to study the structure of the Earth at unprecedented levels. Using advances in solver techniques that run on the GPUs on Titan at the OLCF, scientists are able to perform large-scale seismic inverse modeling and imaging. Using seismic data from global and regional networks from global CMT earthquakes, scientists are using SPECFEM3D_GLOBE to understand the structure of the mantle layer of the Earth. Visualization of the generated data sets provide an effective way to understand the computed wave perturbations which define the structure of mantle in the Earth.

Pugmire, David↗

Lightweight Detection of Cache Conflicts

In memory hierarchies, caches perform an important role in reducing average memory access latency. Minimizing cache misses can yield significant performance gains. As set-associative caches are widely used in modern architectures, capacity and conflict cache misses co-exist. These two types of cache misses require different optimization strategies. While cache misses are commonly studied using cache simulators, state-of-the-art simulators usually incur hundreds to thousands of times a program’s execution runtime. Moreover, a simulator has difficulty in simulating complex real hardware. To overcome these limitations, measurement methods are proposed to directly monitor program execution on real hardware via performance monitoring units. However, existing measurement-based tools either focus on capacity cache misses or do not distinguish capacity and conflict cache misses. In this paper, we design and implement CCProf, a lightweight measurement-based profiler that identifies conflict cache misses and associates them with program source code and data structures. CCProf incurs moderate runtime overhead that is at least an order of magnitude lower than simulators. With the evaluation on a number of representative programs, CCProf is able to guide optimizations on cache conflict misses and obtain nontrivial speedups.

Roy, Probir↗

MBMS1.0: An Open-Source Code for Modeling and Simulation of Membrane-Based Dehumidification and Energy Recovery

Air dehumidification processes are important and significantly impact both the performance of building ventilation and air conditioning systems, and the health of occupants. In a membrane-based dehumidifier, a permselective membrane is adopted to separate the two flow streams at feed and permeate sides. The feed side is continuously supplied with humid air at atmospheric pressure, while the permeate side is connected to a vacuum pump to produce vacuum or a low permeate vapor pressure as a driving force for water vapor permeation. The software MBMS 1.0 enables modeling and simulation of the design and configuration optimization of a membrane-based air-to-air (A2A) or air-to-vapor (A2V) device. A first-order physical-constant model was developed to reasonably account for the fundamental heat and mass transfer processes involved.

Gao, Zhiming↗

MrHyDE v.1.0

SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗

MiniMOD

SAND2025-03854O MiniMod is a user-friendly software tool designed to assess the performance of high-performance computing (HPC) systems. Researchers can use the program to test communication methods and computational tasks to understand how different setups can affect application efficiency. This software is particularly useful for optimizing network performance in scientific research, simulations, and data analysis. MiniMod‘s flexible design allows users to make informed decisions about their computing environments, which can enhance productivity and results in real-world applications. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Dosanjh, Matthew [Sandia National Lab. (SNL-CA), L↗

Defects in MX2 Phases (DMX) Dataset: STEM Digital Twins

DFT is used to optimize defects in monolayer MX2 phases. We calculate ~600 optimized defect structures, then use multislice simulations in abTEM to generate STEM digital twins, which can be used to train experimental ML. The reference paper for procedures and demonstration of their utility is https://arxiv.org/abs/2305.02917, and the images here correspond with the discussed Messy dataset.

36 MATERIALS SCIENCE↗

Defects in MX2 (DMX) 2.0: STEM Digital Twins

DFT is used to optimize defects in monolayer MX2 phases. We calculate ~600 optimized defect structures, then use multislice simulations in abTEM to generate STEM digital twins, which can be used to train experimental ML. The reference paper for procedures and demonstration of their utility is https://arxiv.org/abs/2305.02917, and the images here correspond with the discussed Messy dataset. Version 2 includes an auxiliary dataset matched with experiment.

36 MATERIALS SCIENCE↗

Performance Analysis, Modeling and Scaling of HPC Applications and Tools

E cient use of supercomputers at DOE centers is vital for maximizing system throughput, mini- mizing energy costs and enabling science breakthroughs faster. This requires complementary e orts along several directions to optimize the performance of scienti c simulation codes and the under- lying runtimes and software stacks. This in turn requires providing scalable performance analysis tools and modeling techniques that can provide feedback to physicists and computer scientists developing the simulation codes and runtimes respectively. The PAMS project is using time allocations on supercomputers at ALCF, NERSC and OLCF to further the goals described above by performing research along the following fronts: 1. Scaling Study of HPC applications; 2. Evaluation of Programming Models; 3. Hardening of Performance Tools; 4. Performance Modeling of Irregular Codes; and 5. Statistical Analysis of Historical Performance Data. We are a team of computer and computational scientists funded by both DOE/NNSA and DOE/ ASCR programs such as ECRP, XStack (Traleika Glacier, PIPER), ExaOSR (ARGO), SDMAV II (MONA) and PSAAP II (XPACC). This allocation will enable us to study big data issues when analyzing performance on leadership computing class systems and to assist the HPC community in making the most e ective use of these resources.

97 MATHEMATICS AND COMPUTING↗

Final Technical Report for DE-SC0012297

This is the final report on the work performed in award DE-SC0012297, Cosmic Frontier work in support of the LSST Dark Energy Science Collaboration's work to develop algorithms, simulations, and statistical tests to ensure optimal extraction of the dark energy properties from galaxy clusters observed with LSST. This work focused on effects that could produce a systematic error on the measurement of cluster masses (that will be used to probe the effects of dark energy on the growth of structure). These effects stem from the deviations from pure ellipticity of the gravitational lensing signal and from the blending of light of neighboring galaxies. Both these effects are expected to be more significant for LSST than for the stage III experiments such as the Dark Energy Survey. We calculate the magnitude of the mass error (or bias) for the first time and demonstrate that it can be treated as a multiplicative correction and calibrated out, allowing mass measurements of clusters from gravitational lensing to meet the requirements of LSST's dark energy investigation.

79 ASTRONOMY AND ASTROPHYSICS↗

Quantitative Prediction of Uranium Speciation and Amidoxime Binding in Seawater from Advanced Simulation Techniques

The goal of this project was to quantitatively predict stability of uranium species and thermochemistry of uranium binding with amidoxime ligands in realistic seawater conditions. The specific aims of the project were: (i) determine the stable species of uranium and its solvation structure in realistic seawater conditions; also speciation and solvation of uranium's major competing species in seawater, vanadium; (ii) accurately predict the free energies (and thereby stability constants) of binding between uranium and amidoxime; (iii) accurately predict the reaction enthalpies and entropies of binding between uranium and amidoxime as well as between vanadium and amidoxime. Main results and conclusions are as follows. First-principles molecular dynamic simulations found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable and that one Ca ion binds to the center UO 2 (CO 3 ) 3 4- anion stronger than the other Ca ion. This finding suggests that using time-resolved EXAFS spectra may confirm the asymmetry in binding of the two Ca ions in the aqueous Ca 2 UO 2 (CO 3 ) 3 complex. To consider the common ions, solvation of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater was simulated by classical molecular dynamics simulation. It was found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable in the model seawater. A Na+ ion was found to be closely associated with the Ca 2 UO 2 (CO 3 ) 3 complex by indirectly interacting with one axial oxygen atom of the UO 2 group bridged by a water molecule. In addition, the Na + ion interacts closely with one Ca 2+ ion than the other. The present simulations revealed the key role of common ions such as Na + in impacting the solvation, structure, and apparent charge of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater. The sequential processes of UO 2 2+ binding with the three CO 3 2- groups and the two Ca 2+ ions in pure water and in 0.1 M NaCl were simulated by classical molecular dynamics with both the non-constrained brute-force approach and umbrella sampling. The simulated free energies show excellent agreement with the experiment. The coordination structures of UO 2 (CO 3 ) 3 4- and CaUO 2 (CO 3 ) 3 2- were found to be significantly affected by the presence of Na + ions, leading to a monodentate binding of a carbonate group to U and a water molecule entering the first coordination shell of U. To shed light on the binding between uranyl and glutardiamidoxime (H 2 B), an important model and state-of-the-art ligand for seawater uranium extraction, a suite of computational methods was employed. From molecular dynamics (MD) simulations, it was found that the binding configurations of B 2- with uranyl favor the twofold distorted η 2 binding between the oximate ends (C=N-O-) and U, while HB - prefers the chelating mode for the oximate end with the neutral end being solvated by water. The free energies of sequential ligand binding to form UO 2 B, [UO 2 B 2 ] 2- , and [UO 2 (HB)B] - were simulated with umbrella sampling and very good agreement with the experimental values was achieved, which corroborates the structural insights into the binding mode. State-of-the-art polymeric sorbents employ both amidoximate and carboxylate groups on the side chains to achieve optimal U uptake and selectivity, so we simulated the binding of a model amidoximate–carboxylate bifunctional ligand with uranyl. Classical MD and free-energy simulations in 0.5 M NaCl showed that the carboxylate group binds first to uranyl, leading to a loose intermediate state, and then, the amidoximate group binds, resulting in a more stable and tight chelate state. Binding of the second bifunctional ligand follows a similar process, and the two ligands prefer a trans configuration around the uranyl group. The simulated free energies indicate that the two bifunctional ligands bind with uranyl 14 kcal/mol stronger than the two ligands with only amidoximate groups, confirming an important synergy between amidoximate and carboxylate groups in binding uranyl.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Non-Hydrostatic Variable Resolution Atmospheric Model in ACME (Final Technical Report)

The project goals are divided into five main tasks to be accomplished over three years: Task 1: Implement a nonhydrostatic atmospheric dynamical core in ACME (via HOMME) Task 2: Improve load balancing in variable resolution simulations by extending Zoltan. Task 3: Optimize implicit-explicit time integrators for overcoming the vertical acoustic wave CFL limitation Task 4: Validate the NH dynamical core with DCMIP and AMIP tests on uniform and variable resolution grids Task 5: Apply the NH dynamical core to study and parameterize turbulent mountain stress and orographic gravity wave drag with ultra-high variable resolution grids This report only concerns contributions from the UC Davis team, which includes Co-PI Paul Ullrich, GSR/Postdoc Jorge Guerra, GSR Alan Rhoades, GSR Shiheng Duan, and GSR Jennifer Piaseczny. From the above list, the UC Davis team is responsible for contributions to Tasks 1, 3, 4 and 5.

54 ENVIRONMENTAL SCIENCES↗

Sorption Enhanced Mixed Matrix Membranes for Hydrogen (H 2 ) Purification and Carbon Dioxide (CO 2 ) Capture

The technical objective of this project was to develop sorption enhanced mixed matrix membranes with H 2 permeance of 500 gas permeance units (GPU) and H 2 /CO 2 selectivity of 30 at 150-200 °C. These membranes will be the central component in the design of membrane based systems for 90% capture of CO 2 from coal-derived syngas, with 95% CO 2 purity at a cost of electricity 30% less than baseline capture approaches. The unique approach in this proposal is to design crosslinked polymers containing Pd-based nanoparticles achieving strong H 2 sorption and size sieving ability and thus H 2 /CO 2 selectivity. The specific objectives for each budget period (BP) are described below. BP 1: Identify polymer matrix with strong size sieving ability and palladium (Pd)-containing nanomaterials to prepare freestanding mixed matrix films with H 2 permeability of 50 Barrer and H 2 /CO 2 selectivity of 30 at 150-200°C with simulated syngas. BP 2: Prepare and optimize thin film mixed matrix composite membranes materials with H 2 permeance of 500 GPU and H 2 /CO 2 selectivity of 30 at 150-200 °C, and complete the modification of the membrane test unit for the field test in the BP 3. BP 3: Conduct a 20-day field test of the membranes with real syngas at Center for Advanced Energy Research (CAER) of the University of Kentucky (UKy). During the BP2, we have successfully prepared thin-film composite (TFC) membranes based on mixed matrix materials (MMMs) containing Pd nanoparticles in polymers, and demonstrated their superior and robust performance for H 2 /CO 2 separation at 150 – 225 °C. (1) Production of the Pd based nanoparticles with a diameter of 4 nm has been scaled up to 200 mg/day. (2) We have prepared TFC membranes with H 2 permeance above 500 GPU and H 2 /CO 2 selectivity above 30 at temperatures up to 225 °C, which meet the targets for the BP2. (3) We have conducted parametric studies of TFC membranes with a mixed gas containing H 2 S and H 2 O and demonstrated the stability of the membranes. (4) We have established a new testing plan at the Center for Advanced Energy Studies (CAER) at the University of Kentucky because NCCC decided to shut down their gasifier. During this project, four Ph.D. students received the inter-disciplinary training and graduated, including Shailesh Konda, Maryam Omidvar, Deqiang Yin, and Lingxiang Zhu. One postdoctoral researcher (Dr. Liang Huang) and two Ph.D. students (Abhishek Kumar and Hien Nguyen) are involved in this project. The project leads to one provisional patent application, eight peer-reviewed articles, and one manuscript in preparation. The details are shown below.

01 COAL, LIGNITE, AND PEAT↗

Metaheuristic Optimization Tool

The metaheuristic optimization tool (MOT) is a generic python module for solving a real value, multiobjective, multidimensional optimization problem. Users design their optimization problem by defining search variables, simulations, evaluation metric functions, and constraints. Search variables and constraints are straightforwardly added via python object methods, while simulations and metric functions used for search variable set evaluation must be implemented as a python class. Final optimum result(s) are provided in a tabular text file, as well as search history if needed.

97 MATHEMATICS AND COMPUTING↗

Improving energy yield in photovoltaic modules with photonic structures

When installed outdoors, Si solar cells typically operate 20 – 30 K above ambient conditions. These elevated operating temperatures lead to diminished performance, reducing efficiency by ~0.4%/K for crystalline Si cells and decreasing module lifetime. Since much of the elevated temperature of the modules derives from parasitic absorption of sub-bandgap sunlight by the contacts, encapsulants, or other materials, reflectors that remove the sub-bandgap radiation from the module before it is absorbed promise to reduce the operating temperature. However, there are many competing strategies to reject this light: selective reflectors could be integrated into the outer module glass, at the textured Si-encapsulant interface, or on the rear. Furthermore, many of these approaches are complex and expensive to fabricate. The issue of heating can be even more critical in bifacial modules: high fractions of rear irradiance can lead to higher operating temperatures. Prior to this project, relatively little was known about the optical strategies for temperature reduction in bifacial modules. The goal of this project was to quantitatively assess optical strategies for improving the energy yield via a combination of improved anti-reflection and sub-bandgap light rejection. We studied two complementary concepts: the performance of ideal structures to determine the upper limits to performance, and low-complexity structures made from standard materials as cost-effective implementations. We also studied the performance of these reflectors in different types of modules, including Al BSF, PERC, and bifacial. Here we build from our prior accomplishments in developing mirror optimization techniques, and make use of simulation methods that accurately capture the optical properties of the modules throughout the visible and near-infrared spectrum. We connect these optical properties to energy yield under realistic, outdoor conditions. We also experimentally fabricated mirrors, incorporated them into mini-modules, and performed outdoor testing of performance at NREL. We found definitively that spectrally-selective mirrors on the outside of the module cover glass offer the best performance, both under ideal conditions and for realistic, low- complexity mirrors. Mirrors at the Si cell/encapsulant interface are limited by the multiple reflections needed to remove sub-bandgap light. Mirrors at the cell surface and mirrors on the rear also cannot remove all the sub-bandgap light, since parasitic absorption also occurs in the cover glass, encapsulant, and front contacts, depending on the module configuration. Also, cell texturing places higher demands on any cell-surface mirror because multiple interactions with the mirror are required to reject sub bandgap light which compounds losses. For bifacial modules, mirrors on the back do provide additional cooling and energy yield improvements, and the mirrors can have the same structure. In the monofacial case up to 3.3K of cooling is possible for mirrors on the front, compared to 2.2K at the cell surface and 1.2K at the rear. For bifacial modules, up to 2.4K of cooling is possible when mirrors are included at both the front and back. Our realistic designs utilize 4 – 6 layers of common photovoltaic materials such as SiO 2 , TiO 2 , and SiN x , making them more cost effective. Finally, we used our models to study thermal management in bifacial modules with different back lamination materials, finding that glass-glass laminated modules operated hotter than equivalent glass-polymer backsheet modules, due solely to the increased absorption of light incident from the rear of the module. Improving the energy yield by 2%, which is achievable with these realistic designs, would lead to LCOE reductions of $0.03/kWh by 2030. These benefits make photovoltaic panels more accessible to the public and may reduce associated maintenance costs.

14 SOLAR ENERGY↗

Integrated Multiscale Model for Design of Robust 3D Solid-state Lithium Batteries

In FY23, we successfully established the multiscale modeling framework for probing the effects of materials microstructure on cell performance of 3D solid-state batteries. The framework covers physicochemical processes co-evolving at the atomistic and microstructure scales. Our simulations revealed the mechanism of initial interfacial degradation, formation of secondary phases, and the structure-property relationship for ion transport and mechanical stability at the interface. In addition, we also established the microstructure-performance relationships by performing sensitivity tests of various microstructure features and extracting their impact on cell performance during charge-discharge cycles. We have successfully applied our multiscale, multiphysics modeling capability to common electrode and electrolyte materials that are of interests to VTO and the experimental teams within the US-Germany collaboration. The insights we obtained from these simulations provide valuable design principles to optimize materials properties for advanced 3D solid-state batteries.

25 ENERGY STORAGE↗

RAFT: Reconfigurable Array of High-Efficiency Ducted Turbines for Hydrokinetic Energy Harvesting

Diversifying the energy harvesting portfolio is crucial to achieving the ambitious goal of transitioning to clean energy by 2030. Marine hydrokinetic energy has garnered renewed interest due to its high harvesting potential in the U.S., and the resource's reliability and predictability—remaining relatively constant on a daily basis and available 24/7. However, there are currently few commercial devices capable of harnessing the energy from flowing water. This project aims to bridge that gap by designing and evaluating a novel hydrokinetic turbine concept that can efficiently harvest energy from both rivers and tidal streams. The RAFT (Reconfigurable Array of High-Efficiency Ducted Turbines) concept introduces a duct surrounding the turbine rotor and creates an array of small 5-kW units. The duct serves two primary purposes: (1) it enhances hydrodynamic efficiency by accelerating flow to the rotor, and (2) it functions as a structural component, facilitating the formation of modular arrays that lower costs. This project focuses on demonstrating this concept and validating these benefits through simulations and scaled prototype testing. The project team includes 8 faculty members and over 20 students from 3 universities, organized into three core areas: hydrodynamics, electrical systems, and structural analysis, with additional teams dedicated to system integration, environmental assessment and risk management, and tech-to-market strategy. The team successfully demonstrated the increased hydrodynamic efficiency of a ducted turbine compared to an unducted version using high-fidelity simulations and prototype tests. Moreover, design optimization efforts led to surpassing the SHARKS program's goal of 60% reduction of the levelized cost of energy with a significant margin.

13 HYDRO ENERGY↗

Energy-Transit Nexus Tools for Bus Fleet Electrification (NEXTBUS)

NEXTBUS is an open-source software project that integrates NLR's bus energy modeling and simulation tools with multi-objective optimization for fleet operations. NLR is collaborating with a transit technology startup, ReVolt, to commercialize these capabilities by deploying NEXTBUS in ReVolt's software platform. The goal is to manage the added complexities of running a heterogeneous fleet, encompassing battery electric and diesel buses, across a large, multi-depot transit network.

33 ADVANCED PROPULSION SYSTEMS↗