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NASA NTRSDate not supplied

Development of a Planetary Water Treatment System

As NASA prepares for human missions beyond the International Space Station (ISS), NASA personnel have begun development of a water treatment system that is optimized for operation on the Moon and Mars. This system will take advantage of the partial gravity of surface-based habitats while minimizing resupply mass and ensuring optimum reliability. An overview of expected water uses, waste streams, and architectures for a partial gravity water recovery system (WRS) is presented and compared to the WRS on ISS. This paper summarizes the current progress of evaluating various technologies and architectures for the potential mission profiles currently being considered.

Dean Leslie Muirhead↗

NASA NTRSDate not supplied

AI-Enhanced Computational Tools for Entry Systems Modeling

To advance the understanding of complex atmospheric entry phenomena, NASA’s Entry Systems Modeling (ESM) team [1] has developed high-fidelity computational tools addressing multiscale challenges, from material microstructures to full-scale heatshield response. This abstract highlights a subset of ESM tools, focusing on AI integration to enhance workflows and predictive modeling. - PuMA [2] computes effective material properties from high-resolution micro-CT scans, supporting TPS analysis for NASA missions. - TomoSAM [3] automates 3D tomography dataset segmentation for PuMA using the Segment Anything Model, reducing manual effort and improving accuracy. - PATO [4] models porous reactive materials under extreme conditions, with advancements such as unified solvers, mechanical erosion, and TPS coatings for NASA missions. - arcjetCV [5] employs deep learning to analyze arc jet test footage, measuring recession rates, shape changes, and shock standoff distances, bridging simulations, and experiments to reveal TPS ablation behavior. - ARCHeS [6] simulates arc heater plasma flows, modeling turbulence, radiation, and electromagnetic interactions to optimize arc heater performance, validate TPS under extreme conditions, and serve as a foundation for developing digital twins of arc heater facilities. - SPARTA [7] simulates rarefied hypersonic flows and gas-surface interactions for planetary entry missions, leveraging GPU architectures for scalable and efficient aerothermal and ablation analyses. AI-driven solutions, such as deep learning segmentation, have streamlined workflows in ESM tools and still hold significant potential to further accelerate processes and enhance automation in entry systems modeling. [1] Haskins, J.B. (2023), [2] Ferguson, J.C. (2018), [3] Meurisse, J.B.E. (2018), [4] Semeraro, F. (2023), [5] Quintart, A. (2024) [6] Meurisse, J.B.E. (2022), [7] Plimpton, S.J. (2019)

Predictive Modeling↗

DOE OSTI2025

Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios

This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.

De Souza, Reubun [School of Electrical Engineering↗

DOE OSTI2025

Solid‐State Prealkylation of Electrode Architectures (SPEAR): Direct Control of Prelithiation Levels in Silicon Anodes and Electrochemical Cycling

The Solid-state Prealkylation of Electrode ARchitectures (SPEAR) is different than traditional electrochemical prealkylation processes. Through SPEAR, alkylation is driven by solid-state diffusion without the simultaneous SEI formation concomitant with polarization. Here, we investigate the prelithiation of 80 wt. % Si-based anodes to varying amounts (up to Li 1.38 Si) to understand the trade-off between improved Li capacity and expansion-induced stress. Through dilatometry, we found that solid-state lithiation led to filling of the electrode pores through silicon expansion. This swelling changed the SEI formation process and accessibility of the silicon compared to an electrochemically lithiated electrode. Indeed, optimal prelithiation to Li 0.82 Si increases the initial C/3 cycling capacity post-SEI formation up to 43%, consistent with deeper Si activation through the electrode bulk. Prelithiation and cycling cells prelithiated beyond Li 0.82 Si results in a state of charge (SOC) close to 100% which facilitates parasitic degradation mechanisms and volume expansion of the Si electrode. The results demonstrate a pathway to modify silicon activation/SEI formation to enable high-energy electrodes.

Musgrove, Amanda L. [Oak Ridge National Laboratory↗

DOE OSTI2023

Risk Analysis of Various Design Architectures for High Safety-significant Safety-related Digital Instrumentation and Control Systems of Nuclear Power Plants during Accident Scenarios

This report documents the plus-up activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems to support system design decisions and diversity and redundancy applications, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the LWRS-developed framework instructs nuclear vendors and utilities on how to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). Adding diversity within system or components is the main means to eliminate and mitigate CCFs, but diversity also increases plant complexity and errors and may not address all sources of systematic failures. How to optimize the diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in HSSSR DI&C systems of NPPs and supporting relevant design optimization, the framework provides: ? An integrated best-estimate, risk-informed capability to address new technical digital issues quantitatively, accurately, and efficiently in plan modernization progress, such as software CCFs in HSSSR DI&C systems of NPPs ? A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to efficiently predict and prevent risk in the early design stage of DI&C systems ? Technical bases and risk-informed insights to assist U.S. Nuclear Regulatory Commission (NRC) and industry to address and fulfill the risk-informed alternatives for evaluation of CCFs in HSSSR DI&C systems of NPPs ? An integrated risk-informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The plus-up research and development efforts of this project in FY 2022 are focused on methodology improvement of software CCF modeling and estimation, prevention analysis, importance analysis and risk analysis of various design architectures of HSSSR DI&C systems. This work greatly enhances the capability of the LWRS-developed framework for the risk assessment and design optimization of safety-critical DI&C systems. It should be noted that all the analyses are performed for the demonstration of the LWRS-developed framework, not for the evaluation of relevant systems. Results are obtained based on very limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2024

Optimal Control Allocation for Distributed Electric Propulsion in a Series/Parallel Partial Hybrid Powertrain

The SUbsonic Single Aft eNgine (SUSAN) Electrofan is a NASA concept jet transport aircraft with a 2040 entry-into-service date. It utilizes electrified aircraft propulsion (EAP) to enable propulsive and aerodynamic benefits to reduce fuel usage, emissions, and cost. The powertrain consists of a single thrust producing, boundary layer-ingesting (BLI) turbofan gas turbine engine (GTE) with generators driving a series/parallel partial hybrid EAP system. The architecture includes 16 underwing contrarotating BLI fans, eight on each side, in a mailslot configuration. The 16 fans run on power extracted from the GTE through four 5 MW motor/generators connected to the Low-Pressure Spool, and a single 1 MW motor/generator on the High-Pressure Spool. The distributed fans can be used by the flight control to augment or replace the rudder function. At top of climb, the power extracted from the GTE for the fans is boosted by batteries. The design provides redundancy, and the capacity for boost means that the fans are designed to be able to provide additional thrust when necessary. These features can be leveraged in case of a fan or generator failure. This paper sets up the optimal control problem of setpoint determination for individual fans in the distributed propulsion system, accounting for electrical string efficiencies, saturations, and failures. The solution minimizes power consumption while maintaining thrust and torque on the airframe for maneuvering. Additionally, thrust that would have been lost due to temporary fan speed or power saturation is optimally redistributed to maintain overall desired thrust and torque on the aircraft. The power extraction range constraints derive from the gas turbine engine design and the small amount of variation allowed for the engine to maintain operability. The problem formulation allows the number and location of fan failures for which the thrust and torque can be maintained to be investigated, which has implications for certification. Simulations of a coordinated turn utilizing the distributed electric propulsion for yaw rate control under different failure scenarios demonstrate the robustness of the powertrain design to failures and help define its limitations.

Distributed Electric Propulsion↗

DOE OSTI2025

Toward a persistent event-streaming system for high-performance computing applications

High-performance computing (HPC) applications have traditionally relied on parallel file systems and file transfer services to manage data movement and storage. Alternative approaches have been proposed that use direct communications between application components, trading persistence and fault tolerance for speed. Event-driven architectures, as popularized in enterprise contexts, present a compelling middle ground, avoiding the performance cost and API constraints of parallel file systems while retaining persistence and offering impedance matching between application components. However, adapting streaming frameworks to HPC workloads requires addressing challenges unique to HPC systems. This paper investigates the potential for a streaming framework designed for HPC infrastructures and use cases. We introduce Mofka, a persistent event-streaming framework designed specifically for HPC environments. Mofka combines the capabilities of a traditional streaming service with optimizations tailored to the HPC context, such as support for massively multicore nodes, efficient scaling for large producer-consumer workflows, RDMA-enabled high-performance network communications, specialized network fabrics with multiple links per node, and efficient handling of large scientific data payloads. Built using the Mochi suite of HPC data service components, Mofka provides a lightweight, modular, and high-performance solution for persistent streaming in HPC systems. We present the architecture of Mofka and evaluate its performance against Kafka and Redpanda using benchmarks on diverse platforms, including Argonne's Polaris and Oak Ridge's Frontier supercomputers, showing up to 8× improvement in throughput in some scenarios. We then demonstrate its utility in several real-world applications: a tomographic reconstruction pipeline, a workflow for the discovery of metal-organic frameworks for carbon capture, and the instrumentation of Dask workflows for provenance tracking and performance analysis.

HPC↗

DOE OSTI2024

Exploring code portability solutions for HEP with a particle tracking test code

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools—including compiler pragma-based approaches, abstraction libraries, and other tools—allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions. While in several cases portable implementations perform close to the reference code version, we find that the performance varies significantly depending on the details of the implementation. Achieving optimal performance is not easy, even for relatively simple applications such as the test codes considered in this work. Several factors can affect the performance, such as the choice of the memory layout, the memory pinning strategy, and the compiler used. The compilers and tools are being actively developed, so future developments may be critical for their deployment in HEP experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2017

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. Here, we consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We then present techniques to significantly improve the SpMM and the transpose operation SpMM T by using the compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2014

Integrated Multidisciplinary Optimization Objects

OpenMDAO is an open-source MDAO framework. It is used to develop an integrated analysis and design environment for engineering challenges. This Phase II project integrated additional modules and design tools into OpenMDAO to perform discipline-specific analysis across multiple flight regimes at varying levels of fidelity. It also showcased a refined system architecture that allows the system to be less customized to a specific configuration (i.e., system and configuration separation). By delivering a capable and validated MDAO system along with a set of example applications to be used as a template for future users, this work greatly expands NASA's high-fidelity, physics-based MDAO capabilities and enables the design of revolutionary vehicles in a cost-effective manner. This proposed work complements M4 Engineering's expertise in developing modeling and simulation toolsets that solve relevant subsonic, supersonic, and hypersonic demonstration applications.

Alston, Katherine↗

DOE OSTI2025

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Analyzing the suitability of contemporary 3D-stacked PIM architectures for HPC scientific applications

Scaling off-chip bandwidth is challenging due to fundamental limitations, such as a fixed pin count and plateauing signaling rates. Recently, vendors have turned to 2.5D and 3D stacking to closely integrate system components. Interestingly, these technologies can integrate a logic layer under multiple memory dies, enabling computing capability inside a memory stack. This trend in stacking is making PIM architectures commercially viable. In this work, we investigate the suitability of offloading kernels in scientific applications onto 3D stacked PIM architectures. We evaluate several hardware constraints resulted from the stacked structure. We perform extensive simulation experiments and in-depth analysis to quantify the impact of application locality in TLBs, data caches, and memory stacks. Our results also identify design optimization areas in software and hardware for HPC scientific applications.

Peng, Ivy Bo↗

DOE OSTI2014

AOI [3] High-Temperature Nano-Derived Micro-H 2 and - H 2 S Sensors

The emissions from coal-fired power plants remain a significant concern for air quality. This environmental challenge must be overcome by controlling the emission of sulfur dioxide (SO 2 ) and hydrogen sulfide (H 2 S) throughout the entire coal combustion process. One of the processes which could specifically benefit from robust, low cost, and high temperature compatible gas sensors is the coal gasification process which converts coal and/or biomass into syngas. Hydrogen (H 2 ), carbon monoxide (CO) and sulfur compounds make up 33%, 43% and 2% of syngas, respectively. Therefore, development of a high temperature (>500°C) chemical sensor for in-situ monitoring of H 2 , H 2 S and SO2 2 levels during coal gasification is strongly desired. The selective detection of SO 2 /H 2 S in the presence of H 2 , is a formidable task for a sensor designer. In order to ensure effective operation of these chemical sensors, the sensor system must inexpensively function within harsh temperature and chemical environment. Currently available sensing approaches, which are based on gas chromatography, electrochemistry, and IR-spectroscopy, do not satisfy the required cost and performance targets. This work focused on the development microsensors that can be applied to this application. In order to develop the high- temperature compatible microsensor, this work addressed various issues related to sensor stability, selectivity, and miniaturization. In the research project entitled “High-Temperature Nano-Derived Micro-H 2 and -H 2 S Sensors”, the team worked to develop micro-scale, chemical sensors and sensor arrays composed of nano-derived, metal-oxide composite materials to detect gases like H 2 , SO 2 , and H 2 S within high-temperature environments (>500°C). The research was completed in collaboration with NexTech Materials, Ltd. (Lewis Center, Ohio). NexTech assisted in the testing of the sensors in syngas with contaminate levels of H 2 S. The idea of including nanomaterials as the sensing material within resistive-type chemical sensor platforms was to increase the sensitivity (as shown for room temperature applications). Unfortunately, nanomaterials are not stable at high temperatures due to sintering and coarsening processes that are driven by their high surface to volume ratio. Therefore, new hydrogen and sulfur selective nanomaterial systems with high selectivity and stability properties in the proposed harsh environment were investigated. Different nano-morphologies of zirconate, molybdate, and tungstate compounds were investigated. The fabrication of the microsensors consisted of the deposition of the selective nanomaterial systems over metal based interconnects on an inert substrate. This work utilized the chemi-resistive (resistive- type) microsensor architecture where the chemically and structurally stable, high temperature compatible electrodes were sputtered onto a ceramic substrate. The nanomaterial sensing systems were deposited over the electrodes using a lost mold method patterned by conventional optical lithography. The microsensor configuration with optimized nanomaterial system was tested and compared to a millimeter-size sensor e outcomes of this research will contribute to the economical application of sensor arrays for simultaneous sensing of H 2 , H 2 S, and SO 2 .

47 OTHER INSTRUMENTATION↗

DOE OSTI2017

Carbon Capture Multidisciplinary Simulation Center Trilab Support Team (TST) Fall Meeting 2016 Report

The theme of this year’s meeting was “Predictivity: Now and in the Future”. After welcoming remarks, Erik Draeger gave a talk on the NNSA Labs’ history of predictive simulation and the new challenges faced by upcoming architecture changes. He described an example where the volume of analysis data produced by a set of inertial confinement fusion (ICF) simulations on the Trinity machine was too large to store or transfer, and the steps needed to reduce it to a manageable size. He also described the software re-engineering plan for LLNL’s suite of multiphysics codes and physics packages with a new push toward common components, making collaboration with teams like the CCMSC who already have experience trying to architect complex multiphysics code infrastructure on next-generation architectures all the more important. Phil Smith then gave an overview outlining the goals of the project, namely to accelerate development of new technology in the form of high efficiency carbon capture pulverized coal power generation as well as further optimize existing state of the art designs. He then presented a summary of the Center’s top-down uncertainty quantification approach, in which ultimate target predictivity informs uncertainty targets for lower-level components, and gave data on how close all the different components currently are to their targets. Most components still need an approximately two-fold reduction in uncertainty to hit the ultimate predictivity target, but the current accuracy is already rather impressive.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2018

WBS STPR 04 Milestone 4 Report

This report documents the completion of milestone STPRO4-4 Kokkos back-ends research, collaborations, development, optimization, and documentation. The Kokkos team updated its existing backend to support the software stack and hardware of DOE's Sierra, Summit and Astra machines. They also collaborated with ECP PathForward vendors on developing backends for possible exa-scale architectures. Furthermore, the team ramped up its engagement with the ISO/C++ committee to accelerate the adoption of features important for the HPC community into the C++ standard.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

WBS STPR 04 Milestone 4 Report

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation

Long-term seismic monitoring of carbon capture and storage projects is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. As part of the monitoring program of the Archer Daniels Midland’s large-scale injection of CO 2 in Decatur, Illinois, USA, a continuous seismic monitoring array was installed using a combination of surface orbital vibrator (SOV) sources and fiber-optic cables for distributed acoustic sensing (DAS) acquisition with the objective to build a continuous monitoring array. The aim of the presented project was to build a monitoring array and platform that integrates real-time seismic data with conventional data streams and provides continuous data analysis using dynamic computational models to deliver a comprehensive real-time assessment of subsurface conditions. It is in this context that the Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation project was proposed with the objective to develop an integrated architecture that utilizes a permanent seismic monitoring network, combines the real-time geophysical and process data with reservoir flow and geomechanical models to create a comprehensive monitoring, visualization, and control system that delivers critical information for process surveillance and optimization.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1978

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed.

Beckner, F. L.↗

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