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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Phase segregation of a composite air electrode unlocks the high performance of reversible protonic ceramic electrochemical cells

One breakthrough in developing highly efficient air electrodes for reversible protonic ceramic electrochemical cells (R-PCECs) is optimizing the sluggish oxygen reduction and water oxidation reactions. Here, we present a novel composite material with a nominal formula of high-entropy Ce 0.2 Ba 0.2 Sr 0.2 La 0.2 Ca 0.2 CoO 3–δ (CBSLCC) that spontaneously self-assembles to three-phase electrocatalysts composed of deficient Ce 0.2–y Ba 0.2 Sr 0.2–x La 0.2–x Ca 0.2 CoO 3–δ (CD-CBSLCC), CeO 2 , and La 0.5 Sr 0.5 CoO 3–δ (LSC). Mechanistic studies corroborate that oxygen reduction may occur on entire air electrode surfaces, followed by water formation preferentially at or near CD-CBSLCC. The CeO 2 phase could provide or consume protons to facilitate the oxygen evolution/reduction kinetics in R-PCECs. The developed electrodes demonstrate a record-high electrochemical performance in dual modes of fuel cells and electrolysis cells, delivering a peak power density of 1.66 W cm –2 at 600 °C and a current density of –1.76 A cm –2 at 1.3 V and 600 °C. Finally, excellent operational stabilities of the fuel cell (200 h at 600 °C), electrolysis cell (200 h at 600 °C), and reversible cycling (548 h at 550 °C) provide a promising and reliable step towards realizing the commercialization of R-PCECs.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Developing And Scaling an OpenFOAM Model to Study Turbulent Flow in a HFIR Coolant Channel

Improving the understanding of how computational fluid dynamics (CFD) direct numerical simulations (DNS) of flows in the High Flux Isotope Reactor (HFIR) perform when run in parallel using the high performance computing (HPC) platform Summit at the Oak Ridge Leadership Computing Facility (OLCF) is of particular importance to boost the computational tools used to support HFIR conversion to low enriched fuel (LEU). Evaluation of scaling performance was driven by the increasing importance of graphics processing unit (GPU) usage in HPC, which is becoming the standard for modern supercomputers such as Summit. The desired results are to obtain a strong positive correlation between the computational resources dedicated to a problem and the relative speed-up of the simulation in comparison to a benchmark. This capability will allow substantially improvement in HFIR flow analytical capabilities, specifically when predicting turbulence properties at high Reynolds numbers. The study leverages previous simulation results performed with code PHASTA (finite element) on HPC platforms Cori (NERSC) and Theta (ALCF) [1] with computing options provided in the computing platform OpenFOAM (finite volume) at OLCF. Transitioning from PHASTA to OpenFOAM will (1) eliminate dependence on third-party software for mesh generation and manipulation, (2) reduce resource needs by employing modern architectures, and (3) build expertise for future modeling of HFIR-specific problems like heat transfer in involute geometry, entrance effects, flow structure in channel corners, and so on—all important issues when defining the available thermal margins in the transition to LEU. CPUs and GPUs differ significantly in their architecture and utilization, as discussed in the literature [2]. The most important differences are in the approach to computations and their memory. A single GPU contains a large quantity of cores, enabling it to perform with a much higher throughput than a CPU, but execution requires a different approach. GPU codes execute instructions using the Single-Instruction Multiple-Thread (SIMT) approach in which a single instruction is used for groups of threads called warps. A warp typically consists of 32 threads which must execute the same set of instructions, although on separate threads. Alternately, a CPU has far fewer cores that are much more flexible in their operation, excelling at quickly performing more complex serial computations. This is why GPUs have greater throughput when properly utilized. The second important difference is seen when comparing their memory spaces. Limited memory allocations and CPU–GPU communications cause a significant bottleneck in GPU-accelerated programs. Further study was required to properly take advantage of GPU resources. A comprehensive analysis of code performance and the model-specific features of turbulence constitutes the core of this work. In this study, a DNS simulation of HFIR channel turbulence was performed with the finite volume CFD code OpenFOAM v2112 and CUDA v11.0 on Red Hat Enterprise Linux v8.2. The OpenFOAM installation had AMGx integrated to enable GPU acceleration and utilizes the PETSc4FOAM library. The computational resources and the problem size were scaled on CPU and CPU + GPU architectures to gain a better understanding of the performance of a DNS problem on modern computing hardware. The study aimed to analyze the scaling of the code exclusively on CPUs and then to examine the scaling of the codes with GPU acceleration enabled. Scaling studies included CPU and GPU acceleration on a mesh of varying resolution to analyze the impact of problem size relative to computational resources. In the course of preparing the GPU configuration on Summit, mainly using the AMGX solvers, difficulties were encountered stemming from constant changes resulting from extensive ongoing development activities and the changing environment. This resulted in the inability to complete the GPU portion of the work. The code was compiled and tested, but production runs to assess acceleration were not performed because the used discretional compute time allocation expired as year-end approached. The Summit HPC platform is scheduled for decommissioning in 2024, making it unattractive for future use with Nvidia-based GPUs. Therefore, the work will be moved onto NERSC machines in FY24. An application was prepared and submitted, and sufficient node-hours were awarded to continue the research in the next calendar year. This report summarizes work performed thus far, which mostly focused on CPU OpenFOAM computing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

An Approach to Shared Research Systems

World-leading research requires state-of-the-art systems for research and innovation. Operational excellence is the key component of scientific excellence by implementing ubiquitous security, repeatable processes, streamlined policies, and reliable and robust infrastructure. Building a research environment to meet these needs is a complex and expensive task. In many cases, it can be financially advantageous for one or more programs, projects, and/or stakeholder(s) to pool resources to build a shared research system to meet common goals. However, such a system brings some unique challenges that need to be addressed. This document describes the process to mitigate these challenges by documenting how the interested parties contribute to the overall success of the system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

JUSTIFI: Open-Source Software for Identifying and Quantifying Non-Energy Benefits

The integration of Non-Energy Benefits (NEBs) into energy efficiency initiatives is essential for operational excellence in manufacturing. This presentation and software demonstration explore how quantifying NEBs such as improved safety, increased quality, and enhanced productivity, can strengthen business cases for energy investments, leading to better payback periods and alignment with organizational goals. We introduce JUSTIFI, a free, open-source software by the U.S. Department of Energy that aids in the measurement of NEBs and enhances understanding of their impact on Key Performance Indicators (KPIs) and return on investment (ROI). JUSTIFI features an intuitive interface for identifying NEBs, customizable reporting tools, and comprehensive system cataloging, empowering companies to effectively communicate the value of energy efficiency projects. By leveraging this innovative tool, organizations can better navigate energy efficiency assessments and drive support for their energy management initiatives.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Improving Performance via Energy Efficiency JUSTIFI: Open-Source Software for Identifying and Quantifying Non-Energy Benefits

Energy efficiency is pivotal to achieving operational excellence, as it enhances value while reducing waste. This presentation explores the integration of non-energy benefits (NEBs) into energy efficiency projects, which can lead to risk reduction, value creation, and cost savings. By quantifying NEBs - such as improved safety, decreased pollution, and increased productivity - companies can strengthen their business cases for energy investments, ultimately improving payback periods and aligning with strategic goals. Designed for a diverse audience, from trained auditors to novices in energy assessments, we have developed open-source software called JUSTIFI, NEB finding methodology, and training materials which build on existing frameworks and leverages resources from the U.S. Department of Energy and Better Plants energy system analysis software suite such as MEASUR. This work aims to maximize ROI through NEB identification, utilizing tools like JUSTIFI and the NEBs Discovery Protocol.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Livewire: A Model Platform for Data Quality Assessment and AI Readiness Across DOE Missions

High-quality, well-governed data is essential for accelerating discovery and achieving operational excellence across DOE and national laboratory missions. The Livewire Data Platform is a DOE-supported platform that offers automated assessments of data quality, standardization, provenance, and Artificial Intelligence (AI) readiness. It allows researchers and data practitioners to systematically and easily evaluate datasets against established governance criteria and prepare them for advanced analytics. Livewire addresses critical challenges in DOE's data ecosystem with integrated capabilities for metadata validation, provenance tracking, and schema alignment. This platform's automated workflows assist users in identifying data quality gaps, enhancing interoperability between datasets collected from various stakeholders, and ensuring compliance with DOE data standards, all while reducing manual curation efforts. Additionally, we will discuss its AI readiness framework, which is being developed to prepare datasets for training models, developing advanced analytic tools, and machine learning applications. Using some of the more than one hundred tabular datasets on Livewire, processed with this open-source methodology, we will demonstrate how Livewire can serve as a model for scalable, standards-driven data management. This approach provides a pathway to leverage existing and future datasets within the DOE, boosting innovation and efficiency across national laboratories.

33 - ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Polycation ionic liquid tailored PEO-based solid polymer electrolytes for high temperature lithium metal batteries

Poly(ethylene oxide) (PEO)-based polymer electrolytes are promising candidates for solid-state electrolytes in safer, next generation lithium metal batteries. Despite their benefits however, PEO-based electrolyte exhibits highly crystalline ethylene oxide chains that provide poor ionic conductivity and, thus severely limit its practical application. Here, we report the use of hydroxypropyl trimethylammonium bis(trifluoromethane) sulfonimide chitosan salt (HACC-TFSI), which is an amorphous poly(ionic liquid) based biomass chitosan derivative, as a modifier for PEO-based solid polymer electrolytes (SPEs) to address these deficiencies. Hybrid SPEs with HACC-TFSI display enlarged amorphous regions with enhanced ionic conductivity. Interactions between quaternary ammonium cations and TFSI-anions in hybrid SPEs are also found to promote dissociation between Li + and TFSI - , which further increases ionic mobility. Moreover, the electrochemical stability, mechanical strength, and thermal stability of hybrid SPEs are collectively superior to blank SPEs without HACC-TFSI. LiFePO 4 /SPEs/Li full-cells assembled using 10wt% HACC-TFSI in PEO (10%HACC-TFSI/SPEs) electrolyte provide a capacity of 161.3 mAh g -1 and operate with excellent cycle performances at 0.2 C and 60 C. Finally, even when the temperature is increased to 150 C, LiFePO 4 /SPEs/Li cells with 10%HACC-TFSI/SPEs still display remarkable cycle performance with 73% capacity retention after 100 cycles at 1 C rate.

25 ENERGY STORAGE↗
DOE OSTI2026

Building the Business Case with JUSTIFI: Quantifying Multiple Benefits in the Automotive Industry

The Michigan State University Industrial Training and Assessment Center (MSU ITAC) conducted a pilot study at an automotive parts manufacturer in Michigan. The study identified energy-productivity enhancements through the application of the JUSTIFI software. Key recommendations included replacing six inefficient rooftop units (RTUs) with a new air rotational unit. By quantifying operational savings for this project, the expected payback period went from 6.6 years to 1.4 years. Additionally, the installation of variable frequency drives (VFDs) on condenser tower motors was suggested. By including all operational benefits, the payback period was reduced from 8.2 years to 1.3 years. This comprehensive analysis aims to bolster the manufacturer's goals of reducing energy while enhancing overall operational efficiency.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Surrogate Modeling of Landau Damping with Deep Operator Networks

Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov–Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process—a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets’ robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.

plasma astrophysics↗
DOE OSTI2024

Realizing efficient operations of Ni-cermet-based fuel cells on hydrocarbons via an in situ self-assembled metal/oxide nano-heterostructured catalyst

Operating Ni-cermet-based fuel cells on hydrocarbons is charming by largely hindered by poor coking tolerance and severe deterioration. Here, in this study, we report an effective metal/oxide nano-heterostructured catalyst with a nominal composition of Pr 0.95 Ru 0.05 O 2-δ (PRO), which is in situ self-assembled to a Pr 0.95 Ru 0.05-x O 2-δ oxide frame coated with Ru metallic nanoparticles (denoted as Ru/PRO) under the operation condition. When applied to the Ni-cermet (Ni-YSZ) anodes, the cells achieve decent peak power densities of 1.784 and 1.870 W cm -2 on CH 4 and C 3 H 8 with only 3% H 2 O at 750 °C, respectively. Moreover, the cells with Ru/PRO-coated anode demonstrate excellent durability when operated on CH 4 for ~ 220 h and C 3 H 8 for ~115 h. It is demonstrated that the Ru/PRO generates hydroxyl species that react with carbon species, followed by the formation of COH intermediates on Ni anode surfaces for removing the coking, as confirmed by experiments and computations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Operation of the ATLAS trigger system in Run 2

The ATLAS experiment at the Large Hadron Collider employs a two-level trigger system to record data at an average rate of 1 kHz from physics collisions, starting from an initial bunch crossing rate of 40 MHz. During the LHC Run 2 (2015–2018), the ATLAS trigger system operated successfully with excellent performance and flexibility by adapting to the various run conditions encountered and has been vital for the ATLAS Run-2 physics programme. For proton-proton running, approximately 1500 individual event selections were included in a trigger menu which specified the physics signatures and selection algorithms used for the data-taking, and the allocated event rate and bandwidth. The trigger menu must reflect the physics goals for a given data collection period, taking into account the instantaneous luminosity of the LHC and limitations from the ATLAS detector readout, online processing farm, and offline storage. This document discusses the operation of the ATLAS trigger system during the nominal proton-proton data collection in Run 2 with examples of special data-taking runs. Aspects of software validation, evolution of the trigger selection algorithms during Run 2, monitoring of the trigger system and data quality as well as trigger configuration are presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Physical Sciences Vistas (Issue 3 2020)

In this issue, highlights of our outstanding R&D supporting the Laboratory’s nuclear security mission include the following. 1. The essential nuclear materials science contributions of MST’s Materials Properties Team to a range of Lab missions, operations, and initiatives. 2. Work in the Sigma Complex supporting NNSA’s Advanced Manufacturing Development milestones by members of Sigma Division and colleagues across the Laboratory and other NNSA sites. 3. Greg Dale’s role in the Lab’s molybdenum program and today as a technical lead on the Scorpius project. 4. Measurements that reveal the role thermal interfaces play in dynamic compression experiments. 5. The first-ever synthesis of an actinide framework, which offers opportunities for understanding these structures as potential radioactive waste forms and provides new models of actinide species transport in the environment. This issue also showcases, as part of our commitment to simultaneous excellence in mission operations and community relations, the successful high-hazard repair of the LANSCE accelerator, which resulted in a large team Laboratory Distinguished Performance Award, and a virtual Summer Physics Camp for Young Women that brought together participants from around the world, even as COVID kept them socially distant in their homes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modular Electrochemical Synthesis Using a Redox Reservoir Paired with Independent Half-Reactions

Electrochemical synthesis of high-value chemicals using renewable electricity could lead to sustainability advantages over conventional chemical manufacturing. The tight coupling between paired anodic and cathodic reactions in conventional electrochemical processes, however, constrains the products and their efficiencies. Here, we demonstrate a strategy to achieve modular electrochemical synthesis (ModES) of different chemicals by decoupling and pairing the oxidative and reductive half-reactions with a heterogeneous redox reservoir (RR). Specifically, sodium nickel hexacyanoferrate (NaNiHCF) can serve as a robust and reversible RR that enables the modular production of several strong oxidants, hydrogen peroxide (H 2 O2) and sodium persulfate (Na 2 S 2 O 8 ) or active chlorine, in separate RR-coupled electrochemical processes. The system’s excellent performance and operational stability is demonstrated by continuous operation over 100 cycles (~36 h) to produce H 2 O 2 and Na 2 S 2 O 8 with a high voltage efficiency. The concept presented here highlights the prospects for using RRs to enable efficient, modular, and on-demand electrochemical synthesis of valuable chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Calcium-Doped High-Voltage Spinel Cathode for Long Cycle Life Lithium-Ion Batteries

With the promises of low cost, high operating voltage, and excellent rate capability, the high-voltage spinel material with the formula of LiNi 0.5 Mn 1.5 O 4 (LNMO) has been considered as one of the most promising cathode materials for nextgeneration lithium-ion batteries (LIBs). However, the adoption of LNMO into practical LIBs is greatly hindered due to its rapid capacity decay associated with its bulk structural instability and interfacial side reactions. To address these issues, we proposed to use the cost-effective calcium (Ca) element as a dopant to stabilize the oxygen framework and surface of the LNMO crystal. The experimental results showed that, with moderate Ca doping, the obtained cathode (Ca 0.05 LNMO) retained a specific capacity of ∼121 mAh/g (∼94.4% capacity retention) after 500 cycles at 0.5 C, compared to ∼73% for the baseline bare sample. Furthermore, the Ca 0.05 LNMO cathode retained ∼84% of its initial capacity, vs the baseline with ∼69%, after 150 cycles at the high temperature of 55 °C. The excellent battery performance of the moderately Ca-doped LNMO cathode is ascribed to its structural and kinetic advantages.

25 ENERGY STORAGE↗
DOE OSTI2021

An efficient and durable anode for ammonia protonic ceramic fuel cells

Ammonia protonic ceramic fuel cells (PCFCs) have the potential to be a highly efficient power source with high energy density. However, the inadequate catalytic activity of the existing anodes for utilization of ammonia greatly limits the performance of PCFCs. In this study, we report an Fe-modified state-of-the-art Ni cermet anode with greatly enhanced activity and durability toward utilization of ammonia. Cells with an Fe-decorated Ni-BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3 (Ni–BZCYYb) anode demonstrate an excellent performance, achieving peak power densities of 0.360, 0.723, 1.257, and 1.609 W cm –2 at 550, 600, 650, and 700 °C, respectively, which reveal the highest performance of solid oxide fuel cells fueled on ammonia. In addition, the cells show an excellent durability when operated at a constant current density of 0.5 A cm –2 (or a power density of ~0.435 W cm –2 ) at 650 °C. The superior activity and durability of the Fe-modified Ni/BZCYYb anode are attributed to the alternation of NH 3 adsorption strength and N 2 desorption barrier heights, as confirmed by first-principles based mechanistic and microkinetic modeling. Our research provides a valuable guidance for the development of efficient electro-catalysts for ammonia PCFCs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Neural operator transformers capture bifurcating drift-wave turbulence in fusion plasma simulations

Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator partial differential equation surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa–Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence, and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants: 2nd Annual Report

For economic reasons, the nuclear industry is witnessing premature closure of nuclear power plants, despite excellent safety records. Operations and Maintenance (O&M) activities are some of the largest costs in operating legacy light-water plants. By reducing O&M costs, nuclear energy can become more economically competitive with other energy sources. This can be achieved by leveraging machine-learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose potential faults within the system. Improved accuracy of the models can lead to a reduction in unnecessary maintenance, thus reducing costs associated with parts, labor, and unnecessary planned, forced, or extended outages. To address these challenges, the goal of this project is to perform research and development in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and data science based analytic capabilities, to advance online monitoring and predictive maintenance in nuclear plants and improve plant performance (efficiency gain and economic competitiveness). This report summarizes the fiscal year 2020 research progress encompassing (1) different wireless vibration sensor and data indicators used to assess the health of a plant asset; (2) development of diagnostic models for fault detection; and (3) development of prognostic models for estimating the health of the system up to 7 days ahead.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Review of Nanocarbon-Based Anode Materials for Lithium-Ion Batteries

Renewable and non-renewable energy harvesting and its storage are important components of our everyday economic processes. Lithium-ion batteries (LIBs), with their rechargeable features, high open-circuit voltage, and potential large energy capacities, are one of the ideal alternatives for addressing that endeavor. Despite their widespread use, improving LIBs’ performance, such as increasing energy density demand, stability, and safety, remains a significant problem. The anode is an important component in LIBs and determines battery performance. To achieve high-performance batteries, anode subsystems must have a high capacity for ion intercalation/adsorption, high efficiency during charging and discharging operations, minimal reactivity to the electrolyte, excellent cyclability, and non-toxic operation. Group IV elements (Si, Ge, and Sn), transition-metal oxides, nitrides, sulfides, and transition-metal carbonates have all been tested as LIB anode materials. However, these materials have low rate capability due to weak conductivity, dismal cyclability, and fast capacity fading owing to large volume expansion and severe electrode collapse during the cycle operations. Contrarily, carbon nanostructures (1D, 2D, and 3D) have the potential to be employed as anode materials for LIBs due to their large buffer space and Li-ion conductivity. However, their capacity is limited. Blending these two material types to create a conductive and flexible carbon supporting nanocomposite framework as an anode material for LIBs is regarded as one of the most beneficial techniques for improving stability, conductivity, and capacity. This review begins with a quick overview of LIB operations and performance measurement indexes. It then examines the recently reported synthesis methods of carbon-based nanostructured materials and the effects of their properties on high-performance anode materials for LIBs. These include composites made of 1D, 2D, and 3D nanocarbon structures and much higher Li storage-capacity nanostructured compounds (metals, transitional metal oxides, transition-metal sulfides, and other inorganic materials). The strategies employed to improve anode performance by leveraging the intrinsic features of individual constituents and their structural designs are examined. The review concludes with a summary and an outlook for future advancements in this research field.

25 ENERGY STORAGE↗