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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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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Identification of multiple eigenmode growth rates in DIII-D and EAST tokamak plasmas

Here, the successful application of three-dimensional (3D) magnetohydrodynamic (MHD) spectroscopy in the stable DIII-D and EAST plasmas enables to directly extract the multi-mode plasma transfer function in tokamak experiments. The transfer function not only reveals the contribution of each dominant MHD eigenmode in the multi-model plasma response, but also quantifies the corresponding eigenvalue which is the critical stability index of MHD mode. The method performs the active detection of stable plasma by utilizing the upper and lower rows of internal coils to scan the frequency and poloidal spectrum of the applied 3D field, and reconstructing the multi-mode transfer functions model through the least square fitting of the plasma response measured by 3D-field magnetic sensors distributed at different poloidal locations. The results point to the potential development of an advanced strategy for tracking the plasma stability based on the extracted eigenvalues of stable modes. The improved understanding of the dominant eigenmodes' behavior in multi-mode plasma response through transfer function can also help to optimize the applied 3D fields for the purposes, such as the type-I edge localized mode suppression and the core stability control in future fusion reactors.

3D MHD spectroscopy↗
DOE OSTI2025

Scaling Ultrahigh-Resolution E3SM Land Model for Leadership-Class Supercomputers

This paper presents advancements in scaling the ultrahigh-resolution E3SM Land Model (uELM) for deployment on leadership-class supercomputers, addressing the increased demand for km-scale Earth system modeling. By focusing on km-scale ELM simulations, we enhance predictive capabilities for climate interactions, facilitating improved responses to climate change impacts on energy systems, agriculture, and water resources. Our approach leverages innovative software architecture optimizations, sophisticated data handling techniques, and advanced parallel processing, achieving strong scalability on two leadership supercomputers (2400 nodes (105,600 cores) on Summit, and 1200 nodes (76,800 cores) on Frontier). Results from extensive scalability assessments on the Summit and Frontier also demonstrate outstanding I/O performance (close to 400 GB/s write throughput) and the model's ability to efficiently handle increasing computational demands. This study not only establishes uELM's capability for high-resolution simulations over vast geographical domains, but also sets a foundation for future Earth system modeling breakthroughs.

Wang, Dali [ORNL] (ORCID:0000000168065108)↗
DOE OSTI2021

The Kokkos EcoSystem: Comprehensive Performance Portability For High Performance Computing

State of the art Engineering and Science codes have grown in complexity dramatically over the last two decades. As a consequence application teams have adopted more sophisticated development strategies, leveraging third party libraries, deploying comprehensive testing and using advanced debugging and profiling tools. In todays environment of diverse hardware platforms, these applications also desire performance portability - avoiding the need to duplicate work for various platforms - which makes it necessary that these tools and libraries also work across the various systems. The Kokkos EcoSystem provides that portable software stack. Based on the Kokkos Core Programming Model, the EcoSystem provides math libraries, interoperability capabilities with Python and Fortran, and Tools for analysing, debugging, and optimizing applications. In this paper we will provide an overview of the components, discuss some specific use cases, and highlight how co-designing these components enables a more developer friendly experience.

42 ENGINEERING↗
DOE OSTI2020

Conceptual Design of DUNE Near Detector Superconducting Magnet System

The Deep Underground Neutrino Experiment (DUNE) has formed a near detector design group (NDDG) tasked with delivering a CDR by the end of 2019. The DUNE Near Detectors will be housed in an underground hall on the Fermilab site. The two main detector systems are a liquid argon detector and a high-pressure gas time projection chamber (HPgTPC). The HPgTPC requires a magnet that generates a 0.5 T solenoidal magnetic field in a large volume of 6 m diameter, and 5 m length. Herein, we present a superconducting magnet system design. We investigated: an open air core magnet with three coils, and a five coils system having two active fringe field shielding coils. Coils positions were optimized to obtain the specified field homogeneity in the magnet good field region, while minimizing the Lorentz forces and the superconductor volume. We discuss the magnetic, mechanical, and thermal conceptual designs.

43 PARTICLE ACCELERATORS↗
DOE OSTI2017

Borehole Tool for the Comprehensive Characterization of Hydrate-bearing Sediments

Reservoir characterization and simulation require reliable parameters to anticipate hydrate deposits responses and production rates. The acquisition of the required fundamental properties currently relies on wireline logging, pressure core testing, and/or laboratory observations of synthesized specimens, which are challenged by testing capabilities and innate sampling disturbances. The project reviews hydrate-bearing sediments, properties, and inherent sampling effects, albeit lessen with the developments in pressure core technology, in order to develop robust correlations with index parameters. The resulting information is incorporated into a tool for optimal field characterization and parameter selection with uncertainty analyses. Ultimately, the project develops a borehole tool for the comprehensive characterization of hydrate-bearing sediments at in situ, with the design recognizing past developments and characterization experience and benefited from the inspiration of nature and sensor miniaturization.

03 NATURAL GAS↗
DOE OSTI2022

In-Situ Pipeline Coatings for Methane Emissions Mitigation and Quantification from Natural Gas Pipelines

Addressing the current health of the nation’s existing 3 million miles of pipeline infrastructure is key to preventing further climate change. In 2020, natural gas production exceeded 34 trillion cubic feet (Tcf). Roughly 75% of natural gas consists of methane (CH 4 ), which is up to 25 times more powerful than carbon dioxide (CO 2 ) at trapping heat within the atmosphere over a 100-year period, and studies from the Environmental Defense Fund (EDF) estimate approximately 2% of all the natural gas produced will be lost during normal operations due to unaddressed leaks. This does not even consider the risks of major disaster due to pipeline failure, or the losses and extra fuel costs incurred due to corrosion and scale deposits in under-maintained pipelines. The objective of the proposed research is to demonstrate the protection capabilities and economic benefits of Oceanit’s internal pipe surface treatment, known as DragX™. DragX™ is a chemically resistant, water-and-oil repellent nanocomposite system that can be readily applied in-situ on natural gas transmission and distribution pipelines with a minimum of surface preparation. This makes it an ideal candidate for in-place retrofitting and refurbishment of existing pipelines without the need for expensive extraction and replacement. DragX™ is also able to significantly reduce the surface roughness, and subsequently, the frictional drag forces within a pipeline, improving throughput, decreasing energy costs of pressurization and pumping, and allowing for longer pipeline operation without interruption, reducing the methane emitted during pipe isolation and venting. As part of this project, Oceanit has utilized the Department of Energy’s support to fully develop, de-risk and prove the DragX™ core technology is both economically viable and commercially desirable to pipeline operators and energy companies alike. DragX™ material properties were optimized in this effort both for ease of applicability, to provide value in certain key parameters, and was demonstrated on pilot applications exceeding 2 miles in length. Beyond the already field demonstrated applications, this innovative nanocomposite surface treatment has the potential to be the backbone for CO 2 and Hydrogen transporting pipeline infrastructure. The learnings from this project could accelerate the deployment of surface treatment technologies related to the energy transition infrastructure, thus benefitting the clean energy initiatives in the United States and all around the world.

03 NATURAL GAS↗
DOE OSTI2025

Autonomous Flow Electrochemistry for Accelerated Catalyst Discovery

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Validation of Calibrated k–ε Model Parameters for Jet-in-Crossflow

Previous efforts determined a set of calibrated, optimal model parameter values for Reynolds-averaged Navier–Stokes (RANS) simulations of a compressible jet in crossflow (JIC) using a $k–ε$ turbulence model. These parameters were derived by comparing simulation results to particle image velocimetry (PIV) data of a complementary JIC experiment under a limited set of flow conditions. Here, a $k–ε$ model using both nominal and calibrated parameters is validated against PIV data acquired from a much wider variety of JIC cases, including a realistic flight vehicle. The results from the simulations using the calibrated model parameters showed considerable improvements over those using the nominal values, even for cases that were not used in the calibration procedure that defined the optimal parameters. This improvement is demonstrated using a number of quality metrics that test the spatial alignment of the jet core, the magnitudes of multiple flow variables, and the location and strengths of vortices in the counter-rotating vortex cores on the PIV planes. These results suggest that the calibrated parameters have applicability well outside the specific flow case used in defining them and that with the right model parameters, RANS solutions for the JIC can be improved significantly over those obtained from the nominal model.

42 ENGINEERING↗
DOE OSTI2023

Inferring Type II-P Supernova Progenitor Masses from Plateau Luminosities

Abstract Connecting observations of core-collapse supernova explosions to the properties of their massive star progenitors is a long-sought, and challenging, goal of supernova science. Recently, Barker et al. presented bolometric light curves for a landscape of progenitors from spherically symmetric neutrino-driven core-collapse supernova (CCSN) simulations using an effective model. They find a tight relationship between the plateau luminosity of the Type II-P CCSN light curve and the terminal iron-core mass of the progenitor. Remarkably, this allows us to constrain progenitor properties with photometry alone. We analyze a large observational sample of Type II-P CCSN light curves and estimate a distribution of iron-core masses using the relationship of Barker et al. The inferred distribution matches extremely well with the distribution of iron-core masses from stellar evolutionary models and namely, contains high-mass iron cores that suggest contributions from very massive progenitors in the observational data. We use this distribution of iron-core masses to infer minimum and maximum masses of progenitors in the observational data. Using Bayesian inference methods to locate optimal initial mass function parameters, we find M min = 9.8 − 0.27 + 0.37 and M max = 24.0 − 1.9 + 3.9 solar masses for the observational data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

Curiosity's Sample Analysis at Mars (SAM) Investigation: Overview of Results from the First 120 Sols on Mars

During the first 120 sols of Curiosity s landed mission on Mars (8/6/2012 to 12/7/2012) SAM sampled the atmosphere 9 times and an eolian bedform named Rocknest 4 times. The atmospheric experiments utilized SAM s quadrupole mass spectrometer (QMS) and tunable laser spectrometer (TLS) while the solid sample experiments also utilized the gas chromatograph (GC). Although a number of core experiments were pre-programmed and stored in EEProm, a high level SAM scripting language enabled the team to optimize experiments based on prior runs.

Mahaffy, P. R.↗
DOE OSTI2017

Lawrence Berkeley National Laboratory 2016 Annual Financial Report

FY2016 was a year of significant change and progress at Berkeley Lab. In March, Laboratory Director Michael Witherell assumed his new role when former Lab Director Paul Alivisatos became Vice Chancellor for Research at UC Berkeley. Dr. Witherell has solidified the Lab’s strategy, with a focus on long term science and technology priorities. Large-scale science efforts continued to expand at the Lab, including the Dark Energy Spectroscopic Instrument now heading towards construction, and the LUX-ZEPLIN dark matter detector to be built underground in South Dakota. Another proposed project, the Advanced Light Source-Upgrade, was given preliminary approval and will be the Lab’s largest scientific investment in years. Construction of the Integrative Genomics Building began, and will bring together researchers from the Lab’s Joint Genome Institute, now based in Walnut Creek, and the Systems Biology Knowledgebase (K-Base) under one roof. Investment in the Lab’s infrastructure also continues, informed by the Lab’s Infrastructure Strategic Plan. Another important focus is on developing the next generation of scientists with the talent and diversity needed to sustain Berkeley Lab’s scientific leadership and mission contributions to DOE and the Nation. Berkeley Lab received $897.5M in new FY2016 funding, a 12.5% increase over FY2015, for both programmatic and infrastructure activities. While the Laboratory experienced a substantial increase in funding, it was accompanied by only a modest increase in spending, as areas of growth were partially offset by the completion of several major efforts in FY2015. FY2016 costs were $826.9M, an increase of 1.9% over FY2015. Similar to the prior year, the indirect-funded Operations units worked with generally flat budgets to yield more funding for strategic needs. A key challenge for Berkeley Lab continues to be achieving the best balance to fund essential investments, deliver highly effective operational mission support and remain cost-competitive. Through a comprehensive approach to prioritize competing needs, the Lab ended the year in a favorable financial position. The Office of the Chief Financial Officer (OCFO) played a key role in providing analysis and decision support to Executive Leadership, enabling the Lab to enhance its financial management strategies. In FY2016, the OCFO updated its analytic approaches and models to enhance long term financial projections under various funding and investment scenarios, and to assess total cost of ownership for major proposed investments. These improvements provided the new Lab Director and Senior Leadership with more comprehensive information and analytic support for planning and prioritization efforts. Within the OCFO, we focused on core operations and key initiatives defined in our OCFO Strategic Roadmap. The Lab’s Financial System transitioned from stabilization to optimization, with a focus on expanding the financial reporting capabilities considerably. We completed implementation of the eCommerce platform, achieving a notable outcome for the Lab in close partnership with DOE’s Office of Science. In other accomplishments, we launched a financial literacy program to enable Lab managers and staff to understand and execute their financial management and stewardship responsibilities more effectively; made substantial progress in enhancing our Field Finance model that provides financial support to client divisions and areas; developed a business process governance model to define OCFO business processes, clarify roles, and strengthen service delivery; and implemented a Partners in Leadership training program to build leadership capacity among our staff. We completed a ‘refresh’ of our Strategic Roadmap, which now defines our priorities for FY2017-FY2019. As a part of this effort, we made a subtle but important change to the OCFO mission statement to call out the Lab’s research and stewardship mission to sustain the Lab’s science and technology capabilities now and into the future. Berkeley Lab’s FY2016 progress on all fronts - scientific, operations, and financial management – position the Lab to continue bringing science solutions to the world as we charge into the 21st Century.

Source record↗
DOE OSTI2025

Parametric Optimization of Nanostructured Alumina Forming Austenitic Alloys

Alumina-forming austenitic (AFA) alloys provide excellent oxidation resistance through the formation of a stable alumina scale, but their relatively high Ni content might limit application in core structural components because of helium generation and swelling under irradiation. Increasing the density of nanoscale features in the microstructure is a promising route to improve sink strength and mitigate these effects. Conventional oxide dispersion–strengthened (ODS) steels achieve this through mechanical alloying (MA), but the approach is costly and difficult to scale. In this milestone, additive manufacturing was applied as an alternative pathway to fabricate nanostructured AFA (NAFA) alloys with dual precipitation of oxides and nitrides enabled by reactive atmosphere processing. In FY25, two compositions, NAFA-1 (AFA-05) and NAFA-2, were produced and compared. Both alloys achieved densities above 99% of theoretical values, but NAFA-2 exhibited microcracking associated with Nb-rich intermetallic formation, indicating the need for further chemistry optimization. Nitrogen and oxygen uptake was confirmed on selected builds, demonstrating the intended dual-precipitate dispersion. Mechanical testing showed that AM-processed NAFA alloys exceed conventionally produced ODS steels in high-temperature tensile strength and fracture toughness. Preliminary Cu ion irradiation results further suggest improved resistance compared to wrought austenitic alloys. While the current alloys are promising for their compatibility in high-temperature air or liquid Pb environments, modifications to composition will be required for long-term operation in high-temperature impure sodium systems. Future work will focus on refining alloy chemistry to suppress laser powder bed fusion–induced cracking and scaling production to reactor-relevant dimensions using directed energy deposition as a pathway toward net-shape cladding fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Distributed out-of-memory NMF on CPU/GPU architectures

We propose an efficient distributed out-of-memory implementation of the non-negative matrix factorization (NMF) algorithm for heterogeneous high-performance-computing systems. The proposed implementation is based on prior work on NMFk, which can perform automatic model selection and extract latent variables and patterns from data. In this work, we extend NMFk by adding support for dense and sparse matrix operation on multi-node, multi-GPU systems. The resulting algorithm is optimized for out-of-memory problems where the memory required to factorize a given matrix is greater than the available GPU memory. Memory complexity is reduced by batching/tiling strategies, and sparse and dense matrix operations are significantly accelerated with GPU cores (or tensor cores when available). Input/output latency associated with batch copies between host and device is hidden using CUDA streams to overlap data transfers and compute asynchronously, and latency associated with collective communications (both intra-node and inter-node) is reduced using optimized NVIDIA Collective Communication Library (NCCL) based communicators. Benchmark results show significant improvement, from 32X to 76x speedup, with the new implementation using GPUs over the CPU-based NMFk. Good weak scaling was demonstrated on up to 4096 multi-GPU cluster nodes with approximately 25,000 GPUs when decomposing a dense 340 Terabyte-size matrix and an 11 Exabyte-size sparse matrix of density 10 -6 .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An algorithm for physics informed scan path optimization in additive manufacturing

Site specific microstructure control is a critical research area within the field of additive manufacturing due to its potential to revolutionize part performance. One way to achieve site specific microstructure control is through control of the solidification conditions via the construction of intricate scan paths; however, the search space for such a problem is large. Previous attempts only considered the solidification conditions at the top surface while also requiring either lots of manual-fine tuning or large amounts of computational resources. This paper introduces a general method for scan path optimization which considers the solidification conditions in the bulk of the material without an increase in computational expense. This method consists of three core components:1. A heat transfer model for simulating the temperature field at a given time.2. A surrogate model which takes scan pattern information and temperature data and predicts the solidification conditions of the bulk as well as the meltpool depths for a spot melt.3. A decision algorithm to decide which spot melt should be printed next based on the outputs of the surrogate model.Each of these components can be changed without changing the overall method. Within this work, this method is applied in the creation of an algorithm containing a semi-analytic heat transfer model to simulate the temperature field, a fully convolutional neural network (FCNN) as the surrogate model, and a greedy decision algorithm. The resulting algorithm produced complex scan patterns which gave strong results for simulated microstructure control.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Developing an Ultra-Compact, Topology-Optimized Heat Exchanger using Additive Manufacturing

Traditional heat exchanger designs have relied on a plate-fin architecture, assembled in a crossflow configuration. These HX designs are limited in their maximum allowable temperature and, therefore, efficiency. More advanced designs, such as those containing counter-flow, microtube cores, can yield higher efficiencies, but generally require complex headers. The design of such headers is usually determined based on user experience rather than on rigorous design tools, which can lead to lossy architectures; conventional headers can generate up to 50% of the total pressure loss through the HX. Furthermore, the consequences of manufacturing techniques are rarely considered during the design phase. Our team has developed an ultra-compact, topology-optimized heat exchanger capable of operating in environments with temperatures and pressures up to 800°C (1472°F) and 250 bar (3626 psi), respectively. Throughout the program, the team leveraged extensive additive manufacturing research and experience in supercritical carbon dioxide (sCO 2 ) power generation experience to develop and commercialize the technology. The final power density numbers for the subscale and full-scale heat exchangers are >15 kW/kg and >50 kW/L, which includes the core and the headers. The team has involved the RTX business units, notably Collins Aerospace, in discussions for commercializing the technology.

20 FOSSIL-FUELED POWER PLANTS↗