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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Zero-Carbon Biofuels: An Optimized Two-Stage System for High Productivity Conversion of CO2 to Liquid Fuels

In MIT’s Zero-Carbon Biofuels work, under ARPAe’s EcoSynBio funding, we capitalized on our previously developed technology of the oleaginous yeast, Yarrowia lipolytica, engineered to produce high titer biodiesel-like lipids and alkanes, and set out to reduce or eliminate CO 2 generation during lipid production. We set out to do this by both engineering Y. lipolytica with the necessary enzymes to allow the generation of reducing equivalents from H 2 , formic acid, or methanol, and, in parallel, installing the non-oxidative glycolysis (NOG) pathway. The goal was that combination of these two would enable stoichiometric conversion of glucose and acetate to biodiesel products. Furthermore, our ARPAe Electrofuels funded research, yielded a very innovative two-stage system where gases comprising CO 2 and H 2 are used for the production of liquid fuels. Acetate is produced from CO 2 and H 2 , via acetogenic fermentation, in the first stage as the intermediary product, then fed to Y. lipolytica in the second stage, for conversion to lipids and alkanes. To increase the commercial competitiveness of the two-stage system, by improvement to the overall productivity, we recently showed that synergistic substrate co-feeding drastically enhances CO 2 fixation rates and set out to explore additional co-substrate pairs, including methanol and formic acid, in order to maximize acetate productivity. Finally, given the attractiveness as a biofuel, we also set out to attempt an important increase in the production of alkanes over lipids in Yarrowia.

10 SYNTHETIC FUELS↗
DOE OSTI2025

Direct numerical simulations for hybrid rocket boundary layers: Performance modeling and scaling

This paper presents a comprehensive performance and scaling analysis of direct numerical simulations for reacting boundary layers, focusing on slab burner configurations. Using a PETSc-based finite volume CFD framework, the study evaluates the scalability and computational cost of flow, chemistry, and radiation evaluations across 2D and 3D simulations. Polymethyl methacrylate (PMMA) is the fuel with pure O 2 as the oxidizer, modeled using a detailed chemical kinetics mechanism with 113 species and 660 reactions. A ray-tracing-based radiation solver, designed for distributed memory applications, is implemented to model radiation heat transfer. Parallel scalability is analyzed for the coupled flow, chemistry, and radiation heat transfer processes. Weak and strong scaling studies are conducted on up to 15,000 computational ranks, revealing robust performance when flow cells exceed 200 per rank. Chemistry evaluations dominate the computational cost in large 3D simulations, accounting for approximately 40% of the total runtime, while flow processes contribute around 35%, and radiation solver contributions remain below 10% due to reduced evaluation frequencies. GPU accelerated chemistry evaluation, implemented with Zero-RK, demonstrates significant promise, achieving up to a 4x speedup for workloads exceeding 30,000 cells per GPU. However, diminishing returns are observed for smaller workloads due to CPU-GPU communication overhead. This study identifies key challenges, including memory bottlenecks and the effects of domain partitioning on flow scalability, while highlighting the potential of GPU-accelerated chemistry to reduce computational costs. In conclusion, these findings provide realizable run configurations for 2D, 3D, and GPU-accelerated cases, offering insights for optimizing reactive flow solvers.

CFD Scalability↗
DOE OSTI2026

Dynamic analysis of fully constrained Cable-Driven Parallel Robots for automated prefabricated component installation

This paper presents a dynamic analysis and validation framework to assess a fully constrained six-anchor Cable-Driven Parallel Robot (CDPR) for automated installation of prefabricated facade components. Compared with conventional eight-anchor systems, the six-anchor configuration simplifies setup and reduces cost, but it also reduces control authority, shrinks the wrench-feasible workspace, and tightens orientation limits. Consequently, it is unclear a priori whether dynamically feasible trajectories exist to move the end effector from pickup to the facade. A constrained trajectory optimization is formulated to enforce the system dynamics, cable-tension bounds, and pose/velocity limits, and the framework is evaluated in simulation at three levels: (i) an idealized reference model, (ii) a lab-scale prototype model incorporating measured anchor misalignments and identified damping, and (iii) a full-scale three-story building model with load decomposition for structural feasibility checks. Across these scenarios, the analysis shows that optimal, constraint-satisfying trajectories exist that move the end effector from pickup to installation while maintaining a near-plumb, level orientation at the final pose. Collectively, this multi-scale dynamic analysis and validation framework supports the deployment readiness of the six-anchor CDPR and provides a prototype-based sensitivity case study of how measured anchor placement deviations affect feasibility.

CDPR↗
DOE OSTI2022

Training data selection for accuracy and transferability of interatomic potentials

Abstract Advances in machine learning (ML) have enabled the development of interatomic potentials that promise the accuracy of first principles methods and the low-cost, parallel efficiency of empirical potentials. However, ML-based potentials struggle to achieve transferability, i.e., provide consistent accuracy across configurations that differ from those used during training. In order to realize the promise of ML-based potentials, systematic and scalable approaches to generate diverse training sets need to be developed. This work creates a diverse training set for tungsten in an automated manner using an entropy optimization approach. Subsequently, multiple polynomial and neural network potentials are trained on the entropy-optimized dataset. A corresponding set of potentials are trained on an expert-curated dataset for tungsten for comparison. The models trained to the entropy-optimized data exhibited superior transferability compared to the expert-curated models. Furthermore, the models trained to the expert-curated set exhibited a significant decrease in performance when evaluated on out-of-sample configurations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?

Physics-informed neural networks (PINNs) have become a popular choice for solving high-dimensional partial differential equations (PDEs) due to their excellent approximation power and generalization ability. Recently, extended PINNs (XPINNs) based on domain decomposition methods have attracted considerable attention due to their effectiveness in modeling multiscale and multiphysics problems and their parallelization. However, theoretical understanding of their convergence and generalization properties remains unexplored. In this study, we take an initial step towards understanding how and when XPINNs outperform PINNs. Specifically, for general multilayer PINNs and XPINNs, we first provide a prior generalization bound via the complexity of the target functions in the PDE problem and a posterior generalization bound via the posterior matrix norms of the networks after optimization. Moreover, based on our bounds, we analyze the conditions under which XPINNs improve generalization. Concretely, our theory shows that the key building block of XPINN, namely, the domain decomposition, introduces a tradeoff for generalization. On the one hand, XPINNs decompose the complex PDE solution into several simple parts, which decreases the complexity needed to learn each part and boosts generalization. On the other hand, decomposition leads to less training data being available in each subdomain, and hence such a model is typically prone to overfitting and may become less generalizable. Empirically, we choose five PDEs to show when XPINNs perform better than, similar to, or worse than PINNs, hence demonstrating and justifying our new theory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High-Efficiency and Low-Carbon Energy Storage and Power Generation System for Electric Aviation

This report summarizes the work performed by University of California San Diego (UCSD) – Honeywell Aerospace (Honeywell) team for the U.S. Department of Energy/Advanced Research Projects Agency-Energy (DOE/ARPA-E) under Phase 1 (April 2021 – October 2023) project, Cooperative Agreement DE-AR0001347 entitled “High-Efficiency and Low-Carbon Energy Storage and Power Generation System for Electric Aviation”. The main objective of this project is to develop and demonstrate an energy storage and power generation (ESPG) system operating on bio liquid natural gas (LNG) for electric aviation applications. The ESPG system concept in this project is a fuel cell, battery, and gas turbine hybrid system that incorporates an innovative solid oxide fuel cell (SOFC) technology. This SOFC technology has two main novel elements: (i) a lightweight and compact stack architecture that consists of cells and cell modules in electrical parallel and series connections (the module design) and (ii) exceptionally high performance, direct methane thin-film cells on porous substrate made by sputtering deposition process. This fuel cell has the specific power and volumetric power density suitable for electric aviation applications. Based on the current status of the SOFC technology, the Phase 1 work focused on the following activities: (i) ESPG System Modeling – to design and optimize an aircraft SOFC-based ESPG system concept that met the performance, weight and cost targets; (ii) Cell Material Development and Scaleup – to demonstrate scalability of the sputtering process for manufacture of thin-film SOFC cells of practical sizes, confirm the exceptional performance of sputtered cells, improve cell stability and durability for operation with hydrogen and methane fuel, and develop a suitable electrically conducting porous substrate to replace the current non-conducting ceramic substrate; (iii) Stack Development – to design and manufacture stack components for the stack architecture, evaluate and select a suitable sealant, and build and operate multi-cell stacks to demonstrate stack operation, and (iv) Technology to Market – to develop business models and commercialization plans, conduct various market and technology analysis and estimate SOFC and ESPG system costs.

25 ENERGY STORAGE↗
DOE OSTI2024

Scaling the SciDAC QuantOm Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.

Lersch, Daniel↗
DOE OSTI2025

Convergence and Quantum Advantage of Trotterized MERA for Strongly-Correlated Systems

Strongly-correlated quantum many-body systems are difficult to study and simulate classically. We recently proposed a variational quantum eigensolver (VQE) based on the multiscale entanglement renormalization ansatz (MERA) with tensors constrained to certain Trotter circuits. Here, we determine the scaling of computation costs for various critical spin chains which substantiates a polynomial quantum advantage in comparison to classical MERA simulations based on exact energy gradients or variational Monte Carlo. Algorithmic phase diagrams suggest an even greater separation for higher-dimensional systems. Hence, the Trotterized MERA VQE is a promising route for the efficient investigation of strongly-correlated quantum many-body systems on quantum computers. Furthermore, we show how the convergence can be substantially improved by building up the MERA layer by layer in the initialization stage and by scanning through the phase diagram during optimization. For the Trotter circuits being composed of single-qubit and two-qubit rotations, it is experimentally advantageous to have small rotation angles. We find that the average angle amplitude can be reduced considerably with negligible effect on the energy accuracy. Benchmark simulations suggest that the structure of the Trotter circuits for the TMERA tensors is not decisive; in particular, brick-wall circuits and parallel random-pair circuits yield very similar energy accuracies.

Miao, Qiang [Duke Quantum Center, Duke University,↗
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↗
NASA NTRS1989

Computer simulation and design of a three degree-of-freedom shoulder module

An in-depth kinematic analysis of a three degree of freedom fully-parallel robotic shoulder module is presented. The major goal of the analysis is to determine appropriate link dimensions which will provide a maximized workspace along with desirable input to output velocity and torque amplification. First order kinematic influence coefficients which describe the output velocity properties in terms of actuator motions provide a means to determine suitable geometric dimensions for the device. Through the use of computer simulation, optimal or near optimal link dimensions based on predetermined design criteria are provided for two different structural designs of the mechanism. The first uses three rotational inputs to control the output motion. The second design involves the use of four inputs, actuating any three inputs for a given position of the output link. Alternative actuator placements are examined to determine the most effective approach to control the output motion.

Marco, David↗
NASA NTRS1992

High performance interconnection between high data rate networks

The bridge/gateway system needed to interconnect a wide range of computer networks to support a wide range of user quality-of-service requirements is discussed. The bridge/gateway must handle a wide range of message types including synchronous and asynchronous traffic, large, bursty messages, short, self-contained messages, time critical messages, etc. It is shown that messages can be classified into three basic classes, synchronous and large and small asynchronous messages. The first two require call setup so that packet identification, buffer handling, etc. can be supported in the bridge/gateway. Identification enables resequences in packet size. The third class is for messages which do not require call setup. Resequencing hardware based to handle two types of resequencing problems is presented. The first is for a virtual parallel circuit which can scramble channel bytes. The second system is effective in handling both synchronous and asynchronous traffic between networks with highly differing packet sizes and data rates. The two other major needs for the bridge/gateway are congestion and error control. A dynamic, lossless congestion control scheme which can easily support effective error correction is presented. Results indicate that the congestion control scheme provides close to optimal capacity under congested conditions. Under conditions where error may develop due to intervening networks which are not lossless, intermediate error recovery and correction takes 1/3 less time than equivalent end-to-end error correction under similar conditions.

Foudriat, E. C.↗
NASA NTRS1996

Research on computer systems benchmarking

This grant addresses the topic of research on computer systems benchmarking and is more generally concerned with performance issues in computer systems. This report reviews work in those areas during the period of NASA support under this grant. The bulk of the work performed concerned benchmarking and analysis of CPUs, compilers, caches, and benchmark programs. The first part of this work concerned the issue of benchmark performance prediction. A new approach to benchmarking and machine characterization was reported, using a machine characterizer that measures the performance of a given system in terms of a Fortran abstract machine. Another report focused on analyzing compiler performance. The performance impact of optimization in the context of our methodology for CPU performance characterization was based on the abstract machine model. Benchmark programs are analyzed in another paper. A machine-independent model of program execution was developed to characterize both machine performance and program execution. By merging these machine and program characterizations, execution time can be estimated for arbitrary machine/program combinations. The work was continued into the domain of parallel and vector machines, including the issue of caches in vector processors and multiprocessors. All of the afore-mentioned accomplishments are more specifically summarized in this report, as well as those smaller in magnitude supported by this grant.

Smith, Alan Jay↗
NASA NTRS2001

Aviation System Capacity Program Terminal Area Productivity Project: Ground and Airborne Technologies

Ground and airborne technologies were developed in the Terminal Area Productivity (TAP) project for increasing throughput at major airports by safely maintaining good-weather operating capacity during bad weather. Methods were demonstrated for accurately predicting vortices to prevent wake-turbulence encounters and to reduce in-trail separation requirements for aircraft approaching the same runway for landing. Technology was demonstrated that safely enabled independent simultaneous approaches in poor weather conditions to parallel runways spaced less than 3,400 ft apart. Guidance, control, and situation-awareness systems were developed to reduce congestion in airport surface operations resulting from the increased throughput, particularly during night and instrument meteorological conditions (IMC). These systems decreased runway occupancy time by safely and smoothly decelerating the aircraft, increasing taxi speed, and safely steering the aircraft off the runway. Simulations were performed in which optimal trajectories were determined by air traffic control (ATC) and communicated to flight crews by means of Center TRACON Automation System/Flight Management System (CTASFMS) automation to reduce flight delays, increase throughput, and ensure flight safety.

Giulianetti, Demo J.↗
NASA NTRS2009

Motion Cueing Algorithm Modification for Improved Turbulence Simulation

Atmospheric turbulence cueing produced by flight simulator motion systems has been less than satisfactory because the turbulence profiles have been attenuated by the motion cueing algorithms. Cardullo and Ellor initially addressed this problem by directly porting the turbulence model output to the motion system. Reid and Robinson addressed the problem by employing a parallel aircraft model, which is only stimulated by the turbulence inputs and adding a filter specially designed to pass the higher turbulence frequencies. There have been advances in motion cueing algorithm development at the Man-Machine Systems Laboratory, at SUNY Binghamton. In particular, the system used to generate turbulence cues has been studied. The Reid approach, implemented by Telban and Cardullo, was employed to augment the optimal motion cueing algorithm installed at the NASA LaRC Simulation Laboratory, driving the Visual Motion Simulator. In this implementation, the output of the primary flight channel was added to the output of the turbulence channel and then sent through a non-linear cueing filter. The cueing filter is an adaptive filter; therefore, it is not desirable for the output of the turbulence channel to be augmented by this type of filter. The likelihood of the signal becoming divergent was also an issue in this design. After testing on-site it became apparent that the architecture of the turbulence algorithm was generating unacceptable cues. As mentioned above, this cueing algorithm comprised a filter that was designed to operate at low bandwidth. Therefore, the turbulence was also filtered, augmenting the cues generated by the model. If any filtering is to be done to the turbulence, it will utilize a filter with a much higher bandwidth, above the frequencies produced by the aircraft response to turbulence. The authors have developed an implementation wherein only the signal from the primary flight channel passes through the nonlinear cueing filter. This paper discusses three new algorithms. Testing shows that the new methods provide the pilot with a more realistic sensation of turbulence; the cues are not attenuated by algorithm. Results of offline testing show the credibility of the models. Offline test verification was based primarily on the evaluation of the power spectral density of the outputs and the time response.

Ercole, Anthony V.↗
DOE OSTI2022

Enabling real-time adaptation of machine learning models at x-ray Free Electron Laser facilities with high-speed training optimized computational hardware

The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to experimental sciences. In this manuscript we present the per-epoch and end-to-end training times for an example of a streaming diagnostic that is planned for the upcoming high-repetition rate x-ray Free Electron Laser, the Linac Coherent Light Source-II. We explore the parameter space of batch size and data parallel training across multiple Graphics Processing Units and Reconfigurable Dataflow Units. We show the landscape of training times with a goal of full model retraining in under 15 min. Although a full from scratch retraining of a model may not be required in all cases, we nevertheless present an example of the application of emerging computational hardware for adapting machine learning models to changing environments in real-time, during streaming data acquisition, at the rates expected for the data fire hoses of accelerator-based user facilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2006

Parallel Estimation and Control Architectures for Deep-Space Formation Flying Spacecraft

The formation flying of precisely controlled spacecraft in deep space can be used to implement optical instruments capable of imaging planets in other solar systems. The distance of the formation from Earth necessitates a significant level of autonomy and each spacecraft must base its actions on its estimates of the location and velocity of the other spacecraft. Precise coordination and control is the key requirement in such missions and the flow of information between spacecraft must be carefully designed. Doing this in an efficient and optimal manner requires novel techniques for the design of the on-board estimators. The use of standard Kalman filter-based designs can lead to unanticipated dynamics--which we refer to as disagreement dynamics--in the estimators' errors. We show how communication amongst the spacecraft can be designed in order to control all of the dynamics within the formation. We present several results relating the topology of the communication network to the resulting closed-loop control dynamics of the formation. The consequences for the design of the control, communication and coordination are discussed.

distributed control↗
DOE OSTI2025

High-Throughput Discovery Illuminates Design Principles and Limits for Long-Lived Charged Species in Organic Electrolytes

The chemical stability of charged molecules in all-organic redox flow batteries (RFBs) is required for the prolonged operation of these devices. Molecular engineering and electrolyte optimization are used to mitigate parasitic reactions and extend the lifetimes of the charge carriers. However, how much can structural variation extend the lifetime? To probe this query, we designed a high-throughput kinetic study of the radical cation of N-methylphenothiazinium, guided by statistical sampling and learning algorithms. Using Argonne’s autonomous discovery facility, we conducted over 6,000 kinetic experiments with robotic sample preparation, parallel kinetic measurements, and machine learning inputs, testing 188 solvent molecules selected from a space of over 540 candidates from 11 chemical classes. Algorithmic selections guided us to stable solvent candidates, which were further tested in high concentration with and without supporting electrolyte. Our findings reveal the inherent difficulty of exceeding the current state of the art through solvent variation. The desired stability is statistically rare and poorly predictable. Among the many tested, only three solvents significantly outperformed our baseline, acetonitrile─and none by more than a factor of 3─suggesting a general challenge in achieving the necessary techno-economic targets. Furthermore, we suggest that self-discharge through solvent homolysis is the cause of the observed limitations. Several structural motifs contribute to >1,000 h half-life stability including molecular simplicity, symmetry, oxidation complement, and strategic fluorination. Importantly, this workflow establishes effective assays for diagnosing and predicting oxidative stress for highly stable liquid electrolytes in all batteries.

Batteries↗
DOE OSTI2022

Chemoselective, osmium-free, dihydroxylation/oxidative cleavage of heteroaryl isoprenes by a contemporary Malaprade reaction

The methyl ketone is a central synthetic building block for the construction of advanced heteroaryl scaffolds and systems. Reactions, including oxidative cyclization strategies, are often predicated on efficient access to this ubiquitous moiety. In the context of arenes, standard approaches leveraging Markovnikov hydration/oxidation or oxidative cleavage of the C–C π bond often afford satisfactory performance. However, when the substrate contains an electron-deficient heteroaryl core, the traditional Malaprade reaction, and related oxidative-cleavage strategies, frequently result in diminished performance over carbon-based arenes. In this work we present the development and application of an oxidative cleavage reaction of various pyridinyl isoprenes towards accessing the downstream methyl ketone for utilization in advanced cyclizations for the preparation of soft-N-donor complexant scaffolds. Further, this efficient protocol parallels the principles of Green chemistry by exchanging KMnO 4 for the toxic OsO 4 and offers the end-user an efficient, more environmentally friendly option for accessing heteroaryl methyl ketones in one hour of reaction time using potassium permanganate and sodium paraperiodate as a synergistically potent oxidative cleavage system. The wide substrate scope defined access to simple, as well as advanced heteroaryl methyl ketones. Method development, optimization, substrate scope, preliminary mechanistic observations, and a scale up reaction are delineated herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗