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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Future Circular Hadron Collider FCC-hh: Overview and Status

The Future Circular Collider (FCC) study was launched as a world-wide international collaboration hosted by CERN. Its goal is to push the field to the next energy frontier beyond LHC, increasing by an order of magnitude the mass of particles that could be directly produced, and decreasing by an order of magnitude the subatomic distances to be studied. The FCC study covers two accelerators, namely, an energy-frontier hadron collider (FCC-hh) and a highest luminosity, high-energy lepton collider (FCC-ee). Both rings are hosted in the same 100 km tunnel infrastructure, replicating the CERN strategy for LEP and LHC, i.e. developing a lepton and a hadron ring sharing the same tunnel. This paper is devoted to the FCC-hh and summarizes the key features of the FCC-hh accelerator design, performance reach, and underlying technologies. The material presented in this paper builds on the conceptual design report published in 2019, and extends it, including also the progress made and the results achieved since then.

43 PARTICLE ACCELERATORS↗
NASA NTRS2013

Doppler Lidar System Design via Interdisciplinary Design Concept at NASA Langley Research Center - Part I

Optimized designs of the Navigation Doppler Lidar (NDL) instrument for Autonomous Landing Hazard Avoidance Technology (ALHAT) were accomplished via Interdisciplinary Design Concept (IDEC) at NASA Langley Research Center during the summer of 2013. Three branches in the Engineering Directorate and three students were involved in this joint task through the NASA Langley Aerospace Research Summer Scholars (LARSS) Program. The Laser Remote Sensing Branch (LRSB), Mechanical Systems Branch (MSB), and Structural and Thermal Systems Branch (STSB) were engaged to achieve optimal designs through iterative and interactive collaborative design processes. A preliminary design iteration was able to reduce the power consumption, mass, and footprint by removing redundant components and replacing inefficient components with more efficient ones. A second design iteration reduced volume and mass by replacing bulky components with excessive performance with smaller components custom-designed for the power system. Mechanical placement collaboration reduced potential electromagnetic interference (EMI). Through application of newly selected electrical components and thermal analysis data, a total electronic chassis redesign was accomplished. Use of an innovative forced convection tunnel heat sink was employed to meet and exceed project requirements for cooling, mass reduction, and volume reduction. Functionality was a key concern to make efficient use of airflow, and accessibility was also imperative to allow for servicing of chassis internals. The collaborative process provided for accelerated design maturation with substantiated function.

Boyer, Charles M.↗
DOE OSTI2026

Realizing the scientific program with polarized ion beams at the future BNL Electron Ion Collider

Polarized ion beams at the Electron Ion Collider (EIC) are essential to address some of the most important open questions at the twenty-first century frontiers of understanding of the fundamental structure of matter. Here, in this work, we summarize the science case and identify polarized 2 H, 3 He, 6 Li, and 7 Li ion beams as critical technology that will enable experiments which address the most important science. Furthermore, we discuss the required ion polarimetry and spin manipulation at the EIC. The current EIC accelerator design is presented. We identify a significant research and development effort across national and international laboratories and universities that is required over about a decade to realize the polarized ion beams and estimate (based on previous experience) that it will require about 20 full-time equivalent (FTE) over 10 years (or a total of about 200 FTE-years) of personnel, including graduate students, postdoctoral researchers, technicians, and engineers. Attracting, educating, and training a new generation of physicists in experimental spin techniques will be essential for the successful realization. Artificial intelligence and machine learning are seen as having significant potential for both acceleration of research and development and amplification of discovery in the optimal realization of this unique quantum technology on a cutting-edge collider. The research and development effort is synergistic with research in atomic physics and fusion energy science.

43 PARTICLE ACCELERATORS↗
NASA NTRS1998

Mechanism-based Modeling of Long-term Degradation

The use of composites in high temperature, long lifetime applications requires a basic understanding of composite degradation mechanisms, advances in analytical capabilities, and accurate accelerated and scaled tests. To advance all of these goals, models are proposed based on a variety of fundamental material mechanisms. Thermal, oxygen, and moisture diffusion, chemical reactions, composite micromechanics; modified laminated plate theory, and future mechanics based damage models are used. All models attempt to stay as simple and fundamental as possible. All are coupled, so that interactions between various effects are modeled implicitly. Ongoing efforts at MIT are reviewed here, with some reference to other work, but no attempt is made to do a comprehensive review. Mechanism based models are yielding understanding of the mechanisms behind observed degradation phenomena, are helping to design accelerated tests, and are the first steps towards a predictive capability.

McManus, H. L.↗
DOE OSTI2026

Surrogates for Valve-Controlled Pipe Flow: Accelerating Nuclear Reactor Design

Neural surrogate models are developed to replace expensive steady-state RANS CFD simulations for valve-controlled pipe flow in nuclear reactor design. Using parametric CFD data generated with MOOSE Pronghorn across a range of valve geometry and flow conditions, three approaches are compared: a POD-based reduced-order model, a structured UNet on a cylindrical grid, and unstructured models (DeepONet and BiStride MeshGraphNet) on nondimensionalized point clouds. POD achieves the highest accuracy (99%) with fast inference but requires storing all solution snapshots, while the DeepONet and BSMS-GNN both achieve ~89% accuracy at sub-second inference, with the BSMS-GNN offering superior geometric generalizability. These surrogates enable rapid ranking of candidate valve designs and can warm-start CFD solvers to accelerate convergence, supporting agentic design iteration on the Prometheus platform.

42 - ENGINEERING↗
DOE OSTI2021

AMReX: Block-structured adaptive mesh refinement for multiphysics applications

Block-structured adaptive mesh refinement (AMR) provides the basis for the temporal and spatial discretization strategy for a number of Exascale Computing Project applications in the areas of accelerator design, additive manufacturing, astrophysics, combustion, cosmology, multiphase flow, and wind plant modeling. AMReX is a software framework that provides a unified infrastructure with the functionality needed for these and other AMR applications to be able to effectively and efficiently utilize machines from laptops to exascale architectures. AMR reduces the computational cost and memory footprint compared to a uniform mesh while preserving accurate descriptions of different physical processes in complex multiphysics algorithms. AMReX supports algorithms that solve systems of partial differential equations in simple or complex geometries and those that use particles and/or particle–mesh operations to represent component physical processes. In this article, we will discuss the core elements of the AMReX framework such as data containers and iterators as well as several specialized operations to meet the needs of the application projects. In addition, we will highlight the strategy that the AMReX team is pursuing to achieve highly performant code across a range of accelerator-based architectures for a variety of different applications.

Zhang, Weiqun↗
DOE OSTI2025

Compact X-band split electron linac for cabinet-size small-sample irradiators

Many small-scale industrial radiation facilities rely on hazardous radioisotope-based gamma ray irradiators, which are being gradually replaced by alternative technologies, such as particle accelerators. RadiaBeam has developed a compact, cabinet-size, 3 MeV electron-linac-based irradiator to match the penetration of Co-60 and provide high irradiation throughput. Here the linac utilizes X-band RF power to reduce size, employs an innovative split-linac approach to lower costs, and features a novel X-ray conversion target geometry that spreads the beam out across the target, providing a low dose uniformity ratio. In this paper, we discuss the accelerator design, fabrication, and test results.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Gas-Phase Composition as a Predictive Metric for Calendar Life Behavior of Next-Generation Silicon Anodes

The expansion of renewable technologies and electrification of the transportation sector is driving increased demand for next-generation battery materials that provide higher power and energy density with superior cycling and calendar life stability. Silicon (Si) has a theoretical capacity nearly 10x that of graphite, and is therefore a promising anode material candidate to meet these rigorous performance demands. While leading Si anode battery demonstrations are approaching target metrics for cycle life, a series of complex and interrelated modes of reactivity lead to reduced calendar life and therefore challenge practical adoption of these materials. Deconvoluting the degradation processes that impact Si calendar life is critical to informing the rational and accelerated design of improved Si materials. In the present work, we employ novel sampling techniques and GC-MS-FID characterization to measure gas-phase composition during initial Si cycling, which we tie to selective mechanisms of Si passivation. We utilize a tiered analysis approach to identify and quantify the gas-phase reaction products associated with three advanced Si material candidates under practical operating conditions. Ex situ analysis of Si powders (pure chemical reactivity) is coupled with nondestructive in situ sampling of Si electrodes in a practical pouch-cell format (coupled chemical and electrochemical reactivity). We link the observed gas-phase species evolution to electrochemical behavior and measured calendar life of the three Si materials. Further, we evaluate the voltage-resolved evolution of gas-phase species for one such Si nanomaterial, where nonmonotonic gas generation implies competition between passivating reaction pathways. The measured gas-phase compositional data serves as a critical input for our advanced electrochemical SEI models to identify favorable vs unfavorable reaction pathways to stabilize Si. In addition to bolstering a fundamental understanding of Si reactivity, the present approach informs specific and quantifiable gas-phase metrics tied to calendar life improvements in Si, which can streamline and accelerate the process of next-generation material development.

DIRECT ENERGY CONVERSION,ENERGY STORAGE↗
DOE OSTI2023

Probing individual single atom electrocatalyst sites by advanced analytical scanning transmission electron microscopy

Single atom electrocatalysts (SAEs) are promising next-generation materials for promoting a variety of important reactions, such as the oxygen reduction, nitrogen reduction, and CO 2 reduction reactions. While bulk characterization techniques such as X-ray absorption spectroscopy and Mössbauer spectroscopy have significantly enhanced our understanding of these catalysts, direct probing of individual single metal atom sites at the atomic scale is necessary to understand local variations in the properties of these sites and accelerate design and synthesis of improved SAEs. Aberration-corrected scanning transmission electron microscopy (STEM) has become a powerful tool for providing this type of atomic-scale information about SAE metal sites. These sites are typically unstable under the electron beam, however, which, in combination with conventional acquisition methods and detectors, has limited the type and quantity of information obtainable by spectroscopic STEM techniques. Here, we map multiple individual SAE metal sites in a nitrogen-doped carbon containing atomically dispersed Fe and Re (FeReNC) at the atomic scale by direct electron detection electron energy-loss spectroscopy (EELS). Direct electron detection provides an improved signal-to-noise ratio over conventional scintillator-based detectors and enables detection and real space localization of weak signals. In addition, we demonstrate an automated method for identification of metal atom positions, placement of the probe on these sites, and simultaneous EELS and energy dispersive X-ray spectroscopic (EDS) signal acquisition. This simultaneous acquisition of EELS and EDS provides access to the composition and bonding of a wide range of SAE metal sites. In this study, focusing the probe directly on the metal sites also increases the relevant data acquisition rate by more than an order of magnitude over two-dimensional mapping, enabling improved statistical measurements of site properties. The versatility, sensitivity, and speed that these techniques provide enhances our ability to probe the local elemental and chemical environment of a large number of individual SAE metal site structures at the atomic scale, enabling an improved understanding of the variations in the local properties of these electrocatalysts to be gained. As a result, significantly increased information about individual metal sites will be available to future electrochemical studies through these techniques, accelerating the development of advanced SAEs.

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

A Scalable Reduced‐Order Model for the Steady Navier–Stokes Equations

Scaling up new scientific technologies from laboratory to industry often involves demonstrating performance on a larger scale. Computer simulations can accelerate design and predictions in the deployment process, though traditional numerical methods are computationally intractable even for intermediate pilot plant scales. Recently, the component reduced order modeling method has been developed to tackle this challenge by combining projection reduced order modeling and discontinuous Galerkin domain decomposition. However, while many scientific or engineering applications involve nonlinear physics, this method has only been demonstrated for various linear systems. In this work, the component reduced order modeling method is extended to steady Navier–Stokes flow, with application to general nonlinear physics in view. The large‐scale, global domain is decomposed into a combination of small‐scale unit component. Linear subspaces for flow velocity and pressure are identified via proper orthogonal decomposition over sample snapshots collected from each small‐scale unit component. Velocity bases are augmented with a pressure supremizer to satisfy the inf–sup condition for stable pressure prediction. Two different nonlinear reduced order modeling methods are employed and compared for efficient evaluation of nonlinear advection: A third‐order tensor projection operator and the empirical quadrature procedure. The proposed method is demonstrated on the flow over arrays of five different unit objects, achieving a 23‐fold speedup with less than 4% relative error in domains up to 256 times larger than the unit components. Furthermore, a numerical experiment with the pressure supremizer strongly indicates the need for a supremizer for stable pressure prediction. A comparison between the tensorial approach and the empirical quadrature procedure revealed a slight advantage of the empirical quadrature procedure. The framework is compared with an alternating Schwarz‐based reduced‐order approach, demonstrating improved efficiency and robustness for the DG‐based global solver while retaining flexibility for sub‐scale iterative solvers. The method is further extended to a coupled advection–diffusion and Navier–Stokes system, illustrating its applicability to multi‐physics problems and its potential for more general, inter‐coupled nonlinear systems.

42 ENGINEERING↗
DOE OSTI2022

Feature engineering for machine learning enabled early prediction of battery lifetime

Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. Here, a mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.

25 ENERGY STORAGE↗
DOE OSTI2021

Bridging Scales in Bioenergy and Catalysis: A Review of Mesoscale Modeling Applications, Methods, and Future Directions

Between the molecular and reactor scales, which are familiar to the chemical engineering community, lies an intermediate regime, here termed the “mesoscale,” where transport phenomena and reaction kinetics compete on similar time scales. Bioenergy and catalytic processes offer particularly important examples of mesoscale phenomena owing to their multiphase nature and the complex, highly variable porosity characteristic of biomass and many structured catalysts. In this review, we overview applications and methods central to mesoscale modeling as they apply to reaction engineering of biomass conversion and catalytic processing. A brief historical perspective is offered to put recent advances in context. Applications of mesoscale modeling are described, and several specific examples from biomass pyrolysis and catalytic upgrading of bioderived intermediates are highlighted. Methods including reduced order modeling, finite element and finite volume approaches, geometry construction and import, and visualization of simulation results are described; in each category, recent advances, current limitations, and areas for future development are presented. Owing to improved access to high-performance computational resources, advances in algorithm development, and sustained interest in reaction engineering to sustainably meet societal needs, we conclude that a significant upsurge in mesoscale modeling capabilities is on the horizon that will accelerate design, deployment, and optimization of new bioenergy and catalytic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Transferring a Molecular Foundation Model for Polymer Property Predictions

Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. Further, state-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Towards accurate prediction of configurational disorder properties in materials using graph neural networks

Abstract The prediction of configurational disorder properties, such as configurational entropy and order-disorder phase transition temperature, of compound materials relies on efficient and accurate evaluations of configurational energies. Previous cluster expansion methods are not applicable to configurationally-complex material systems, including those with atomic distortions and long-range orders. In this work, we propose to leverage the versatile expressive capabilities of graph neural networks (GNNs) for efficient evaluations of configurational energies and present a workflow combining attention-based GNNs and Monte Carlo simulations to calculate the disorder properties. Using the dataset of face-centered tetragonal gold copper without and with local atomic distortions as an example, we demonstrate that the proposed data-driven framework enables the prediction of phase transition temperatures close to experimental values. We also elucidate that the variance of the energy deviations among configurations controls the prediction accuracy of disorder properties and can be used as the target loss function when training and selecting the GNN models. The work serves as a fundamental step toward a data-driven paradigm for the accelerated design of configurationally-complex functional material systems.

Chemistry↗
DOE OSTI2024

Protons on the line

Stable regions in four-dimensional phase space were observed by following the motion of accelerated proton beams subject to nonlinear forces. Here, this provides insights into the physics of dynamical systems and may lead to improved accelerator designs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Benchmarking of hydrodynamic plasma waveguides for multi-GeV laser-driven electron acceleration

Hydrodynamic plasma waveguides initiated by optical field ionization have recently become a key component of multi-GeV laser wakefield accelerators. Here, we present the most complete and accurate experimental and simulation-based characterization to date, applicable to current multi-GeV experiments and future 100 GeV-scale laser plasma accelerators. Crucial to the simulations is the correct modeling of intense Bessel beam interaction with meter-scale gas targets, the results of which are used as initial conditions for hydrodynamic simulations. The simulations are in good agreement with our experiments measuring evolving plasma and neutral hydrogen density profiles using two-color short pulse interferometry, enabling realistic determination of the guided mode structure for application to laser-driven plasma accelerator design. Published by the American Physical Society 2024

Physics↗
DOE OSTI2022

ASAP: Automatic Synthesis of Area-Efficient and Precision-Aware CGRAs

Coarse-grained reconfigurable accelerators (CGRAs) are a promising accelerator design choice that strikes a balance between performance and adaptability to different computing patterns across various applications domains. Designing a CGRA for a specific application domain involves enormous software/hardware engineering effort. Recent research works explore loop transformations, functional unit types, network topology, and memory size to identify optimal CGRA designs given a set of kernels from a specific application do- main. Unfortunately, the impact of functional units with different precision support has rarely been investigated. To address this gap, we propose ASAP – a hardware/software co-design framework that automatically identifies and synthesizes optimal precision-aware CGRA for a set of applications of interest. Our evaluation shows that ASAP generates specialized designs 3.2×, 4.21×, and 5.8× more efficient (in terms of performance per unit of energy or area) than non-specialized homogeneous CGRAs, for the scientific computing, embedded, and edge machine learning domains, respectively, with limited accuracy loss. Moreover, ASAP provides more efficient designs than other state-of-the-art synthesis frameworks for specialized CGRAs.

artificial intelligence↗
DOE OSTI2021

20-24 GeV FFA CEBAF Energy Upgrade

A proposal was formulated to increase the CEBAF energy from the present 12 GeV to 20-24 GeV by replacing the highest-energy arcs with Fixed Field Alternating Gradient (FFA) arcs. The new pair of arcs would provide six or seven new beam passes, going through this magnet array, allowing the energy to be nearly doubled using the existing CEBAF SRF cavity system. One of the immediate accelerator design tasks is to develop a proof-of-principle FFA arc magnet lattice that would support simultaneous transport of 6-7 passes with energies spanning a factor of two. We also examine the possibility of using combined function magnets to configure a cascade, six-way beam split switchyard. Finally, a novel multi-pass linac optics based on a weakly focusing lattice is being explored.

Bogacz, Slawomir Alex↗