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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 OSTI2025

A Synthesis Methodology for Intelligent Memory Interfaces in Accelerator Systems

Domain-specific systems improve the performance of a specific set of applications compared to general-purpose processing systems by deploying custom hardware accelerators. These hardware accelerators are generated using high-level synthesis (HLS) tools. The HLS tools enable a comprehensive design space exploration to optimize the compute performance of the generated accelerators. However, they often ignore the challenges of implementing the accelerators in a system-on-chip, particularly how the accelerators access memory. Our work introduces a buffering system design that improves accelerators' memory accesses by intelligently employing burst transactions to prefetch useful data from external memory to on-chip local buffers. Our design is dynamic, parametric, and transparent to the accelerators generated by HLS tools. We derive the buffering system parameters using appropriate compiler-based analysis passes and memory channel latency constraints. The proposed buffering system design results in, on average, 8.8x performance improvements while lowering memory channel utilization on average by 53.2% for a set of PolyBench kernels.

Limaye, Ankur M. (ORCID:0000000194062584)↗
DOE OSTI2023

Accelerating Battery Simulations by Using High Performance Computing and Opportunities with Machine Learning

The US Department of Energy (DOE) estimates that the battery pack cost of $60 per kilowatt-hour while increasing the driving range to over 300 miles and vehicle charging under 15 min or less would enable mass penetration of electric vehicles in the USA by 2030. These projections are based on the currently available high-density cell chemistry combined with a system level design and optimized electrodes. Electrodes for current state-of-the-art lithium-ion cell technology are fabricated from electrode slurry comprising of active material, polymeric binder, and conductive diluent such as carbon black that are coated on metal current collectors such as copper and aluminum. Given these challenging requirements for development of electrical energy storage devices for future transportation needs, a predictive simulation capability which can accelerate design by considering performance and safety implications of different geometry, materials, and chemistry choices is required. In this chapter, we discuss our state-of-the-art three-dimensional modeling framework, providing examples on battery performance and safety simulations. We also present approaches to use machine learning in the context of large datasets that are becoming available.

Allu, Srikanth↗
DOE OSTI2025

Predicting Si-Anode Calendar Life Using Machine Learning: Correlating Electrolyte Properties and Electrochemical Signals

This study evaluates novel electrolytes tailored for Si-containing anodes to promote calendar-life. Drawing inspiration from advancements in electrolytes for Li-metal cells, the work investigates correlations between predicted electrolyte properties and measured electrochemical performance using several machine-learning models. By leveraging machine learning and advanced modeling techniques, this study aims to establish predictive frameworks that accelerate calendar-aging experiments and inform rational electrolyte design for Si-containing cells. In the present study, fifteen different electrolytes are evaluated in a Si-containing cell using an accelerated calendar-life protocol. For each electrolyte considered, 87 properties (features) from the Advanced Electrolyte Model were produced to identify key property/performance relationships. In this study, the best performing electrolytes were generally those formulations that included non-coordinating fluoroether solvents, and the most predictive features for long-term calendar-life were features related to salt concentration and electrolyte viscosity as well as early capacity, ionic conductivity, and Coulombic efficiency measurements. The framework developed in this study correlating electrolyte properties to measured electrochemical performance is expected to accelerate electrolyte design for Si-containing anodes and ultimately enable high-energy-density, long-life Li-ion batteries.

25 - ENERGY STORAGE↗
DOE OSTI2023

Charge per Micro-Pulse Calculation for Ideal Lujan and WNR beams

A complete redesign of the LANSCE front-end is currently under development for the Los Alamos Modernization Project (LAMP). This includes the replacement of the traditional Cockroft-Walton injection system to the newer radiofrequency quadrupole (RFQ) standard. LANSCE accelerates both H- and H+ beams and therefore requires an unconventional application for RFQ systems. A new facility could consider using an independent RFQ for each beam species. However, with the tight space of the LANSCE injector facility, it would prove difficult to instrument. Our initial studies have shown that a single RFQ could be used. The negatively- and positively-charged direct current (DC) beams are shaped, bunched, and accelerated out of phase to each other, with very little interaction. This would work for all possible H+ beam gates and most H- Beam gates. However, a challenge arises with the Weapons Neutron Research (WNR) beam structure that requires the acceleration of bunched beams at higher charge density per pulse. The Long Bunch Enable Gate (LBEG) used for the Lujan center is the best example of a standard injector beam gate. The beam gate, or Macro-Pulse (MP), is 625-us long, separated by at least 8.3 ms. A chopper is used to dice the MP into 1750 mini-Pulses (mP), which are 290-ns wide. After chopping, the MP contains 100,000 micro-Pulses (uP), which are generated by the linac acceleration structure modulated at 201.25 MHz; that is, the micro-Pulses are separated 5 ns apart from each other. The H+ beam structure will be very similar once a chopper is added to its transport. However, the Multi-Pulse Enable Gate (MPEG) used for WNR is significantly different in structure. In this case, the mP is chopped to be only 35-ns wide. This width is then rotated into a single 201.25 MHz RF bucket using the Low Frequency Buncher (LFB). This is proving difficult for the RFQ design in the LAMP upgrade. We began investigating the amount of current sent to WNR for two reasons. Firstly, we wanted to find a solution to avoid using the Low Frequency Buncher to simplify the LAMP RFQ design. It is simple to inject and accelerate a 35 mA DC beam in the LAMP RFQ (this is done in numerous accelerator facilities). However, injecting a short bunch with a larger current (once compressed to 5 ns to meet the RFQ frequency) may be difficult. Second, we have a technique called PSR2WNR in which we do not employ the Low Frequency Buncher, but rather accumulate 5 LBEG uP (5 ns) in the PSR and deliver the accumulated beam to WNR every 1.8 us. As a result, we could provide to WNR with five accumulated LBEG uPs every shot. The goal here is to create a rapid kicker with a frequency response of 555 kHz. DARHT-II and other accelerator facilities have constructed kickers of this type for other uses, and our engineers consider this technology as feasible.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

FPGA-Accelerated Range-Limited Molecular Dynamics

Long timescale Molecular Dynamics (MD) simulation of small molecules is crucial in drug design and basic science. To accelerate a small data set that is executed for a large number of iterations, high-efficiency is required. Recent work in this domain has demonstrated that among COTS devices only FPGA-centric clusters can scale beyond a few processors. The problem addressed here is that, as the number of on-chip processors has increased from fewer than 10 into the hundreds, previous intra-chip routing solutions are no longer viable. We find, however, that through various design innovations, high efficiency can be maintained. These include replacing the previous broadcast networks with ring-routing and then augmenting the rings with out-of-order and caching mechanisms. Others are adding a level of hierarchical filtering and memory recycling. Two novel optimized architectures emerge, together with a number of variations. These are validated, analyzed, and evaluated. We find that in the domain of interest speed-ups over GPUs are achieved. Finally, the potential impact is that this system promises to be the basis for scalable long timescale MD with commodity clusters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A new method for measuring refractory corrosion of ceramics in glass

Abstract Nuclear waste glass vitrification furnaces are lined with refractory ceramic blocks to contain the molten glass. The refractory liner is susceptible to corrosion and has a finite service lifetime. For this reason, predicting the refractory corrosion in contact with molten glass is integral to estimating melter service lifetime. Standardized laboratory tests varying time and temperature are commonly performed to estimate refractory material loss as a function of glass composition. These data are time and resource‐intensive to collect and are susceptible to considerable measurement error. In order to accelerate glass formulation and design for nuclear waste vitrification, methods are needed to increase laboratory‐scale throughput while maintaining data quality. In this work, a method to remove the residual glass from a corroded coupon using hydrofluoric acid is presented that accelerates the throughput of sample analysis while simultaneously facilitating more accurate measurements.

Amoroso, Jake W. [Savannah River National Laborato↗
DOE OSTI2025

Software For Advanced Large-scale Analysis Of Magnetic Confinement For Numerical Design, Engineering & Research (salamander)

As magnetic confinement fusion energy gains traction internationally to enable abundant energy production, designing components for fusion systems is a pressing challenge. During the planned lifetime of a fusion device, components evolve in extreme environments and must withstand large, repeated thermal loads and bombardment by 14 MeV neutrons, plasma ions, and neutral particles (deuterium, tritium, and helium), corrosive conditions, etc. All these physical processes take place simultaneously, interact in intricate ways, and impose important constraints that can affect performance. Experimental data is rare and costly to obtain, making design particularly challenging. Predictive computational frameworks must be an integral part of an accelerated and cost-effective design process by modeling fusion system performance in simulated environments. To better understand component degradation and operational impacts on their performance, the Software for Advanced Large-scale Analysis of MAgnetic confinement for Numerical Design, Engineering & Research (SALAMANDER) is designed as an open-source, fully integrated, multiphysics, multiscale, NQA-1 compliant framework facilitating 3D, high-fidelity fusion system modeling. To that end, SALAMANDER is a MOOSE-based framework, and therefore leverages MOOSE upstream libraries such as PETSc and libMesh to deliver sophisticated finite element, finite volume, and nonlinear solver technology for fusion energy simulations. SALAMANDER couples MOOSE physics module capabilities—such as thermal hydraulics, heat conduction, Navier-Stokes, and thermomechanics—with tritium transport via TMAP8, neutronics via Cardinal, and nascent particle-in-cell capabilities. Direct simulation Monte Carlo methods will be used to address neutral transport near the walls. By coupling all these physics in an integrated application, SALAMANDER will enable high-fidelity modeling of irradiation levels and plasma exposure conditions of plasma facing components and their impact on heat and tritium distributions, as well as the resulting mechanical constraints experienced by the plasma facing components and performance of blanket systems. Furthermore, SALAMANDER will be particularly suited for engineering studies thanks to the stochastic tool module readily available in MOOSE, allowing for extended uncertainty quantification and risk analysis studies. It is also able to use computer-aided design (CAD) meshes to model complex geometries, which is indispensable for fusion systems. SALAMANDER therefore supports design, safety, engineering, and research projects for magnetic confinement fusion systems

Simon, Pierre-Clement [Idaho National Laboratory (↗
DOE OSTI2025

Challenges and Gaps in the Development of Pulsed Power for Fusion Applications: A Preroadmapping Perspective From Industry, Academia, and National Laboratory Experts

Fusion energy meets the twenty-first century World Grand Challenge of sustainable, ubiquitous, and safer energy sources. However, harnessing the promise of fusion energy has proven elusive. The competing approaches to fusion power plant design include inertial confinement fusion, National Ignition Facility (ICF-NIF, Z machine, etc.,) magnetic confinement fusion (MCF-Tokamak, stellarators, etc.), and other approaches that show promise in small- (flow stabilized Z pinches) or large-scale applications. These approaches are being accelerated with private and public funding and seek to demonstrate the feasibility of different approaches to fusion-based power plants. Yet, how can the necessary pulsed power technologies for these disruptive technology bases be accelerated with no clear “Dominant Design?” Roadmapping holds the promise to identify and develop common critical pulsed power components for laboratory, prototype, and commercial fusion, and can accelerate the commercialization of fusion reactor designs. A preroadmapping Workshop on Pulsed Power for Fusion was held at the IEEE International Pulsed Power Conference in San Antonio, TX, USA, in June 2023. The workshop had 177 attendees. Here, the common elements for many of the ICF technologies vying for dominant design were identified. The advancement of these technologies through roadmapping will enhance commercial expectations that require their rapid and innovative development in the next five years, as well as the next five to ten years. The key technologies identified that underpin and limit the advancement of fusion power include pulsed power technologies such as energy storage, high-voltage switching, additive manufacturing, and modular pulsed power circuit topologies. In conclusion, they are the focus of our effort in the following roadmap scenario, which will delineate potential paths to technology development.

Curry, Randy D. [I-Pulse Group, Albuquerque, NM (U↗
DOE OSTI2023

Batched Sparse Linear Algebra (Final Report for Subcontract B648960)

This report finalizes design specifications for developing batched kernels for small tensor operations for unassembled matrix-free iterative solvers, batched solvers for partially assembled operators, and batched solvers with support for various sparse formats. The outcome of the project milestones is a set of interfaces to Batched Sparse LA solvers running on hardware accelerators for use in ECP Libraries and Applications. It is part of the development of sparse batched kernels, solvers/preconditioners as well as creating interoperability in xSDK libraries with sparse and dense batched functions to benefit ECP applications. The participants included representatives from ECP libraries (not limited to the xSDK project), applications, and vendors (AMD, Intel, and NVIDIA). Batched sparse linear algebra solvers form the new frontier for algorithmic development and performance engineering. Many applications (ECP and non-ECP alike) require simultaneous solutions of small linear systems of equations that are structurally sparse. To move towards high hardware utilization, it is important to provide these applications with appropriate interfaces to efficient batched sparse solvers running on modern hardware accelerators. We present interface designs in use by HPC software libraries supporting batched sparse linear algebra and the development of sparse batched kernel codes for solvers and preconditioners. We also address the potential interoperability opportunities to keep the software portable between the major hardware accelerators from AMD, Intel, and NVIDIA. The presented interface specifications includes batched band, sparse iterative, and sparse direct solvers. This report summarizes progress in Kokkos Kernels and the xSDK libraries MAGMA, Ginkgo, hypre, SUNDIALS, and SuperLU_dist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders

We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the model on a large full simulation dataset from one detector design, and subsequently fine-tune the model on a sample with a different collider and detector design. Specifically, we use the Compact Linear Collider detector (CLICdet) model for the initial training set and demonstrate successful knowledge transfer to the CLIC-like detector (CLD) proposed for the Future Circular Collider in electron-positron mode. We show that with an order of magnitude less samples from the second dataset, we can achieve the same performance as a costly training from scratch, across particle-level and event-level performance metrics, including jet and missing transverse momentum resolution. Furthermore, we find that the fine-tuned model achieves comparable performance to the traditional rule-based particle-flow approach on event-level metrics after training on 100,000 CLD events, whereas a model trained from scratch requires at least 1 million CLD events to achieve similar reconstruction performance. To our knowledge, this represents the first full-simulation cross-detector transfer learning study for particle-flow reconstruction. These findings offer valuable insights towards building large foundation models that can be fine-tuned across different detector designs and geometries, helping to accelerate the development cycle for new detectors and opening the door to rapid detector design and optimization using machine learning.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

A fast computational framework for the design of solvent-based plastic recycling processes

Multicomponent plastics cannot be processed using mechanical recycling technologies, hindering efforts to deal with plastic waste. Multicomponent plastics include multilayer plastic films, which are widely used for food and healthcare packaging. Multilayer films combine several layers (potentially dozens) of different polymers to protect products from external factors (e.g., oxygen, water, temperature, shock, and light). Solvent-based separation processes have emerged as a promising alternative to recycle these complex materials. For instance, the Solvent-Targeted Recovery and Precipitation (STRAP TM ) process uses sequential solvent washes to selectively dissolve and separate constituent polymers from multicomponent plastic waste, including films. STRAP TM process design (separation sequence, type of solvents, and operating conditions) changes significantly depending on the design of the multilayer plastic film (e.g., number, types, and proportions of polymers). The ability to quickly quantify the economic and environmental benefits of diverse STRAP TM process designs is essential to accelerate the development of sustainable recycling processes and more recyclable multilayer film products. In this work, we present a fast computational framework that integrates molecular-scale models, process modeling, and techno-economic and life cycle analysis to quickly evaluate STRAP TM designs. The computational framework is general and can be used to study the processing of complex multilayer plastic waste streams that contain many layers. Furthermore, we highlight the different uses of the framework via targeted case studies.

Computational framework↗
DOE OSTI2025

Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids

Ionic liquids (ILs) are a novel class of solvents that have attracted significant attention due to their unique and tunable properties. Among their physiochemical characteristics, surface tension plays a critical role in various industrial applications including electrolytes, heat transfer fluids, and separation processes. However, because of the exploratory nature of IL design and the vast combinatorial space of possible anion–cation pairs, the experimental determination of these properties is often impractical, being both time-consuming and costly. To overcome these challenges, computational approaches are increasingly employed to develop accurate predictive models that can accelerate IL discovery and design. In this study, we present two deep learning (DL) models for predicting the surface tension of ILs across a broad temperature range at a constant pressure. The models use simplified molecular input line entry system, SMILES, representations of ILs to extract molecular features as inputs. Both DL models demonstrate excellent agreement with experimental data, achieving an R 2 value of 0.990 and a root-mean-square error of 0.792 mN/m. In conclusion, these results offer valuable insights for the rapid screening and rational design of ILs with tailored surface tension values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Conceptual Design of a HTS Dipole Insert Based on Bi2212 Rutherford Cable

The U.S. Magnet Development Program (US-MDP) is aimed at developing high-field accelerator magnets with magnetic fields beyond the limits of Nb 3 Sn technology. Recent progress with composite wires and Rutherford cables based on the first generation high-temperature superconductor Bi 2 Sr 2 CaCu 2 O 8-x (Bi2212) allows considering them for this purpose. However, Bi2212 wires and cables are sensitive to transverse stresses and strains, which are large in high-field accelerator magnets. This requires magnet designs with stress management concepts to control azimuthal and radial strains in the coil windings and prevent the degradation of the current carrying capability of Bi2212 conductor or even its permanent damage. This paper describes a novel stress management approach, which was developed at Fermilab for high-field large-aperture Nb 3 Sn accelerator magnets, and is now being applied to high-field dipole inserts based on Bi2212 Rutherford cables. The insert conceptual design and main parameters, including the superconducting wire and cable, as well as the coil stress management structure, key technological steps and approaches, test configurations and their target parameters, are presented and discussed.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Design of Ionization Profile Monitors at the Integrable Optics Test Accelerator Facility at Fermilab

The Integrable Optics Test Accelerator (IOTA) at Fermilab is transitioning from an electron beam facility to a proton beam facility for studies in nonlinear accelerator optics and space-charge dominated proton beams. This project involves the commissioning and fabrication of Ionization Profile Monitors (IPMs) to enable beam profile measurements at IOTA. In general, IPMs work on principle of residual gas ionization by the beam to generate beam profile. This work focuses on a mechanical design that leverages a controlled injection of noble gases, primarily Argon, as the ultra-high vacuum of the IOTA ring provides insufficient residual gas for ionization. Efforts to understand vacuum integration to ensure compatibility with the storage ring environment, the integration of real-time data acquisition systems and the commissioning of the IPMs will be discussed. This project provides a versatile diagnostic tool, supporting IOTA’s role as a testbed for larger-scale accelerator facilities and contributing to the broader understanding of beam physics in high-intensity, high-space-charge regimes.

Mwaniki, M. W. [IIT, Chicago] (ORCID:0000000169057↗