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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Taking Down a Giant: 699 Tons of SLAC’s Accelerator Removed for Upgrade

For the first time in more than 50 years, a door opened at the western end of the historic linear accelerator at the Department of Energy’s SLAC National Accelerator Laboratory casts light on four empty walls stretching as far as the eye can see. This end of the linac – a full kilometer of it – has been stripped of all its equipment both above and below ground. Over the next two years it will be re-equipped with new technology to power another wonder of modern science: an X-ray laser that will fire a million pulses per second.

42 ENGINEERING↗
DOE OSTI2023

Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU System

The deep learning revolution has been enabled in large part by GPUs, and more recently accelerators, which make it possible to carry out computationally demanding training and inference in acceptable times. As the size of machine learning networks and workloads continues to increase, multi-GPU machines have emerged as an important platform offered on High Performance Computing and cloud data centers. Since these machines are shared among multiple users, it becomes increasingly important to protect applications against potential attacks. In this paper, we explore the vulnerability of Nvidia's DGX multi-GPU machines to covert and side channel attacks. These machines consist of a number of discrete GPUs that are interconnected through a combination of custom interconnect (NVLink) and PCIe connections. We reverse engineer the interconnected cache hierarchy and show that it is possible for an attacker on one GPU to cause contention on the L2 cache of another GPU. We use this observation to first develop a covert channel attack across two GPUs, achieving the best bandwidth of around 4 MB/s. We also develop a prime and probe attack on a remote GPU allowing an attacker to recover the cache access pattern of another workload. This access pattern can be used in any number of side channel attacks: we demonstrate a proof of concept attack that fingerprints the application running on the remote GPU, with high accuracy. We also develop a proof of concept attack to extract hyperparameters of a machine learning workload. Our work establishes for the first time the vulnerability of these machines to microarchitectural attacks and can guide future research to improve their security.

Dutta, Sankha↗
DOE OSTI2016

Review of the Current State of Knowledge on the Effects of Radiation on Concrete

A review of the current state of knowledge on the effects of radiation on concrete in nuclear applications is presented. Emphasis is placed on the effects of radiation damage as reflected by changes in engineering properties of concrete in the evaluation of the long-term operation (LTO) and for Plant Life or Aging Management of nuclear power plants (NPPs) in Japan, Spain, and the United States. National issues and concerns are described for Japan and the US followed by a discussion of the fundamental understanding of the effects radiation on concrete. Specifically, the effects of temperature, moisture content, and irradiation on ordinary Portland cement paste and the role of temperature and neutron energy spectra on radiation induced volumetric expansion (RIVE) of aggregate-forming minerals are described. This is followed by a discussion of the bounding conditions for extended operation, the significance of accelerated irradiation conditions, the role of temperature, creep, and how these issues are being incorporated into numerical and meso-scale models. From these insights on radiation damage, analyses of these effects on concrete structures are reviewed and the current status of work in Japan and the US are described. Also discussed is the recent formation of a new international scientific and technical organization, the International Committee on Irradiated Concrete (ICIC), to provide a forum for timely information exchanges among organizations pursuing the identification, quantification, and modeling of the effects of radiation on concrete in commercial nuclear applications. Lastly, the paper concludes with a discussion of research gaps including: 1) interpreting test-reactor data, 2) evaluating service-irradiated concrete for aging management and to inform radiation damage models with the Zorita NPP (Spain) serving as the first comprehensive test case, 3) irradiated-assisted alkali-silica reactions, and 4) RIVE under constrained conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

FY2017 Materials Annual Progress Report

The Materials Technology subprogram supports the Vehicle Technology Office’s mission to help consumers and businesses reduce their transportation energy costs while meeting or exceeding vehicle performance expectations. The Propulsion Materials research portfolio seeks to develop higher performance materials that can withstand increasingly extreme environments and address the future properties needs of a variety of high efficiency powertrain types, sizes, fueling concepts, and combustion modes. Advanced Lightweight Materials research enables improvements in fuel economy by providing properties that are equal to or better than traditional materials at a lower weight. Because it takes less energy to accelerate a lighter object, replacing cast iron and traditional steel components with lightweight materials such as high-strength steel, magnesium (Mg), aluminum (Al), and polymer composites can directly reduce a vehicle’s fuel consumption. Materials technology activities focus on the following cost and performance targets: (1) enable a 25 percent weight reduction for light-duty vehicles including body, chassis, and interior as compared to a 2012 baseline at no more than a $5/lb-saved increase in cost; and (2) validate a 25 percent improvement in high temperature (300°C) component strength relative to components made with 2010 baseline cast Al alloys (A319 or A356) for improved efficiency light-duty engines.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Production and Distribution of {sup 44}Ti and {sup 56}Ni in a Three-dimensional Supernova Model Resembling Cassiopeia A

The spatial and velocity distributions of nuclear species synthesized in the innermost regions of core-collapse supernovae can yield important clues about explosion asymmetries and the operation of the still disputed explosion mechanism. Recent observations of radioactive {sup 44}Ti with high-energy satellite telescopes ( Nuclear Spectroscopic Telescope Array [ NuSTAR ], INTEGRAL ) have measured gamma-ray line details, which provide direct evidence of large-scale explosion asymmetries in SN 1987A and in Cassiopeia A (Cas A) even by mapping of the spatial brightness distribution ( NuSTAR ). Here we discuss a 3D simulation of a neutrino-driven explosion, using a parameterized neutrino engine, whose {sup 44}Ti distribution is mostly concentrated in one hemisphere pointing opposite to the neutron star (NS) kick velocity. Both exhibit intriguing resemblance to the observed morphology of the Cas A remnant, although neither the progenitor nor the explosion was fine-tuned for a perfect match. Our results demonstrate that the asymmetries observed in this remnant can, in principle, be accounted for by a neutrino-driven explosion, and that the high {sup 44}Ti abundance in Cas A may be explained without invoking rapid rotation or a jet-driven explosion, because neutrino-driven explosions generically eject large amounts of high-entropy matter. The recoil acceleration of the NS is connected to mass ejection asymmetries and is opposite to the direction of the stronger explosion, fully compatible with the gravitational tugboat mechanism. Our results also imply that Cas A and SN 1987A could possess similarly “one-sided” Ti and Fe asymmetries, with the difference that Cas A is viewed from a direction with large inclination angle to the NS motion, whereas the NS in SN 1987A should have a dominant velocity component pointing toward us.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

SODA: a New Synthesis Infrastructure for Agile Hardware Design of Machine Learning Accelerators

Next generation systems, such as edge devices, will have to provide efficient processing of machine learning (ML) algorithms along several metrics, including energy, performance, area, and latency. However, the quickly evolving field of ML makes it extremely difficult to generate accelerators able to support a wide variety of algorithms. At the same time, designing accelerators in hardware description languages (HDLs) by hand is hard and time consuming, and does not allow quick exploration of the design space. This paper discusses the SODA synthesizer, an automated open source high-level ML framework-to-Verilog compiler targeting ML Application-Specific Integrated Circuits (ASICs) chiplets based on the LLVM infrastructure. The SODA synthesizers will allow implementing optimal designs by combining templated and fully tunable IPs and macros, and fully custom components generated through high-level synthesis. All these components will be provided through an extendable resource library, characterized with both commercial and open source logic design flows. Through a closed loop design space exploration engine, developers will be able to quickly explore their hardware designs along different dimension

Minutoli, Marco↗
DOE OSTI2022

Hot-Casting-Assisted Liquid Additive Engineering for Efficient and Stable Perovskite Solar Cells

High-performance inorganic–organic lead halide perovskite solar cells (PSCs) are often fabricated with a liquid additive such as dimethyl sulfoxide (DMSO), which retards crystallization and reduces roughness and pinholes in the perovskite layers. However, DMSO can be trapped during perovskite film formation and induce voids and undesired reaction byproducts upon later processing steps. Here, it is shown that the amount of residual DMSO can be reduced in as-spin-coated films significantly through use of preheated substrates, or a so-called hot-casting method. Hot casting increases the perovskite film thickness given the same concentration of solutions, which allows for reducing the perovskite solution concentration. By reducing the amount of DMSO in proportion to the concentration of perovskite precursors and using hot casting, it is possible to fabricate perovskite layers with improved perovskite–substrate interfaces by suppressing the formation of byproducts, which increase trap density and accelerate degradation of the perovskite layers. The best-performing PSCs exhibit a power conversion efficiency (PCE) of 23.4% (23.0% stabilized efficiency) under simulated solar illumination. Furthermore, encapsulated devices show considerably reduced post-burn-in decay, retaining 75% and 90% of their initial and post-burn-in efficiencies after 3000 h of operation with maximum power point tracking (MPPT) under high power of ultraviolet (UV)-containing continuous light exposure.

perovskite-substrate interface↗
DOE OSTI2016

On the Relationships Between the Fundamental Parameters of Calculation Accelerograms

Analysis of published data on the fundamental parameters of actual accelerograms of strong earthquakes having peak ground acceleration A{sub max}, predominant period T{sub pr}, and duration τ{sub 0.5} at 0.5A{sub max} determined that, for earthquakes of intensity greater than 6.5 – 7.0, the relationship between these quantities is sufficiently well described by the parameters B = ATτ and C = AτT{sup −1.338}, the former of which depends little on earthquake intensity I and is almost completely determined by the earthquake magnitude, while the latter, on the contrary, weakly depends on magnitude and is determined principally by the quantity I. Methods are proposed for using the parameters B and C to improve the reliability of determining parameters of accelerograms used to calculate the seismic resistance of hydraulic engineering facilities.

42 ENGINEERING↗
DOE OSTI2020

Range cost-effectiveness of plug-in electric vehicle for heterogeneous consumers: An expanded total ownership cost approach

Plug-in electric vehicles (PEV) appears to have sales momentum in major personal vehicle markets but are still at the early market stage. Opportunities to accelerate PEV adoption can be discovered through comprehensive total cost of ownership (TCO) analysis. Understanding the cost-effective electric ranges of PEVs for consumers, manufacturers, and the society is critical for any discussion of PEV mass markets. This study expanded the traditional TCO approach by (1) fully considering heterogeneous consumer driving patterns, (2) quantifying the charging inconvenience and range anxiety cost of battery-electric vehicles (BEVs), and (3) monetizing both tangible and intangible PEV policies. Uncertainties were handled through Monte Carlo simulation. The results suggest that BEVs with an electric range of 250–350 km have the lowest TCO in cities with government-enacted purchase limitations, and internal combustion engine vehicles (ICEVs) have the lowest TCO in cities without purchase limitations, even when considering PEV subsidies. The lowest TCO for some consumer groups is obtained by BEVs with an electric range of 400–450 km, especially in northern China, where the weather is colder. The cost-effective all-electric range for BEVs in each city in 2025 will decrease due to improved battery performance in cold environments and an expanded charging infrastructure. Finally, based on TCO, plug-in hybrid electric vehicles (PHEVs) are currently more suitable for drivers with a high average daily mileage or a large mileage variance. However, by 2025, BEVs with a long driving range may become a more cost-effective choice for these drivers.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Bayesian Optimization of Catalysis with In-Context Learning

Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning (ICL), allowing the model to observe query-relevant examples at inference time and eliminating the need for additional weight updates to generalize beyond its original training data. We extend this capability to regression with uncertainty estimation using frozen LLMs (e.g., GPT-4o, Gemini), enabling Bayesian optimization (BO) in natural language without explicit model training or feature engineering. We apply this to materials discovery by representing materials as synthesis and testing procedures for use in natural language prompts. This Bayesian, design-first approach prioritizes optimization toward target material properties before detailed characterization, in contrast to conventional experimental workflows that often emphasize characterization of suboptimal materials. On benchmarks like aqueous solubility and oxidative coupling of methane (OCM), BO-ICL matches or outperforms Gaussian processes. In live experiments on the reverse water–gas shift (RWGS) reaction, BO-ICL identifies multimetallic catalysts that approach equilibrium CO yield within 6 and 10 iterations from a pool of 3,700 and 360,000 candidates, respectively. Our method redefines materials representation and accelerates discovery, with broad applications across catalysis, materials science, and AI.

Calibration↗
DOE OSTI2016

Component Database for the APS Upgrade

The Advanced Photon Source Upgrade (APS-U) project will replace the existing APS storage ring with a multi-bend achromat (MBA) lattice to provide extreme transverse coherence and extreme brightness x-rays to its users. As the time to replace the existing storage ring accelerator is of critical concern, an aggressive one-year removal/installation/testing period is being planned. To aid in the management of the thousands of components to be installed in such a short time, the Component Database (CDB) application is being developed with the purpose to identify, document, track, locate, and organize components in a central database. Three major domains are being addressed: Component definitions (which together make up an exhaustive "Component Catalog"), Designs (groupings of components to create subsystems), and Component Instances (“Inventory”). Relationships between the major domains offer additional "system knowledge" to be captured that will be leveraged with future tools and applications. It is imperative to provide sub-system engineers with a functional application early in the machine design cycle. Topics discussed in this paper include the initial design and deployment of CDB, as well as future development plans.

Veseli, S.↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
DOE OSTI2017

Additive Manufacturing of Wind Turbine Molds

The objective of this project was to explore the utility of Big Area Additive Manufacturing (BAAM) for low cost manufacturing of wind turbine molds. Engineers at Oak Ridge National Laboratory (ORNL) and TPI Composites (TPI) collaborated to design and manufacture a printed mold that can be used for resin infusion of wind turbine components. Specific focus was on required material properties (operating temperatures and pressures, coefficient of thermal expansion (CTE), thermal conductivity), surface finish (accuracy and coatings) and system integration (integrated vacuum ports, and heating element). The project began with a simple proof of principle components, targeting surface coatings and material properties for printing a small section (approximately 4’ x 4’ x 2’) of a mold. Next, the second phase scaled up and integrated with the objective of capturing all of the necessary components (integrated heating to accelerate cure time, and vacuum, sealing) for resin infusion on a mold of significant size (8’ x 20’ x 6’).

17 WIND ENERGY↗
DOE OSTI2025

Orchestrating Spontaneous Emission With Metasurfaces: Recent Advances in Engineering Thermal, Luminescent, and Quantum Emissions

Metasurfaces have emerged as powerful tools for controlling spontaneous emission, offering unprecedented control over light-matter interactions at sub-wavelength scales. While metasurfaces are traditionally utilized for shaping coherent electromagnetic waves, they have recently extended their capabilities to control incoherent or spontaneous emission. This examines review how metasurfaces can enhance and precisely control properties of thermal, luminescent, and quantum emission. In thermal emission, metasurfaces enable control over spatial, temporal, and spin coherence, offering new possibilities for applications such as energy harvesting, radiative cooling and heat assisted ranging and detection. For luminescent emission, metasurfaces significantly improve emission rates, quantum efficiency, and directionality, driving innovations in lighting and display technologies. For controlling quantized spontaneous emission, metasurfaces are instrumental in enhancing single-photon sources and enabling novel functionalities in quantum states through photon-pair generation, which is vital for quantum communication, meteorology, and computing. Here, despite these advancements several challenges to increase the operational bandwidths, accelerate and develop simulation strategies, and fabrication complexities persist. Emerging trends are also discussed, such as dynamic metasurfaces and their integration with nanophotonic platforms, which could further expand the capabilities of light-emitting metasurfaces.

Luminescence↗
DOE OSTI2022

Strain localization criteria for viscoplastic geomaterials

This report presents a viscoplastic localization criterion to detect quasi-instantaneous (i.e., load-induced) and delayed (creep-induced) strain localization in rate-dependent solids. The study is based on the theory of controllability and a viscoplastic description of the mechanical response. Analytical precursors of unstable states are defined through systems of ordinary differential equations (OEDs). The use of the proposed criteria is illustrated at the material point level through a set of strain localization analyses simulating active strain localization of a porous rock. In addition, full-field finite element simulations of compression tests conducted under various pressures are reported to demonstrate the role of local unstable viscoplasticity in the spontaneous propagation of deformation bands under stationary boundary conditions. The study shows that the viscoplastic localization criterion maintains a negative sign as long as the behavior is unstable, that is, the rate of deformation is accelerating. The sign switch coincides with the transition to decelerating deformation. The analyses revealed that pulses of overstress always emerge in correspondence with the growth of unstable behavior, and the peak matches the transition to stable behavior. The local responses recovered from full-field analyses were consistent with those observed in analyses at material point level and the predictions of the presented theory.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Moving small files in a networked environment

Globally distributed computing infrastructures, such as clouds and supercomputers, are currently used to manage data that is generated with an unprecedented speed from a variety of resources. Coping with this trend, the volume of data exchanged across distant sites increases substantially. To accelerate data transfer, high-speed networks are provided to connect remote sites. Most existing data movement solutions are optimized for moving large files. However, it is still challenging to transfer a large number of small files across networks. This disadvantage not only lowers data transfer performance, but also decreases overall system utilization. Here, we identify that moving small files is mainly constrained by degraded file system throughput, not just network performance as might be suspected. We have built a data transfer pipeline model to analyze the impact of small network I/O and storage I/O on data movement. Extending one of the widely used open source data movement solutions, GridFTP, we demonstrate several appropriate engineering approaches that mitigate the bottleneck and increase data transfer efficiency. We show optimizations that improve data transfer performance more than 5 times. In comparison to existing solutions, our approaches can save a significant amount of system resources for moving lots of small files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Comparison of URANS and LES predictions for the open phase of the OECD NEA CSNI fluid structure interaction CFD benchmark

The OECD NEA CSNI WGAMA CFD Task Group ran a benchmark in 2020 and 2021 to assess the predictive capabilities of coupled fluid structure interaction (FSI) CFD analysis methods. This paper presents the predictions made for the open phase of the benchmark using URANS and LES turbulence modelling approaches, and a comparison of the results to the experimental data. The benchmark comprised a channel containing two inline cylinders in cross-flow. The cylinders were fixed at one end, free at the other, and had measured resonant frequencies and damping properties. The URANS modelling used ANSYS Fluent 2-way coupled to ANSYS Mechanical. The LES modelling used Nek5000, 1-way coupled to Diablo. Comparisons with cross-channel velocity profiles are presented, both for the mean flow and its RMS. Comparisons are also made to the frequency spectra for point measurements of fluid velocity and pressure, and for the accelerations of the free end of each cylinder. URANS predicts the average velocity profiles relatively well, and is able to predict the velocity and acceleration spectra at the shedding frequency. However, the frequency content at the 4th harmonic of the shedding frequency is low in the URANS flow fields, and so does not excite accelerations at the resonant frequency of the cylinders. LES makes better predictions of the average profiles, and the velocity spectra agree well at both the shedding frequency and at higher frequencies. In conclusion, the 1-way coupled LES results show good agreement for acceleration spectra.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Analog optical computing primitives in silicon photonics

Optical computing accelerators help alleviate bandwidth and power consumption bottlenecks in electronics. In this paper, we show an approach to implementing logarithmic-type analog co-processors in silicon photonics and use it to perform the exponentiation operation and the recovery of a signal in the presence of multiplicative distortion. Finally, the function is realized by exploiting nonlinear-absorption-enhanced Raman amplification saturation in a silicon waveguide.

42 ENGINEERING↗