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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Application of Geant4 Monte Carlo simulation in dose calculations for small radiosurgical fields

The Geant4 toolkit was used to develop a Monte Carlo (MC)-based engine for accurate dose calculations in small radiation field sizes. The Geant4 toolkit (version 10.1.p02) was used to simulate 6-MV photon beam of a Varian2100C linear accelerator that is being used for stereotactic radiosurgery (SRS) treatment with small radiation fields. Geometric models of 3 in-house designed radiosurgical divergent cones, with the diameters of their projections at the isocenter being 10, 20, and 30 mm, were simulated. The accuracy of the MC simulation technique was examined by reproducing several different simulated dosimetric parameters of the primary beams with the experimental data. The dose distributions are first checked for single beams for each cone, then standard multiple field (SMF) techniques are applied. A sample set of DICOM files from computed tomography (CT) scan imaging of a patient's head was converted to the Geant4 geometry format to implement MC-based engine for a clinical test. To validate the accuracy of the MC-based calculations for SMF arrangements, the isodose lines from MC simulation in water phantom were compared with the measured isodose lines using EBT3 Gafchromic film in Solid Water phantoms. Agreements between measured and simulated depth dose values and beam profiles for SRS cones were generally within 2%/2 mm. For output factors, the largest discrepancy was observed for 10 mm SRS cone, which was 1.7%. For SMF techniques, in SRS cones, the MC simulation and EBT3 Gafchromic film dosimetry were in acceptable agreement (5%/5 mm). Excellent agreement between the results of the MC-based and measured dose values for both single and SMF techniques in SRS cones indicates the ability of the Geant4 toolkit to be applied as the platform for treatment planning of advanced radiotherapy techniques.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Comparing total cost of ownership of battery electric vehicles and internal combustion engine vehicles

The technological advance of electrochemical energy storage and the electric powertrain has led to rapid growth in the deployment of electric vehicles. The high cost and the added weight of the batteries have limited the size (energy storage capacity) and, therefore, the driving range of these vehicles. However, consumers are steadily purchasing these vehicles because of the fast acceleration, quiet ride, and high energy efficiency. The higher pack-to-wheel efficiency and the lower energy cost per mile, as well as the lower expense for maintenance and repair, translate to operating savings over conventional vehicles. Here we compare battery electric vehicles with internal combustion engine vehicles based on the total cost of ownership. It is seen that the higher initial cost of electric vehicles can be recovered in as little as 5 years. This is especially true for electric vehicles with shorter driving ranges. Specifically, a vehicle with an electric driving range under 200 miles may achieve cost parity with an equivalent internal combustion engine vehicle in 8 years or less.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Report on the Integration of Experimental and Modeling Data for Initial Equivalence Study of Mechanical Performance in Irradiated LPBF 316 Stainless Steel

To ensure the rapid development, deployment, and use of advanced nuclear technologies, faster qualification approaches are needed. Typically, the primary pathway uses traditional data packages consistent with the American Society of Mechanical Engineers Boiler and Pressure Vessel Code, which does not consider the environmental effects the material will experience such as corrosion and radiation damage. Examining radiation effects requires a significant amount of space in US facilities at the Advanced Test Reactor and the High Flux Isotope Reactor (HFIR) and suffers from natural gradients in temperature and neutron flux profiles. Ion irradiation may enable rapid assessment of radiation-induced damage to a material and is proposed as part of an accelerated materials qualification framework through the Advanced Materials and Manufacturing Technologies program. To enhance the utility of ion irradiation as an examination tool, this report provides the initial assessment of engineering-relevant properties of microstructures produced from ion irradiation in the near-surface volume. Nanoindentation, Vickers hardness, and known tensile properties were brought together with simple mathematical models and experimental data for an initial equivalence study of the mechanical performance of irradiated laser powder bed fusion (LPBF) 316 stainless steels across length scales. Direct observation of the calculated ion irradiation yield stress and measured neutron irradiation yield stress at 2 dpa showed that both datasets exhibit the same trend with irradiation temperature and overlap within an acceptable band of stress values. Ion irradiations at 10 dpa serve as a prediction of properties to compare to postirradiation examination of HFIR-irradiated LPBF 316H further in the program. This work is a significant demonstration of the Licensing Approach with Ions and Neutrons, which uses ion irradiations to generate mechanical property information more rapidly than through neutron irradiations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Neural message-passing for objective-based uncertainty quantification and optimal experimental design

Various real-world scientific applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufficient and the cost of acquiring additional data may be high. In such cases, based on a Bayesian paradigm, we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantification (objective-UQ) based on MOCU (mean objective cost of uncertainty) provides an effective means for quantifying uncertainty in complex systems, the high computational cost of estimating MOCU has been a challenge in applying it to real-world scientific/engineering problems. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to five orders of magnitude, without any visible performance loss compared to the state-of-the-art. The proposed approach applies to general OED tasks, beyond the Kuramoto model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

An MLIR-based Compiler Flow for System-Level Design and Hardware Acceleration

The generation of custom hardware accelerators for applications implemented within high-level productive programming frameworks requires considerable manual effort. To automate this process, we introduce \sodaopt, a compiler tool that extends the MLIR infrastructure. \sodaopt automatically searches, outlines, tiles, and pre-optimizes relevant code regions to generate high-quality accelerators through high-level synthesis. \sodaopt can support any high-level programming framework and domain-specific language that interface with the MLIR infrastructure. By leveraging MLIR, \sodaopt solves compiler optimization problems with specialized abstractions. Backend synthesis tools connect to \sodaopt through progressive intermediate representation lowerings. \sodaopt interfaces to a design space exploration engine to identify the combination of compiler optimization passes and options that provides high-performance generated designs for different backends and targets. We demonstrate the practical applicability of the compilation flow by exploring the automatic generation of accelerators for deep neural networks operators outlined at arbitrary granularity and by combining outlining with tiling on large convolution layers. Experimental results with kernels from the PolyBench benchmark show that \sodaopt high-level optimizations improve execution delays of synthesized accelerators up to 60x. We also show that for the selected kernels, our solution outperforms the current of state-of-the art in more than 70% of the benchmarks and provides better average speedup in 55% of them.

Bohm Agostini, Nicolas↗
DOE OSTI2024

Self-similar Reynolds-averaged mechanical–scalar turbulence models for Rayleigh–Taylor mixing induced by power-law accelerations in the small Atwood number limit

Analytical self-similar solutions to two-, three-, and four-equation Reynolds-averaged mechanical–scalar turbulence models describing turbulent Rayleigh–Taylor mixing driven by a temporal power-law acceleration are derived in the small Atwood number (Boussinesq) limit. The solutions generalize those previously derived for constant acceleration Rayleigh–Taylor mixing for models based on the turbulent kinetic energy K and its dissipation rate ε, together with the scalar variance S and its dissipation rate χ [O. Schilling, “Self-similar Reynolds-averaged mechanical–scalar turbulence models for Rayleigh–Taylor, Richtmyer–Meshkov, and Kelvin–Helmholtz instability-induced mixing in the small Atwood number limit,” Phys. Fluids 33, 085129 (2021)]. The turbulent fields are expressed in terms of the model coefficients and power-law exponent, with their temporal power-law scalings obtained by requiring that the self-similar equations are explicitly time-independent. Mixing layer growth parameters and other physical observables are obtained explicitly as functions of the model coefficients and parameterized by the exponent of the power-law acceleration. Values for physical observables in the constant acceleration case are used to calibrate the two-, three-, and four-equation models, such that the self-similar solutions are consistent with experimental and numerical simulation data corresponding to a canonical (i.e., constant acceleration) Rayleigh–Taylor turbulent flow. The calibrated four-equation model is then used to numerically reconstruct the mean and turbulent fields, and turbulent equation budgets across the mixing layer for several values of the power-law exponent. Finally, the reference solutions derived here can be used to understand the model predictions for strongly accelerated or decelerated Rayleigh–Taylor mixing in the large Reynolds number limit.

42 ENGINEERING↗
DOE OSTI2013

LDRD Annual Report 2013

Target normal sheath acceleration (TNSA) by self-guided laser pulses is demonstrated at a range of Al target thickness (2-10 microns), and proton spectra are compared with those accelerated by un-guided laser pulses. For un-guided pulses, the maximum proton energy decreases with the decrease of target thickness, exhibiting the detrimental effect of laser pre-pulse on TNSA. On the other hand, the dependence of proton energy on target thickness was not observed when using self-guided laser pulses, which is possibly due to the suppressed pre-pulse intensity. However the laser pulse appears to lose a significant amount of energy (>~50%) during ~4mm self-guiding in the plasma, and thus the accelerated proton energy is much lower than the case of unguided pulses.

42 ENGINEERING↗
DOE OSTI2020

Fabricated ecosystem workshop: bridging laboratory to field science. Workshop report.

Lawrence Berkeley National Laboratory (Berkeley Lab) scientists held a workshop at the DOE BER Genomic Sciences PI meeting on the use of Fabricated Ecosystems in Washington D.C. on February 25th, 2020. Ecosystem fabrication is an approach to creating controlled microbial, soil and plant ecologies within a laboratory setting that enable discovery and dissection of environmental variables, activities, and interactions. At Berkeley Lab, we are developing two systems which span spatial and temporal scales — the EcoFAB and the EcoPOD. The participants of the workshop discussed the potential applications and challenges of using fabricated ecosystems as tools to tackle BER-relevant scientific questions. Specifically, they identified (1) research challenges that would benefit from ready access to this infrastructure (2) identified and prioritized technical challenges that currently limit these systems and (3) discussed how to use them to bridge the gap between lab and field research. To ensure that the fabricated ecosystems , in particular the larger-scale EcoPODs, are of use to as much of the BER community as possible, the workshop participants made the following recommendations: 1. Produce publicly available datasets for a set of control variables. Use for benchmarking, and assessing reproducibility between systems. 2. Implement data standards. 3. Develop/leverage nano/micro sensors and in situ root imaging. 4. Encourage the development of interdisciplinary teams to develop EcoPOD experiments. 5. Develop fabricated ecosystems with size and complexity that sits between the EcoFAB and EcoPOD to accelerate use and access.

42 ENGINEERING↗
DOE OSTI2015

Cold plate with combined inclined impingement and ribbed channels

Heat transfer devices and methods for making the same that include a first enclosure having at least one inlet port; a second enclosure having a bottom plate and one or more dividing walls to establish channels, at least one internal surface of each channel having rib structures to create turbulence in a fluid flow; and a jet plate connecting the first enclosure and the second enclosure having impinging jets that convey fluid from the first enclosure to the channels, said impinging jets being set at an angular deviation from normal to cause local acceleration of fluid and to increase a local heat transfer rate.

42 ENGINEERING↗
DOE OSTI2020

Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes

Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.

42 ENGINEERING↗
DOE OSTI2020

Cold plate with combined inclined impingement and ribbed channels

Heat transfer devices and methods for making the same that include a first enclosure having at least one inlet port; a second enclosure having a bottom plate and one or more dividing walls to establish channels, at least one internal surface of each channel having rib structures to create turbulence in a fluid flow; and a jet plate connecting the first enclosure and the second enclosure having impinging jets that convey fluid from the first enclosure to the channels, said impinging jets being set at an angular deviation from normal to cause local acceleration of fluid and to increase a local heat transfer rate.

42 ENGINEERING↗
DOE OSTI2025

Practical Implementation of GPU-based Computing at the Grid Edge for Resilience Scenarios

This paper presents a practical implementation of GPU-accelerated computing at the grid edge to enhance power system resilience through next-generation smart meters. Advanced Metering Infrastructure (AMI) systems rely predominantly on centralized processing architectures, which limit real-time response capabilities during grid disturbances. This work proposes the integration of GPU-enabled computational platforms directly within smart meter to enable local execution support for power system analytics, fault detection algorithms, and optimization routines. The proposed framework uses the Julia programming language to leverage highperformance parallel computing capabilities while maintaining code portability and development efficiency. We use two experimental scenarios to benchmark the computational feasibility of this approach: sparse linear system solutions representative of power flow analyses, and multi-stage production cost simulations incorporating unit commitment and economic dispatch operations. Results demonstrate that computationally intensive power system algorithms, such as those supporting resilience scenario calculations, can be effectively executed at the distribution edge using commercially available embedded GPU hardware. Keywords—GPU acceleration, edge computing, smart meters, grid resilience, AMI, resilience.

De Souza, Reubun [School of Electrical Engineering↗
DOE OSTI2012

Acceleration of Monte Carlo Criticality Calculations Using Deterministic-Based Starting Sources

A new automatic approach that uses approximate deterministic solutions for providing the starting fission source for Monte Carlo eigenvalue calculations was evaluated in this analysis. By accelerating the Monte Carlo source convergence and decreasing the number of cycles that has to be skipped before the tally's estimation, this approach was found to increase the efficiency of the overall simulation, even with the inclusion of the extra computational time required by the deterministic calculation. This approach was also found to increase the reliability of the Monte Carlo criticality calculations of loosely coupled systems because the use of the better starting source reduces the likelihood of producing an undersampled k eff due to the inadequate source convergence. The efficiency improvement was demonstrated using two of the standard test problems devised by the OECD/NEA Expert Group on Source Convergence in Criticality-Safety Analysis to measure the source convergence in Monte Carlo criticality calculations. For a fixed uncertainty objective, this approach increased the efficiency of the overall simulation by factors between 1.2 and 3 depending on the difficulty of the source convergence in these problems. The reliability improvement was demonstrated in a modified version of the 'k eff of the world' problem that was specifically designed to demonstrate the limitations of the current Monte Carlo power iteration techniques. For this problem, the probability of obtaining a clearly undersampled k eff decreased from 5% with a uniform starting source to zero with a deterministic starting source when batch sizes with more than 15,000 neutron/cycle were used.

42 ENGINEERING↗
DOE OSTI2021

Morphogene-assisted transformation of Sorghum bicolor allows more efficient genome editing

Sorghum bicolor (L.) Moench, the fifth most important cereal worldwide, is a multi-use crop for feed, food, forage and fuel. To enhance the sorghum and other important crop plants, establishing gene function is essential for their improvement. For sorghum, identifying genes associated with its notable abiotic stress tolerances requires a detailed molecular understanding of the genes associated with those traits. The limits of this knowledge became evident from our earlier in-depth sorghum transcriptome study showing that over 40% of its transcriptome had not been annotated. Here, we describe a full spectrum of tools to engineer, edit, annotate and characterize sorghum’s genes. Efforts to develop those tools began with a morphogene-assisted transformation (MAT) method that led to accelerated transformation times, nearly half the time required with classical callus-based, non-MAT approaches. These efforts also led to expanded numbers of amenable genotypes, including several not previously transformed or historically recalcitrant. Another transformation advance, termed altruistic, involved introducing a gene of interest in a separate Agrobacterium strain from the one with morphogenes, leading to plants with the gene of interest but without morphogenes. The MAT approach was also successfully used to edit a target exemplary gene, phytoene desaturase. To identify single-copy transformed plants, we adapted a high-throughput technique and also developed a novel method to determine transgene independent integration. These efforts led to an efficient method to determine gene function, expediting research in numerous genotypes of this widely grown, multi-use crop.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Athermal glass work at the nanoscale: Engineered electron-beam-induced viscoplasticity for mechanical shaping of brittle amorphous silica

Amorphous silica deforms viscoplastically at elevated temperatures, which is common for brittle glasses. The key mechanism of viscoplastic deformation involves interatomic bond switching, which is thermally activated. Here, for this study, we precisely control the mechanical shaping of brittle amorphous silica at the nanoscale via engineered electron–matter interactions without heating. We observe a ductile plastic deformation of amorphous silica under a focused scanning electron beam with low acceleration voltages (few to tens of kilovolts) during in-situ compression studies, with unique dependence on the acceleration voltage and beam current. By simulating the electron–matter interaction, we show that the deformation of amorphous silica depends strongly on the volume where inelastic scattering occurs. The electron–matter interaction via e-beam irradiation alters the Si–O interatomic bonds, enabling the high-temperature deformation behavior of amorphous silica to occur athermally. Finally, by systematically controlling the electron–matter interaction volume, it is possible to mechanically shape the brittle amorphous silica on a small scale at room temperature to a level comparable to glass shaping at high temperatures. The findings can be extended to develop new fabrication processes for nano- and microscale brittle glasses.

36 MATERIALS SCIENCE↗