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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Automated and highly parallelized Bayesian optimization scheme for direct drive fusion experiments on OMEGA

Finding the optimal implosion design on existing experimental facilities for inertial confinement fusion requires an exhaustive search of the vast design parameter space. This is infeasible both with experiments and with simulations. Consequently, a large fraction of the experimentally realizable design space remains unexplored, and new design schemes are challenging to optimize in a reasonable time frame. On the OMEGA laser facility, predictive machine learning models have been developed to accurately forecast the result of an experiment using only inexpensive simulations and the large dataset of prior experimental data. However, the full design space remains vast enough to be unassailable with simple optimization techniques. Here we develop an automated and optimally parallel Bayesian optimization algorithm that can entirely optimize the target and pulse shape of a direct-drive ICF implosion under a given design paradigm. We use this algorithm to find a markedly improved design for the performance implosions on OMEGA that is predicted to hydroequivalently scale to ignition at 2.15 MJ.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Design Optimization of a Criticality Experiment for the Molten Chloride Reactor Experiment Facility

Neutronics simulations of Molten Chloride Fast Reactors have quantifiable biases that arise from nuclear data, modeling choices, or numerical methods. The multiphysics nature of molten salt reactors makes it challenging to disentangle neutronics modeling biases from biases originating from other physical phenomena. In comparison to a mock-up reactor, criticality experiments can specifically assess the neutronics modeling bias while limiting multiphysics effects. The criticality experiment must be neutronically representative of the full-scale reactor to be valuable. Here, in this paper, we describe the design of a criticality experiment to validate only the neutronics of TerraPower’s Molten Chloride Reactor Experiment (MCRE) and its criticality safety upset scenarios. The proposed experiment uses different chlorine-containing materials to maximize its similarity to the MCRE. The design process uses a constrained Bayesian optimization algorithm to investigate different objective functions that use covariance information for 35 Cl nuclear data. The experiments could reduce the nuclear data–induced uncertainty in k eff of the MCRE from 2161 to 886 pcm. They would also increase the upper subcritical limit of the MCRE criticality safety upset scenario from 0.94101 to 0.94476 when using the WHISPER analysis framework.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Sustainable bioleaching of lithium-ion batteries for critical metal recovery: Process optimization through design of experiments and thermodynamic modeling

Recycling spent lithium-ion batteries (LIBs) could alleviate supply risks for critical metals and be less harmful to the environment compared to new production of metals from mining. Developing a cost-effective LIB bioleaching process could be a promising alternative to traditional energy-intensive recycling technologies. Here, this study aimed to optimize bioleaching conditions for maximum economic competitiveness through design of experiments using iterative response surface methodology (RSM), assisted by thermodynamic modeling. The optimal condition was identified as 2.5% pulp density in 75 mM gluconic acid biolixiviant at 55°C for 30 h which could recover 57%–84% of nickel, 71%–86% of cobalt, and 100% of lithium and manganese, yielding a 17%–26% net profit margin. The recommended pulp density and acid concentrations, together with the observed metal solubilization, were supported by thermodynamic modeling predictions. Our study demonstrated that combining RSM with thermodynamic simulations could be a powerful tool for optimizing bioleaching conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Optimizing ablator thickness for laser shock experiments

In laser shock experiments, a well-defined, flat-top shock wave at the ablator/sample interface is important for accurately probing material response under uniaxial strain compression. However, the relationship between the ablator thickness and the resulting shock wave characteristics is insufficiently understood, limiting the ability to design optimal experiments. To address this need, we conducted a systematic experimental study using a 100 J laser to shock-compress polyimide ablators to peak stresses ranging from 20.4 to 111.6 GPa. Laser interferometry diagnostics measured the transmitted wave profiles at the ablator/sample interface, consistently showing a single jump followed by a constant peak state before the arrival of release waves. Here, by analyzing shock transit time, flat-top duration, and stress, our results establish a framework for selecting ablator thickness to maximize the flat-top duration, improving the precision and reproducibility of laser shock experiments.

design of experiments↗
DOE OSTI2024

Modeling, Optimization, and Design of Experiments of a Rotary Packed Bed Contactor for NGCC–Based CO2 Capture Using Solid Sorbents

This presentation will be given at the 2024 AICHE annual meeting on October 30th. This presentation focuses on modeling a rotary packed bed contactor for CO2 capture. The RPB is an alternative contactor to fixed beds and optimization is performed to minimize the energy requirement. A design of experiments case study of the RPB is also performed.

Hughes, Ryan↗
DOE OSTI2025

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Reducing Richtmyer–Meshkov instability jet velocity via inverse design

In this work, we detail a novel application of inverse design and advanced manufacturing to rapidly develop and experimentally validate modifications to a shaped charge jet analog. The shaped charge jet analog comprises a copper liner, a high explosive (HE), and a silicone buffer. Here, we apply a genetic algorithm to determine an optimal buffer design that can be placed between the liner and the HE that results in the largest possible change in jet velocity. The use of a genetic algorithm allows for discoveries of unintuitive, complex, yet optimal buffer designs. Experiments using the optimal design verified the effectiveness of the buffer and validated the machine learning approach to hydrodynamic design optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optimization Algorithm for Criticality Experiment Design Using Whisper

Many criticality experiments performed to aid in nuclear data evaluation are designed to maximize the sensitivity of the system’s effective neutron multiplication factor to a certain nuclide reaction pair over an energy region of interest. This is typically done by evaluating possible designs in a transport code such as MCNP and selecting the one with the highest desired sensitivity. A designer has many tools to try to maximize this sensitivity such as different moderators, reflectors, fuels, and geometries. This balancing act of identifying a critical and maximally sensitive system become very computationally expensive as more variables are added and higher precisions are desired. In order to identify these optimal configurations more efficiently a Particle Swarm Optimization (PSO) algorithm coupled with MCNP has been developed by Los Alamos National Laboratory (LANL). This algorithm has been used to design two upcoming criticality experiments that will be performed at the National Criticality Experiments Research Center (NCERC), located at the Nevada National Security Site, and operated by LANL, the only general-purpose critical experiments laboratory in the United States. PSO uses a population (swarm) of candidate solutions (particles) on a search space of dimensions such as moderator and reflector thicknesses or enrichments and concentrations. These particles move around the search space from generation to generation according to simple rules. Eventually, the swarm converges on the configuration that is both critical and maximally sensitive to a piece of nuclear data. PSO is well suited for criticality experiments as the algorithm is agnostic to the underlying physics, meaning it is effective on many different experimental setups. This algorithm has been modified to maximize the nuclear data similarity coefficient between an application case and an experiment aimed at replicating the application case using WHISPER, a nuclear criticality safety analysis tool. This allows for the efficient design of critical experiments informed by nuclear data sensitives.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Maximized Information Gain of Next Generation Pulsed Power Using Optimized Design of Z-Machine Experiments

This project develops a Bayesian optimization approach to extracting insights from Z Machine experimental data to determine if and how these insights can be used to extrapolate to a larger facility. The primary goal is to address the scientific challenge of informing how confidently experimental conditions can be predicted on a next generation facility, the design of which requires the reliable extrapolation of current high energy density technologies to regimes yet unobserved, except by costly high-fidelity computational models. Maximizing the use of presently available data and understanding how it informs future endeavors is critically important to enable transformative pulsed power and the science of extreme conditions. We explore a Bayesian optimization approach to experimental design which combines information theory, experimental data, and computational modeling to explore how information gain can be maximized.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimization of the N ∥ Upshift in the DIII-D high field side lower hybrid current drive experiment

High field side lower hybrid current drive (LHCD) is one potential candidate for efficient non-inductive current drive in tokamak power plants, and the first test of this technology will occur on the DIII-D tokamak during the 2024 campaign. Previous LFS launch experiments operated in the multi-pass regime and relied on scrape-off layer interactions to close the spectral gap. In the DIII-D experiment, single-pass damping is achievable via an upshift in the parallel refractive index N ∥ caused by mode converting twice (slow → fast → slow). This mode conversion affects the ray trajectories and can lead to enhanced N ∥ upshift depending on where mode conversion occurs. Compared to multi-pass absorption experiments, the optimization of launched N ∥ and plasma parameters can be counter-intuitive: increased density may increase efficiency and smaller N ∥,launch tend to damp closer to the separatrix. A hard x-ray camera installed to measure the bremsstrahlung (50–250 keV) radiation from LHCD-generated fast electrons is capable of verifying the trends reporting in this paper through comparison to the ray-tracing/Fokker–Planck codes GENRAY/CQL3D.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Weighted greedy-optimal design of computer experiments for kernel-based and Gaussian process model emulation and calibration

This article is concerned with the approximation of high-dimensional functions by kernel-based methods. Motivated by uncertainty quantification, which often necessitates the construction of approximations that are accurate with respect to a probability density function of random variables, we aim at minimizing the approximation error with respect to a weighted $L^p$-norm. We present a greedy procedure for designing computer experiments based upon a weighted modification of the pivoted Cholesky factorization. The method successively generates nested samples with the goal of minimizing error in regions of high probability. Numerical experiments validate that this new importance sampling strategy is superior to other sampling approaches, especially when used with non-product probability density functions. We also show how to use the proposed algorithm to efficiently generate surrogates for inferring unknown model parameters from data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PIP-II High Beta 650 Prototype Cryomodule Assembly Experience and Related Design Optimization

After shipment to the Daresbury Lab and return to Fermilab, the prototype HB650 cryomodule underwent another phase of 2K RF testing to ascertain any performance issues that may have arisen from the transport of the cryomodule. While measurements taken at room temperature after the conclusion of shipment indicated that there were no negative impacts on cavity alignment, beamline vacuum, or cavity frequency, testing at 2K was required to validate other aspects such as tuner operation, cavity coupling, cryogenic system integrity, and cavity performance. Results of this latest round of limited 2K testing will be presented.

Ozelis, J.↗
DOE OSTI2022

Particle Swarm Optimization Algorithm for Critical Experiment Design

Nuclear criticality experiments are used to validate nuclear cross section data used by simulation software. This is typically achieved by designing a critical system with a high sensitivity to a certain material’s cross section. Once the experiment has been carried out, a high fidelity model of the system is developed into a benchmark. When this benchmark model is simulated by a transport code, some of the difference between the experimental and computational effective neutron multiplication factor can be attributed to inaccurate nuclear data. Nuclear data evaluators then can make adjustments accordingly to improve cross section data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Optimized Test Section for Helium Flow Loop Experiment

Phase 3 of this project uses the knowledge and techniques gained from Phase 1 and Phase 2. The first outcome of Phase 3 is a new test section which can be manufactured and tested in the Helium Flow Loop Experiment to verify the results of the optimization study. The second outcome of Phase 3 is analyzing the features of this design and determining its performance under simulated TBM conditions: heat flux, pressure, mass flow rate, one-sided and volumetric heating. This will be accomplished by first using the results from the Phase 1 analyses and then applying the optimization strategy from Phase This phase uses the STAR-CCM+ Design Manager tool to apply the optimization strategy to the Helium Flow Loop Experiment (HFLE). The optimization algorithm used by the Design Manager tool is Simultaneous Hybrid Exploration that is Robust, Progressive, and Adaptive (SHERPA search algorithm). In addition to demonstrating the optimization features, a simple parameter sweep is also included in this phase. This report details the applied single objective optimization.

42 ENGINEERING↗