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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
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 OSTI2019

NSUF CoMET Project, Implementation Plan FY2019

The Combined Materials and Experiment Toolkit (CoMET) is an NSUF information management resource designed to give users access to the information and tools necessary for designing meaningful experiments and writing clear, structured proposals. NSUF has a milestone to deploy an experiment design expert system to the NSUF website to support applications for the FY2020 CINR, due on August 30, 2019. To meet this milestone, the CoMET project work for FY2019 focuses on three main objectives: 1.) Integrate the three NSUF web applications by linking data fields in one application with the data found in the others. The NSUF web applications are: The Rapid Turnaround Experiment (RTE) Proposal Solicitation system, the Nuclear Fuels and Materials Library (NFML) and the Nuclear Energy Infrastructure Database (NEID) and the researcher and technical staff account and profile management systems. 2.) Build an Experiment Design Wizard (expert system) that will guide users through the process of writing a statement of work for a CINR or RTE experiment. 3.) Build an Irradiation Effects Calculator to help a PI choose the best neutron irradiation facility for their needs, estimate the time needed to achieve their goals, and estimate the resulting radioactivity of their samples, post-irradiation. These objectives are designed to support the mission of the NSUF and optimize the experiment design portion of the proposal processes. The new researcher database will make finding collaborators easier. The wizard will provide the PI with early NSUF expertise so that their proposed experiments, at the “Letter of Intent” and “Pre-Application” stages are better designed to work within the available NSUF facilities. Utilization of these tools should reduce the proportion of proposals that are judged to be infeasible or “not ready” for NSUF access. It should also improve the efficiency of the NSUF technical leads since the PI will be starting with a better statement of work for the project. The tools will also provide resources for NSUF staff to more efficiently screen and score incoming proposals. This implementation plan will layout the work breakdown structure, activities, resources required, and plan for completion of the activities for CoMET in FY2019.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Applying Constrained Bayesian Optimization to the Design of Critical Experiments

Often when planning a criticality experiment, many design configurations are iteratively investigated with a Monte Carlo transport code. The goal is that the experiment will be optimal with respect to some variable, like the fraction of fissions occurring at a certain energy range, while simultaneously being critical. Unfortunately, the Monte Carlo transport simulations are expensive, which can ultimately limit the number of configurations that can be explored. In this work, we present how Gaussian processes (GPs) can be used as a reduced-order model in a constrained Bayesian optimization (CBO) algorithm to design a criticality experiment. The GPs replace the Monte Carlo transport simulations that explore the design space. The CBO algorithm efficiently identifies new points in the design space to run the Monte Carlo transport code while respecting the criticality constraint. It does so in a manner that both improves the accuracy of the GP and finds the approximate global optimum. We demonstrate the performance of CBO with the design of a Thermal Epithermal eXperiment (TEX) for the criticality safety validation of nuclear waste models of the Hanford Tank Farm.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Expert‐in‐the‐loop design of integral nuclear data experiments

Abstract Nuclear data are fundamental inputs to radiation transport codes used for reactor design and criticality safety. The design of experiments to reduce nuclear data uncertainty has been a challenge for many years, but advances in the sensitivity calculations of radiation transport codes within the last two decades have made optimal experimental design possible. The design of integral nuclear experiments poses numerous challenges not emphasized in classical optimal design, in particular, constrained design spaces (in both a statistical and engineering sense), severely under‐determined systems, and optimality uncertainty. We present a design pipeline to optimize critical experiments that uses constrained Bayesian optimization within an iterative expert‐in‐the‐loop framework. We show a successfully completed experiment campaign designed with this framework that involved two critical configurations and multiple measurements that targeted compensating errors in 239 Pu nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

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

Constrained Bayesian Optimization of Criticality Experiments [Slides]

The design of criticality experiments is typically an iterative process that employs a Monte Carlo transport code. The goal is to find a design that optimizes some variable, like the sensitivity of a response to a cross section, while simultaneously ensuring criticality. The high fidelity of the Monte Carlo code is a great asset, but it makes exploring the design space computationally expensive. Herein, we present how a constrained Bayesian optimization algorithm can be used to efficiently design a criticality experiment. It uses Gaussian processes as a surrogate model to probe the design space and to reduce the number of code executions that are needed to find the optimum. We demonstrate constrained Bayesian optimization with a Pu-239/polyethylene solution system and a TEX experiment that is designed for criticality safety validation of a nuclear waste model at the Hanford Site. For both systems, a global optimum was found within 75 Monte Carlo simulations.

42 ENGINEERING↗
NASA NTRS2006

Optimizing Crystal Volume for Neutron Diffraction

Neutron diffraction is uniquely sensitive to hydrogen positions and protonation state. In that context structural information from neutron data is complementary to that provided through X-ray diffraction. However, there are practical obstacles to overcome in fully exploiting the potential of neutron diffraction, Le. low flux and weak scattering. Several approaches are available to overcome these obstacles and we have investigated the simplest: increasing the diffracting volume of the crystals. Volume is a quantifiable metric that is well suited for experiment design and optimization techniques. By using response surface methods we have optimized xylose isomerase crystal volume, enabling neutron diffraction while we determined the crystallization parameters with the minimum of experiments. Our results suggest a systematic means of enabling neutron diffraction studies for a larger number of samples that require information on hydrogen position and/or protonation state.

Snell, Edward H.↗
NASA NTRS1992

Multidisciplinary design optimization using response surface analysis

Aerospace conceptual vehicle design is a complex process which involves multidisciplinary studies of configuration and technology options considering many parameters at many values. NASA Langley's Vehicle Analysis Branch (VAB) has detailed computerized analysis capabilities in most of the key disciplines required by advanced vehicle design. Given a configuration, the capability exists to quickly determine its performance and lifecycle cost. The next step in vehicle design is to determine the best settings of design parameters that optimize the performance characteristics. Typical approach to design optimization is experience based, trial and error variation of many parameters one at a time where possible combinations usually number in the thousands. However, this approach can either lead to a very long and expensive design process or to a premature termination of the design process due to budget and/or schedule pressures. Furthermore, one variable at a time approach can not account for the interactions that occur among parts of systems and among disciplines. As a result, vehicle design may be far from optimal. Advanced multidisciplinary design optimization (MDO) methods are needed to direct the search in an efficient and intelligent manner in order to drastically reduce the number of candidate designs to be evaluated. The payoffs in terms of enhanced performance and reduced cost are significant. A literature review yields two such advanced MDO methods used in aerospace design optimization; Taguchi methods and response surface methods. Taguchi methods provide a systematic and efficient method for design optimization for performance and cost. However, response surface method (RSM) leads to a better, more accurate exploration of the parameter space and to estimated optimum conditions with a small expenditure on experimental data. These two methods are described.

Unal, Resit↗
DOE OSTI2024

Sustainable production of 2,3,5,6-Tetramethylpyrazine at high titer in engineered Corynebacterium glutamicum

The industrial amino acid production workhorse, Corynebacterium glutamicum naturally produces low levels of 2,3,5,6-tetramethylpyrazine (TMP), a valuable flavor, fragrance, and commodity chemical. Here, we demonstrate TMP production (~0.8 g L –1 ) in C. glutamicum type strain ATCC13032 via overexpression of acetolactate synthase and/or α-acetolactate decarboxylase from Lactococcus lactis in CGXII minimal medium supplemented with 40 g L –1 glucose. This engineered strain also demonstrated growth and TMP production when the minimal medium was supplemented with up to 40% (v v –1 ) hydrolysates derived from ionic liquid-pretreated sorghum biomass. Further, a key objective was to take the fully engineered strain developed in this study and interrogate medium parameters that influence the production of TMP, a critical post-strain engineering optimization. Design of experiments in a high-throughput plate format identified glucose, urea, and their ratio as significant components affecting TMP production. These two components were further optimized using response surface methodology. In the optimized CGXII medium, the engineered strain could produce up to 3.56 g L –1 TMP (4-fold enhancement in titers and 2-fold enhancement in yield, mol mol –1 ) from 80 g L –1 glucose and 11.9 g L –1 urea in shake flask batch cultivation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluating pulse-shaping capabilities of next-generation pulsed power architectures

This project evaluated the pulse shaping capabilities of next-generation pulsed power (NGPP) architectures. NGPP architectures share several common attributes including multiple independent pulse-generation lines, a radial water-insulated impedance transformer, and a central vacuum insulated load region. A multi-module circuit model was developed, incorporating independent pulse-generation lines and a 2-D transmission line mesh of the radial impedance transformer to assess the effects of azimuthal asymmetry in pulse-shaped experiments. Circuit model simulations demonstrated that NGPP architectures are able to produce the the desired current pulse shapes for exemplar NGPP experiments. Additionally, the project explored automated methods for experiment design, including derivative -ree optimization and machine learning. Pulse-shaped experiments require designers to determine machine parameters that reliably produce the desired current pulse at the load, a process that typically relies on expert knowledge and iterative adjustments using the Z circuit model. Given the increased complexity of NGPP systems, this manual approach may be impractical. While the evaluated methods do not eliminate the need for manual iteration, they can reduce the time required for experiment design. Derivative-free optimization automates much of the trial-and-error process, providing a close starting point for manual adjustments or making small modifications to near-final designs. Meanwhile, deep neural network methods can generate a good qualitative match to the desired current pulse in under one second without requiring circuit model simulations.

42 ENGINEERING↗
DOE OSTI2022

Optimizing the accuracy of viscoelastic characterization with AFM force–distance experiments in the time and frequency domains

Atomic Force Microscopy (AFM) force-distance (FD) experiments have emerged as an attractive alternative to traditional micro-rheology measurement techniques owing to their versatility of use in materials of a wide range of mechanical properties. Here, we show that the range of time dependent behaviour which can reliably be resolved from the typical method of FD inversion (fitting constitutive FD relations to FD data) is inherently restricted by the experimental parameters: sampling frequency, experiment length, and strain rate. Specifically, we demonstrate that violating these restrictions can result in errors in the values of the parameters of the complex modulus. In the case of complex materials, such as cells, whose behaviour is not specifically understood a priori, the physical sensibility of these parameters cannot be assessed and may lead to falsely attributing a physical phenomenon to an artifact of the violation of these restrictions. We use arguments from information theory to understand the nature of these inconsistencies as well as devise limits on the range of mechanical parameters which can be reliably obtained from FD experiments. The results further demonstrate that the nature of these restrictions depends on the domain (time or frequency) used in the inversion process, with the time domain being far more restrictive than the frequency domain. Lastly, we demonstrate how to use these restrictions to better design FD experiments to target specific timescales of a material's behaviour through our analysis of a polydimethylsiloxane (PDMS) polymer sample.

information theory↗
NASA NTRS2001

The fungicidal and phytotoxic properties of benomyl and PPM in supplemented agar media supporting transgenic arabidopsis plants for a Space Shuttle flight experiment

Fungal contamination is a significant problem in the use of sucrose-enriched agar-based media for plant culture, especially in closed habitats such as the Space Shuttle. While a variety of fungicides are commercially available, not all are equal in their effectiveness in inhibiting fungal contamination. In addition, fungicide effectiveness must be weighed against its phytotoxicity and in this case, its influence on transgene expression. In a series of experiments designed to optimize media composition for a recent shuttle mission, the fungicide benomyl and the biocide "Plant Preservative Mixture" (PPM) were evaluated for effectiveness in controlling three common fungal contaminants, as well as their impact on the growth and development of arabidopsis seedlings. Benomyl proved to be an effective inhibitor of all three contaminants in concentrations as low as 2 ppm (parts per million) within the agar medium, and no evidence of phytotoxicity was observed until concentrations exceeded 20 ppm. The biocide mix PPM was effective as a fungicide only at concentrations that had deleterious effects on arabidopsis seedlings. As a result of these findings, a concentration of 3 ppm benomyl was used in the media for experiment PGIM-01 which flew on shuttle Columbia mission STS-93 in July 1999.

Non-NASA Center↗
DOE OSTI2024

Automated Calibration for Rapid Optical Spectroscopy Sensor Development for Online Monitoring

An automated platform has been developed to assist researchers in the rapid development of optical spectroscopy sensors to quantify species from spectral data. This platform performs calibration and validation measurements simultaneously. Real-time, in situ monitoring of complex systems through optical spectroscopy has been shown to be a useful tool; however, building calibration models requires development time, which can be a limiting factor in the case of radiological or otherwise hazardous systems. While calibration time can be reduced through optimized design of experiments, this study approached the challenge differently through automation. The ATLAS (Automated Transient Learning for Applied Sensors) platform used pneumatic control of stock solutions to cycle flow profiles through desired calibration concentrations for multivariate model construction. Additionally, the transients between desired concentrations based on flow calculations were used as validation measurements to understand model predictive capabilities. This automated approach yielded an incredible 76% reduction in model development time and a 60% reduction in sample volume versus estimated manual sample preparation and static measurements. The ATLAS system was demonstrated on two systems: a three-lanthanide system with Pr/Nd/Ho representing a use case with significant overlap or interference between analyte signatures and an alternate system containing Pr/Nd/Ni to demonstrate a use case in which broad-band corrosion species signatures interfered with more distinct lanthanide absorbance profiles. Both systems resulted in strong model prediction performance (RMSEP < 9%). Lastly, ATLAS was demonstrated as a tool to simulate process monitoring scenarios (e.g., column separation) in which models can be further optimized to account for day-to-day changes as necessary (e.g., baseline correction). Ultimately, ATLAS offers a vital tool to rapidly screen monitoring methods, investigate sensor fusion, and explore more complex systems (i.e., larger numbers of species).

47 OTHER INSTRUMENTATION↗