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Results for “Experiment Optimization”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Recent experiences using finite-element-based structural optimization

Structural optimization has been available to the structural analysis community as a tool for many years. The popular use of displacement method finite-element techniques to analyze linearly elastic structures has resulted in an ability to calculate the weight and constraint gradients inexpensively for numerical optimization of structures. Here, recent experiences in the investigation and use of structural optimization are discussed. In particular, experience with the commercially available ADS/NASOPT code is addressed. An overview of the ADS/NASOPT procedure and how it was implemented is given. Two example problems are also discussed.

Paul, B. K.↗
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.↗
NASA NTRS1979

Supercritical wing design using numerical optimization and comparisons with experiment

A numerical minimization scheme is used in conjunction with two-dimensional and three-dimensional inviscid transonic flow analysis codes to provide procedures for wing leading edge aerodynamic design. The procedures are demonstrated in the design of a new leading edge to improve C-141 cruise performance. For the high aspect ratio moderately swept C-141 wing, the 2-D procedure is shown to yield results which are in close agreement with those obtained using the 3-D technique. Although the 2-D approach uses much less computation time than the 3-D technique, the latter requires fewer manhours than the former. Comparisons of predicted and wind tunnel measured performance improvements are presented which verify the design procedures.

Lores, M. E.↗
NASA NTRS1984

Experiences performing conceptual design optimization of transport aircraft

Optimum Preliminary Design of Transports (OPDOT) is a computer program developed at NASA Langley Research Center for evaluating the impact of new technologies upon transport aircraft. For example, it provides the capability to look at configurations which have been resized to take advantage of active controls and provide and indication of economic sensitivity to its use. Although this tool returns a conceptual design configuration as its output, it does not have the accuracy, in absolute terms, to yield satisfactory point designs for immediate use by aircraft manufacturers. However, the relative accuracy of comparing OPDOT-generated configurations while varying technological assumptions has been demonstrated to be highly reliable. Hence, OPDOT is a useful tool for ascertaining the synergistic benefits of active controls, composite structures, improved engine efficiencies and other advanced technological developments. The approach used by OPDOT is a direct numerical optimization of an economic performance index. A set of independent design variables is iterated, given a set of design constants and data. The design variables include wing geometry, tail geometry, fuselage size, and engine size. This iteration continues until the optimum performance index is found which satisfies all the constraint functions. The analyst interacts with OPDOT by varying the input parameters to either the constraint functions or the design constants. Note that the optimization of aircraft geometry parameters is equivalent to finding the ideal aircraft size, but with more degrees of freedom than classical design procedures will allow.

Arbuckle, P. D.↗
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↗
DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Adaptive and self-optimizing intelligent systems such as digital twins are increasingly important in science and engineering. Digital twins utilize mathematical models to provide added precision to decision-making. However, physics-informed models are challenging to build, calibrate, and validate with existing data science methods. Model-based design of experiments (MBDoE) is a popular framework for optimizing data collection to maximize parameter precision in mathematical models and digital twins. In this work, we apply MBDoE, facilitated by the open-source package Pyomo.DoE, to train and validate mathematical models for batch crystallization. We quantitatively examined the estimability of the model parameters for experiments with different cooling rates. This analysis provides a quantitative explanation for the heuristic of using multiple experiments at different cooling rates.

Lynch, Hailey↗
NASA NTRS1989

Optimal trajectories for the aeroassisted flight experiment. Part 4: Data, tables, and graphs

The determination of optimal trajectories for the aeroassisted flight experiment (AFE) is discussed. Data, tables, and graphs relative to the following transfers are presented: (IA) indirect ascent to a 178 NM perigee via a 197 NM apogee; and (DA) direct ascent to a 178 NM apogee. For both transfers, two cases are investigated: (1) the bank angle is continuously variable; and (2) the trajectory is divided into segments along which the bank angle is constant. For case (2), the following subcases are studied: two segments, three segments, four segments, and five segments; because the time duration of each segment is optimized, the above subcases involve four, six, eight, and ten parameters, respectively. Presented here are systematic data on a total of ten optimal trajectories (OT), five for Transfer IA and five for Transfer DA. For comparison purposes and only for Transfer IA, a five-segment reference trajectory RT is also considered.

Miele, A.↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key factors influencing PR consideration: safety, service experience, privacy, traffic/environment, and time/cost. Additional research examined ways to optimize PR experiences by identifying four critical factors: comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. To measure the impact of these factors, a statistical model called the Pooled Rideshare Acceptance Model (PRAM) was developed, providing insights into how each element influences PR adoption. Further analysis using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) revealed how demographic characteristics such as age, gender, income, and past rideshare experience shape PR perceptions. Some key findings from the multigroup analyses showed that younger users valued technological features and environmental benefits, while older users prioritized reliability and service transparency. Additionally, privacy concerns were more significant for female users, while convenience was critical for higher-income groups. These results emphasize that a 'onesize-fits-all' approach to PR service design is not effective, highlighting the need for tailored strategies to address different user segments. Further, workshops were conducted with researchers and students to translate the findings into real-world solutions. These workshops and 3 all the statistical analyses led to the development of 95 actionable recommendations. The recommendations focus on key areas such as safety, service reliability, user education, and accessibility, offering tangible improvements to PR services. The insights from this study provide valuable guidance for policymakers, transportation network companies (TNCs), and researchers aiming to make PR services safer, more accessible, and widely accepted. By addressing user concerns, PR can become a more viable transportation option, supporting sustainable urban mobility and reducing reliance on private vehicles. Additionally, these findings emphasize the importance of user-centric service design in encouraging broader PR adoption. Future research should explore evolving trends in PR preferences, technological advancements, and policy changes to ensure continued improvements. By implementing these recommendations, PR services can better align with user expectations, enhance trust in shared mobility, and contribute to a more efficient transportation ecosystem.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1989

Maximum likelihood techniques in QELS

A framework for the analysis of Quasi Elastic Light Scattering (QELS) experiments designed to be used in microgravity environment is derived. Example calculations of the type to be used to design the QELS system are given. The framework for the analysis is based on the concepts of parameter estimation typified by Maximum Likelihood Estimation methods. These methods not only serve as the template for parameter estimation algorithms, but can also be used for optimal design of the experiments. Optimal design of experiments is facilitated by the fact that these methods not only give procedures for parameter estimation, but also estimates of the errors associated with the parameter estimation.

Edwards, Robert V.↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.

Gangadharaiah, Rakesh↗
NASA NTRS1989

Optimal trajectories for the aeroassisted flight experiment. Part 3: Formulation, results, and analysis

The determination of optimal trajectories for the aero-assisted flight experiment (AFE) is investigated. The intent of this experiment is to simulate a GEO-to-LEO transfer, where GEO denotes a geosynchronous Earth orbit and LEO denotes a low Earth orbit. The trajectories of an AFE spacecraft are analyzed in a 3D-space, employing the full system of 6 ODEs describing the atmospheric pass. The atmospheric entry conditions are given, and the atmospheric exit conditions are adjusted in such a way that the following conditions are satisfied: (1) the atmospheric velocity depletion is such that, after exiting, the AFE spacecraft first ascends to a specified apogee and then descends to a specified perigee; and (2) the exit orbital plane is identical with the entry orbital plane. The final maneuver, not analyzed here, includes the rendezvous with and the capture by the space shuttle.

Miele, A.↗