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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 199 records · Page 11

All sources NASA NTRS DOE OSTI

DOE OSTI2023

The Development of the Pooled Rideshare Acceptance Model (PRAM)

Due to the advancements in real-time information communication technologies and sharing economies, rideshare services have gained significant momentum by offering dynamic and/or on-demand services. Rideshare service companies evolved from personal rideshare, where riders traveled solo or with known individuals, into pooled rideshare (PR), where riders can travel with one to multiple unknown riders. Similar to other shared economy services, pooled rideshare is beneficial as it efficiently utilizes resources, resulting in reduced energy usage, as well as reduced costs for the riders. However, previous research has demonstrated that riders have concerns about using pooled rideshare, especially regarding personal safety. A U.S. national survey with 5385 participants was used to understand human factor-related barriers and user preferences to develop a novel Pooled Rideshare Acceptance Model (PRAM). This model used a covariance-based structural equation model (CB-SEM) to identify the relationships between willingness to consider PR factors (time/cost, privacy, safety, service experience, and traffic/environment) and optimizing one’s experience of PR factors (vehicle technology/accessibility, convenience, comfort/ease of use, and passenger safety), resulting in the higher-order factor trust service. We examined the factors’ relative contribution to one’s willingness/attitude towards PR and user acceptance of PR. Privacy, safety, trust service, and convenience were statistically significant factors in the model, as were the comfort/ease of use factor and the service experience, traffic/environment, and passenger safety factors. The only two non-significant factors in the model were time/cost and vehicle technology/accessibility; it is only when a rider feels safe that individuals then consider the additional non-significant variables of time, cost, technology, and accessibility. Privacy, safety, and service experience were factors that discouraged the use of PR, whereas the convenience factor greatly encouraged the acceptance of PR. Despite the time/cost factor’s lack of significance, individual items related to time and cost were crucial when viewed within the context of convenience. This highlights that while user perceptions of privacy and safety are paramount to their attitude towards PR, once safety concerns are addressed, and services are deemed convenient, time and cost elements significantly enhance their trust in pooled rideshare services. This study provides a comprehensive understanding of user acceptance of PR services and offers actionable insights for policymakers and rideshare companies to improve their services and increase user adoption.

dynamic rideshare system↗

NASA NTRS2003

Design of Experiments for the Thermal Characterization of Metallic Foam

Metallic foams are being investigated for possible use in the thermal protection systems of reusable launch vehicles. As a result, the performance of these materials needs to be characterized over a wide range of temperatures and pressures. In this paper a radiation/conduction model is presented for heat transfer in metallic foams. Candidates for the optimal transient experiment to determine the intrinsic properties of the model are found by two methods. First, an optimality criterion is used to find an experiment to find all of the parameters using one heating event. Second, a pair of heating events is used to determine the parameters in which one heating event is optimal for finding the parameters related to conduction, while the other heating event is optimal for finding the parameters associated with radiation. Simulated data containing random noise was analyzed to determine the parameters using both methods. In all cases the parameter estimates could be improved by analyzing a larger data record than suggested by the optimality criterion.

Crittenden, Paul E.↗

DOE OSTI2021

The Usage of Observing System Simulation Experiments and Reinforcement Learning to Optimize Experimental Design and Operation

Various organizations regularly conduct field campaigns across the globe designed to probe and improve atmospheric process understanding. However, these campaigns are mostly designed ad-hoc, and rely on anecdotes and knowledge of what was successful in previous campaigns. Such ad-hoc experiment design results in sub-optimal instrument siting and operation strategies. Inadequate targeted data collection of extreme weather is a major limitation to Earth and Environmental Systems Science Division (EESSD)’s goals of data model integration (4.5.3), which limits the knowledge gained through the collected observations, holding back significant advances in predictability. While advances in instrument design are always chipping away at these limitations, we are not optimally utilizing the instrumentation we currently possess.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Optimal experimental designs for the estimation of thermal properties of composite materials

Reliable estimation of thermal properties is extremely important in the utilization of new advanced materials, such as composite materials. The accuracy of these estimates can be increased if the experiments are designed carefully. The objectives of this study are to design optimal experiments to be used in the prediction of these thermal properties and to then utilize these designs in the development of an estimation procedure to determine the effective thermal properties (thermal conductivity and volumetric heat capacity). The experiments were optimized by choosing experimental parameters that maximize the temperature derivatives with respect to all of the unknown thermal properties. This procedure has the effect of minimizing the confidence intervals of the resulting thermal property estimates. Both one-dimensional and two-dimensional experimental designs were optimized. A heat flux boundary condition is required in both analyses for the simultaneous estimation of the thermal properties. For the one-dimensional experiment, the parameters optimized were the heating time of the applied heat flux, the temperature sensor location, and the experimental time. In addition to these parameters, the optimal location of the heat flux was also determined for the two-dimensional experiments. Utilizing the optimal one-dimensional experiment, the effective thermal conductivity perpendicular to the fibers and the effective volumetric heat capacity were then estimated for an IM7-Bismaleimide composite material. The estimation procedure used is based on the minimization of a least squares function which incorporates both calculated and measured temperatures and allows for the parameters to be estimated simultaneously.

Scott, Elaine P.↗

NASA NTRS1989

Optimal trajectories for the Aeroassisted Flight Experiment. Part 2: Equations of motion in an inertial system

The determination of optimal trajectories for the aeroassisted flight experiment (AFE) is discussed. The AFE refers to the study of the free flight of an autonomous spacecraft, shuttle-launched and shuttle-recovered. Its purpose is to gather atmospheric entry environmental data for use in designing aeroassisted orbital transfer vehicles (AOTV). It is assumed that: (1) the spacecraft is a particle of constant mass; (2) the Earth is rotating with constant angular velocity; (3) the Earth is an oblate planet, and the gravitational potential depends on both the radial distance and the latitude (harmonics of order higher than four are ignored); and (4) the atmosphere is at rest with respect to the Earth. Under these assumptions, the equations of motion for hypervelocity atmospheric flight (which can be used not only for AFE problems, but also for AOT problems and space shuttle problems) are derived in an inertial system. Transformation relations are supplied which allow one to pass from quantities computed in an inertial system to quantities computed in an Earth-fixed system and vice versa.

Miele, A.↗

NASA NTRS1973

Research study of materials processing in space Experiment M512

Ground based studies of two proposed experiments which were to be conducted aboard the Skylab Laboratory are reported; metals melting experiment, and sphere forming experiment. These studies were conducted with the objectives of clarifying the effects of gravity and optimizing the experiments within the constraints of existing hardware. All process phenomena were considered, with particular attention given to adhesion-cohesion studies with emphasis on the fluid dynamics of the molten metal. Detailed thermal histories were generated for three-dimensional specimen geometry including radiation, conduction and vaporization losses with allowances for variable properties. In addition quantitative techniques were used to establish the gravitational level (magnitude and direction) for KC-135 aircraft flights and the actual Skylab mission for both experiments.

Brashears, M. R.↗

NASA NTRS1989

Optimal trajectories for the Aeroassisted Flight Experiment. Part 1: Equations of motion in an Earth-fixed system

The determination of optimal trajectories for the aeroassisted flight experiment (AFE) is discussed. The AFE refers to the study of the free flight of an autonomous spacecraft, shuttle-launched and shuttle-recovered. Its purpose is to gather atmospheric entry environmental data for use in designing aeroassisted orbital transfer vehicles (AOTV). It is assumed that: (1) the spacecraft is a particle of constant mass; (2) the Earth is rotating with constant angular velocity; (3) the Earth is an oblate planet, and the gravitational potential depends on both the radial distance and the latitude (harmonics of order higher than four are ignored); and (4) the atmosphere is at rest with respect to the Earth. Under these assumptions, the equations of motion for hypervelocity atmospheric flight (which can be used not only for AFE problems, but also for AOT problems and space shuttle problems) are derived in an Earth-fixed system. Transformation relations are supplied which allow one to pass from quantities computed in an Earth-fixed system to quantities computed in an inertial system, and vice versa.

Miele, A.↗

DOE OSTI2024

An open-source data storage and visualization platform for collaborative qubit control

Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource utilization efficiently. However, the lack of dedicated data management platforms presents a significant obstacle to progress, highlighting the necessity for essential assistive tools tailored for this purpose. Current qubit control systems are unable to handle complicated management of extensive calibration data and do not support effectively visualizing intricate quantum experiment outcomes. In this paper, we introduce Qubit Control Storage and Visualization ( QubiCSV ), a platform specifically designed to meet the demands of quantum computing research, focusing on the storage and analysis of calibration and characterization data in qubit control systems. As an open-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data versioning capabilities for data storage and allowing researchers and programmers to interact with qubits in real time. The insightful visualization are developed to interpret complex quantum experiments and optimize qubit performance. QubiCSV not only streamlines the handling of qubit control system data but also improves the user experience with intuitive visualization features, making it a valuable asset for researchers in the quantum computing domain.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1982

Development of a large area space solar cell assembly

The development of a large area high efficiency solar cell assembly is described. The assembly consists of an ion implanted silicon solar cell and glass cover. The important attributes of fabrication are the use of a back surface field which is compatible with a back surface reflector, and integration of coverglass application and cell fabrications. Cell development experiments concerned optimization of ion implantation processing of 2 ohm-cm boron-doped silicon. Process parameters were selected based on these experiments and cells with area of 34.3 sq cm wre fabricated. The average AMO efficiency of the twenty-five best cells was 13.9% and the best bell had an efficiency of 14.4%. An important innovation in cell encapsulation was also developed. In this technique, the coverglass is applied before the cell is sawed to final size. The coverglass and cell are then sawed as a unit. In this way, the cost of the coverglass is reduced, since the tolerance on glass size is relaxed, and costly coverglass/cell alignment procedures are eliminated. Adhesive investigated were EVA, FEP-Teflon sheet and DC 93-500. Details of processing and results are reported.

Spitzer, M. B.↗

DOE OSTI2022

Reactivity Coefficient Measurements and Sensitivity Studies [Abstract]

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Validation of Jezebel Reactivity Coefficients and Sensitivity Analysis

Nuclear data validation is often performed today using criticality measurements. The gold standard for criticality measurements is the International Criticality Safety Benchmark Experiment Project (ICSBEP). The validation specifically focuses on the effective multiplication factor ($k_{eff}$). $K_{eff}$ is a relatively easy parameter to infer and has reduced uncertainty due to being at or above critical. However, while $k_{eff}$ is the most documented parameter and its uncertainties and sensitivities have been evaluated in great detail, it cannot be used as a standalone metric to determine inaccuracies in nuclear data (e.g., cross section data, PFNS, nu), which is based on theory, physics, and differential measurements. The Experiments Underpinned by Computational Learning for Improvements in nuclear Data (EUCLID) project aims to identify compensating errors in specific isotope nuclear data by optimally designing experiments that are, or are not sensitive to a suite of measurement parameters beyond $k_{eff}$. By identifying parameters that are sensitive to each other, oppositely sensitive, or have substantial magnitude differences in sensitivity, experiments can be designed to constrain questionable nuclear data. One sensitivity that is of particular interest to this project includes the sensitivity of reactivity coefficients. Reactivity coefficients compare reactivity, which is related to $k_{eff}$ at two different states therefore being sensitive to small changes in the system. The most common type of reactivity coefficient measurements is comparison to void for a small sample within the assembly. It is key that the sample sizes are small enough to not affect the flux of the full system. Reactivity coefficients were evaluated for many early experiments to better understand transport corrected cross sections. In fact, ICSBEP includes reactivity coefficient results as “Supplemental Measurements” in appendices for a handful of older benchmarks. One of those benchmarks is Jezebel, the bare Pu critical assembly. This paper compares new simulations of reactivity coefficients for Jezebel, and explores the sensitivity of reactivity coefficients to small changes in nuclear data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Scalable Bayesian optimization with randomized prior networks

Several fundamental problems in science and engineering consist of global optimization tasks involving unknown high-dimensional (black-box) functions that map a set of controllable variables to the outcomes of an expensive experiment. Bayesian Optimization (BO) techniques are known to be effective in tackling global optimization problems using a relatively small number objective function evaluations, but their performance suffers when dealing with high-dimensional outputs. To overcome the major challenge of dimensionality, here we propose a deep learning framework for BO and sequential decision making based on bootstrapped ensembles of neural architectures with randomized priors. Using appropriate architecture choices, we show that the proposed framework can approximate functional relationships between design variables and quantities of interest, even in cases where the latter take values in high-dimensional vector spaces or even infinite-dimensional function spaces. In the context of BO, we augmented the proposed probabilistic surrogates with re-parameterized Monte Carlo approximations of multiple-point (parallel) acquisition functions, as well as methodological extensions for accommodating black-box constraints and multi-fidelity information sources. We test the proposed framework against state-of-the-art methods for BO and demonstrate superior performance across several challenging tasks with high-dimensional outputs, including a constrained multi-fidelity optimization task involving shape optimization of rotor blades in turbo-machinery.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Flexible formulation of value for experiment interpretation and design

Not Available

36 MATERIALS SCIENCE↗

DOE OSTI2023

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization

Here, we consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Nuclear-level effective theory of 𝜇 → 𝑒 conversion: Inelastic process

Mu2e and COMET will search for electrons produced via the neutrinoless conversion of stopped muons bound in 1s atomic orbits of 27 Al, improving existing limits on charged lepton flavor violation (CLFV) by roughly four orders of magnitude. Conventionally, 𝜇 → 𝑒 conversion experiments are optimized to detect electrons originating from transitions where the nucleus remains in the ground state, thereby maximizing the energy of the outgoing electron. Clearly, detection of a positive signal in forthcoming experiments would stimulate additional work—including subsequent conversion experiments using complementary nuclear targets—to further constrain the new physics responsible for CLFV. Here we argue that additional information can be extracted without the need for additional experiments, by considering inelastic conversion in 27 Al. Transitions to low-lying nuclear excited states can modify the near-endpoint spectrum of conversion electrons, with the ratio of the elastic and inelastic responses being sensitive to the underlying CLFV operator. We extend the nuclear effective theory of 𝜇 → 𝑒 conversion to the inelastic case, which adds five new response functions to the six that arise for the elastic process. We evaluate these nuclear response functions in 27 Al and calculate the resulting conversion-electron signal, taking into account the resolution anticipated in Mu2e/COMET. We find that 27 Al is an excellent target choice from the perspective of the new information that can be obtained from inelastic 𝜇 → 𝑒 conversion.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Improving Self-Driving Labs: Quantifying System-Level Experiment Repeatability and Broadening Instrument-Level Compatibility

Modular Autonomous Research System (MARS) is a self-driving laboratory (SDL) which performs wet-lab science with peptide-lanthanide combinations in an automated and, ultimately, an autonomous manner to aid in soil analysis for domestic lithium mining. Autonomous experimentation involves automated experimentation, experiment planning, and active learning. MARS consists of a 6-axis robotic arm (UR5e) on a linear rail, pipette robots (Opentrons 2), and microplate readers. These components transport, operate on, and collect data with chemical solutions in standard labware. For effective autonomy, MARS must perform system-level labware operations repeatably, plan experiments autonomously, and be portable between research-domains. Repeatability is evaluated by labware placement precision, such that future operations can properly locate labware, as well as the elapsed time, so that low variance mean estimates of experiment duration can inform high-level researcher decision making. Autonomous experiment planning is the next step to decouple experimentation from human management; however, there is a conflict between the ideal system-level experiment goals and the constraints imposed by instruments’ limitations. Sub-domain portability is a long-term goal to extend MARS’ research beyond the chemistry of peptide-lanthanide binding to other sub-domains without having to invest significant overhead to system retrofitting. To address these goals, we manually trained the robotic arm labware placement and modelled statistical failurerate and uncertainty Additionally, we benchmarked the duration and variance of each experiment sub-operation as a heuristic for research decision making. Next, we use a parameterized geometric program (PGP) approach to design experiments that optimize system-level objectives and satisfy instrument-level constraints. Lastly, we proposed a Python framework to maximize MARS’ extensibility to other scientific sub-domains through a JSON-based experiment specification.

36 MATERIALS SCIENCE↗

NASA NTRS1998

Continuation of the University of Michigan Sounding Rocket Program

This interim report gives the activity on this grant through 31 October 1998, and the planned activities for the next fiscal year. A Project Initiation Conference (PIC) was held at the Wallops Flight Facility (WFF), with planning for the launch of flight 36.174 in Fall 1998. However, following the PIC, the WFF personnel decided that the experiment telemetry system had to be completely rewired, which required substantial design and wiring changes on the experiment side of the payload. These costs had not been anticipated in the FY98 budget, and used up considerable resources of the experimenters. In addition, the telemetry re-design was not completed until Fall 1998, so that there was no opportunity for a launch in 1998 (the Jupiter window closes in late November 1998 and it will reopen in late summer 1999). A Design Review was held by telecon with WFF personnel on 26 October 1998, with plans for the 36.174 launch now in late August 1999 with a launch window which extends into winter 2000. On a more positive note, we have been developing a vacuum UV calibration chamber in Michigan's Space Physics Research Lab, for future measurements of our experiment components, and this facility is now undergoing initial measurements of various calibration lamps in preparation for testing of the telescope UV reflective efficiency. This facility will greatly increase our ability to optimize our experiment for UV efficiency, which has been lacking in the past, and it will be made available for support of other NASA missions at Michigan and for visitors from other institutions. For the next flight 36.174, the following activities will be accomplished: (1) prepare the experiment, including replacement and upgrading of critical components, at University of Michigan; (2) pre-integrate the payload at the Wallops Flight Facility; (3) perform final far-UV calibration of instrument (4) perform final alignment, integration, and electrical checks at WSMR; (5) launch payload from WSMR and check condition of recovered payload; (6) perform post-flight calibration, if necessary and applicable (7) reduce and analyze flight data. The following pages contain information from the experimenter package for the Design Review for the next flight, 36.174, listing the plans, changes, and schedule for this flight.

Clarke, John↗

DOE OSTI2021

Computational design of thermoelectric alloys through optimization of transport and dopability

Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba 2(1–x) Sr 2x CdP 2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. Here, we assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.

36 MATERIALS SCIENCE↗