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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

FuSED – Users Manual – (V.5.26)

The Fusion of Simulation, Experiment, and Data (FuSED) team provides a set of tools for solving inverse problems in structural dynamics (InverseSD) and thermal physics (InverseAria), a sensor placement optimization tool via Optimal Experimental Design (OED), and a decision boundary tool using SVMs (TRACE). These methods are used for designing experiments, model calibration, and verification/validation analysis of systems. This document provides a user’s guide.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Physical Activity Types and Programs Recommended by Primary Care Providers Treating Adults With Arthritis, DocStyles 2018

Primary care providers (PCPs) can offer counseling to adults with arthritis on physical activity, which can reduce pain and improve physical function, mental health, and numerous other health outcomes. We analyzed cross-sectional 2018 DocStyles data for 1,366 PCPs who reported they always or sometimes recommend physical activity to adults with arthritis. Most PCPs sampled (88.2%) recommended walking, swimming, or cycling; 65.5% did not recommend any evidence-based, arthritis-appropriate physical activity programs recognized by the Centers for Disease Control and Prevention. Opportunities exist for public health awareness campaigns to educate PCPs about evidence-based physical activity programs proven to optimize health for adults with arthritis when more than counseling is needed.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1977

Development of a parameter optimization technique for the design of automatic control systems

Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.

Whitaker, P. H.↗
NASA NTRS1978

Missions to the sun and to the earth

The program outlined in the present paper represents an optimized plan of solar terrestrial physics. It is constrained only in the sense that it involves not more than one new major mission per year for the Solar Terrestrial Division during the 1980-1985 period. However, the flight activity proposed, if accepted by the Agency and by Congress, would involve a growth in the existing Solar Terrestrial budget by more than a factor of 2. Thus, the program may be considered as somewhat optimistic when viewed in the broader context of the NASA goals and budget. The Agency's integrated FY 1980 Five Year Plan will show how many missions proposed will survive this planning process.

Timothy, A. F.↗
NASA NTRS1982

Preliminary test results of electrical charged particle generator for application to fog dispersal

A charged particle generator for use in fog dispersal applications was built and preliminary tests were carried out. The parameter used as a measure of performance was the current measured with a needle probe positioned in the charged jet connected to ground through an ammeter. The needle was movable and allowed the current profile throughout the jet to be determined. The measured current is referred to as the current output. The major independent parameters were liquid water injection rate, plenum pressure, and corona voltage. Optimum current output was achieved at the approximate pressure of 30 psig, corona voltage of 5600 volts, and liquid water injection rate of 6 cc/min. The results of the test with the prototype charged particle generator clearly demonstrate that a current on the order of 20 microamperes can be routinely achieved with the system. This measurement of current does not necessarily represent the total issuing from the nozzle current which is expected to be larger. From these results, confidence was established that a charged particle generator which will operate continuously and consistently can be designed, constructed, and operated. Further work is required, however, to better understand the physical mechanisms involved and to optimize the system for fog dispersal application.

Frost, W.↗
NASA NTRS1986

MAX '91: An advanced payload for the exploration of high energy processes on the active sun

The results of a NASA science working group established to study a follow-on to the Solar Maximum Mission are given. A complement of instruments is suggested, with the primary objective of studying the physics of energetic processes in cosmic plasmas by observing high-energy phenomena in solar flares. High-quality flare observations will be possible with these instruments during the next peak in solar activity expected to last from 1990 through at least 1995. The primary objective of MAX '91 is to study energetic processes in cosmic plasmas by observing high-energy phenomena in solar flares. These processes, which are of general astrophysical importance, include energy release, particle acceleration, and energy transport. Results from comprehensive observing programs conducted during the last solar cycle have demonstrated the great scientific potential of high-energy emissions for addressing these central physical processes. Consequently, a payload optimized for observations of high-energy solar flare phenomena is suggested for MAX '91. It consists of the following four specific instruments: (1) a Fourier-transform X-ray and gamma-ray imager covering the energy range from a few keV to 1 MeV with arcsecond spatial resolution; (2) a cooled germanium X-ray and gamma-ray spectrometer with keV spectral resolution covering the energy range from 10 keV to 50 MeV; (3) Bragg spectrometers with high spectral resolution at wavelengths between 1 and 9 angstrons; and (4) a soft X-ray, EUV, or UV imaging instrument with arcsecond spatial resolution.

Source record↗
NASA NTRS1986

Oxide-dispersion-strengthened turbine blades, volume 1

The objective of Project 4 was to develop a high-temperature, uncooled gas turbine blade using MA6000 alloy. The program objectives were achieved. Production scale up of the MA6000 alloy was achieved with a fair degree of tolerance to nonoptimum processing. The blade manufacturing process was also optimized. The mechanical, environmental, and physical property evaluations of MA6000 were conducted. The ultimate tensile strength, to about 704 C (130 F), is higher than DS MAR-M 247 but with a corresponding lower tensile elongation. Also, above 982 C (180 F) MA6000 tensile strength does not decrease as rapidly as MAR-M 247 because the ODS mechanism still remains active. Based on oxidation resistance and diffusional stability considerations, NiCrAlY coatings are recommended. CoCrAly coating should be applied on top of a thin NiCrAlY coating. Vibration tests, whirlpit tests, and a high-rotor-rig test were conducted to ensure successful completion of the engine test of the MA6000 TFE731 high pressure turbine blades. The results of these tests were acceptable. In production quantities, the cost of the Project 4 MA6000 blade is estimated to be about twice that of a cast DS MAR-M 247 blade.

Millan, P. P., Jr.↗
NASA NTRS1987

Response of joint dominated space structures

An approximate method is developed for estimating the transient response of nonlinear systems in terms of linearized modes of response. Its advantages are that it is computationally more efficient than the time integration method and that it is possible to view the design problem in the more traditional physical terms of modal response. The major drawback of the approximate method is loss of accuracy. It seems that both approximate methods and time integration have their roles in design. Approximate methods provide efficient tools for performing parametric studies and they supply physical insights into how to optimize system performance that are not easily inferred from strictly numerical methods. Time integration provides a method for assessing the accuracy of the approximate solution for key simulations and for fine tuning the final design. In the procedure presented the nonlinear system is approximated by an equivalent linear system in which the system parameters are constant over the range of transient response.

Source record↗
NASA NTRS1987

Oxide-dispersion-strengthened turbine blades. Volume 2

The overall objective of Project 4 was to develop and test a high-temperature, uncooled gas turbine blade using MA6000 alloy. Production scale up of the MA6000 alloy was achieved with a fair degree of tolerance to non-optimum processing. The blade manufacturing process was also optimized. The mechanical, environmental, and physical property evaluations of MA6000 were conducted. The ultimate tensile strength, to about 704 C (1300 F), is higher than DS MAR-M 247 but with a corresponding lower tensile elongation. Also, above 982 C (1800 F) MA6000 tensile strength does not decrease as rapidly as MAR-M 247 because the ODS mechanism still remains active. Based on oxidation resistance and diffusional stability considerations, NiCrAlY coatings are recommended. CoCrAlY coating should be applied on top of a thin NiCrAlY coating if hot corrosion is expected. Vibration, whirlpit, and high-rotor-rig tests were conducted to ensure successful completion of the engine test of the MA6000 TFE731 high pressure turbine blades. Test results were acceptable. In production quantities, the cost of the Project 4 MA6000 blade is estimated to be twice that of a cast DS MAR-M 247 blade.

Millan, P. P., Jr.↗
NASA NTRS1990

Comparison of FDMA and CDMA for second generation land-mobile satellite communications

Code Division Multiple Access (CDMA) and Frequency Division Multiple Access (FDMA) (both analog and digital) systems capacities are compared on the basis of identical link availabilities and physical propagation models. Parameters are optimized for a bandwidth limited, multibeam environment. For CDMA, the benefits of voice activated carriers, antenna discrimination, polarization reuse, return link power control and multipath suppression are included in the analysis. For FDMA, the advantages of bandwidth efficient modulation/coding combinations, voice activated carriers, polarization reuse, beam placement, and frequency staggering were taken into account.

Yongacoglu, A.↗
NASA NTRS1992

Photoexcited escape probability, optical gain, and noise in quantum well infrared photodetectors

We present a detailed and thorough study of a wide variety of quantum well infrared photodetectors (QWIPs), which were chosen to have large differences in their optical and transport properties. Both n- and p-doped QWIPs, as well as intersubband transitions based on photoexcitation from bound-to-bound, bound-to-quasi-continuum, and bound-to-continuum quantum well states were investigated. The measurements and theoretical analysis included optical absorption, responsivity, dark current, current noise, optical gain, hot carrier mean free path; net quantum efficiency, quantum well escape probability, quantum well escape time, as well as detectivity. These results allow a better understanding of the optical and transport physics and thus a better optimization of the QWIP performance.

Levine, B. F.↗
NASA NTRS2022

Design of Materials with Alchemite

Machine learning models that establish the relationships between materials processing and properties can enable inverse design of materials through active learning. Alchemite is a commercial software that can perform inverse materials design on sparse data. Here we evaluate Alchemite’s performance on a dataset of shape memory alloys and a dataset of heat exchangers compared to baseline random forest models. Alchemite had higher accuracy when making predictions on sparse data and was more accurate or nearly as accurate as random forests on complete datasets while also quantifying uncertainty. The software was also used to suggest processing steps and design parameters to optimize properties and performance; however, physical validation of the suggested design parameters was beyond the scope of this work. Several useful design insights were gained about the impact of the design parameters on properties and performance including the importance of dopant choice and amount for shape memory alloys and the importance of height and weight on the thermal resistance of heat exchangers.

Machine learning↗
DOE OSTI2024

A Workflow to Optimize Fast Neutron Irradiation in A Thermal Neutron Spectrum Test Reactor Leveraging Open-Source Tools

The Advanced Test Reactor (ATR) located at Idaho National Laboratory (INL) is one of the key nuclear engineering research and testing facilities within the US Department of Energy (DOE). The ATR is one of few high-power research reactors in the world with different application including accelerated testing of nuclear fuel, materials irradiation in a very high neutron flux environment, and medical radioisotope production [1]. Also, the ATR offers opportunities for testing fast spectrum fission and fusion reactor materials. The key challenges in this area are in further detailing and optimizing a fast spectrum environment within a thermal test reactor. This challenge involves researching, developing, and testing novel concepts for the multiplying of neutron populations into ever higher energy spectra in high flux test reactors like ATR. The main objective of this work is to investigate candidate materials for establishing a fast neutron experiment irradiation in thermal neutron spectrum test reactors which can be accomplished by filtering thermal and epithermal neutrons and boosting fast neutrons at designated irradiation positions. However, adding these filters will render the neutron spectrum and the criticality of the system. The selection of the thickness and material layers should be accomplished by developing an optimization design algorithm that is applicable for ATR to enhance the fast neutron spectrum irradiation utilizing high-fidelity Monte Carlo methods along with advanced machine learning capabilities. This paper presents workflow for design optimization to enhance fast neutron irradiation in the ATR. The workflow leverages open-source tools to develop an algorithm that is viable to ATR and can be leveraged in other reactors. The following sections discuss the development of the experiment design optimization workflow and its application to ATR irradiation positions.

42 - ENGINEERING↗
NASA NTRS1992

Multidisciplinary design and optimization

Mutual couplings among the mathematical models of physical phenomena and parts of a system such as an aircraft complicate the design process because each contemplated design change may have a far reaching consequence throughout the system. This paper outlines techniques for computing these influences as system design derivatives useful to both judgmental and formal optimization purposes. The techniques facilitate decomposition of the design process into smaller, more manageable tasks and they form a methodology that can easily fit into existing engineering optimizations and incorporate their design tools.

Sobieszczanski-Sobieski, Jaroslaw↗
DOE OSTI2024

Deep kernel methods learn better: from cards to process optimization

Abstract The ability of deep learning methods to perform classification and regression tasks relies heavily on their capacity to uncover manifolds in high-dimensional data spaces and project them into low-dimensional representation spaces. In this study, we investigate the structure and character of the manifolds generated by classical variational autoencoder (VAE) approaches and deep kernel learning (DKL). In the former case, the structure of the latent space is determined by the properties of the input data alone, while in the latter, the latent manifold forms as a result of an active learning process that balances the data distribution and target functionalities. We show that DKL with active learning can produce a more compact and smooth latent space which is more conducive to optimization compared to previously reported methods, such as the VAE. We demonstrate this behavior using a simple cards dataset and extend it to the optimization of domain-generated trajectories in physical systems. Our findings suggest that latent manifolds constructed through active learning have a more beneficial structure for optimization problems, especially in feature-rich target-poor scenarios that are common in domain sciences, such as materials synthesis, energy storage, and molecular discovery. The Jupyter Notebooks that encapsulate the complete analysis accompany the article.

97 MATHEMATICS AND COMPUTING↗