SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
These case studies provide tangible examples within the development field that show how true innovation in real estate and property development hinges on shaping and optimizing diverse connections and relationships to create widely available, market-viable, and high-performance buildings.
One of the primary objectives of the Energy Storage effort is the development of durable and affordable advanced batteries (and ultracapacitors) for use in a full range of vehicle applications, from start/stop to full-power HEVs, EVs, and PHEVs. The battery technology development activity spans three areas: system development of full battery systems; benchmark testing of emerging technologies in order to remain abreast of the latest industry developments; and Small Business Innovative Research (SBIR) to fund early-stage R&D for small businesses/entrepreneurs.
Discussed in order after the introduction are solar components and systems (collectors, storage, service hot water systems, space heating with liquid and air systems, space cooling, heat pumps and controls); computer programs for system optimization; local solar and weather data; a description of buildings and plants in Southern California applying solar technology; current Federal and California solar legislation; standards, codes and performance testing information; a listing of manufacturers, distributors, and professional services available in Southern California region; and information access. Finally, solar design check lists for those engineers who wish to design their own systems. The program for the Solar Workshop for the Plant Engineer, March 30, 1978, Los Angeles, California is included.
This paper discusses power electronics developed under the ARPA-E GAMOW program to support nuclear fusion power production. The goal of this project was to develop and assess the potential for wide-bandgap (WBG) semiconductor devices in power electronics to enable high-efficiency and high-voltage solid-state systems for fusion plasma generation, heating, and control. The power electronics use an architecture in which multiple high-power boards can be combined to produce megawatt-level power, where using multiple boards provides high reliability. Two main areas of power electronics boards are developed in this project for fusion plasma heating and control applications: (1) pulse generation and control and (2) radiofrequency generation. The first area is for boards capable of driving high-voltage millisecond pulses at high duty cycles. The envisioned application of these pulses is in plasma control of magnetohydrodynamic instabilities, plasma position, and edge-localized modes. Pulse-width modulation allows for the implementation of a wide variety of linear and nonlinear control systems. The boards developed for this project could actuate control coils based on digital input signals and can be parallelized to provide megawatts of output power. The design of the pulse generator is a low-side load switch. A load switch was designed and constructed that utilized 2-kV-rated field-effect transistor (FET)-based cascodes developed by Qorvo under this project to perform initial testing of these cascodes. The second area is being implemented using class E amplifiers with WBG devices and a reactance steering network to handle inductive or capacitive plasma loads. Applications include ion cyclotron resonance heating (ICRH) and high-harmonic fast-wave (HHFW) heating. A class E reactance steering network is demonstrated in modeling and experiment with a resistive-inductive load that models an inductively-coupled plasma. Power combining of boards with class E reactance steering networks is also simulated and demonstrated experimentally, to enable scaling up to high power. Modeling of high-power-density cooling and remaining useful life is conducted to enable reliable, effectively cooled high-power electronics for fusion applications.
DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.
Not provided.
As the state of the art of the design of spacecraft has progressed, one of the difficult problems which has arisen is the positioning and articulating of spacecraft elements which must “see” in different directions without interference. Some of these elements are: solar panels or collectors, Sun sensors, communication antennas, star seekers, planet tracking and scanning devices, rocket motors, attitude-control jets, and scientific instruments. This Report presents a method of testing a spacecraft design to determine how well it satisfies these look-angle requirements. This method is applied to both simple and complex cases, and examples of its use are presented. A means of assessing spacecraft constraints on trajectories is discussed. The method was developed for the Mariner interplanetary spacecraft, but should be applicable to other cases.
Explore the source record for details and available documents.
The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La 1−x Sr x FeO 3 . We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.
Boundary-layer cloud interactions involving shortwave-absorbing aerosols remain one of the least understood aerosol influences on climate. Here, we find the highest stratocumulus cloud droplet number concentrations over the southeast Atlantic occur when agricultural fires coincide with synoptically-weakened surface warming over Angola, occurring June-early August. Dry convection fills a shallow continental boundary layer with smoke, and a nighttime (local solar time 2-9) land breeze transports the aerosol into the marine boundary layer. Offshore aerosol transport is strengthened by low-level easterlies from a continental pressure high southeast of Angola. Simultaneously, the South Atlantic subtropical high is weaker, allowing extensive dispersal of aerosol offshore into the boundary layer, obscuring cloud brightening from shipping. Meteorological co-variation at synoptic scales compensates for cloud brightening by the smoke. Outgoing shortwave radiation increases by 15–20% of the monthly mean in June and July when offshore droplet numbers are less but the stratocumulus deck is more developed.
Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.
This is the level 4 milestone report (M4) for the work package SF-25SN01030409 in the Geologic Disposal Safety Assessment (GDSA) program.
The Space Programs Summary is a six-volume bimonthly publication designed to report on JPL space exploration programs and related supporting research and advanced development projects. The titles of all volumes of the Space Programs Summary are: Vol. I. The Lunar Program (Confidential) Vol. 11. The Planetary-Interplanetary Program (Confidential) Vol. 111. The Deep Space Network (Unclassified) Vol. IV. Supporting Research and Advanced Development (Unclassified) Vol. V. Supporting Research and Advanced Development (Confidential) Vol. VI. Space Exploration Programs and Space Sciences (Unclassified) The Space Programs Summary, Vol. VI, consists of: an unclassified digest of appropriate material from Vols. I, II, and III; an original presentation of the JPL quality assurance and reliability efforts, and the environmental- and dynamic-testing facility-development activities; and a reprint of the space science instrumentation studies of Vols. I and 11. This instrumentation work is conducted by the JPL Space Sciences Division and also by individuals of various colleges, universities, and other organizations. All such projects are supported by the Laboratory and are concerned with the development of instruments for use in the NASA space flight programs.
Abstract Desert tortoises play ecologically significant roles, including plant seed dispersal and mineral cycling, and yet little is known about microbial members that are critical to their gut and overall health. Tortoises consume recalcitrant plant material, which their gut microbiota degrades and converts into usable metabolites and nutrients for the tortoise. Findings from tortoise gut microbiomes may translate well into biotechnological applications as these microbes have evolved to efficiently degrade recalcitrant substrates and generate useful products. In this study, we cultivated microbial communities from desert tortoise fecal samples following a targeted anaerobic enrichment for microbes involved in deconstruction and utilization of plant biomass. We employed 16S rRNA amplicon sequencing to compare cultivated communities to initial fecal source material and found high abundances of Firmicutes and Bacteroidota typically associated with biomass deconstruction in all cultivated samples. Significantly decreased microbial diversity was observed in the cultivated microbial communities, yet several key taxa thrived in lignocellulose enrichments, includingLachnospiraceaeandEnterococcus. Additionally, cultivated communities produced short-chain fatty acids under anaerobic conditions, and their growth and metabolic output provide evidence of their viability in the initial fecal communities. Overall, this study adds to the limited understanding of reptilian herbivore microbiota, and offers a path towards biotechnological translation based on the ability of the cultivated communities to convert lignocellulose directly to acetate, propionate, and butyrate.
In the spring of 1962, engineers from the Engineering Mechanics Division of the Jet Propulsion Laboratory gave a series of lectures on spacecraft design at the Engineering Design seminars conducted at the California Institute of Technology. Several of these lectures were subsequently given at Stanford University as part of the Space Technology seminar series sponsored by the Department of Aeronautics and Astronautics. Presented here are notes taken from these lectures. The lectures were conceived with the intent of providing the audience with a glimpse of the activities of a few mechanical engineers who are involved in designing, building, and testing spacecraft. Engineering courses generally consist of heavily idealized problems in order to allow the more efficient teaching of mathematical technique. Students, therefore, receive a somewhat limited exposure to actual engineering problems, which are typified by more unknowns than equations. For this reason it was considered valuable to demonstrate some of the problems faced by spacecraft designers, the processes used to arrive at solutions, and the interactions between the engineer and the remainder of the organization in which he is constrained to operate. These lecture notes are not so much a compilation of sophisticated techniques of analysis as they are a collection of examples of spacecraft hardware and associated problems. They will be of interest not so much to the experienced spacecraft designer as to those who wonder what part the mechanical engineer plays in an effort such as the exploration of space.
All-solid-state lithium–metal batteries have attracted significant attention, owing to their high energy density and superior safety. However, lithium–metal penetration through the solid electrolyte, leading to short-circuiting, remains a critical failure mode that demands comprehensive mitigation strategies. Most existing strategies are effective only prior to the initiation of lithium-dendrite formation and fail once dendrites begin to propagate through the electrolyte. In this study, we propose a self-healing mechanism in which the penetrated lithium reacts with a self-healing agent to form a passivating layer along the particle boundaries. Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) was incorporated into a Li 6 PS 5 Cl solid electrolyte as the self-healing agent to suppress lithium-dendrite propagation even after dendrite formation initiated under high current densities. The self-healing induced by LiTFSI was verified through comprehensive experimental analyses and was further demonstrated in a full-cell configuration. Moreover, LiTFSI incorporation plays an important role in increasing the critical current density by reducing the overall electronic conductivity of the solid electrolyte and facilitating the formation of a robust LiF-containing solid-electrolyte interphase.
Study to determine technical and financial feasibility of implementing anaerobic digestion of wastewater biosolids and food waste to produce renewable energy
Negative emissions technologies will be essential for limiting anthropogenic global temperature increases to 2 °C in the later years of the 21st century. Carbonic anhydrase (CA) metalloenzymes catalyze the otherwise slow conversion of CO 2 into carbonic acid (H 2 CO 3 ), suggesting their utility in the rapid hydration and downstream capture of dissolved CO 2 in aqueous media for a variety of CO 2 capture methods, such as thermal and pH swings and mineralization. The possibility of driving the rapid capture of CO 2 by catalyzing the CO 2 hydration bottleneck carries real potential for realizing efficient direct air capture (DAC) and direct ocean capture (DOC) systems. However, scaled application of CAs will be dependent on some way of economically sourcing the enzymes at volumes relevant to scaled DAC/DOC operations. In this perspective, we consider the prospect of catalyzing CO 2 hydration using a CA that is bound to the outer membrane of a cyanobacterial host, engineered constructs we call CyCAMs. Coupling extracellular CA expression to a marine, photosynthetic cyanobacterial host results in a CO 2 hydration catalyst that is continuously refreshed during the bacterial growth cycle, without the need for enzyme purification or harvesting steps. In the case of marine cyanobacteria, sunlight and the solutes native to seawater provide the respective energy, nutrients and sources of inorganic carbon essential to maintaining the bacterial population. We consider the potential impact of CA-mediated CO 2 hydration on a variety of DAC processes, with CO 2 mineralization explored as a primary use case.