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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Satellite communications provisions on NASA Ames instrumented aircraft platforms for Earth science research/applications

Earth science activities at NASA Ames are research in atmospheric and ecosystem science, development of remote sensing and in situ sampling instruments, and their integration into scientific research platform aircraft. The use of satellite communications can greatly extend the capability of these agency research platform aircraft. Current projects and plans involve satellite links on the Perseus UAV and the ER-2 via TDRSS and a proposed experiment on the NASA Advanced Communications Technology Satellite. Provisions for data links on the Perseus research platform, via TDRSS S-band multiple access service, have been developed and are being tested. Test flights at Dryden are planned to demonstrate successful end-to-end data transfer. A Unisys Corp. airborne satcom STARLink system is being integrated into an Ames ER-2 aircraft. This equipment will support multiple data rates up to 43 Mb/s each via the TDRS S Ku-band single access service. The first flight mission for this high-rate link is planned for August 1995. Ames and JPL have proposed an ACTS experiment to use real-time satellite communications to improve wildfire research campaigns. Researchers and fire management teams making use of instrumented aircraft platforms at a prescribed burn site will be able to communicate with experts at Ames, the U.S. Forest Service, and emergency response agencies.

Shameson, L.↗
NASA NTRS2004

New Screening Test Developed for the Blanching Resistance of Copper Alloys

NASA's extensive efforts towards more efficient, safer, and more affordable space transportation include the development of new thrust-cell liner materials with improved capabilities and longer lives. For rocket engines fueled with liquid hydrogen, an important metric of liner performance is resistance to blanching, a phenomenon of localized wastage by cycles of oxidation-reduction due to local imbalance in the oxygen-fuel ratio. The current liner of the Space Shuttle Main Engine combustion chamber, a Cu-3Ag-0.5Zr alloy (NARloy-Z) is degraded in service by blanching. Heretofore, evaluating a liner material for blanching resistance involved elaborate and expensive hot-fire tests performed on rocket test stands. To simplify that evaluation, researchers at the NASA Glenn Research Center developed a screening test that uses simple, in situ oxidation-reduction cycling in a thermogravimetric analyzer (TGA). The principle behind this test is that resistance to oxidation or to the reduction of oxide, or both, implies resistance to blanching. Using this test as a preliminary tool to screen alloys for blanching resistance can improve reliability and save time and money. In this test a small polished coupon is hung in a TGA furnace at the desired (service) temperature. Oxidizing and reducing gases are introduced cyclically, in programmed amounts. Cycle durations are chosen by calibration, such that all copper oxides formed by oxidation are fully reduced in the next reduction interval. The sample weight is continuously acquired by the TGA as usual.

Thomas-Ogbuji, Linus U.↗
DOE OSTI2020

Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization

Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.

42 ENGINEERING↗
NASA NTRS2017

Investigating Access Performance of Long Time Series with Restructured Big Model Data

Data sets generated by models are substantially increasing in volume, due to increases in spatial and temporal resolution, and the number of output variables. Many users wish to download subsetted data in preferred data formats and structures, as it is getting increasingly difficult to handle the original full-size data files. For example, application research users such as those involved with wind or solar energy, or extreme weather events are likely only interested in daily or hourly model data at a single point (or for a small area) for a long time period, and prefer to have the data downloaded in a single file. With native model file structures, such as hourly data from NASA Modern-Era Retrospective analysis for Research and Applications Version-2 (MERRA-2), it may take over 10 hours for the extraction of parameters-of-interest at a single point for 30 years. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is exploring methods to address this particular user need. One approach is to create value-added data by reconstructing the data files. Taking MERRA-2 data as an example, we have tested converting hourly data from one-day-per-file into different data cubes, such as one-month, or one-year. Performance is compared for reading local data files and accessing data through interoperable services, such as OPeNDAP. Results show that, compared to the original file structure, the new data cubes offer much better performance for accessing long time series. We have noticed that performance is associated with the cube size and structure, the compression method, and how the data are accessed. An optimized data cube structure will not only improve data access, but also may enable better online analysis services

reanalysis↗
DOE OSTI2015

Analysis of the Effects of a Flexible Ramping Ancillary Service Product on Power System Operations: Preprint

The recent increased interest in utilizing variable generation (VG) resources such as wind and solar in power systems has motivated investigations into new operating procedures. Although these resources provide desirable value to a system (e.g., no fuel costs or emissions), interconnecting them provides unique challenges. Their variable, non-controllable nature in particular requires significant attention, because it directly results in increased power system variability and uncertainty. One way to handle this is via new operating reserve schemes. Operating reserves provide upward and downward generation and ramping capacity to counteract uncertainty and variability prior to their realization. For instance, uncertainty and variability in real-time dispatch can be accounted for in the hour-ahead unit commitment. New operating reserve methodologies that specifically account for the increased variability and uncertainty caused by VG are currently being investigated and developed by academia and industry. This paper examines one method inspired by the new operating reserve product being proposed by the California Independent System Operator. The method is based on examining the potential ramping requirements at any given time and enforcing those requirements via a reserve demand curve in the market-clearing optimization as an additional ancillary service product.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1984

Report of the Panel on Materials

Materials and manufacturing technology are critical to advanced aircraft and permeate all disciplines. Current aircraft systems employ a variety of materials, each selected to provide the best vehicle design in terms of performance, safety, reliability, manufacturability, and life cycle cost. However, a mistake in materials selection could bankrupt an airframe or engine manufacturer. Thus, the introduction of new materials is a slow process. Generally, new materials are used initially in noncritical components until their performance in service can be confirmed. Then, as confidence grows, they are used in more and more critical applications. Finally, if appropriate, new materials are used in critical, static elements and then in dynamic components. Thus, because the nominal time for development of a conventional monolithic material ranges from 5 to 10 years there is a 10- to 15- year lag between laboratory effort and introduction into service. Therefore, to assure the availability of materials suitable for production aircraft and engines in the year 2000, the concepts already must have been identified and must be progressing along evolutionary paths toward application. Trends, actual and projected, in the use of materials for commercial engines are shown in Figure 4-1. Related military applications are projected to follow similar paths. The new actor will be composites. The panel examined a wide range of materials important to all aspects of aircraft development, airframe structures, propulsion systems and for other important aircraft subsystems. These are addressed in the body of the report in terms of the current state of the art, opportunities for improvement, and barriers to achievement of projected benefits. This is followed by projections of the progress of technology that could be realized by the year 2000 with the application of appropriate resources.

Diefendorf, Russell J.↗
NASA NTRS2016

CDDIS: NASA's Archive of Space Geodesy Data and Products Supporting GGOS

The Crustal Dynamics Data Information System (CDDIS) supports data archiving and distribution activities for the space geodesy and geodynamics community. The main objectives of the system are to store space geodesy and geodynamics related data and products in a central archive, to maintain information about the archival of these data,to disseminate these data and information in a timely manner to a global scientific research community, and provide user based tools for the exploration and use of the archive. The CDDIS data system and its archive is a key component in several of the geometric services within the International Association of Geodesy (IAG) and its observing systemthe Global Geodetic Observing System (GGOS), including the IGS, the International DORIS Service (IDS), the International Laser Ranging Service (ILRS), the International VLBI Service for Geodesy and Astrometry (IVS), and the International Earth Rotation and Reference Systems Service (IERS). The CDDIS provides on-line access to over 17 Tbytes of dataand derived products in support of the IAG services and GGOS. The systems archive continues to grow and improve as new activities are supported and enhancements are implemented. Recently, the CDDIS has established a real-time streaming capability for GNSS data and products. Furthermore, enhancements to metadata describing the contents ofthe archive have been developed to facilitate data discovery. This poster will provide a review of the improvements in the system infrastructure that CDDIS has made over the past year for the geodetic community and describe future plans for the system.

CNS↗
DOE OSTI2024

Lignin molecular weights of Populus trichocarpa residues after CELF pretreatment

Here we present a dataset of molecular weights of lignin from a woody energy crop (Populus trichocarpa) residues after a series of co-solvent enhanced lignocellulosic fractionation (CELF) pretreatment. The natural poplar variant GW-9947 from the Center for Bioenergy Innovation (CBI) was used. The poplar was knife milled and passed through a 1 mm sieve and CELF pretreatment was performed in a Parr autoclave reactor with 7.5 wt % solids loading, 0.5 wt% H2SO4 as catalyst at 150°C with various time. Tetrahydrofuran was added in a 1:1 mass ratio with water as the pretreatment solvent. Lignin was isolated from the pretreated samples after ball-milling in a porcelain jar with ceramic balls via Retsch PM 200 at 580 rpm for 2.5 h followed by enzymatic hydrolysis in acetate buffer (pH 4.8, 50 °C) for 48 h. The solid residue was isolated by centrifugation and hydrolyzed again with freshly added buffer and enzymes for another 48 h. After filtration, the solid residue was extracted twice with 96% (v/v) 1,4-dioxane/water mixture at room temperature overnight. The extracts were combined, rotary evaporated, and freeze-dried to recover lignin. The lignin samples were then derivatized in an acetic anhydride/pyridine (1:1, v/v) mixture and stirred at room temperature for 24 h. Ethanol was added to the reaction mixture, left for 30 min and then removed with a rotary evaporator. The addition and removal of ethanol was repeated at least 3 times until all traces of acetic acid were removed. Acetylated lignin samples were then dissolved in tetrahydrofuran (THF) at a concentration of 1.0 mg/mL. The molecular weight of acetylated lignin was measured by a gel permeation chromatography (GPC) on a PSS-Polymer Standards Service (Warwick, RI, USA) GPC SECurity 1200 system featuring Agilent HPLC 1200 components equipped with four Waters Styragel columns (HR1, HR2, HR4 and HR6) and an UV detector (270 nm). Tetrahydrofuran was used as the mobile phase and flow rate was 0.3 mL/min. The Polymer Standards Service WinGPC Unity software (Build 6807) was used for data processing for all the samples. The data provides information about the effects of CELF pretreatment time at 150 ºC on lignin molecular weights.

09 BIOMASS FUELS↗
DOE OSTI2021

SunLine Transit Agency Fuel Cell Electric Bus Progress Report (Data Period Focus Jan-Jun 2020) [Slides]

This report presents early results from a deployment of fuel cell electric buses (FCEBs) operated by SunLine Transit Agency in the Coachella Valley area of California. The five FCEBs, produced by New Flyer, feature an electric drive propulsion system powered by a Ballard fuel cell system. The project team is collaborating with the U.S. Department of Energy (DOE) and DOE's National Renewable Energy Laboratory (NREL) to evaluate the buses in revenue service. The goal of this evaluation is to compare the FCEB performance to that of conventional technology and to track progress over time toward meeting the technical targets set by DOE and the Department of Transportation (DOT). The FCEBs were delivered beginning in mid-2019. The data period covers January 2020 through July 2020. NREL collects data on five 2019 model year compressed natural gas (CNG) buses as a baseline comparison at SunLine. These new CNG buses were phased into service beginning in April 2020.

25 ENERGY STORAGE↗
DOE OSTI2022

SunLine Transit Agency Fuel Cell Electric Bus Progress Report - Data Period Focus: Jan. 2020 through Dec. 2021 [Slides]

This report presents early results from a deployment of fuel cell electric buses (FCEBs) operated by SunLine Transit Agency in the Coachella Valley area of California. The five FCEBs, produced by New Flyer, feature an electric drive propulsion system powered by a Ballard fuel cell system. The project team is collaborating with the U.S. Department of Energy (DOE) and DOE's National Renewable Energy Laboratory (NREL) to evaluate the buses in revenue service. The goal of this evaluation is to compare the FCEB performance to that of conventional technology and to track progress over time toward meeting the technical targets set by DOE and the Department of Transportation (DOT). The FCEBs were delivered beginning in mid-2019. The data period covers January 2020 through December 2021. NREL collects data on five 2019 model year compressed natural gas (CNG) buses as a baseline comparison at SunLine. These new CNG buses were phased into service beginning in April 2020.

08 HYDROGEN↗
DOE OSTI2020

The impact of autonomous vehicles on commute ridesharing with uncertain work end time

The uncertainty in work end time can prevent ridesharing between two commuters with identical home and work locations. This effect can be alleviated by autonomous vehicles (AV): when two commuters share a ride from home to work, if the realized work end times result in a long wait between the two, an additional AV can be easily repositioned from home to work, whereas with regular human-driven vehicles one has to either bear the long wait or request expensive taxi service. This observation implies that AVs have great potential for increasing commute ridesharing. To study this effect, we develop evening commute bottleneck models with uncertain work end time and establish mode choice equilibrium for the round-trip commute. We find that, with regular vehicles, because of the expected waiting cost caused by work end time uncertainty, commuters typically do not share rides even though they have the same home and work locations. With AVs, commuters always share rides for the morning commute, and they also share rides for the evening commute if their work end times turn out to be close; otherwise, they reposition an additional AV to pick one of them up. Finally, our numerical examples show that AVs can encourage commute ridesharing and significantly reduce the expected round-trip travel costs for commuters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Moore vs. Murphy: Tradeoffs between complexity and reliability in distributed energy system scheduling using software-as-a-service

Software-based optimization of building control strategies, including scheduling, has the potential to improve the performance of existing complex heating, ventilation, and air conditioning (HVAC), storage, and other systems—especially if temporally variable energy production, such as solar thermal or photovoltaics, is included. If reductions in energy bills can be achieved using optimized control strategies that take advantage of cost-saving opportunities, such as time-of-use pricing, the additional bill savings can cover further efficiency investment costs. As computer processing becomes cheaper over time (Moore's Law), opportunities to perform complex control optimization become more abundant, and these can be performed remotely as software-as-a-service (SaaS). However, by “perfecting” our control strategies, we run an increased risk that when something unexpected happens (Murphy's Law), the consequences of failure are greater. This study used simulation to explore the potential benefits of HVAC schedule optimization, delivery, and implementation using a SaaS paradigm, at various levels of complexity. Implementing optimal schedules in a model of an efficient building's HVAC system, the study predicts energy cost savings of up to 10% compared to the naïve reference control strategy. Optimizing more system control variables increases the potential energy cost savings; however, these savings could be compromised by failures in communication inherent in delivering schedules via SaaS. The additional cost of energy resulting from the risk of increased demand charges generally increased with increased communication failure to a much larger extent than the risk of increased energy use charges. This work suggests that moderate improvements in performance, achieved at low cost by simple means, may be more effective than highly optimized schemes, which are more susceptible to failure due to their dependence on complex interactions between systems.

42 ENGINEERING↗
DOE OSTI2019

Modeling and Analysis of a Fast Charging Station and Evaluation of Service Quality for Electric Vehicles

The deployment of public charging infrastructure networks has been a major factor in enabling electric vehicle (EV) technology transition and must continue to support the adoption of this technology. DC fast charging (DCFC) increases customer convenience by lowering charging time, enables long-distance EV travel, and could allow the electrification of high-mileage fleets. Yet, high capital costs and uneven power demand have been major challenges to the widespread deployment of DCFC stations. There is a need to better understand DCFC stations' loading and customer service quality. Furthermore, the relationship between the initial investment decision on building certain number of ports and customer satisfaction should be quantified. This paper aims to analyze these aspects using one million vehicle days of travel data within the Columbus, OH, USA, region. Monte Carlo analysis is carried out in three types of areas - urban, suburban, and rural - to quantify the effect of uncertain parameters on DCFC station loading and service quality. Additional simulations based on a homogeneous vehicle population are carried out, and closed-form equations are derived therefrom to estimate charging duration and waiting time in the queue. Optimization of DCFC station design is also addressed through the number and capacity of ports.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Adaptive Algebraic Derivative Estimation for Battery Electric Buses Energy Consumption Forecasting

The limited service life of onboard batteries for EVs is a challenge, underscoring the need for real-time battery usage prediction. This paper proposes an adaptive Algebraic Derivative Estimation (ADE) approach for forecasting the energy consumption of battery electric buses. By dynamically adjusting the sliding window length, the adaptive ADE retains the fixed-length ADE’s key advantage—namely, operating online without reliance on extensive historical datasets—while substantially bolstering forecast accuracy by actively trading estimation bias off estimation variance. Comparative experiments against both the conventional ADE with a fixed length and a representative machine learning algorithm, XGBoost, were conducted, with performance evaluated via root mean square error, mean absolute error, and the coefficient of determination. The results demonstrate that the proposed approach significantly outperforms baseline methods.

Cui, Tianyang [The University of Texas at Dallas]↗
DOE OSTI2026

Conserving asphalt resources: Rethinking rejuvenator performance evaluation through peptizing efficiency

Timely rejuvenation and restoration of asphalt are essential conservation practices that help preserve and extend the service life of roads, bridges, driveways, and parking lots. The performance of asphalt rejuvenators is often assessed based on their diffusion rates and softening power, yet these metrics alone fail to capture true rejuvenation potential. Here, this study integrates density functional theory (DFT) modeling with experimental analysis to show that rejuvenation effectiveness is primarily governed by molecular interactions with oxidized asphaltene nanoaggregates. DFT results revealed that amide- and unsaturated-chain compounds, such as hexadecanamide, oleic acid, and 9,17-octadecadienal, act cooperatively to disrupt π–π stacking and exfoliate asphaltene layers, reducing binding strength and enhancing dispersion. Unlike bulky, rigid molecules that remain trapped, these components remain mobile and repeatedly interact with multiple aggregation sites. Experimental validation using rheometry and FTIR confirmed that such molecularly compatible rejuvenators restore the binder flexibility and polydispersity, even when diffusion is relatively slow. Building on this mechanistic foundation, six rejuvenators (A2, A5, A7, A8, A9, and A10) were evaluated using a three-metric performance framework encompassing cracking resistance (Glover–Rowe parameter), UV stability, and surface hydrophobicity. Among the six rejuvenators evaluated, A9 exhibited the highest overall performance, reducing the Glover–Rowe cracking parameter by 85% (from 351 kPa to 53 kPa), demonstrating the greatest resistance to UV-induced aging with a stability index of 4.17 h·kPa⁻¹, and increasing surface hydrophobicity to a contact angle of 103.6°. Its superior performance is primarily attributed to its amide- and unsaturated-chain components, which act cooperatively to disrupt π–π stacking interactions and exfoliate asphaltene layers, thereby promoting molecular deagglomeration and enhancing long-term durability. These results shift the criteria for selecting rejuvenators: effective candidates must pair the electronic capability to unlock aged asphaltenes with sufficient structural stability to resist secondary aging and restore hydrophobicity. Restoring hydrophobicity is critical, as aging reduces the asphalt’s water repellency and increases water diffusion, which in turn accelerates moisture-related damage; consequently, a high-performing rejuvenator must effectively restore the binder’s hydrophobic characteristic. Collectively, these findings provide a framework for the rational design of next-generation of bio-based rejuvenators that enhance pavement longevity and promote long-term sustainability.

Aging↗
DOE OSTI2015

PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data

Geospatial intelligence has traditionally relied on the use of archived and unvarying data for planning and exploration purposes. In consequence, the tools and methods that are architected to provide insight and generate projections only rely on such datasets. Albeit, if this approach has proven effective in several cases, such as land use identification and route mapping, it has severely restricted the ability of researchers to inculcate current information in their work. This approach is inadequate in scenarios requiring real-time information to act and to adjust in ever changing dynamic environments, such as evacuation and rescue missions. In this work, we propose PlanetSense, a platform for geospatial intelligence that is built to harness the existing power of archived data and add to that, the dynamics of real-time streams, seamlessly integrated with sophisticated data mining algorithms and analytics tools for generating operational intelligence on the fly. The platform has four main components – i) GeoData Cloud – a data architecture for storing and managing disparate datasets; ii) Mechanism to harvest real-time streaming data; iii) Data analytics framework; iv) Presentation and visualization through web interface and RESTful services. Using two case studies, we underpin the necessity of our platform in modeling ambient population and building occupancy at scale.

big data architecture↗
DOE OSTI2021

Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service

Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO 2 emission annually.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗