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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Development, Application, and Transition of Aerosol and Trace Gas Products Derived from Next-Generation Satellite Observations to Operations

NASA Short-term Prediction Research and Transition (SPoRT) Center has a history of successfully transitioning unique observations and research capabilities to the operational weather community to improve short-term forecasts. SPoRTstrives to bridge the gap between research and operations by maintaining interactive partnerships with end users to develop products that match specific forecast challenges, provide training, and assess the products in the operational environment. This presentation focuses on recent product development, application, and transition of aerosol and trace gas products to operations for specific forecasting applications. Recent activities relating to the SPoRT ozone products, aerosol optical depth composite product, sulfur dioxide, and aerosol index products are discussed.

remote sensing↗
NASA NTRS2016

Applications of Earth Remote Sensing for Identifying Tornado and Severe Weather Damage

NASA SPoRT (Short-term Prediction Research and Transition Center) provided MODIS (Moderate Resolution Imaging Spectrometer) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery to WFOs (Weather Forecast Offices) in Alabama to support April 27th, 2011 damage assessments across the state. SPoRT was awarded a NASA Applied Science: Disasters Feasibility award to investigate the applicability of including remote sensing imagery and derived products into the NOAA/NWS (National Oceanic and Atmospheric Administration/National Weather System) Damage Assessment Toolkit (DAT). Proposal team was awarded the 3-year proposal to implement a web mapping service and associate data feeds from the USGS (U.S. Geological Survey) to provide satellite imagery and derived products directly to the NWS thru the DAT. In the United States, NOAA/NWS is charged with performing damage assessments when storm or tornado damage is suspected after a severe weather event. This has led to the development of the Damage Assessment Toolkit (DAT), an application for smartphones, tablets and web browsers that allows for the collection, geo-location, and aggregation of various damage indicators collected during storm surveys.

Satellite Remote Sensing↗
NASA NTRS2018

New, Multi-Spectral (RGB) Imagery Products from GOES-16 Usher in a New Era of Satellite Meteorology

Otherwise known as "R-G-B" imagery, these new products provide a paradigm shift in short-term nowcasting and situational awareness via remote sensing. SPoRT used NASA instruments like MODIS and S-NPP VIIRS to demonstrate RGB capabilities prior to GOES-16. Now, with geostationary RGBs in operations, SPoRT has collaborated with select forecasters to assess the impact of this new GOES-16 capability.

Fuell, Kevin K.↗
NASA NTRSDate not supplied

Dust Machine Learning Probability and Assessment

- NASA SPoRT introduced the "Dust RGB" via NASA satellites to demonstrate GOES-R ABI capabilities and then evaluated the impact in operations (Fuell et al. 2016) - The Dust RGB allows for continued dust detection at night, but the cooling ground surface limits the effectiveness as night progresses. - SPoRT has developed a 'Machine Learning' (ML) model using a physically-based approach which can correctly label 85% of dust pixels and 99% of no-dust pixels

Dust↗
NASA NTRSDate not supplied

Role of Antecedent Soil Moisture and Vegetation Stress in Lightning-Initiated Wildfires

Lightning-caused wildfires are a small percentage of all wildfire events within the Conterminous U.S. (CONUS), but they account for over 56% of the acreage burned. The atmospheric conditions favoring wildfire and rapid growth are well understood: large dewpoint depressions, unstable planetary boundary layer, strong winds, etc. However, antecedent land surface conditions affecting dead and live fuel moisture is more difficult to quantify. This study examines over 20 years of antecedent land surface, vegetation stress, and wildfire characteristic data associated with nearly 77,000 lightning-initiated wildfires from the U.S. Forest Service Wildfire Database. We will invoke two in-house databases generated by the NASA Short-term Prediction Research and Transition (SPoRT) Center: an observations-driven, climatological run of the Noah land surface model within the NASA Land Information System (i.e., SPoRT-LIS) to depict soil moisture deficits / anomalies, and a satellite-constrained Evaporative Stress Index (ESI) product to denote areas of stressed vegetation. We will mine these datasets associated with lightning-caused (and null) events to determine important relationships, distributions, and delineators that correspond to elevated threat areas for lightning-initiated wildfires.

Wildfire↗
NASA NTRSDate not supplied

Modeling and Simulation Credibility Assessment of Finite Element Models for Fall From Heights Injury Scenarios

Finite element (FE) computational models of the whole human body are being used to investigate the injury modalities and mechanisms arising from suited astronauts’ ExtraVehicular Activities (EVAs). These FE whole body models – simplified Elemance and THUMS pedestrian versions – have undergone a series of verification and validation (V&V) assessments for applications in automotive, domestic, and contact sports arenas. However, the FE models’ Modeling and Simulation (M&S) credibility assessments for simulating suited astronaut injuries due to fall from heights in altered gravity environments are limited. The current study evaluates the M&S credibility of simplified Elemance and THUMS pedestrian FE models based on eight credibility factors – data pedigree, input pedigree, code verification, solution verification, conceptual validation, referent validation, results uncertainty, and results robustness – as per NASA-STD-7009A [1]. These credibility factors were assigned ordinal scores ranging between 0 to 4, with a score of 0 in the case of insufficient evidence and 4 when all data for the model is known, used appropriately, and traceable to the Real-World System (RWS) with acceptable accuracy, precision, and uncertainty. The credibility factor sufficiency thresholds were assigned upon guidance from NASA EVA subject matter experts. The factor scores were ascertained through the evaluation of M&S credibility assessment procedures described in the published literature and FE model reports. The M&S credibility scores for the Elemance and THUMS FE models vary between 1 and 3 for data pedigree, input pedigree, and referent validation credibility factors, and between 0 and 1 for code verification, solution verification, conceptual validation, results uncertainty and results robustness. The results show that these FE models are credible for simulating fall from heights injury scenarios in specific kinematic and kinetic ranges that correlate with highway and contact sports-related external conditions. Multiple credibility elevation strategies are prescribed to improve the Elemance and THUMS models’ credibility for use in the EVA fall from height injury scenarios.

R A Perkins↗
NASA NTRSDate not supplied

Evaluating Meteorological Dust Events and Machine-Learning Based Dust Identification in Geostationary Satellite Imagery

NASA scientists in the Short-term Prediction Research and Transition Center (SPoRT) developed a physically-based machine learning approach to identify dust in satellite imagery with a focus on night-time dust detection (Berndt et al. 201; DustTracker-AI). NASA/NOAA Geostationary Environmental Operational Satellite-16 (GOES-16) imagery was used for training and model inputs. The training, testing and validation data set consists of 28 events in the Southwest United States, capturing dust and null events in the region from 2018-2020.With 83 distinct images and millions of pixels a random forest model was trained and validated, correctly labeling 85% of dust pixels.For the first time, the model was run in near-real time production during the spring of 2022 and dust probability visualizations were made available to NOAA National Weather Service (NWS) forecasters to assess its utility for dust forecasting. Results indicated the model helped increase the confidence in the presence of dust and enabled dust tracking for a longer period of time into the night-time hours. Forecaster assessment and running the model in near real-time allowed for the team to determine the types of events missed, captured, and false alarms. To gain additional context on model performance,the SPoRT team sought to gather more detailed information on the training database(e.g., meteorological characteristics and drivers). The goal of this project was to identify the meteorological drivers for the dust events and create a database which synthesized information from observations, forecaster discussions, and analyses pertaining to the dust events to understand the types of events currently used to train the model. A more detailed meteorological synopsis was created for each dust event in the training, testing, and validation datasets. Following the completion of the database and documentation, the classification details revealed that 88% of the dust events were synoptically driven while mesoscale events were less prevalent in model datasets. Meteorological conditions found such as mixing layer depth and wind velocity had mean values of 645mb and 21kt respectively.With conditions of deep mixed layers and moderate to strong surface winds a mesoscale thunderstorm outflow event was considered and subsequently added to the model training data set to test the impact of additional mesoscale training data. The model was retrained and then qualitatively tested on a sample thunderstorm outflow case that the original model was unable to identify. Preliminary results showed potential that the addition of more mesoscale events included in the training data could help to better identify indistinct and localized dust events.

Connor Welch↗
NASA NTRSDate not supplied

Hourly Stream Heights – A Short-term Deep Learning Prediction Model

River flooding can have a detrimental impact on a community by causing loss of life, loss or damage to property, and damage to infrastructure. Having the capability to forecast flooding events can prevent the loss of life and mitigate damage to property. A programmatic, data-driven approach using deep learning to forecast a stream’s gauge height every 6 hours has been developed by NASA’s Short-term Prediction Research and Transition Center (SPoRT) and is currently in operation. Based on our end user engagement and feedback, this medium-term product has been successfully adopted by several National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC). SPoRT is currently researching and developing a short-term hourly deep learning model which will be useful in forecasting flash flood events.

Michael Antia↗
NASA NTRSDate not supplied

Short-term Prediction Research and Transition Center

SPoRT: Translating science to user-centered products and capabilities that enable Earth Action. SPoRT was established in 2002 as part of the ESD R&A Weather Focus Area with the mission to accelerate the adoption of NASA Earth Science satellite data within the National Weather Service (NWS).

Emily Berndt↗
NASA NTRSDate not supplied

Development of TEMPO Products and Tools to Support Air Quality Management Decisions

The TEMPO mission has been observing air pollutants every hour during the daytime across its Field of Regard (FoR) covering greater North America since First Light on August 2, 2023. The highly anticipated public release of TEMPO data occurred on May 20, 2024, consisting of level 2 and level 3 trace gas data products of nitrogen dioxide, formaldehyde, and ozone. Our project at the NASA SPoRT Center is developing value-added products and tools to support the TEMPO mission and Early Adopters program with special attention on the air quality management community. The initial focus of this project is evaluating the TEMPO products over stakeholder target areas using Pandora and surface monitor observations. Methods for oversampling TEMPO data to 1 km resolution are being applied over the target areas to resolve fine-scale emission sources and pollutant gradients. Machine learning techniques using TEMPO, surface monitor, and model data to estimate surface-level nitrogen dioxide concentrations are being developed over the target areas. Our SPoRT viewer has been updated to include visualizations of the TEMPO products and an ArcGIS dashboard is being designed for enabling air quality management stakeholders to efficiently analyze TEMPO data. Training materials including user guides are being developed to ensure the effective and sustained use of TEMPO data in air quality management applications. The major outcome of this project is to support the inclusion of TEMPO data in exceptional event demonstrations by active engagement with stakeholders and ultimately enable more informed air quality management decisions in the future. This talk will provide an update on our project activities and showcase use cases of TEMPO data for monitoring different emission sources including wildland fire smoke.

air quality↗
NASA NTRS2026

Smart Process Planning for Automated Fiber Placement

Many industries, including aerospace, automotive, wind energy, maritime, and sporting goods, rely on strong, lightweight materials called composites. These materials are made by layering fibers, which can come in the form of narrow strips or wider sheets, and setting them in a polymer matrix. One of the most advanced ways to make these parts is through automated fiber placement, where a machine lays down the fibers in precise patterns. This method can create very efficient and strong designs, but it is complex, expensive, and often depends heavily on the experience of skilled engineers. Today, the design, manufacturing, and inspection stages of composite production are usually handled separately. This separation means that important information, such as how a part will be built or what defects might occur, is not always shared between stages. As a result, parts may not be as lightweight, strong, or defect-free as possible, and the process can take longer and cost more. This research develops a smart process planning system that connects design, manufacturing, and inspection into one continuous process. Built as software that works with existing tools, the system can automatically plan how the fibers are placed, predicting and reducing defects while improving both manufacturability and strength. The system optimizes not only individual layers but also how defects are distributed across all layers, preventing them from stacking up in ways that weaken the final part. It also uses inspection results from completed parts to improve future designs, creating a feedback loop where each stage informs the others. The system was tested by designing a composite panel using this new approach and comparing it to a panel made with state-of-the-art manual planning methods. The results showed that the system could intentionally control where defects appeared and increase the efficiency of the planning process. By unifying design, manufacturing, and inspection, this research shows a way to make advanced composite manufacturing more efficient, consistent, and cost-effective. This approach lowers the barrier to using automated fiber placement and opens the door for its wider adoption not only in aerospace but also in industries such as automotive, wind energy, maritime, and sporting goods, where strong and lightweight structures are essential.

Computer-Aided Process Planning↗
DOE OSTI2021

Modeling the Metabolic Costs of Heavy Military Backpacking

Existing predictive equations underestimate the metabolic costs of heavy military load carriage. Metabolic costs are specific to each type of military equipment, and backpack loads often impose the most sustained burden on the dismounted warfighter. This study aimed to develop and validate an equation for estimating metabolic rates during heavy backpacking for the US Army Load Carriage Decision Aid (LCDA), an integrated software mission planning tool. Thirty healthy, active military-age adults (3 women, 27 men; age, 25 ± 7 yr; height, 1.74 ± 0.07 m; body mass, 77 ± 15 kg) walked for 6–21 min while carrying backpacks loaded up to 66% body mass at speeds between 0.45 and 1.97 m·s -1 . A new predictive model, the LCDA backpacking equation, was developed on metabolic rate data calculated from indirect calorimetry. Model estimation performance was evaluated internally by k-fold cross-validation and externally against seven historical reference data sets. We tested if the 90% confidence interval of the mean paired difference was within equivalence limits equal to 10% of the measured metabolic rate. Estimation accuracy and level of agreement were also evaluated by the bias and concordance correlation coefficient (CCC), respectively. Estimates from the LCDA backpacking equation were statistically equivalent ( P < 0.01) to metabolic rates measured in the current study (bias, -0.01 ± 0.62 W·kg -1 ; CCC, 0.965) and from the seven independent data sets (bias, -0.08 ± 0.59 W·kg -1 ; CCC, 0.926). The newly derived LCDA backpacking equation provides close estimates of steady-state metabolic energy expenditure during heavy load carriage. These advances enable further optimization of thermal-work strain monitoring, sports nutrition, and hydration strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Inadvertent Hatchery Rainbow Trout Oncorhynchus mykiss Discovered in Native Westslope Cutthroat Trout Oncorhynchus clarkii lewisi Habitat: A Cautionary Tale

Abstract Fish hatcheries across North America release billions of fish into the wild every year to augment harvest opportunities in sport, commercial, and tribal fisheries, as well as to recover depressed populations. Despite these benefits, wild fish populations may be negatively affected when hatchery fish compete and/or hybridize with native species. Native Westslope Cutthroat Trout Oncorhynchus clarkii lewisi have decreased by 90% across their historic range; however, the Flathead River in Montana is a rangewide stronghold for these imperiled fish. The primary threat to Westslope Cutthroat Trout is hybridization with invasive Rainbow Trout O. mykiss, which leads to hybrid swarm formation and genomic extinction. We collected Rainbow Trout in Mill Creek, a tributary of the Flathead River, downstream from a hatchery that produces the species, and used otolith 87Sr/86Sr to determine whether individual fish were inadvertently introduced from the hatchery. Differences in 87Sr/86Sr among water samples from Mill Creek and the marine-derived hatchery food provided high-classification accuracy to determine the natal origins of Mill Creek Rainbow Trout. We found that 83 of the 106 Rainbow Trout collected (78%) were of hatchery origin. Otolith microstructure analysis indicated these fish had been in Mill Creek for ~1–32 weeks prior to capture. Our results illustrate the utility of otolith 87Sr/86Sr to identify hatchery fish in natural habitats and point to a source of invasive Rainbow Trout that pose conservation risks to native fishes in the drainage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Plug-in electric vehicles in China and the USA: a technology and market comparison

As the top two plug-in electric vehicle (PEV) markets in the world, China and the United States of America (USA) have developed different market structures that are influenced by government policies, test procedures, customer acceptance, and vehicle performance. There are differences in PEV test procedures and vehicle class definitions as well. This paper quantifies such differences and compares PEV characteristics and markets in the two countries using actual data collected over several years. First, although China surpasses the USA in annual PEV production because of generous PEV policies and higher charging infrastructure availability, the USA has a higher PEV adoption rate per capita (2.3 versus 0.81 per 1000 people at the end of 2017). Second, the most popular vehicle classes are A00 battery electric vehicles (BEVs) in China but long-range mid-size BEVs in the USA. Moreover, China's electric and plug-in hybrid sport utility vehicle market is growing quickly. Third, the electricity consumption rated under Chinese test procedures is 20%-40% lower than that rated under US procedures. The sales-weighted electricity consumption of an average BEV in China is 23% lower than that in the USA because of larger proportions of small and micro BEVs in China. Fourth, PEV battery cost in the two countries is currently close to $210-$220 kWh. Finally, China has higher numbers of charging infrastructure, and the ratio of PEVs to public chargers is 9.0 in China versus 17.9 in the USA at the end of 2018.

China EV market↗
DOE OSTI2024

Analysis of the turbostratic structures in PAN-based carbon fibers with wide-angle x-ray diffraction

Carbon fiber (CF) is a versatile material renowned for its excellent mechanical, thermal, and electrical properties. Polyacrylonitrile (PAN)-based CFs dominate the market due to their high tensile strength, rendering them suitable for structural applications in a wide variety of applications ranging from sporting goods to aerospace. Over five decades of commercial development of PAN-based CF has resulted in a range of varieties with different tensile moduli and tensile strengths. The microstructures, nanostructures, and crystal structures of PAN-based CF play pivotal roles in the macroscale properties of this material. In particular, the crystal structure and crystallite orientation in CF is closely related to the mechanical properties. The crystal structure of PAN-based CF generally consists of turbostratic carbon, which is a disordered form of graphite, the characteristics of which can be effectively characterized in a bulk format through wide-angle x-ray diffraction (WAXD). In this work, we employed a three-part approach to the analysis of WAXD patterns collected from four intermediate modulus PAN-based CFs. The approach incorporates a Scherrer analysis, a Debye analysis, and an orientational analysis to provide precise estimates of crystallite sizes, crystallite distributions, and crystallite orientations with the fiber axis. Furthermore, the results presented here suggest that intermediate modulus PAN-based CF mostly consists of small turbostratic crystallites (<4 nm), with larger crystallites having increased orientation with the fiber axis. The results here imply the presence of curvature and/or wrinkling of the turbostratic layers within the CF structure.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Evaluating consumer 3D printing nozzles as a low cost alternative for mesophase pitch-derived carbon fiber production

Synthetic fibers, such as Kevlar fibers, SiC fibers, and carbon fibers, are essential components for constructing high performance structures. Whether for engineering, sports, energy storage (batteries and supercapacitors), or aerospace applications, fiber microstructure plays a critical role in fiber properties and functionalities. However, studying fiber nozzle configurations and spinning parameters to achieve the desired microstructure remains challenging, costly, and time consuming. Here, mesophase pitch-derived fibers were used as an example to demonstrate that low cost, commercially available 3D printer nozzles can “print” fibers. Four different nozzles were used to “print” fibers and the effects of their features on fiber properties were observed and compared to other lab spun and commercial pitch- derived CF. A longer orifice length resulted in higher modulus fiber whereas a larger draw-down ratio yielded a stronger fiber. Furthermore, the findings provide a new opportunity for 3D printer hardware application and open up opportunities for developing low-cost fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The 3D printing and modeling of functionally graded Kelvin foams for controlling crushing performance

Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.

3D printing↗
DOE OSTI2025

Total cost of ownership of vehicle electrification and fuel switching options for light-duty and heavy-duty vehicles

Projecting the transition from combustion engines to battery-based powertrains is complex becuase it involves numerous interdependent decisions. This study estimates total cost of ownership (TCO) to assess the economic viability of powertrain electrification, focusing exclusively on advances in vehicle and fuel technologies. Under two bounding technology-progress scenarios, we develop vehicle designs and fuel cost trajectories, which serve as inputs to TCO projections for selected classes from 2021 to 2050. We analyzed a small sport utility vehicle (SUV) to represent the light-duty vehicle (LDV) sector, and four medium- and heavy-duty vehicle (MHDV) classes: Class 6 box delivery, Class 8 drayage, Class 8 long-haul, and Class 8 transit bus. For each class, we compared the TCO of battery electric vehicles (BEVs) and fuel cell hybrid electric vehicles (FCHEVs) against conventional internal combustion engine vehicles (ICEVs). The results show that modern ICEVs generally have lower TCO; however, BEVs and FCHEVs could match or have lower TCOs than ICEVs over time, depending on technological progress. In LDVs, BEV300 is projected to deliver the lowest TCO by 2050, particularly under the high-progress scenario. In MHDVs, both BEVs and FCHEVs could become more cost-competitive than ICEVs by 2050 in the high-progress case. Beyond these results, the findings suggest further investigation is warranted for BEV charging infrastructure, FCHEV hydrogen refueling infrastructure, and MHDV charging strategies. In conclusion, these factors could reduce the fuel-cost share of TCO and enhance the competitiveness of BEVs and FCHEVs relative to ICEVs.

Battery electric vehicle↗