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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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Analyzing urban ecosystem variation in the City of Dongguan: A stepwise cluster modeling approach

Highlights: • A stepwise cluster modeling approach is proposed for urban ecosystem simulation. • Nonlinear relationship is established among NDVI and multiple drivers. • Vegetation conditions get better gradually from the north to the south of Dongguan. • Ecosystem in the east of Dongguan is sensitive to growth of economy and population. In this study, a stepwise cluster modeling approach (SCMA) is developed for analyzing urban ecosystem variation via Normalized Difference Vegetation Index (NDVI). NDVI is an indicator of vegetation growth and coverage and useful in reflecting urban ecosystem. SCMA is established on a cluster tree that can characterize the complex relationship between independent and dependent variables. SCMA is applied to the City of Dongguan for simulating the urban NDVI and identifying associated drivers of human activity, topography and meteorology without specific functions. Results show that SCMA performances better than conventional statistical methods, illustrating the ability of SCMA in capturing the complex and nonlinear features of urban ecosystem. Results disclose that human activities play negative effects on NDVI due to the destruction of green space for pursuing more space for buildings. NDVI reduces gradually from the south part to the north part of Dongguan due to increased gross domestic product and population density, indicating that the ecosystem in Dongguan is better in the south part. NDVI in the northeast part (dominated by agriculture) is sensitive to the growth of economy and population. More attention should be paid to this part for sustainable development, such as increasing afforestation, planting grass and constructing parks. Precipitation has a positive effect on NDVI due to the promotion of soil moisture that is beneficial to plants’ growth. Awareness of these complexities is helpful for sustainable development of urban ecosystem.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Kip, Version 1.0

This high-performance ray tracing library provides very fast rendering, compact code, type flexibility via C++ 'generic programming' techniques, and ease of use through an application programming interface (API) that functions independently of any GUI, on-screen display, or other enclosing application. Kip supports constructive solid geometry models based on any of several built-in shapes and operators and allows for new, user-defined shapes and operators. Additional features include basic texturing, input/output of models using a simple human-readable file format with full error checking and detailed diagnostics, and support for shared data parallelism. Kip is written in pure, ANSI Standard C++, is entirely platform independent, and is very easy to use, as it requires no build system, configuration or installation scripts, wizards, special preprocessing, makefiles, or external libraries.

Staley, Martin↗
DOE OSTI2023

An extended focused assessment with sonography in trauma ultrasound tissue-mimicking phantom for developing automated diagnostic technologies

Medical imaging-based triage is critical for ensuring medical treatment is timely and prioritized. However, without proper image collection and interpretation, triage decisions can be hard to make. While automation approaches can enhance these triage applications, tissue phantoms must be developed to train and mature these novel technologies. Here, we have developed a tissue phantom modeling the ultrasound views imaged during the enhanced focused assessment with sonography in trauma exam (eFAST). The tissue phantom utilized synthetic clear ballistic gel with carveouts in the abdomen and rib cage corresponding to the various eFAST scan points. Various approaches were taken to simulate proper physiology without injuries present or to mimic pneumothorax, hemothorax, or abdominal hemorrhage at multiple locations in the torso. Multiple ultrasound imaging systems were used to acquire ultrasound scans with or without injury present and were used to train deep learning image classification predictive models. Performance of the artificial intelligent (AI) models trained in this study achieved over 97% accuracy for each eFAST scan site. We used a previously trained AI model for pneumothorax which achieved 74% accuracy in blind predictions for images collected with the novel eFAST tissue phantom. Grad-CAM heat map overlays for the predictions identified that the AI models were tracking the area of interest for each scan point in the tissue phantom. Overall, the eFAST tissue phantom ultrasound scans resembled human images and were successful in training AI models. Tissue phantoms are critical first steps in troubleshooting and developing medical imaging automation technologies for this application that can accelerate the widespread use of ultrasound imaging for emergency triage.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1996

Downsizing Visualization Platforms: From Crays to Indigos and Beyond

Despite its increasingly negative connotation in other domains, "downsizing" is a positive trend as it applies to the computer hardware necessary to perform scientific visualization. In this talk, we will consider how the visualization of a particular data set, the Digital Terrain Model (DTM) derived from the Viking Orbiter imagery, has been realized in three distinct projects over the past decade. These examples serve to demonstrate how the vast improvements in computational performance both decrease the cost of such visualization efforts, and permit an increasing level of interactivity. We then consider how even today's graphical systems require the visualization designer to make intelligent choices and trade-offs in database rendering. Finally, we discuss how insights from an understanding of human visual perception can guide these design decisions, and suggest new options for visualization hardware and software.

Kaiser, Mary K.↗
NASA NTRS2008

Engineering America's Current and Future Space Transportation Systems: 50 Years of Systems Engineering Innovation for Sustainable Exploration

Over the past 50 years, the National Aeronautics and Space Administration (NASA) has delivered space transportation solutions for America's complex missions, ranging from scientific payloads that expand knowledge, such as the Hubble Space Telescope, to astronauts and lunar rovers destined for voyages to the Moon. Currently, the venerable Space Shuttle, which has been in service since 1981, provides the United States' (U.S.) capability for both crew and heavy cargo to low-Earth orbit to' construct the International Space Station, before the Shuttle is retired in 2010. In the next decade, NASA will replace this system with a duo of launch vehicles: the Ares I Crew Launch Vehicle and the Ares V Cargo Launch Vehicle (Figure 1). The goals for this new system include increased safety and reliability coupled with lower operations costs that promote sustainable space exploration for decades to come. The Ares I will loft the Orion Crew Exploration Vehicle, while the heavy-lift Ares V will carry the Altair Lunar Lander and the equipment and supplies needed to construct a lunar outpost for a new generation of human and robotic space pioneers. This paper will provide details of the in-house systems engineering and vehicle integration work now being performed for the Ares I and planned for the Ares V. It will give an overview of the Ares I system-level test activities, such as the ground vibration testing that will be conducted in the Marshall Center's Dynamic Test Stand to verify the integrated vehicle stack's structural integrity and to validate computer modeling and simulation (Figure 2), as well as the main propulsion test article analysis to be conducted in the Static Test Stand. These activities also will help prove and refine mission concepts of operation, while supporting the spectrum of design and development work being performed by Marshall's Engineering Directorate, ranging from launch vehicles and lunar rovers to scientific spacecraft and associated experiments. Ultimately, fielding a robust space transportation solution that will carry international explorers and essential payloads will pave the way for a new century of scientific discovery beyond planet Earth.

Dmbacher, Daniel L.↗
NASA NTRS2016

Lunar COTS: Using the Moon's Resources to Enable An Economical and Sustainable Pathway to Mars and Beyond

To support NASAs goal of sending humans to Mars, a new plan was constructed to develop and demonstrate cislunar capabilities and services in partnership with commercial industry using the well-proven Commercial Orbital Transportation Services (COTS) Program acquisition model. The NASA COTS Program was a very successful program that developed and demonstrated cost-effective commercial cargo transportation services to the International Space Station (ISS). As a result of NASAs COTS program, two new launch vehicles and spacecraft were developed and have been successfully performing cargo transportation missions to the ISS since 2012. The COTS acquisition strategy utilized a new model than normally accepted in traditional procurement practices. This new model used Space Act Agreements where NASA entered into partnerships with industry to jointly share cost, development and operational risks to demonstrate new capabilities for mutual benefit. This model proved to be very beneficial to both NASA and its industry partners as NASA saved significantly in development and operational costs, as much as tenfold, while industry partners successfully expanded their market share of the global launch transportation business for significant economic benefit. Using the COTS acquisition model as a basis, a new plan, notionally referred to as Lunar Commercial Orbital Transfer Services (or Lunar COTS), has been developed to determine the potential benefits and challenges of a new Lunar COTS plan[1]. The proposed plan includes low-cost, commercial-enabled missions to prospect for resources, determine the economic viability of extracting those resources and assess the value proposition of using these resources in future exploration architectures such as Mars. These missions would be accomplished in partnership with industry to meet these exploration goals but will also have the capability to carry payloads to meet science goals as well.

Zuniga, Allison↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden Szeto↗
NASA NTRSDate not supplied

Transcriptomic Analysis of ISS Crewmembers’ Peripheral Blood Mononuclear Cells Reveals Homeostatic Regulations in Space

The impact of spaceflight on the immune system has been investigated for decades. Studies conducted in cell models, animals and humans suggest that the spaceflight environment affects the innate and acquired immune systems, as the ability to recognize antigens, defend against foreign invaders, and orchestrate repair is significantly hindered. However, the molecular mechanisms behind spaceflight-induced immune dysregulations are still unclear. In this study, blood from eleven (11) International Space Station (ISS) crewmembers was collected before, during and after long duration space missions, as well as from 11 matched ground control subjects. Transcriptomic analysis was performed in isolated peripheral blood mononuclear cells (PBMCs) using the RNA-sequencing technique. In comparison to the blood samples collected from the crewmembers pre-flight, a total of ~1000 genes were found to be upregulated and ~1000 genes downregulated in PBMC collected between 4 and 6 months after they were in space. The most significantly DEGs (differentially expressed genes) include activation of RUBCNL which is an autophagy enhancer and inhibition of GRASP which regulates cell trafficking. Genes involved in cell adhesion, cell cycle progression and other functions were also dysregulated. Pathway analysis of the DEGs indicates mitochondria dysfunction, particularly reduced ATP production in the electron transport chain. Other pathways impacted by spaceflight include glycolysis, autophagy and inflammatory response. Our results suggest that, in space, blood cells may have also experienced energy depletion and reduced metabolism. Consequently, the cells may become autophagic, which is a known homeostatic mechanism for blood cells to become quiescent, but to stay alive. Further analysis of the data shows recovery of the crewmembers after mission and potential differential responses between genders to the space environment. Our data potentially explains some of the physiological changes that have been observed in space such as mitochondria dysfunction, inhibition of T cell activation and telomere lengthening. Comparison of our results with other transcriptomics studies of ISS crewmembers’ blood cells will also be presented.

Maria Moreno Villanueva↗
NASA NTRS2001

Switching between simple cognitive tasks: the interaction of top-down and bottom-up factors

How do top-down factors (e.g., task expectancy) and bottom-up factors (e.g., task recency) interact to produce an overall level of task readiness? This question was addressed by factorially manipulating task expectancy and task repetition in a task-switching paradigm. The effects of expectancy and repetition on response time tended to interact underadditively, but only because the traditional binary task-repetition variable lumps together all switch trials, ignoring variation in task lag. When the task-recency variable was scaled continuously, all 4 experiments instead showed additivity between expectancy and recency. The results indicated that expectancy and recency influence different stages of mental processing. One specific possibility (the configuration-execution model) is that task expectancy affects the time required to configure upcoming central operations, whereas task recency affects the time required to actually execute those central operations.

Cognition↗
DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗
DOE OSTI2025

Report of the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science

This report summarizes insights from the 2025 Workshop on Next-Generation Ecosystems for Scientific Computing: Harnessing Community, Software, and AI for Cross-Disciplinary Team Science, which convened more than 40 experts from national laboratories, academia, industry, and community organizations to chart a path toward more powerful, sustainable, and collaborative scientific software ecosystems. To address urgent challenges at the intersection of high-performance computing (HPC), AI, and scientific software, participants envisioned agile, robust ecosystems built through socio-technical co-design—the intentional integration of social and technical components as interdependent parts of a unified strategy. This approach combines advances in AI, HPC, and software with new models for cross-disciplinary collaboration, training, and workforce development. Key recommendations include building modular, trustworthy AI-enabled scientific software systems; enabling scientific teams to integrate AI systems into their workflows while preserving human creativity, trust, and scientific rigor; and creating innovative training pipelines that keep pace with rapid technological change. Pilot projects were identified as near-term catalysts, with initial priorities focused on hybrid AI/HPC infrastructure, cross-disciplinary collaboration and pedagogy, responsible AI guidelines, and prototyping of public-private partnerships. This report presents a vision of next-generation ecosystems for scientific computing where AI, software, hardware, and human expertise are interwoven to drive discovery, expand access, strengthen the workforce, and accelerate scientific progress.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Information processing

Theoretical models of sensory-information processing by the human brain are reviewed from a human-factors perspective, with a focus on their implications for aircraft and avionics design. The topics addressed include perception (signal detection and selection), linguistic factors in perception (context provision, logical reversals, absence of cues, and order reversals), mental models, and working and long-term memory. Particular attention is given to decision-making problems such as situation assessment, decision formulation, decision quality, selection of action, the speed-accuracy tradeoff, stimulus-response compatibility, stimulus sequencing, dual-task performance, task difficulty and structure, and factors affecting multiple task performance (processing modalities, codes, and stages).

Wickens, Christopher D.↗
NASA NTRS1995

Model-based VQ for image data archival, retrieval and distribution

An ideal image compression technique for image data archival, retrieval and distribution would be one with the asymmetrical computational requirements of Vector Quantization (VQ), but without the complications arising from VQ codebooks. Codebook generation and maintenance are stumbling blocks which have limited the use of VQ as a practical image compression algorithm. Model-based VQ (MVQ), a variant of VQ described here, has the computational properties of VQ but does not require explicit codebooks. The codebooks are internally generated using mean removed error and Human Visual System (HVS) models. The error model assumed is the Laplacian distribution with mean, lambda-computed from a sample of the input image. A Laplacian distribution with mean, lambda, is generated with uniform random number generator. These random numbers are grouped into vectors. These vectors are further conditioned to make them perceptually meaningful by filtering the DCT coefficients from each vector. The DCT coefficients are filtered by multiplying by a weight matrix that is found to be optimal for human perception. The inverse DCT is performed to produce the conditioned vectors for the codebook. The only image dependent parameter used in the generation of codebook is the mean, lambda, that is included in the coded file to repeat the codebook generation process for decoding.

Manohar, Mareboyana↗
NASA NTRS2006

Human Immune Function and Microbial Pathogenesis in Human Spaceflight

This oral presentation was requested by Conference conveners. The requested subject is microbial risk assessment considering changes in the human immune system during flight and microbial diversity of environmental samples aboard the International Space Station (ISS). The presentation will begin with an introduction discussing the goals and limitations of microbial risk assessment during flight. The main portion of the presentation will include changes in the immune system that have been published, historical data from microbial analyses, and initial modeling of the environmental flora aboard ISS. The presentation will conclude with future goals and techniques to enhance our ability to perform microbial risk assessment on long duration missions.

Pierson, Duane J.↗
NASA NTRSDate not supplied

Crew Autonomy through Self-Scheduling: Scheduling Performance Pilot Study

Within the domain of human spaceflight, crew scheduling for International Space Station (ISS) remains a human-driven planning task. Large teams of flight controllers (called Ops Planners) spend weeks creating violation-free schedules for all crewmembers. As NASA considers long-duration exploration missions, the necessary shift of scheduling and planning management from Ops Planners to crew members requires significant research and investigation of crew performance to complete these scheduling tasks. This pilot study was conducted to evaluate non-expert human performance for the task of planning and scheduling, focusing on scheduling problems that increased in complexity based on the number of activities to be scheduled and the number of planning constraints. Nine non-expert planners were recruited to complete scheduling tasks using Playbook, a scheduling software. The results of this pilot study show that scheduling performance decreased as scheduling workload (i.e. number of activities and percent of activities with planning constraints) increased. This paper provides evidence towards developing a model for scheduling task difficulty and identifies potential implications for future automated aids for flight crew scheduling.

planning↗
NASA NTRSDate not supplied

Crew Autonomy Through Self-Scheduling: Scheduling Performance Pilot Study

Within the domain of human spaceflight, crew scheduling for International Space Station (ISS) remains a human-driven planning task. Large teams of flight controllers (called Ops Planners) spend weeks creating violation-free schedules for all crewmembers. As NASA considers long-duration exploration missions, the necessary shift of scheduling and planning management from Ops Planners to crew members requires significant research and investigation of crew performance to complete these scheduling tasks. This pilot study was conducted to evaluate non-expert human performance for the task of planning and scheduling, focusing on scheduling problems that increased in complexity based on the number of activities to be scheduled and the number of planning constraints. Nine non-expert planners were recruited to complete scheduling tasks using Playbook, a scheduling software. The results of this pilot study show that scheduling performance decreased as scheduling workload (i.e. number of activities and percent of activities with planning constraints) increased. This paper provides evidence towards developing a model for scheduling task difficulty and identifies potential implications for future automated aids for flight crew scheduling.

planning↗
NASA NTRSDate not supplied

Earth-Independent Medical Operations (EIMO) Concept of Operations

Compared to the current paradigm for crew health in low-Earth orbit and Lunar missions that rely on constant communication with Mission Control, there is an anticipated shift in medical operations for deep-space exploration missions. This shift stems from mission constraints imposed by the considerable distance from Earth, which include resource limitations due to a lack of resupply, mass, power, volume, and data limitations, challenges imposed by communication latency and the inability to evacuate in case of emergencies. To transition towards a more self-reliant medical approach, a comprehensive strategy is essential to progressively enable crew autonomy and mitigate mission success risks in the challenging environment of space. This transformative shift is collectively referred to as "Earth-Independent Medical Operations" (EIMO), signifying the gradual transfer of medical care and decision-making from terrestrial resources to space-based assets. This transition is aimed at bolstering astronaut health and performance while simultaneously reducing the overall risks associated with space missions. The constraints related to EIMO necessitate an integrated development of medical systems, featuring interoperability with mission planning, vehicle design, spacesuit design, and data architecture. This integration is vital in establishing a robust medical infrastructure that not only supports the well-being of astronauts but also ensures the success of the mission as a whole. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has developed a model-based Concept of Operations (ConOps) outlining an initial vision for EIMO. Within this ConOps, a comprehensive view is presented, encompassing stakeholder needs, system objectives, and system goals associated with EIMO. Additionally, it provides illustrative examples of the various activities (scenarios) for which the system will be employed during missions. The selection of these activities has been meticulous, aiming to encompass a wide spectrum of medical conditions, including those falling under different risk categories, such as low-likelihood-low-consequence, low-likelihood-high-consequence, and high-likelihood-low-consequence. The selection of these activities (scenarios) effectively encompasses the wide range of medical events situated within an assumed probability-consequence bell curve. In each scenario, at least one of the five main EIMO components identified is captured. Those EIMO components are: Pre-mission Planning, Acute and Emergent Management Decision Making, Prolonged Medical Management Decision Making, Supplies and Resource Management, and Task Load Management. The ConOps was developed by a multidisciplinary team consisting of systems engineers, scientists, and clinicians across NASA and aims to serve as an initial recommendation to gradually and safely enabling crew autonomy for Mars missions and beyond.

Earth Independent Medical Operations↗