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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Lessons Learned from the Design, Certification, and Operation of the Space Shuttle Integrated Main Propulsion System (IMPS)

The Space Shuttle Integrated Main Propulsion System (IMPS) consists of the External Tank (ET), Orbiter Main Propulsion System (MPS), and Space Shuttle Main Engines (SSMEs). The IMPS is tasked with the storage, conditioning, distribution, and combustion of cryogenic liquid hydrogen (LH2) and liquid oxygen (LO2) propellants to provide first and second stage thrust for achieving orbital velocity. The design, certification, and operation of the associated IMPS hardware have produced many lessons learned over the course of the Space Shuttle Program (SSP). A subset of these items will be discussed in this paper for consideration when designing, building, and operating future spacecraft propulsion systems. This paper will focus on lessons learned related to Orbiter MPS and is the first of a planned series to address the subject matter.

Martinez, Hugo E.↗
DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗
NASA NTRS2017

Detection of Hail Storms in Radar Imagery Using Deep Learning

In 2016, hail was responsible for 3.5 billion and 23 million dollars in damage to property and crops, respectively, making it the second costliest weather phenomenon in the United States. In an effort to improve hail-prediction techniques and reduce the societal impacts associated with hail storms, we propose a deep learning technique that leverages radar imagery for automatic detection of hail storms. The technique is applied to radar imagery from 2011 to 2016 for the contiguous United States and achieved a precision of 0.848. Hail storms are primarily detected through the visual interpretation of radar imagery (Mrozet al., 2017). With radars providing data every two minutes, the detection of hail storms has become a big data task. As a result, scientists have turned to neural networks that employ computer vision to identify hail-bearing storms (Marzbanet al., 2001). In this study, we propose a deep Convolutional Neural Network (ConvNet) to understand the spatial features and patterns of radar echoes for detecting hailstorms.

natural hazard↗
DOE OSTI2025

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles

Iron carbide (Fe x C y ) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe 182 C 88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe 182 C 88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (H ads ), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting H ads . Furthermore, using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles.

Adsorption↗
DOE OSTI2025

Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting

Abstract Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to application domains like computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and improves the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to achieve desirable outcomes in the healthcare field.

Biochemistry & Molecular Biology↗
NASA NTRS2002

Solving and Learning Soft Temporal Constraints: Experimental Setting and Results

Soft temporal constraints problems allow to describe in a natural way scenarios where events happen over time and preferences are associated to event distances and durations. However, sometimes such local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. Machine learning techniques can be useful in this respect. In this paper we describe two solvers (one more general and the other one more efficient) for tractable subclasses of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and representational power. Finally, we present a learning module and we show its behavior on randomly-generated examples.

Rossi, F.↗
NASA NTRS2004

Identifying, Assessing, and Mitigating Risk of Single-Point Inspections on the Space Shuttle Reusable Solid Rocket Motor

In the production of each Space Shuttle Reusable Solid Rocket Motor (RSRM), over 100,000 inspections are performed. ATK Thiokol Inc. reviewed these inspections to ensure a robust inspection system is maintained. The principal effort within this endeavor was the systematic identification and evaluation of inspections considered to be single-point. Single-point inspections are those accomplished on components, materials, and tooling by only one person, involving no other check. The purpose was to more accurately characterize risk and ultimately address and/or mitigate risk associated with single-point inspections. After the initial review of all inspections and identification/assessment of single-point inspections, review teams applied risk prioritization methodology similar to that used in a Process Failure Modes Effects Analysis to derive a Risk Prioritization Number for each single-point inspection. After the prioritization of risk, all single-point inspection points determined to have significant risk were provided either with risk-mitigating actions or rationale for acceptance. This effort gave confidence to the RSRM program that the correct inspections are being accomplished, that there is appropriate justification for those that remain as single-point inspections, and that risk mitigation was applied to further reduce risk of higher risk single-point inspections. This paper examines the process, results, and lessons learned in identifying, assessing, and mitigating risk associated with single-point inspections accomplished in the production of the Space Shuttle RSRM.

Greenhalgh, Phillip O.↗
NASA NTRS2002

NASA Langley/CNU Distance Learning Programs

NASA Langley Research Center and Christopher Newport University (CNU) provide, free to the public, distance learning programs that focus on math, science, and/or technology over a spectrum of education levels from K-adult. The effort started in 1997, and we currently have a suite of five distance-learning programs. We have around 450,000 registered educators and 12.5 million registered students in 60 countries. Partners and affiliates include the American Institute of Aeronautics and Astronautics (AIAA), the Aerospace Education Coordinating Committee (AECC), the Alliance for Community Media, the National Educational Telecommunications Association, Public Broadcasting System (PBS) affiliates, the NASA Learning Technologies Channel, the National Council of Teachers of Mathematics (NCTM), the Council of the Great City Schools, Hampton City Public Schools, Sea World Adventure Parks, Busch Gardens, ePALS.com, and Riverdeep. Our mission is based on the "Horizon of Learning," a vision for inspiring learning across a continuum of educational experiences. The programs form a continuum of educational experiences for elementary youth through adult learners. The strategic plan for the programs will evolve to reflect evolving national educational needs, changes within NASA, and emerging system initiatives. Plans for each program component include goals, objectives, learning outcomes, and rely on sound business models. It is well documented that if technology is used properly it can be a powerful partner in education. Our programs employ both advances in information technology and in effective pedagogy to produce a broad range of materials to complement and enhance other educational efforts. Collectively, the goals of the five programs are to increase educational excellence; enhance and enrich the teaching of mathematics, science, and technology; increase scientific and technological literacy; and communicate the results of NASA discovery, exploration, innovation and research. All pre-college distance learning programs support the national mathematics, science, and technology standards; support K-12 systemic change; involve educators in their development, implementation, and evaluation; and are based on alliances and partnerships. In addition the programs seek to invoke a sense of geographic, ethnic and cultural diversity by featuring schools from all over the U.S.; schools from urban, suburban, and rural areas; public, private, and religious schools; and schools with large populations of African-American, Asian and Hispanic students.

Caton, Randall↗
NASA NTRS2004

Real-Time Payload Operations on the International Space Station

This paper will focus on the challenges that Payload Operations have faced as Payload Utilization has evolved over the last three years of real-time experience. Specifically the paper will focus on the planning challenges and the constant battle over program allocation between core systems and payloads, the process of developing and implementing crew training in a centralized location that is being support by a payload development community spread out all over the US, the unique challenges associated with deployed pressurized payload payloads that are not located within an ISPR, and the importance of documenting specific requirements that the payload development community must implement to get through the crew reviews associated with training and procedures. The authors will focus on specific lessons learned and improvements that have been made in both the streamlining of the processes and the associated documentation.

Cissom, Rick D.↗
NASA NTRS1974

Student experiments on Skylab

Review of the background and experimental results of the Skylab student project, and assessment of its overall benefits. Virtually all of the objectives of the experiments adopted from student proposals were met. NASA benefited from the program by learning that meaningful experiments could be incorporated into a carry-on or suitcase concept at low cost and within very short time periods. The students benefited greatly from their association with the Skylab program by being afforded a learning opportunity and contact with the real world of research, normally beyond their reach. In addition, hometowns, states, and regions developed a close identification with space exploration.

Floyd, H. B.↗
NASA NTRS1987

Performance improvement of robots using a learning control scheme

Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

Krishna, Ramuhalli↗
NASA NTRS2008

Lessons Learned From Atomic Oxygen Interaction With Spacecraft Materials in Low Earth Orbit

There have been five Materials International Space Station Experiment (MISSE) passive experiment carriers (PECs) (MISSE 1-5) to date that have been launched, exposed in space on the exterior of International Space Station (ISS) and then returned to Earth for analysis. An additional four MISSE PECs (MISSE 6A, 6B, 7A, and 7B) are in various stages of completion. The PECs are two-sided suitcase to size sample carriers that are intended to provide information on the effects of the low Earth orbital environment on a wide variety of materials and components. As a result of post retrieval analyses of the retrieved MISSE 2 experiments and numerous prior space experiments, there have been valuable lessons learned and needs identified that are worthy of being documented so that planning, design, and analysis of future space environment experiments can benefit from the experience in order to maximize the knowledge gained. Some of the lessons learned involve the techniques, concepts, and issues associated with measuring atomic oxygen erosion yields. These are presented along with several issues to be considered when designing experiments, such as the uncertainty in mission duration, scattering and contamination effects on results, and the accuracy of measuring atomic oxygen erosion.

Banks, Bruce A.↗
NASA NTRS2013

Investigating Actuation Force Fight with Asynchronous and Synchronous Redundancy Management Techniques

Within distributed fault-tolerant systems the term force-fight is colloquially used to describe the level of command disagreement present at redundant actuation interfaces. This report details an investigation of force-fight using three distributed system case-study architectures. Each case study architecture is abstracted and formally modeled using the Symbolic Analysis Laboratory (SAL) tool chain from the Stanford Research Institute (SRI). We use the formal SAL models to produce k-induction based proofs of a bounded actuation agreement property. We also present a mathematically derived bound of redundant actuation agreement for sine-wave stimulus. The report documents our experiences and lessons learned developing the formal models and the associated proofs.

Hall, Brendan↗
NASA NTRSDate not supplied

Medical Lessons Learned from the Exploration Atmospheres Study

Background: The National Aeronautics and Space Administration’s (NASA) Exploration Atmospheres study (EA) was done to evaluate alternative cabin atmospheres for future spacecraft designs and planetary surface exploration of the Moon, Mars and beyond. Deep space exploration involves creating habitats and environments safe for human occupancy and means to explore the outside environment (extravehicular activities, EVA). In order to validate alternative atmospheres and pre-spacewalk procedures, the EA study was conducted to evaluate factors such as hypoxia risks, denitrogenation protocols, food limitations, medications, and the impact of other factors on human performance. Overview: Space travel is constrained by mass, volume, power and the cost of vehicle development, which creates tradeoffs in various capabilities, including breathing gasses. Higher atmospheric pressure in a vehicle means more gas, and a stronger containment vessel/habitat; while lower pressure requires higher oxygen partial pressure, which may increase fire risk. NASA’s EA study evaluated a proposed alternative cabin environment (8.2 psia, 34% Oxygen), for future spacecraft habitat, and planetary EVAs. EA included both a 3-day and a 11-day trial. These trials included a depressurization and saturation to 8.2psi at 34% O2 with additional depresses to 4.3 psia at 85% O2 for simulated EVAs, (1 EVA during the 3-day and 5 during the 11-day trials). Discussion: Planning for and executing the medical monitoring and response plan for a trial of this scope was a huge undertaking with no prior practice to fall back on. Food obstacles, sleeping issues, medications, joint injury, equipment limitations, medical privacy, multiple cases of decompression sickness, and even a COVID outbreak among the support team proved challenging. Conclusion: Testing of this nature is an essential part NASA’s preparation for the upcoming Lunar Artemis missions. As spaceflight transitions beyond low earth orbit, to planetary, even more trials of this nature will be required to learn what are the optimal atmospheric and associated operational constraints to maintain the optimal health of the crew and achieve mission objectives.

R Sanders↗
NASA NTRS1992

Environmental effects consideration: A case study - Lessons learned

This paper discusses the importance for serious consideration of environmental effects and associated risks by management early in the development cycle of a facility. A case study on the Space Shuttle provides information with regard to some of the environmental effects issues encountered and the lesson learned. The importance of early management action to enable the acceptance of known environmental risks, or to make program adjustments to avoid their potential consequences, is emphasized.

Vaughan, William W.↗
NASA NTRS2004

Environmental Remediation Technologies Derived from Space Industry Research

Beginning in the 1950s and 1960s, an abundance of effort and initiative was focused on propelling the space industry outward for planetary exploration and habitation. During these early years, the push to take space science to new levels indirectly contributed to the evolution of another science field that would not fully surface until the early 1980s, environmental remediation. This field is associated with the remediation or cleanup of environmental resources such as groundwater, soil, and sediment. Because the space-exploration initiative began prior to the establishment of the U.S. Environmental Protection Agency (EPA) in December of 1970, many NASA Centers as well as space-related support contractors allowed for the release of spent chemicals into the environment. Subsequently, these land owners have been directed by the EPA to responsibly initiate cleanup of their impacted sites. This paper will focus on the processes and lessons learned with the development, testing, and commercialization initiatives associated with four remediation technologies. The technologies include installation techniques for permeable reactive barriers (PRBs), the use of ultrasound to improve long-term performance of PRBs, emulsified zero-valent iron for product-level solvent degradation, and emulsion technologies for application to metal and polychlorinated biphenyl contaminated media. Details of the paper cover technology research, evaluation, and testing; contracts and grants; and technology transfer strategies including patenting, marketing, and licensing.

Quinn, Jacqueline↗