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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Trash Compaction Processing System (TCPS) Technology Demonstration and Risk Reduction Updates FY23-FY24 Presentation

The Next STEP Phase BTrash Compaction Processing System (TCPS) is being developed for a technology demonstration on the International Space Station(ISS) to process common spacecraft consumables trash such as food packaging and clothing to reduce trash storage volume, recover water, safen(e.g. reduce biological activity), and shape the trash for storage, jettison, reuse (e.g.,radiation shielding) and/or recycling. Sierra Space is developing the flight demonstration hardware and National Aeronautics and Space Administration(NASA) continues to conduct risk reduction activities to exploreoperational scenarios, setting the stage for successful on-orbit tests. After the flight demonstration on ISS, the TCPS becomes available to be infused into NASA short-and long-term missions.This paper will discuss the rationale for the updated requirement definitions outlined in the 2023 International Conference on Environmental System TCPS paper and the ongoing risk reduction activities. In addition, drawing from lessons learned in the development of the TCPS, a discussion of other compression technologies will be introduced for various mission types.

Trash Compaction Processing System↗
DOE OSTI2022

Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives

Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit about one-third of greenhouse gases. In the last few years, machine learning has achieved a notable momentum that, if properly harnessed, may unleash its potential for advanced analytics and control of smart buildings, enabling the technique to scale up for supporting the decarbonization of the building sector. In this perspective, transfer learning aims to improve the performance of a target learner exploiting knowledge in related environments. The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction, (2) occupancy detection and activity recognition, (3) building dynamics modeling, and (4) energy systems control. Furthermore, the review highlighted the role of deep learning in transfer learning applications that has been used in more than half of the analyzed studies. The paper also discusses how to integrate transfer learning in a smart building's ecosystem, identifying, for each application area, the research gaps and guidelines for future research directions.

Pinto, G↗
NASA NTRS2012

Launch Pad Closeout Operations for the Mars Science Laboratory's Heat Rejection System

The Mars Science Laboratory (MSL) rover was launched on an Atlas V on November 26, 2011. Preparations were carried out prior to launch in order to closeout the spacecraft's complex heat rejection system (HRS), which consists of two mechanically pumped CFC-11 fluid loops. The first HRS loop, onboard the Curiosity rover, was fully integrated, filled with CFC-11, and successfully operated prior to launch pad operations; however, the second thermal loop, called the cruise HRS loop, required final mechanical and thermal integration activities to occur while on the launch pad in order to accommodate the last minute installation of the rover's Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) power source. In order to prevent overheating of propellant tanks and critical avionics equipment buried deep within the spacecraft's aeroshell, the MMRTG needed to be pre-cooled using a separate non-flight mechanically pumped fluid loop prior to and during the final closeout and subsequent startup of the flight loop. This paper outlines the various steps that took place to safely install the MMRTG while carefully transitioning from the pre-cooling operation to the final startup and operation of the flight cruise HRS loop. Temperature data of the launch pad thermal transition from the ground support loop activity to the final flight loop operation is presented. Some background development of the ground support loop and lessons learned are also discussed. This successful launch pad integration activity required a close-knit coordination between NASA KSC, JPL, the Department of Energy, Idaho National Labs, Pratt and Whitney Rocketdyne Inc., Teledyne Technologies Inc., ULA, and Advanced Thermal Sciences Corp.

thermal↗
DOE OSTI2018

Machine learning properties of binary wurtzite superlattices

The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present paper, we show that fast and accurate predictions of a wide range of properties of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. Finally, while the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Graphene-supported single atom catalysts for high performance lithium-oxygen batteries

The optimal choice of d-block metals in single atom catalysts (SACs) is crucial for designing efficient electrocatalysts for activating the Oxygen reduction reaction (ORR)/ Oxygen evolution reaction (OER) in lithiumoxygen batteries (LOBs). Herein, we used the Quantum Mechanics methods to understand the origin of reactivity for a series of 16 d-block metals supported on nitrogen-doped graphene as SACs for ORR and OER in LOBs. Based on the Gibbs free energy calculations, we found that among the 16 SACs investigated, Zn-SAC exhibits the highest electrochemical activity with the lowest overpotential of 0.17 V. Here we then used machine learning (ML) to develop an intrinsic descriptor, phi, that correlates the catalytic activity with electronic and chemical properties of the catalytic centers at the M-N 4 active site on graphene surface. We established a linear relationship between phi and the catalytic activity that provides guidance for designing efficient SACs for electrocatalysis in LOBs. To validate these predictions, we report electrochemical measurements showing that Zn-SAC exhibits an ultra-stable cyclability with reduced overpotentials over Mo-SAC and nitrogen-doped graphene (NG), confirming our theoretical prediction. This fundamental work provides a deep understanding on the rational design of efficient SACs for OER/ ORR in LOBs.

25 ENERGY STORAGE↗
DOE OSTI2025

Cartesian equivariant representations for learning and understanding molecular orbitals

Qualitative and quantitative orbital properties such as bonding/antibonding character, localization, and orbital energies are critical to how chemists understand reactivity, catalysis, and excited-state behavior. Despite this, representations of orbitals in deep learning models have been very underdeveloped relative to representations of molecular geometries and Hamiltonians. Here, we apply state-of-the-art equivariant deep learning architectures to the task of assigning global labels to orbitals, namely energies characterizations, given the molecular coefficients from Hartree–Fock or density functional theory. The architecture we have developed, the Cartesian Equivariant Orbital Network (CEONET), shows how molecular orbital coefficients are readily featurized as equivariant node features common to all graph-based machine-learned potentials. We find that CEONET performs well at predicting difficult quantitative labels such as the orbital energy and orbital entropy. Furthermore, we find that the CEONET representation provides an intuitive latent space for differentiating orbital character for the qualitative assignment of e.g. bonding or antibonding character. In addition to providing a useful representation for further integrating deep learning with electronic structure theory, we expect CEONET to be useful for automatizing and interpreting the results of advanced electronic structure methods such as complete active space self-consistent field theory. In particular, the ability of CEONET to infer multireference character via the orbital entropy paves the way toward the machine-learned selection of active spaces.

chemical reactions↗
DOE OSTI2020

Stability of heterogeneous single-atom catalysts: a scaling law mapping thermodynamics to kinetics

Heterogeneous single-atom catalysts (SACs) hold the promise of combining high catalytic performance with maximum utilization of often precious metals. We extend the current thermodynamic view of SAC stability in terms of the binding energy (E bind ) of single-metal atoms on a support to a kinetic (transport) one by considering the activation barrier for metal atom diffusion. A rapid computational screening approach allows predicting diffusion barriers for metal–support pairs based on E bind of a metal atom to the support and the cohesive energy of the bulk metal (E c ). Metal–support combinations relevant to contemporary catalysis are explored by density functional theory. Assisted by machine-learning methods, we find that the diffusion activation barrier correlates with (E bind ) 2 /E c in the physical descriptor space. This diffusion scaling-law provides a simple model for screening thermodynamics to kinetics of metal adatom on a support.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Achieving Improved Reliability with Failure Analysis

Reliability is the ability of a product to properly function, within specified performance limits, for a specified period of time, under the life cycle application conditions. Failure analysis is a vital tool in the effort to ensure reliability of electronic products and systems throughout their product lifecycle. Today, organizations involved in activities within the electronics supply chain are facing new challenges, not just from complex assembly styles, harsher lifecycle environments, and sophisticated supply chains, but also from customers who are demanding a quicker turn-around. Unfortunately, root cause failure analysis is often performed incompletely, leading to a poor understanding of failure mechanisms and causes and, customer dissatisfaction due to recurring failures. The PDC starts with an introduction to reliability concepts, physics of failure and an overview of failure mechanisms that affect PCBs, PCBAs and components. The PDC then dives into root cause hypothesizing techniques (Pareto, FMEA, fishbone, FTA), non-destructive and destructive analysis and, materials characterization will be discussed. Numerous failure analysis case studies will be used to illustrate the techniques and analysis principles to arrive at the root cause(s) of field failures on printed circuit boards, active components, and assemblies. What Will You Learn: Topics include: Overview of Reliability Concepts Failure mechanisms of electronic products Root cause analysis Failure analysis techniques -Non-destructive techniques (optical, CSAM etc.) -Destructive analysis (DPA, Decap, FIB etc.) -Materials characterization (XRF, EDS, TMA/DSC etc.) Who Will Benefit: Reliability engineers, failure analysis engineers, engineering managers, design engineers, component engineers, quality assurance functions and, personnel involved with reliability activities within their company.

non-destructive techniques↗
NASA NTRS2001

Studying Earth's Environment From Space: Classroom and Laboratory Activities with Instructor Resources

Standard, text-book based learning for earth, ocean, and atmospheric sciences has been limited by the unavailability of quantitative teaching materials. While a descriptive presentation, in a lecture format, of discrete satellite images is often adequate for high school classrooms, this is seldom the case at the undergraduate level. In order to address these concerns, a series of numerical exercises for the Macintosh was developed for use with satellite-derived Sea Surface Temperature, pigment and sea ice concentration data. Using a modified version of NIH Image, to analyze actual satellite data, students are able to better understand ocean processes, such as circulation, upwelling, primary production, and ocean/atmosphere coupling. Graphical plots, image math, and numerical comparisons are utilized to substantiate temporal and spatial trends in sea surface temperature and ocean color. Particularly for institutions that do not offer a program in remote sensing, the subject matter is presented as modular units, each of which can be readily incorporated into existing curricula. These materials have been produced in both CD-ROM and WWW format, making them useful for classroom or lab setting. Depending upon the level of available computer support, graphics can be displayed directly from the CD-ROM, or as a series of color view graphs for standard overhead projection.

Smith, Elizabeth A.↗
DOE OSTI2024

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗
NASA NTRS1993

Synthetic vision system flight test results and lessons learned

Honeywell Systems and Research Center developed and demonstrated an active 35 GHz Radar Imaging system as part of the FAA/USAF/Industry sponsored Synthetic Vision System Technology Demonstration (SVSTD) Program. The objectives of this presentation are to provide a general overview of flight test results, a system level perspective that encompasses the efforts of the SVSTD and Augmented VIsual Display (AVID) programs, and more importantly, provide the AVID workshop participants with Honeywell's perspective on the lessons that were learned from the SVS flight tests. One objective of the SVSTD program was to explore several known system issues concerning radar imaging technology. The program ultimately resolved some of these issues, left others open, and in fact created several new concerns. In some instances, the interested community has drawn improper conclusions from the program by globally attributing implementation specific issues to radar imaging technology in general. The motivation for this presentation is therefore to provide AVID researchers with a better understanding of the issues that truly remain open, and to identify the perceived issues that are either resolved or were specific to Honeywell's implementation.

Radke, Jeffrey↗
NASA NTRS1998

Neural learning rules for the vestibulo-ocular reflex

Mechanisms for the induction of motor learning in the vestibulo-ocular reflex (VOR) were evaluated by recording the patterns of neural activity elicited in the cerebellum by a range of stimuli that induce learning. Patterns of climbing-fiber, vestibular, and Purkinje cell simple-spike signals were examined during sinusoidal head movement paired with visual image movement at stimulus frequencies from 0.5 to 10 Hz. A comparison of simple-spike and vestibular signals contained the information required to guide learning only at low stimulus frequencies, and a comparison of climbing-fiber and simple-spike signals contained the information required to guide learning only at high stimulus frequencies. Learning could be guided by comparison of climbing-fiber and vestibular signals at all stimulus frequencies tested, but only if climbing fiber responses were compared with the vestibular signals present 100 msec earlier. Computational analysis demonstrated that this conclusion is valid even if there is a broad range of vestibular signals at the site of plasticity. Simulations also indicated that the comparison of vestibular and climbing-fiber signals across the 100 msec delay must be implemented by a subcellular "eligibility" trace rather than by neural circuits that delay the vestibular inputs to the site of plasticity. The results suggest two alternative accounts of learning in the VOR. Either there are multiple mechanisms of learning that use different combinations of neural signals to drive plasticity, or there is a single mechanism tuned to climbing-fiber activity that follows activity in vestibular pathways by approximately 100 msec.

NASA Discipline Neuroscience↗
DOE OSTI2025

Low responsiveness of machine learning models to critical or deteriorating health conditions

Machine learning (ML) based mortality prediction models can be immensely useful in intensive care units. Such a model should generate warnings to alert physicians when a patient’s condition rapidly deteriorates, or their vitals are in highly abnormal ranges. Before clinical deployment, it is important to comprehensively assess a model’s ability to recognize critical patient conditions. We develop multiple medical ML testing approaches, including a gradient ascent method and neural activation map. We systematically assess these machine learning models’ ability to respond to serious medical conditions using additional test cases, some of which are time series. Guided by medical doctors, our evaluation involves multiple machine learning models, resampling techniques, and four datasets for two clinical prediction tasks. We identify serious deficiencies in the models’ responsiveness, with the models being unable to recognize severely impaired medical conditions or rapidly deteriorating health. For in-hospital mortality prediction, the models tested using our synthesized cases fail to recognize 66% of the injuries. In some instances, the models fail to generate adequate mortality risk scores for all test cases. Our study identifies similar kinds of deficiencies in the responsiveness of 5-year breast and lung cancer prediction models. Using generated test cases, we find that statistical machine-learning models trained solely from patient data are grossly insufficient and have many dangerous blind spots. Most of the ML models tested fail to respond adequately to critically ill patients. How to incorporate medical knowledge into clinical machine learning models is an important future research direction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Deep Learning without Global Optimization by Random Fourier Neural Networks

Here we introduce a new training algorithm for deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control. It consistently attains the theoretical approximation rate for residual networks with complex exponential activation functions, determined by network complexity. Additionally, it enables efficient learning of multiscale and high-frequency features, producing interpretable parameter distributions. Despite using sinusoidal basis functions, we do not observe Gibbs phenomena in approximating discontinuous target functions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

Rodent Habitat On ISS: Spaceflight Effects On Mouse Behavior

The NASA Decadal Survey (2011), Recapturing a Future for Space Exploration: Life and Physical Sciences Research for a New Era, emphasized the importance of expanding NASA life sciences research to long duration, rodent experiments on the International Space Station (ISS). To accomplish this objective, flight hardware, operations, and science capabilities supporting mouse studies in space were developed at NASA Ames Research Center. The first flight experiment carrying mice, Rodent Research Hardware and Operations Validation (Rodent Research-1), was launched on Sept 21, 2014 in an unmanned Dragon Capsule, SpaceX4, exposing the mice to a total of 37 days in space. Ground control groups were maintained in environmental chambers at Kennedy Space Center. Mouse health and behavior were monitored for the duration of the experiment via video streaming. Here we present behavioral analysis of two groups of five C57BL/6 female adult mice viewed via fixed camera views compared with identically housed Ground Controls. Flight (Flt) and Ground Control (GC) mice exhibited the same range of behaviors, including eating, drinking, exploratory behavior, self- and allo-grooming, and social interactions at similar or greater levels of occurrence. Mice propelled themselves freely and actively throughout the Habitat using their forelimbs to push off or by floating from one cage area to another, and they quickly learned to anchor themselves using tails and/or paws. Overall activity was greater in Flt as compared to GC mice, with spontaneous ambulatory behavior including the development of organized ‘circling’ or ‘race-tracking’ behavior that emerged within the first few days of flight and encompassed the primary dark cycle activity for the remainder of the experiment. We quantified the bout frequency, duration and rate of circling with respect to characteristic behaviors observed in the varying stages of the progressive development of circling: flipping utilizing two sides of the habitat, circling, multi-lap circling and group-circling. Once begun, mice did not regress to flipping behavior or other previous behavioral milestones for the remainder of flight. An overall upward trend in circling frequency, rate, duration, participation, and organization was observed over the course of the 37-day spaceflight experiment. In this presentation, we will summarize qualitative observations and quantitative comparisons of mice in microgravity and 1g conditions. Behavioral analyses provide important insights into the overall health and adaptation of mice to the space environment, and identify unique behaviors and social interactions to guide future habitat development and research on rodents in space.

Mouse↗
DOE OSTI2020

What's the big deal with TA-55's trash?

There's a beast of hazardous waste, and we're learning how to tame it. Plutonium and production activities are ongoing at TA-55, which means the beast of hazardous waste is always looming around the corner. The Laboratory must dispose of this delicate type of waste properly and swiftly. Learning from previous snags and pitfalls in an incredibly complex process, TA-55 leadership and staff have worked together to greatly improve and streamline waste processes. The result: As of the last week of fiscal year 2020, 42 shipments containing 1,275 containers of transuranic (TRU) waste were sent to the Waste Isolation Pilot Plant (WIPP) in southern New Mexico, the nation’s only repository for defense-generated TRU waste. In FY 2019, we shipped less than half that amount.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2005

Engineering Lessons Learned and Systems Engineering Applications

Systems Engineering is fundamental to good engineering, which in turn depends on the integration and application of engineering lessons learned and technical standards. Thus, good Systems Engineering also depends on systems engineering lessons learned from within the aerospace industry being documented and applied. About ten percent of the engineering lessons learned documented in the NASA Lessons Learned Information System are directly related to Systems Engineering. A key issue associated with lessons learned datasets is the communication and incorporation of this information into engineering processes. Systems Engineering has been defined (EINIS-632) as "an interdisciplinary approach encompassing the entire technical effort to evolve and verify an integrated and life-cycle balanced set of system people, product, and process solutions that satisfy customer needs". Designing reliable space-based systems has always been a goal for NASA, and many painful lessons have been learned along the way. One of the continuing functions of a system engineer is to compile development and operations "lessons learned" documents and ensure their integration into future systems development activities. They can produce insights and information for risk identification identification and characterization. on a new project. Lessons learned files from previous projects are especially valuable in risk

Gill, Paul S.↗