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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Next Generation Microbiology Requirements

As humans continue to explore deep into space, microorganisms will travel with them. The primary means to mitigate the risk of infectious disease are a combination of prudent spacecraft design and rigorous operational controls. The effectiveness of these methods are evaluated by microbiological monitoring of spacecraft, food, water, and the crew that is performed preflight, in-flight, and post-flight. Current NASA requirements associated with microbiological monitoring are based on culture-based methodology where microorganisms are grown on a semi-solid growth medium and enumerated. Subsequent identification of the organisms requires specialized labor and large equipment, which historically has been performed on Earth. Requirements that rely strictly on culture-based units limit the use of non-culture based monitoring technology. Specifically, the culture-based "measurement criteria" are Colony Forming Units (CFU, representing the growth of one microorganism at a single location on the agar medium) per a given volume, area, or sample size. As the CFU unit by definition is culture-based, these requirements limit alternative technologies for spaceflight applications. As spaceflight missions such as those to Mars extend further into space, culture-based technology will become difficult to implement due to the (a) limited shelf life of the culture media, (b) mass/volume necessary to carry these consumables, and (c) problems associated with the production of biohazardous material in the habitable volume of the spacecraft. In addition, an extensive amount of new knowledge has been obtained during the Space Shuttle, NASA-Mir, and International Space Station Programs, which gave direction for new or modified microbial control requirements for vehicle design and mission operations. The goal of this task is to develop and recommend a new set of requirements for vehicle design and mission operations, including microbiological monitoring, based upon "lessons learned" and new technology. During 2011, this study focused on evaluating potable water requirements by assembling a forum of internal and external experts from NASA, other federal agencies, and academia. Key findings from this forum included: (1) Preventive design and operational strategies should be stringent and the primary focus of NASA's mitigation efforts, as they are cost effective and can be attained with conventional technology. (2) Microbial monitoring hardware should be simple and must be able to measure the viability of microorganisms in a sample. Multiple monitoring technologies can be utilized as long as at the microorganisms being identified can also be confirmed as viable. (3) Evidence showing alterations in the crew immune function and microbial virulence complicates risk assessments and creates the need for very conservative requirements. (4) One key source of infectious agents will always be the crew, and appropriate preventative measures should be taken preflight. (5) Water systems should be thoroughly disinfected (sterilized if possible) preflight and retain a residual biocide throughout the mission. Future forums will cover requirements for other types of samples, specifically spaceflight food and environmental samples, such as vehicle air and vehicle and cargo surfaces. An interim report on the potable water forum has been delivered to the Human Research Program with a final report on the recommendations for all sample types being delivered in September 2013.

Ott, C. M.↗
NASA NTRS2014

SpaceOps 2012 Plus 2: Social Tools to Simplify ISS Flight Control Communications and Log Keeping

A paper written for the SpaceOps 2012 Conference (Simplify ISS Flight Control Communications and Log Keeping via Social Tools and Techniques) identified three innovative concepts for real time flight control communications tools based on social mechanisms: a) Console Log Tool (CoLT) - A log keeping application at Marshall Space Flight Center's (MSFC) Payload Operations Integration Center (POIC) that provides "anywhere" access, comment and notifications features similar to those found in Social Networking Systems (SNS), b) Cross-Log Communication via Social Techniques - A concept from Johnsson Space Center's (JSC) Mission Control Center Houston (MCC-H) that would use microblogging's @tag and #tag protocols to make information/requests visible and/or discoverable in logs owned by @Destination addressees, and c) Communications Dashboard (CommDash) - A MSFC concept for a Facebook-like interface to visually integrate and manage basic console log content, text chat streams analogous to voice loops, text chat streams dedicated to particular conversations, generic and position-specific status displays/streams, and a graphically based hailing display. CoLT was deployed operationally at nearly the same time as SpaceOps 2012, the Cross- Log Communications idea is currently waiting for a champion to carry it forward, and CommDash was approved as a NASA Iinformation Technoloby (IT) Labs project. This paper discusses lessons learned from two years of actual CoLT operations, updates CommDash prototype development status, and discusses potential for using Cross-Log Communications in both MCC-H and/or POIC environments, and considers other ways for synergizing console applcations.

Cowart, Hugh S.↗
DOE OSTI2024

A Dataset of CFD Simulated Industrial Furnace Images for Conditional Automatic Generation with GANs

The steel industry is constantly looking for ways to automate processes and improve efficiency. A standard practice in industry is to simulate how complex systems will operate before they are actually used. Some complex systems, including steel industry processes such as blast furnaces, require complex physics-based simulations utilizing computational fluid dynamics (CFD). These CFD physics-based simulations are very accurate but can take significant time and computational resources to process, resulting in challenges for the implementation of the models in real-world operational environments. In recent years, deep learning (DL) has been considered as a substitute for these CFD models. DL models can be trained on validated CFD simulation data and then used for industrial process inference. Previous DL-based solutions have made great contributions for industrial automation but are currently missing the additional visualization component that CFD simulations also provide. In this paper, we propose a dataset for simple DL generative approaches that can help to address this issue. The dataset and methodology under development to approach this prediction are discussed in this work.

Calix, Ricardo↗
DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fast meta-solvers for 3D complex-shape scatterers using neural operators trained on a non-scattering problem

Three-dimensional target identification using scattering techniques requires high accuracy solutions and very fast computations for real-time predictions in some critical applications. We first train a deep neural operator (DeepONet) to solve wave propagation problems described by the Helmholtz equation in a domain without scatterers but at different wavenumbers and with a complex absorbing boundary condition. We then design two classes of fast meta-solvers by combining DeepONet with either relaxation methods, such as Jacobi and Gauss-Seidel, or with Krylov methods, such as GMRES and BiCGStab, using the trunk basis of DeepONet as a coarse-scale preconditioner. We leverage the spectral bias of neural networks to account for the lower part of the spectrum in the error distribution while the upper part is handled inexpensively using relaxation methods or fine-scale preconditioners. The meta-solvers are then applied to solve scattering problems with different shape of scatterers, at no extra training cost. We first demonstrate that the resulting meta-solvers are shape-agnostic, fast, and robust, whereas the standard standalone solvers may even fail to converge without the DeepONet. We then apply both classes of meta-solvers to scattering from a submarine, a complex three-dimensional problem. We achieve very fast solutions, especially with the DeepONet-Krylov methods, which require orders of magnitude fewer iterations than any of the standalone solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Diffractive optical computing in free space

Abstract Structured optical materials create new computing paradigms using photons, with transformative impact on various fields, including machine learning, computer vision, imaging, telecommunications, and sensing. This Perspective sheds light on the potential of free-space optical systems based on engineered surfaces for advancing optical computing. Manipulating light in unprecedented ways, emerging structured surfaces enable all-optical implementation of various mathematical functions and machine learning tasks. Diffractive networks, in particular, bring deep-learning principles into the design and operation of free-space optical systems to create new functionalities. Metasurfaces consisting of deeply subwavelength units are achieving exotic optical responses that provide independent control over different properties of light and can bring major advances in computational throughput and data-transfer bandwidth of free-space optical processors. Unlike integrated photonics-based optoelectronic systems that demand preprocessed inputs, free-space optical processors have direct access to all the optical degrees of freedom that carry information about an input scene/object without needing digital recovery or preprocessing of information. To realize the full potential of free-space optical computing architectures, diffractive surfaces and metasurfaces need to advance symbiotically and co-evolve in their designs, 3D fabrication/integration, cascadability, and computing accuracy to serve the needs of next-generation machine vision, computational imaging, mathematical computing, and telecommunication technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated Electric Vehicle Fleet Operations for On-Demand Service: Challenges and Opportunities

Automated/autonomous vehicle fleet operations within automated mobility districts have been studied over the past five years by the National Renewable Energy Laboratory, a US Department of Energy federally funded research and development center. This paper extends the analysis in this third phase of research underway to include considerations for electric vehicle operations and charging within a fleet of right-sized automated vehicles providing on-demand public mobility services. The current focus of research is on the operational complexities and associated challenges for automated/autonomous vehicles employing electric drivetrains and the resultant need for an efficient battery charging process while vehicles are operating in an "on-demand" mode of service. The blossoming of microtransit with shared-ride and point-to-point dispatching of each vehicle instills complex operations, with multiple mobility-on-demand transit operating sites being deployed, studied, and analyzed across North America. As a starting point, the authors' experience over the past 20 years with the analysis of automated transit network systems operating on and within dedicated and protected transitways provides initial insights into the system-level operational implications for maintaining a sufficient battery charge for a fleet of automated vehicles. Lessons learned through the prior analyses of automated transit network systems operating in on-demand service are identified, along with the capital cost implications for the requisite operating fleet size and charging station infrastructure for various approaches. These costs are summarized in juxtaposition with the benefits of realizing the higher goals of reducing environmental impacts and energy use within automated mobility districts as automated/autonomous vehicle technology matures. Finally, the discussion addresses key aspects of battery-electric propulsion for managed fleets in fully automated operation that will be studied as the third phase of research continues.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Machine learning surrogate for charged particle beam dynamics with space charge based on a recurrent neural network with aleatoric uncertainty

In this work, we develop a machine learning (ML) model with aleatoric uncertainty for the low energy beam transport (LEBT) region of the LANSCE linear accelerator in which we model the transport of a space-charge-dominated 750 keV proton beam through a lattice of 22 quadrupole magnets. Our ML model is developed based on data generated by a Kapchinsky–Vladimirsky (KV) envelope model of beam transport. We show that a recurrent neural network can be used as a dynamical surrogate model for fast prediction of the LEBT beam envelope. Furthermore, we endow the model with the prediction of aleatoric uncertainty and compare three different approaches. We demonstrate that the ML-based uncertainty quantification models are well calibrated and produce good estimates of the regions where the model is less certain about its predictions. This ML framework is a necessary step in the development of a real-time virtual diagnostic tool with uncertainty quantification that can be integrated into more complex downstream tasks (e.g., adaptive control or learning flexible control policies via reinforcement learning) for improved efficiency in beam operations. In future work, we plan to expand on this preliminary study by considering more realistic envelope models that include longitudinal momentum spread and dispersive effects in bending magnets, as well as particle tracking codes with 3D space charge (such as and ). Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Efficient Interdependent Systems Recovery Modeling with DeepONets

Modeling the recovery of interdependent critical infrastructure is a key component of quantifying and optimizing societal resilience to disruptive events. However, simulating the recovery of large-scale interdependent systems under random disruptive events is computationally expensive. Therefore, we propose the application of Deep Operator Networks (DeepONets) in this paper to accelerate the recovery modeling of interdependent systems. DeepONets are ML architectures which identify mathematical operators from data. The form of governing equations DeepONets identify and the governing equation of interdependent systems recovery model are similar. Therefore, we hypothesize that DeepONets can efficiently model the interdependent systems recovery with little training data. We applied DeepONets to a simple case of four interdependent systems with sixteen states. DeepONets, overall, performed satisfactorily in predicting the recovery of these interdependent systems for out of training sample data when compared to reference results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

DDStore

DDStore is designed to addresses random, read-oriented, global shuffle operations for data-intensive deep learning applications. By preserving global shuffling, DDStore ensures that deep learning model maintains high accuracy and generalizability when it is trained with large volume of data using HPC facilities.

Choi, Jong Youl [Oak Ridge National Laboratory (OR↗
DOE OSTI2022

Scalable Predictive And Risk Technologies

The research involves developing scalable technologies for risk-informed predictive analytics to achieve condition-based monitoring and maintenance strategies to reduce overall maintenance costs. The research utilizes data (real-time data, periodic data, and institutional knowledge) related to a particular plant asset from a specific nuclear plant site to develop technologies to scale risk-informed predictive analytic algorithms across different plant assets at the plant site and across the nuclear fleet. The developed algorithms and codes are used to optimize the maintenance strategy and estimate/forecast generation costs based on the state of health of the plant asset. Developed codes specifically include 1. Parameter estimation using plant operation data 2. Federated and Transfer learning model 3. Feature group based Multi-kernel SVM 4. Three state markov model

Manjunatha, KoushikAraseethota↗
DOE OSTI2020

Concentrating Solar Power Best Practices Study

The primary objective of this Concentrating Solar Power Best Practices Study is to publish best practices and lessons learned from the engineering, construction, commissioning, operations, and maintenance of existing concentrating solar power (CSP) parabolic trough and power tower systems. To accomplish this objective, information was solicited from many owners, operators, and engineering, procurement, and construction (EPC) contractors, independent engineers, and other stakeholders of parabolic trough and central receiver plants (also commonly referred to as power towers). Information was gathered through in-person or online meetings, site visits, and questionnaires sent to participants. The project team held about 50 information-gathering sessions with participants over the course this effort; and in the process, it collected information from participants representing nearly 80% of CSP plants operating worldwide.

14 SOLAR ENERGY↗
DOE OSTI2023

ULTIMATE FY22 Creep Test Facility Report (FY23 ARPA-E Milestone Report)

In the 18-month Phase 1 of this project, a facility was assembled for creep testing of refractory metals at 1300°C. This report focuses on the four creep frames that are operational and using an inert gas system (argon) to limit oxidation of the refractory metal specimens being tested. This report details the operations for the first period of operation including the results and lessons learned for Nb- and Mo-based alloy specimens tested by the ORNL team. For an alloy development program, a particularly unique feature of the ORNL facility is more than 40 years of experience conducting mechanical properties testing on sub-sized test specimens (25 mm long dogbones). Due to proprietary constraints, only one representative set of team data are provided. The primary concern for testing Nb-based alloys is oxygen ingress. Despite several strategies to reliably limit O ingress, including getters and Ar gas purity, the experimentally informed strategy currently being developed is a new load train design intended to better seal the system. Hardware for the new design is currently being fabricated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Intermediate Molecular Phenotypes to Identify Genetic Markers of Anthracycline-Induced Cardiotoxicity Risk

Cardiotoxicity due to anthracyclines (CDA) affects cancer patients, but we cannot predict who may suffer from this complication. CDA is a complex trait with a polygenic component that is mainly unidentified. We propose that levels of intermediate molecular phenotypes (IMPs) in the myocardium associated with histopathological damage could explain CDA susceptibility, so variants of genes encoding these IMPs could identify patients susceptible to this complication. Thus, a genetically heterogeneous cohort of mice (n = 165) generated by backcrossing were treated with doxorubicin and docetaxel. We quantified heart fibrosis using an Ariol slide scanner and intramyocardial levels of IMPs using multiplex bead arrays and QPCR. We identified quantitative trait loci linked to IMPs (ipQTLs) and cdaQTLs via linkage analysis. In three cancer patient cohorts, CDA was quantified using echocardiography or Cardiac Magnetic Resonance. CDA behaves as a complex trait in the mouse cohort. IMP levels in the myocardium were associated with CDA. ipQTLs integrated into genetic models with cdaQTLs account for more CDA phenotypic variation than that explained by cda-QTLs alone. Allelic forms of genes encoding IMPs associated with CDA in mice, including AKT1, MAPK14, MAPK8, STAT3, CAS3, and TP53, are genetic determinants of CDA in patients. Two genetic risk scores for pediatric patients (n = 71) and women with breast cancer (n = 420) were generated using machine-learning Least Absolute Shrinkage and Selection Operator (LASSO) regression. Thus, IMPs associated with heart damage identify genetic markers of CDA risk, thereby allowing more personalized patient management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach

False alerts due to misconfigured or compromised intrusion detection systems (IDS) in industrial control system (ICS) networks can lead to severe economic and operational damage. However, research using deep learning to reduce false alerts often requires the physical and cyber sensor data to be trustworthy. Implicit trust is a major problem for artificial intelligence or machine learning (AI/ML) in cyber-physical system (CPS) security, because when these solutions are most urgently needed is also when they are most at risk (e.g., during an attack). To address this, the Inter-Domain Evidence theoretic Approach for Inference (IDEA-I) is proposed that reframes the detection problem as how to make good decisions given uncertainty. Specifically, an evidence theoretic approach leveraging Dempster–Shafer (DS) combination rules and their variants is proposed for reducing false alerts. A multi-hypothesis mass function model is designed that leverages probability scores obtained from supervised-learning classifiers. Using this model, a location-cum-domain-based fusion framework is proposed to evaluate the detector’s performance using disjunctive, conjunctive, and cautious conjunctive rules. The approach is demonstrated in a cyber-physical power system testbed, and the classifiers are trained with datasets from Man-In-The-Middle attack emulation in a large-scale synthetic electric grid. For evaluating the performance, we consider plausibility, belief, pignistic, and general Bayesian theorem-based metrics as decision functions. To improve the performance, a multi-objective-based genetic algorithm is proposed for feature selection considering the decision metrics as the fitness function. Finally, we present a software application to evaluate the DS fusion approaches with different parameters and architectures.

42 ENGINEERING↗
DOE OSTI2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

Aubourg, Eric [APC, Paris] (ORCID:000000025592023X↗
NASA NTRS1977

Launch processing system transition from development to operation

The Launch Processing System has been under development at Kennedy Space Center since 1973. A prototype system was developed and delivered to Marshall Space Flight Center for Solid Rocket Booster checkout in July 1976. The first production hardware arrived in late 1976. The System uses a distributed computer network for command and monitoring and is supported by a dual large scale computer system for 'off line' processing. A high level of automation is anticipated for Shuttle and Payload testing and launch operations to gain the advantages of short turnaround capability, repeatability of operations, and minimization of operations and maintenance (O&M) manpower. Learning how to efficiently apply the system is our current problem. We are searching for more effective ways to convey LPS system performance characteristics from the designer to a large number of users. Once we have done this, we can realize the advantages of LPS system design.

Paul, H. C.↗
NASA NTRS1993

RighTime: A real time clock correcting program for MS-DOS-based computer systems

A computer program is described which effectively eliminates the misgivings of the DOS system clock in PC/AT-class computers. RighTime is a small, sophisticated memory-resident program that automatically corrects both the DOS system clock and the hardware 'CMOS' real time clock (RTC) in real time. RighTime learns what corrections are required without operator interaction beyond the occasional accurate time set. Both warm (power on) and cool (power off) errors are corrected, usually yielding better than one part per million accuracy in the typical desktop computer with no additional hardware, and RighTime increases the system clock resolution from approximately 0.0549 second to 0.01 second. Program tools are also available which allow visualization of RighTime's actions, verification of its performance, display of its history log, and which provide data for graphing of the system clock behavior. The program has found application in a wide variety of industries, including astronomy, satellite tracking, communications, broadcasting, transportation, public utilities, manufacturing, medicine, and the military.

Becker, G. Thomas↗