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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Toward Transition Modeling in a Hypersonic Boundary Layer at Flight Conditions

An accurate physics-based transition prediction method integrated with computational fluid dynamics (CFD) solvers is pursued for hypersonic boundary layer flows over slender hypersonic vehicles at flight conditions. The geometry and flow conditions are selected to match relevant trajectory locations from the ascent phase of the HIFiRE-1 flight experiment, namely, a 7-degree half-angle cone with 2.5 mm nose radius, freestream Mach numbers in the range of 3.8 – 5.5 and freestream unit Reynolds numbers in the range of 3.3 × 10(exp 6) – 21.4 × 10(exp 6) m(exp -1). Earlier research had shown that the onset of transition during the HIFiRE-1 flight experiment correlated with an amplification factor of N ≈ 13.5 for the planar Mack modes. However, to incorporate the N-factor correlations into a CFD code, we investigate surrogate models for disturbance amplification that avoid the direct computation of stability characteristics. A commonly used approach for low-speed flows is based on an a priori database of stability characteristics for locally similar profiles. However, the results presented in this paper demonstrate that the application of this approach to hypersonic boundary layers over blunt spherical nose-tip cones leads to large, unacceptable errors in the predictions of amplification factors, mainly due to its failure in accounting for the effects of the entropy layer on the boundary-layer profiles along the length of the model. We propose and demonstrate an alternate approach that employs the stability computations for a canonical set of blunt cone configurations to train a physics-informed convolutional neural network model that is shown to provide substantially improved transition predictions for hypersonic flow configurations with entropy-layer effects. Furthermore, the excellent performance of the neural network model is also confirmed for cone configurations with nose radius and half-angle values that do not correspond to those used to build the database. Finally, the convolutional neural network model is shown to outperform the linear stability calculations for underresolved basic states.

Pedro Paredes↗
NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Atmospheric 3D winds in the horizontal and vertical directions are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we use a state-of-the-art convolutional neural network for optical flow (FlowNetS) in a physics-guided deep learning framework for predicting AMVs in the horizontal direction. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations (DYAMOND) for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

geostationary↗
NASA NTRSDate not supplied

Towards Physics Guided Optical Flow for Tracking Atmospheric Motion

Observations of atmospheric 3D winds are critical for improving short-range and long-range forecasting. Such advancement in forecasting directly applies to research in a number of areas including convective processes, wildfire plumes and tornado prediction. Atmospheric Motion Vectors (AMVs) provide a passively sensed approach to quantifying horizontal motion and cloud heights, which are typically sourced from geostationary sensors due to the availability of high frequency observations. Recent work has shown that estimating AMVs by tracking individual pixels with dense optical flow is a promising new direction. In this work, we compare state-of-the-art convolutional neural networks for optical flow in a physics-guided deep learning framework for predicting AMVs. The approach is semi-supervised and uses physically informed wind vectors from high-resolution numerical simulations for supervised learning followed by fine-tuning though warping and reconstruction of full-disk geostationary images (GOES-16). In the vertical direction, we use labels from the CALIPSO low-earth orbit satellite to predict cloud height from 16-band geostationary images with a neural network. We present results for both tasks on held-out time periods and secondary datasets.

Geostationary↗
NASA NTRSDate not supplied

High Temperature Material Property Data and Challenges to Thermal Process Model Predictions and In-Situ/Ex-Situ Measurements for Metallic Additive Manufacturing

Understanding and predicting performance properties of parts produced by metallic additive manufacturing has improved significantly over the past decade; however, difficult to measure material properties and process outcomes continue to be challenges. The qualification or certification of aerospace parts require extensive measures to quantify variable part properties in order to buy down the risk of component failure. The variability, inherent to the additive manufacturing, process adds unwanted uncertainty in the production of load critical structural components. Process modeling has proven valuable in providing predictions and context for understanding outcomes of the additive manufacturing process; however, these physically informed process models require material properties at temperatures that are difficult to measure and rarely available. Further, calibrating or validating such models is difficult because the process itself is challenging to measure. This talk will explore some of the challenges resulting from difficult to acquire input data by relating thermal process model predictions to in-situ and ex-situ optical microscopy measurements.

Process Model↗
NASA NTRSDate not supplied

Additive Manufacturing Model-Based Process Metrics: Reduced Order Modeling of the Laser Powder Bed Fusion Process

The multi-scale and complex process of printing additively manufactured (AM) parts can have unexpected, but predictable, build conditions that result in material microstructure variability. In this work, we describe a fully parallel reduced order modeling approach that has been developed to evaluate the evolution of AM processes, termed the AM moment measure method. This method couples the known sequence of the AM process with a physically informed nearest neighbors’ calculation to map the conditions of a part-scale build. The result is a map of the build that is derived directly from build files or in-situ process monitoring sensors. The methodology and terminology of the approach will be described, and computed build maps will be calculated and compared for various laser powder bed fusion (LPBF) builds of Ti-6Al-4V. Such comparative results develop understanding of how the sequential process actions can affect the LPBF-AM build quality and microstructure variability.

Laser Powder Bed Fusion↗
NASA NTRSDate not supplied

Power Hibernation for Low-Cost Solar Powered Lunar Missions

Because the surface of the Moon drops to cryogenic temperatures, no solar-powered lunar spacecraft have reliably operated beyond a single lunar day. Passive thermal control cannot keep a spacecraft sufficiently warm for the 354-hour lunar night, and active thermal control requires a dramatic increase in battery mass at the expense of payload mass. Extreme conditions seen on the lunar surface suggest a radioisotope solution is ideal, but mass, cost, and schedule are inconsistent with low-cost frequent flight intent of the commercial lunar payload services (CLPS) program. To solve the issue of lunar night survivability without radioisotope sources of power and heat, a lunar power hibernation approach is being developed at the Glenn Research Center, which exploits the ability of common 18650 Lithium-ion cells to passively survive cryogenic freeze-thaw cycles and recover without apparent performance degradation. A key aspect of this hibernation approach is the use of cryogenically operable electronics that safely manage the restoration of the battery thermal environment at lunar dawn. A spacecraft utilizing this strategy will operate into the lunar night on batteries until the state of charge or spacecraft temperature reaches a predetermined threshold. At this point, systems are shut down and the battery is isolated from the main bus to prevent charge or discharge during the freezing and thawing transitions. The system remains passive until lunar dawn, where temperatures can reach as low as 50 K. All electronics must be tolerant to these conditions. When the solar arrays are finally illuminated at lunar dawn, the main bus power electronics will initiate a “cold start” and begin regulating array power. The main bus electronics must be designed to operate at cryogenic temperatures. Array power is used to warm the battery and passive electronics back to operational temperatures. Once batteries are returned to normal temperatures, diagnostics and precharging is performed, as needed, and the battery is reconnected. The overall spacecraft system reboots and returns to nominal operations until lunar night returns. To assure that we can develop batteries suited for many hibernation freeze/thaw cycles, STMD Space Technology Research Grant Program (STRG) has selected two principal investigators that will thoroughly characterize of the Li-ion cell through the freeze-thaw process, investigate degradation mechanisms, and identify potential diagnostic techniques. STMD STRG is also funding an investigation of Gallium-Nitride semiconductors for cryogenic power applications. This work includes physics-informed modeling that considers cryogenic conductivity, carrier mobility, and quantum effects that govern semiconductor performance at cryogenic temperatures. These models can enable engineers to develop accurate cryogenic simulation models that assist in the design of power controls stable over the entire lunar surface temperature range. Meanwhile, Glenn is performing cryogenic testing of batteries and electronics, establishing design guidelines for power applications in extreme cold lunar environment, and potentially developing a hibernation technology demonstrator. The hibernation approach will enable low-cost lunar robotic missions to extend their operating lifetime to many months while minimizing development costs and impact on payload capacity. The need for cryogenically operable electronics is restricted to only main bus power and battery controls, as the majority of systems simply need to passively tolerate cryogenic temperatures. This allows developers to continue to exploit the cost savings of legacy and COTS hardware with minimum modification. For these reasons, lunar power hibernation is a viable near-term solution for lunar night survivability for solar powered commercial landers.

Space power↗
NASA NTRSDate not supplied

Powder Bed Fusion Laser Beam Metals Additive Manufacturing: Process Monitoring Approaches for Qualification and Certification

The use of in-situ process monitoring is of interest to lower the cost of inspection for the qualification of powder bed fusion laser beam metal (PBF-LB/M) additively manufactured (AM) parts. Precise monitoring of the PBF-LB/M AM build process constitutes a multi-scale and multi-discipline task. There are several significant challenges to the in-situ approach: the synchronization of sensor signals to process steps; the physical interpretation and classification of sensor signals; managing very large datasets; and comparing the inputs with the observed monitoring signals. At NASA Langley Research Center, a configurable architecture additive testbed has been developed to monitor the build process with synchronized sensors. The philosophy and method adopted for the synchronization of the cameras with laser power and position throughout a complex PBF-LB/M AM build will be described. The synchronized in-situ monitoring signals are compared with ex-situ nondestructive inspection, x-ray computed tomography (XCT). Such comparisons permit a better understanding of how the sequential process actions of LPBF-AM can affect build quality. The multi-scale and complex process of printing additively manufactured (AM) parts can have unexpected, but predictable, build conditions that result in material microstructure variability. This presentation will describe an additive manufacturing model-based process metric (AM-PM) computational method that is a fully parallel reduced order modeling approach developed to evaluate the evolution of AM processes. This method couples the known sequence of the AM process with a physically informed nearest neighbors’ calculation to map the conditions of a part-scale build. The result is a map of the build that is derived directly from build files or in-situ process monitoring sensors. The methodology of the approach will be described and mapped to the porosity observed from XCT for a complex PBF-LB/M build. Such comparative results develop understanding of how the sequential process actions can affect the PBF-LB/M AM build quality and microstructure variability.

Laser Powder Bed Fusion↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
DOE OSTI2024

Challenges in Training PINNs: A Loss Landscape Perspective

This paper explores challenges in training Physics Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process. We examine difficulties in minimizing the PINN loss function, particularly due to ill conditioning caused by differential operators in the residual term. We compare gradient-based optimizers Adam, L-BFGS, and their combination Adam+L-FGS, showing the superiority of Adam+L-BFGS, and introduce a novel secondorder optimizer, NysNewton-CG (NNCG), which significantly improves PINN performance. Theoretically, our work elucidates the connection between ill-conditioned differential operators and ill-conditioning in the PINN loss and shows the benefits of combining first- and second-order optimization methods. Our work presents valuable insights and more powerful optimization strategies for training PINNs, which could improve the utility of PINNs for solving difficult partial differential equations.

Rathore, Pratik↗
DOE OSTI2024

A Fully Decentralized Modulation Scheme for Modular Multilevel Converters

As the number of submodules rapidly increase, control architecture of Modular Multilevel Converters (MMCs) is transforming from centralized to distributed in order to address the challenges of tremendous computational burden and massive wiring. In this digest, we propose a modulation scheme for the MMCs with distributed controls, where each submodule is able to handle both controls and modulation in a decentralized manner. It enables the distributed controllers to synchronize in terms of fast-switching pulse width modulation (PWM). Aiming at this key technical challenge, this research develops a physics-informed PWM control strategy of mirroring the spontaneous synchronization feature of coupled oscillator’s behavior at switching frequency. Thanks to less reliance on wirings and communications, the proposed approach has the potential of i) improving reliability of MMC systems, and ii) reducing cost and space requirements for MMCs. The proposed approach has been preliminarily verified through a simulation tool for power electronics systems.

Lu, Minghui [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2026

DESI Data Release 2 ELGs: Property-dependent subsamples, imaging systematics, and clustering

Using emission-line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we evaluate a property-dependent correction to imaging systematics. We derive systematic weights following the same linear regression method used for other DESI tracers, but do so separately on ELG subsamples to provide a physically-informed alternative to the fiducial, neural-network-based approach. In doing so, we show that the deeper imaging in the Dark Energy Survey (DES) footprint leads to a higher overall number density but a lack of targets with extreme $g-r$ and $r-z$ colors. ELGs in the DES region also show a distinct redshift distribution when subsampled by position in the $g-r$ vs. $r-z$ plane. To address these effects, we implement a separate treatment of the DES footprint within the DESI catalog production pipeline, which is generally well-motivated and, in some cases, imperative for accurate clustering measurements. With DES treated separately, we find that property-dependent systematic weights further mitigate spurious clustering signal in $\sim$10% of subsamples, while the fiducial scheme remains optimal for the full sample.

Hagen, T. [Utah U.]↗
NASA NTRSDate not supplied

INFLUENCE OF ACTN3 GENE ON MUSCLE HEALTH AND PHYSICAL FITNESS TO INFORM FUTURE INTERVENTIONS IN SPACEFLIGHT MISSIONS

INTRODUCTION: Exposure to reduced gravity environments leads to diminished muscle size, strength, and endurance. Preservation of physical fitness is critical for mission essential tasks such as extravehicular activities or adaptation to changes in gravity loads. Current countermeasures to maintain ISS astronauts’ muscle mass and fitness include dedicated time for a combination of cardiovascular exercise and resistance training. Countermeasure effectiveness is monitored through VO2 max and isometric mid-thigh pulls. It is important to understand both environmental and genetic contributors to astronaut physical fitness. Multiple genes, including ACTN3, are known to correlate with exercise phenotypes and have the potential to help personalize countermeasures based on an astronaut’s genotypic makeup. TOPIC: The ACTN3 gene encodes a protein expressed only in fast-twitch muscles and correlates with sprint and power phenotypes. A common polymorphism in ACTN3 gene is R577X (rs1815739), produced by a C−to−T base substitution resulting in a nonsense mutation from arginine (R) to a premature stop codon (X) present in approximately 18% of the population. This polymorphism causes an absence of α−actinin−3 in type II muscle fibers but does not lead to a disease phenotype. The RR genotype is associated with elite athletes, especially in sprint and power sports. The XX genotype is believed to be more common in endurance athletes. The ACTN3 gene also has potential associations with training adaptation, post-exercise recovery, and exercise-associated injuries. APPLICATION: Understanding how ACTN3 and other genetic determinates of fitness phenotypes affect astronaut physical performance can inform personalized exercise and recovery prescriptions. For example, individuals with the RR or RX genotype may respond better to high-load and low repetition exercise, while XX may benefit from high repetition with low weight exercise. Since the wild-type protein may confer more resistance to muscle damage, those individuals with the RR or RX genotype may benefit from high intensity interval training for improved VO2 max, whereas those with an XX genotype may benefit from low intensity, high volume endurance activity. Additionally, omics data related to health and performance could be used as biomarkers to monitor astronaut fitness and the effectiveness of training regimens during a mission. Learning objectives: • Learn about the phenotypic effects on muscle function and fitness secondary to R577X mutation in the ACTN3 gene • Learn how mutations in the ACTN3 gene may inform future countermeasures for future spaceflight missions

Lynn K Stanwyck↗
DOE OSTI2024

Blending Behavioral Science and Physics-Based Models Inform Equitable Decarbonization Pathways in the US Housing Stock: Preprint

A just energy transition is an imperative of the Biden-Harris Administration, emphasizing the equitable distribution of benefits through energy-efficient and decarbonizing household technologies. Understanding the factors that increase a household's willingness to adopt these technologies helps policymakers implement more targeted and effective approaches. Our research addresses this by blending 550,000 housing stock types and energy simulation data with a nationally representative survey on residential technology adoption and decision-making (n=10,000). We identify energy equity gaps across tenure and income, highlighting disparities in energy burdens and insecurity. Findings show that households with prior modification experience are more willing to renovate, suggesting that small-scale retrofit programs could foster greater willingness. Energy secure but burdened homeowners are least willing to modify, highlighting the need to consider energy bill perceptions in policy design. Nearly half of US households that are energy burdened also face energy insecurity, with a significant gap in assistance for low-income households. We emphasize the need to understand household perceptions to improve policy. This research underscores the importance of understanding household behaviors to improve policy effectiveness, offering actionable insights for policymakers to promote equitable housing upgrades and advance a decarbonized future.

behavioral science↗
NASA NTRS1973

Physics in perspective, volume 2. Part B: The interfaces

Detailed information of physics subfields and interface areas are presented. Topics discussed include: astrophysics and relativity, earth and planetary physics, physics in chemistry, physics in biology, instrumentation, education, and dissemination and use of the information of physics. For Vol. 1, see N72-28706; for excerpt from Vol. 1, see N72-29689; for Vol. 2, Pt. A, see N73-15706.

Source record↗
DOE OSTI2025

Regulatory Considerations for Domestic Reprocessing Facility Physical Security

U.S. advanced non-light-water reactor vendors may pursue collocated on-site reprocessing activities. Therefore, these facilities are likely to possess formula quantities, or Category I quantities, of special nuclear material (SNM) during normal operations. The U.S. Nuclear Regulatory Commission (U.S. NRC) has yet to formally establish a regulatory framework for commercial reprocessing. While Category I requirements would explicitly not apply in this circumstance under current regulatory requirements, regulatory certainty does not exist. A novel framework should be developed to ensure public health and safety while also risk-informing the physical security requirements. This report reviews the relevant background of related rulemaking activities and proposes risk-informed physical protection requirements to satisfy these objectives. Insights from NRC security-related rulemaking activities provide a substantial technical basis to approach potential establishment of physical security requirements for reprocessing facilities. If a licensee can provide justification that the material satisfies a sufficient self-protecting radiation dose threshold, the material may not be subject to theft or diversion requirements and only potential sabotage requirements would apply. Furthermore, if the material can be justified to be moderately dilute, a set of risk-informed requirements could provide adequate protection of public health and safety. A revised performance objective for prevention of theft of moderately dilute Category I SNM may be detection to allow prompt recovery by a local law enforcement agency. However, a significant caveat to the proposed categorization scheme is the unknown integration of radiological sabotage with requirements for the protection against theft. Future licensees should consult with the NRC regarding treatment of this regulatory topic. Additionally, the self-protecting radiation dose threshold (either the existing or a proposed future threshold) would need to be considered. An integrated approach may apply graded potential requirements for protection against the design basis threat of radiological sabotage currently applicable to commercial nuclear power plants and Category I SNM facilities defined within 10 CFR 73.1(a).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗