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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems

Long-term operation of heating, ventilation, and air conditioning (HVAC) systems will eventually lead to a range of HVAC system failures, resulting in excessive energy consumption and maintenance costs. Here, to avoid HVAC malfunctioning, fault detection diagnostic (FDD) is utilized as a common practice. Machine learning methods have lately received considerable interest for FDD analysis of HVAC systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets has also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing the accuracy of the models which leads to a high number of false positives (misleading alarms) in the system. In order to enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. By proposing this framework, three persistent challenges are addressed: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

AI in Astrophysics: Tackling Domain Shift, Model Robustness and Uncertainty

Artificial Intelligence (AI) is revolutionizing physics research from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This talk will introduce common challenges in applying AI across domains and present solutions based on domain adaptation a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery.

Ciprijanvoic, Aleksandra [Fermilab] (ORCID:0000000↗
NASA NTRS2020

Cloud-Computing and Machine Learning in Support of Country-Level Land Cover and Ecosystem Extent Mapping in Liberia and Gabon

Liberia and Gabon joined the Gaborone Declaration for Sustainability in Africa (GDSA), established in 2012, with the goal of incorporating the value of nature intonational decision making by estimating the multiple services obtained from ecosystems using the natural capital accounting framework. In this study, we produced 30-m resolution 10 classes land cover maps for the 2015 epoch for Liberia and Gabon using the Google Earth Engine (GEE) cloud platform to support the ongoing natural capital accounting efforts in these nations. We pro-pose an integrated method of pixel-based classification using Landsat 8 data, the Random Forest(RF) classifier and ancillary data to produce high quality land cover products to fit abroad range of applications, including natural capital accounting. Our approach focuses on a pre-classification filtering (Masking Phase) based on spectral signature and ancillary data to reduce the number of pixels prone to be misclassified; therefore, increasing the quality of the final product. The proposed approach yields an overall accuracy of 83% and 81% for Liberia and Gabon, respectively, out performing prior land cover products for these countries in both thematic content and accuracy. Our approach, while relatively simple and highly replicable, was able to produce high quality land cover products to fill an observational gap in up to date land cover data at national scale for Liberia and Gabon.

Celio de Sousa↗
DOE OSTI2025

Shift Happens: Building Robust AI Models with Domain Adaptation

Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we'll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.

Ciprijanovic, A. [Fermilab] (ORCID:000000031281719↗
NASA NTRS2017

Saffire: A Novel Approach to Study of Spacecraft Fire Safety Using Un-Manned Spacecraft

The Saffire Experiment represents the first test of spacecraft fires at a scale relevant to real spacecraft. Although large-scale fire tests on Earth are conducted for every type of habitable structure (buildings, planes, trains, automobiles, ships and mines) they had never been attempted in a space experiment for obvious reasons of practicality and safety. This is despite the fact that fire is a catastrophic hazard for spaceflight where the crew has very limited or no escape options. The spread and growth of a fire, combined with its interactions with the vehicle cannot be expected to scale linearly from small-scale test data, and so there is a substantial gap in our ability to predict the behavior of spacecraft fire and also its impact on the spacecraft habitability and operability. The experiments were performed aboard the Cygnus vehicle, a large unmanned resupply spacecraft to the International Space Station (ISS). The experiment was installed in the Cygnus vehicle, and remained dormant during docked operations. Once the vehicle was reloaded with ISS trash, it unberthed and moved to a separate orbit. The Saffire experiment was initiated by ground command and downlinked the data before the vehicle re-entered into the earth's atmosphere. The downloaded test data show resolved the question of whether flame growth will achieve a steady size and demonstrated that the size of the enclosure surrounding the sample has more impact on the flame spread than anticipated.

microgravity↗
DOE OSTI2024

A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many of these models prioritize high utility performance, such as accuracy, with a lack of privacy consideration, which is a major concern in modern society where privacy attacks are rampant. To address this issue, researchers have started to develop privacy-preserving GNNs. Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain. In this survey, we aim to address this gap by summarizing the attacks on graph data according to the targeted information, categorizing the privacy preservation techniques in GNNs, and reviewing the datasets and applications that could be used for analyzing/solving privacy issues in GNNs. We also outline potential directions for future research in order to build better privacy-preserving GNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Scale‐Dependent Influence of Permafrost on Riverbank Erosion Rates

Abstract Whether permafrost systematically alters the rate of riverbank erosion is a fundamental geomorphic question with significant importance to infrastructure, water quality, and biogeochemistry of high‐latitude watersheds. For over four decades, this question has remained unanswered due to a lack of data. Using remotely sensed imagery, we addressed this knowledge gap by quantifying riverbank erosion rates across the Arctic and subarctic. To compare these rates to non‐permafrost rivers, we assembled a global data set of published riverbank erosion rates. We found that erosion rates in rivers influenced by permafrost are on average nine times lower than non‐permafrost systems; erosion rate differences increase up to 40 times for the largest rivers. To test alternative hypotheses for the observed erosion rate difference, we examined differences in total water yield and erosional efficiency between these rivers and non‐permafrost rivers. Neither of these factors nor differences in river sediment loads provided compelling alternative explanations, leading us to conclude that permafrost limits riverbank erosion rates. This conclusion was supported by field investigations of rates and patterns of erosion along three rivers flowing through discontinuous permafrost in Alaska. Our results show that permafrost limits maximum bank erosion rates on rivers with stream powers greater than 900 Wm −1 . On smaller rivers, however, hydrology rather than thaw rate may be the dominant control on bank erosion. Our findings suggest that Arctic warming and hydrological changes should increase bank erosion rates on large rivers but may reduce rates on rivers with drainage areas less than a few thousand km 2 .

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2009

Human Research Program Human Health Countermeasures Element Sensorimotor Risk Standing Review Panel (SRP) Final Report

The Sensorimotor Risk Standing Review Panel (SRP) met at the NASA Johnson Space Center on October 4-6, 2009 to discuss the areas of future research targeted by the Human Health Countermeasures (HHC) Element of the Human Research Program (HRP). Using evidence-based knowledge as a background for risks, NASA had identified gaps in knowledge to address those risks. Ongoing and proposed tasks were presented to address the gaps. The charge to the Sensorimotor Risk SRP was to review the gaps, evaluate whether the tasks addressed these gaps and to make recommendations to NASA s HRP Science Management Office regarding the SRP's review. The SRP was requested to evaluate the practicality of the proposed efforts in light of the realistic demands placed on the HRP. In short, all tasks presented in the Integrated Research Plan (IRP) should address specific risks related to the challenges faced by the astronauts as a result of prolonged exposure to microgravity. All tasks proposed to fill the gaps in knowledge should provide applied, translational data necessary to address the specific risks. Several presentations were made to the SRP during the site visit and the SRP spent sufficient time to address the panel charge, either as a group or in separate sessions. The SRP made a final debriefing to the HRP Program Scientist. Taking the evidence and the risk as givens, the SRP reached the following conclusions: 1) the panel is very supportive of and endorses the present activities of the Sensorimotor Risk; and the panel is likewise supportive of the gaps and associated tasks in the IRP; 2) overall, the tasks addressed the gaps in the IRP; 3) there were some gaps and tasks that merit further enhancement and some new gaps/tasks that the SRP recommends.

Peterson, Barry↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2014

Constraining the Structure of the Transition Disk HD 135344B (SAO 206462) by Simultaneous Modeling of Multiwavelength Gas and Dust Observations

Context: Constraining the gas and dust disk structure of transition disks, particularly in the inner dust cavity, is a crucial step toward understanding the link between them and planet formation. HD 135344B is an accreting (pre-)transition disk that displays the CO 4.7 micrometer emission extending tens of AU inside its 30 AU dust cavity. Aims: We constrain HD 135344B's disk structure from multi-instrument gas and dust observations. Methods: We used the dust radiative transfer code MCFOST and the thermochemical code ProDiMo to derive the disk structure from the simultaneous modeling of the spectral energy distribution (SED), VLT/CRIRES CO P(10) 4.75 Micrometers, Herschel/PACS [O(sub I)] 63 Micrometers, Spitzer/IRS, and JCMT CO-12 J = 3−2 spectra, VLTI/PIONIER H-band visibilities, and constraints from (sub-)mm continuum interferometry and near-IR imaging. Results: We found a disk model able to describe the current gas and dust observations simultaneously. This disk has the following structure. (1) To simultaneously reproduce the SED, the near-IR interferometry data, and the CO ro-vibrational emission, refractory grains (we suggest carbon) are present inside the silicate sublimation radius (0.08 is less than R less than 0.2 AU). (2) The dust cavity (R is less than 30 AU) is filled with gas, the surface density of the gas inside the cavity must increase with radius to fit the CO ro-vibrational line profile, a small gap of a few AU in the gas distribution is compatible with current data, and a large gap of tens of AU in the gas does not appear likely. (4) The gas-to-dust ratio inside the cavity is >100 to account for the 870 Micrometers continuum upper limit and the CO P(10) line flux. (5) The gas-to-dust ratio in the outer disk (30 is less than R less than 200 AU) is less than 10 to simultaneously describe the [O(sub I)] 63 Micrometers line flux and the CO P(10) line profile. (6) In the outer disk, most of the gas and dust mass should be located in the midplane, and a significant fraction of the dust should be in large grains. Conclusions: Simultaneous modeling of the gas and dust is required to break the model degeneracies and constrain the disk structure. An increasing gas surface density with radius in the inner cavity echoes the effect of a migrating Jovian planet in the disk structure. The low gas mass (a few Jupiter masses) throughout the HD 135344B disk supports the idea that it is an evolved disk that has already lost a large portion of its mass.

inner dust cavity↗
NASA NTRS1991

Data compression for near Earth and deep space to Earth transmission

Key issues of data compression for near Earth and deep space to Earth transmission discussion group are briefly presented. Specific recommendations as made by the group are as follows: (1) since data compression is a cost effective way to improve communications and storage capacity, NASA should use lossless data compression wherever possible; (2) NASA should conduct experiments and studies on the value and effectiveness of lossy data compression; (3) NASA should develop and select approaches to high ratio compression of operational data such as voice and video; (4) NASA should develop data compression integrated circuits for a few key approaches identified in the preceding recommendation; (5) NASA should examine new data compression approaches such as combining source and channel encoding, where high payoff gaps are identified in currently available schemes; and (6) users and developers of data compression technologies should be in closer communication within NASA and with academia, industry, and other government agencies.

Erickson, Daniel E.↗
NASA NTRS2016

Design and Analysis of Map Relative Localization for Access to Hazardous Landing Sites on Mars

Human and robotic planetary lander missions require accurate surface relative position knowledge to land near science targets or next to pre-deployed assets. In the absence of GPS, accurate position estimates can be obtained by automatically matching sensor data collected during descent to an on-board map. The Lander Vision System (LVS) that is being developed for Mars landing applications generates landmark matches in descent imagery and combines these with inertial data to estimate vehicle position, velocity and attitude. This paper describes recent LVS design work focused on making the map relative localization algorithms robust to challenging environmental conditions like bland terrain, appearance differences between the map and image and initial input state errors. Improved results are shown using data from a recent LVS field test campaign. This paper also fills a gap in analysis to date by assessing the performance of the LVS with data sets containing significant vertical motion including a complete data set from the Mars Science Laboratory mission, a Mars landing simulation, and field test data taken over multiple altitudes above the same scene. Accurate and robust performance is achieved for all data sets indicating that vertical motion does not play a significant role in position estimation performance.

Lander Vision System↗
NASA NTRS2019

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to “things going right” in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human “success” data generated by the aviation system, from personnel and within the supporting infrastructure.

Holbrook, Jon B.↗
DOE OSTI2020

Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study

Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2011

The SEEDS of Planet Formation: Observations of Transitional Disks

As part of its 5-year study, the Strategic Exploration of Exoplanets and Disk Systems (SEEDS) has already observed a number of YSOs with circumstellar disks, including 13 0.5-8 Myr old A-M stars with indications that they host wide gaps or central cavities in their circumstellar disks in millimeter or far-IR observations, or from deficits in warm dust thermal emission. For 8 of the disks, the 0.15" inner working angle of HiCIAO+A0188 samples material in the millimeter or mid-IR identified cavity. In one case we report detection of a previously unrecognized wide gap. For the remaining 4 stars, the SEEDS data sample the outer disk: in 3 cases, we present the first NIR imagery of the disks. The data for the youngest sample members 1-2 Myr) closely resemble coeval primordial disks. After approximately 3 Myr, the transitional disks show a wealth of structure including spiral features, rings, divots, and in some cases, largely cleared gaps in the disks which are not seen in coeval primordial disks. Some of these structural features are predicted consequences of Jovian-mass planets having formed in the disk, while others are novel features. We discuss the implications for massive planet formation timescales and mechanisms.

Grady, Carol A.↗
NASA NTRS2011

The SEEDS of Planet Formation: Observations of Transitional Disks

As part of its 5-year study, the Strategic Exploration of Exoplanets and Disk Systems (SEEDS) has already observed a number of YSOs with circumstellar disks, including 13 0.5- 8 Myr old A-M stars with indications that they host wide gaps or central cavities in their circumstellar disks in millimeter or far-IR observations, or from deficits in warm dust thermal emission. For 8 of the disks, the 0.15" inner working angle of HiCIAO+A0188 samples material in the millimeter or mid-IR identified cavity. In one case we reprt detection of a previously unrecognized wide gap. For the remaining 4 stars, the SEEDS data sample the outer disk: in 3 cases, we present the first NIR imagery of the disks. The data for the youngest sample members (less than 1-2 Myr) closely resemble coeval primordial disks. After approximately 3 Myr, the transitional disks show a wealth of structure including spiral features, rings, divots, and in some cases, largely cleared gaps in the disks which are not seen in coeval primordial disks. Some of these structural features are predicted consequences of lovianmass planets having formed in the disk, while others are novel features. We discuss the implications for massive planet formation timescales and mechanisms.

Grady, Carol↗
DOE OSTI2022

Materials Scorecards, Phase 2: Advanced Materials and Manufacturing Technology

This report presents updated material score cards with detailed justification of the proposed rating provided and quantifies the readiness of the advanced manufacturing of materials for nuclear applications. Based on an expanded literature review of available data, the scores in this report (Phase 2) have either been modified or remained constant as compared with Phase 1 scoring. The rating justification are provided for each scoring criteria whereas the maturity quantification of additively manufactured alloys and the knowledge gaps are discussed with reference traceability. This report focuses on the detailed justification and traceability for the adoption of additively manufactured 316SS, SS304, Alloy 800H, Graphite C/C, Alloy N, Silicon Carbide, HT9, Alloy 617, and Alloy 718. Significant variability in data and knowledge gaps exist among the identified materials. The scorecards are therefore based on published literature data, industry response to a survey, stakeholder input collected at workshops, and expert opinion. The interpretation and assessment were somewhat challenged as experimental process parameters and methodology varied and best judgment was exercised for score quantification. This assessment reflects the overall level of technical maturity for AM materials and their deployment in Gen-VI nuclear systems. These Phase 2 scorecards are likely to be revised based on additional input from stakeholders and as new research results become available.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra

Changes in vegetation distribution, structure, and function can modify the canopy properties of terrestrial ecosystems, with potential consequences for regional and global climate feedbacks. In the Arctic, climate is warming twice as fast as compared to the global average (known as ‘Arctic amplification’), likely having stronger impacts on arctic tundra vegetation. In order to quantify these changes and assess their impacts on ecosystem structure and function, methods are needed to accurately characterize the canopy properties of tundra vegetation types. However, commonly used ground-based measurements are limited in spatial and temporal coverage, and differentiating low-lying tundra plant species is challenging with coarse-resolution satellite remote sensing. The collection and processing of multi-sensor data from unoccupied aerial systems (UASs) has the potential to fill the gap between ground-based and satellite observations. To address the critical need for such data in the Arctic, we developed a cost-effective multi-sensor UAS (the ‘Osprey’) using off-the-shelf instrumentation. The Osprey simultaneously produces high-resolution optical, thermal, and structural images, as well as collecting point-based hyperspectral measurements, over vegetation canopies. In this paper, we describe the setup and deployment of the Osprey system in the Arctic to a tundra study site located in the Seward Peninsula, Alaska. We present a case study demonstrating the processing and application of Osprey data products for characterizing the key biophysical properties of tundra vegetation canopies. In this study, plant functional types (PFTs) representative of arctic tundra ecosystems were mapped with an overall accuracy of 87.4%. The Osprey image products identified significant differences in canopy-scale greenness, canopy height, and surface temperature among PFTs, with deciduous low to tall shrubs having the lowest canopy temperatures while non-vascular lichens had the warmest. The analysis of our hyperspectral data showed that variation in the fractional cover of deciduous low to tall shrubs was effectively characterized by Osprey reflectance measurements across the range of visible to near-infrared wavelengths. Therefore, the development and deployment of the Osprey UAS, as a state-of-the-art methodology, has the potential to be widely used for characterizing tundra vegetation composition and canopy properties to improve our understanding of ecosystem dynamics in the Arctic, and to address scale issues between ground-based and airborne/satellite observations.

54 ENVIRONMENTAL SCIENCES↗