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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Applications systems verification and transfer project. Volume 4: Operational applications of satellite snow cover observations. Colorado Field Test Center

The study was conducted on six watersheds ranging in size from 277 km to 3460 km in the Rio Grande and Arkansas River basins of southwestern Colorado. Six years of satellite data in the period 1973-78 were analyzed and snowcover maps prepared for all available image dates. Seven snowmapping techniques were explored; the photointerpretative method was selected as the most accurate. Three schemes to forecast snowmelt runoff employing satellite snowcover observations were investigated. They included a conceptual hydrologic model, a statistical model, and a graphical method. A reduction of 10% in the current average forecast error is estimated when snowcover data in snowmelt runoff forecasting is shown to be extremely promising. Inability to obtain repetitive coverage due to the 18 day cycle of LANDSAT, the occurrence of cloud cover and slow image delivery are obstacles to the immediate implementation of satellite derived snowcover in operational streamflow forecasting programs.

Shafer, B. A.↗
NASA NTRS1987

Two-color method for optical astrometry - Theory and preliminary measurements with the Mark III stellar interferometer

The two-color method for interferometric astrometry provides a means of reducing the error in a stellar position measurement attributable to atmospheric turbulence. The primary limitation of the method is shown to be turbulent water vapor fluctuations. Secondary atmospheric effects caused by diffraction from small refractive-index inhomogeneities and differential refraction for the observation of stars away from zenith are shown to introduce errors that behave as white noise and which should not be significant. Other potential error sources due to photon noise, systematic instrumental effects, and imperfect data reduction are also considered. The improvement in accuracy possible with the two-color method is estimated as a factor of 5-10 over the corresponding one-color measurement. Some preliminary two-color measurements with the Mark III stellar interferometer at Mt. Wilson are presented, which demonstrate a factor of about 5 reduction in the amplitude of the atmospheric fluctuations in a stellar position measurement.

Colavita, M. Mark↗
NASA NTRS2000

A New Method for Reduction of Photomultiplier Signal-Induced Noise

For lidar measurements of ozone, photomultiplier tube (PMT) detector signal-induced noise represents a fundamental problem that complicates the extraction of information from lidar data. A new method is developed to significantly reduce signal-induced noise in lidar receiver PMT detectors. The electron optics of the lidar photomultiplier detector is modified to filter the source of signal-induced noise. A mesh electrode external to the PMT is utilized to control photoemission and disorient electron trajectories from the photocathode to the first dynode. Experiments were taken both with simulated and actual lidar return signals at Langley Research Center. Results show at least 40 percent more accurate ozone number density values with a mesh voltage of 60 V applied than with no voltage applied.

Koble, Andrea↗
NASA NTRS2013

Evolution of International Space Station Program Safety Review Processes and Tools

The International Space Station Program at NASA is constantly seeking to improve the processes and systems that support safe space operations. To that end, the ISS Program decided to upgrade their Safety and Hazard data systems with 3 goals: make safety and hazard data more accessible; better support the interconnection of different types of safety data; and increase the efficiency (and compliance) of safety-related processes. These goals are accomplished by moving data into a web-based structured data system that includes strong process support and supports integration with other information systems. Along with the data systems, ISS is evolving its submission requirements and safety process requirements to support the improved model. In contrast to existing operations (where paper processes and electronic file repositories are used for safety data management) the web-based solution provides the program with dramatically faster access to records, the ability to search for and reference specific data within records, reduced workload for hazard updates and approval, and process support including digital signatures and controlled record workflow. In addition, integration with other key data systems provides assistance with assessments of flight readiness, more efficient review and approval of operational controls and better tracking of international safety certifications. This approach will also provide new opportunities to streamline the sharing of data with ISS international partners while maintaining compliance with applicable laws and respecting restrictions on proprietary data. One goal of this paper is to outline the approach taken by the ISS Progrm to determine requirements for the new system and to devise a practical and efficient implementation strategy. From conception through implementation, ISS and NASA partners utilized a user-centered software development approach focused on user research and iterative design methods. The user-centered approach used on the new ISS hazard system utilized focused user research and iterative design methods employed by the Human Computer Interaction Group at NASA Ames Research Center. Particularly, the approach emphasized the reduction of workload associated with document and data management activities so more resources can be allocated to the operational use of data in problem solving, safety analysis, and recurrence control. The methods and techniques used to understand existing processes and systems, to recognize opportunities for improvement, and to design and review improvements are described with the intent that similar techniques can be employed elsewhere in safety operations. A second goal of this paper is to provide and overview of the web-based data system implemented by ISS. The software selected for the ISS hazard systemMission Assurance System (MAS)is a NASA-customized vairant of the open source software project Bugzilla. The origin and history of MAS as a NASA software project and the rationale for (and advantages of) using open-source software are documented elsewhere (Green, et al., 2009).

Ratterman, Christian D.↗
DOE OSTI2025

Bayesian Optimized Deep Ensemble for Uncertainty Quantification of Deep Neural Networks: a System Safety Case Study on Sodium Fast Reactor Thermal Stratification Modeling

Deep neural networks (DNNs) are increasingly important to scientific computing and engineering system simulations. Accurate uncertainty quantification (UQ) for DNNs is critical in safety-sensitive engineering domains. Traditional Deep Ensemble (DE) methods, while easy to implement, frequently suffer from poorly calibrated uncertainty estimates and limited predictive accuracy due to reliance on fixed architectures with varied weight initializations. To address these issues, we introduce a workflow that combines Bayesian Optimization (BO) and DE. The workflow is modular, scalable, and integrates parallel BO initialized with Sobol sequences to individually optimize the hyperparameters of each ensemble member. This method enhances ensemble diversity, improves predictive accuracy, and provides reliable uncertainty estimates. We evaluate the proposed BODE approach in a sodium fast reactor thermal stratification modeling case study, where we used a densely connected convolutional neural network to predict turbulent viscosity during the reactor transient with consideration of data noise. We benchmark its performance against several optimization approaches, including baseline deep ensemble, evolutionary algorithm-optimized ensemble, ensemble formed via random search combined with greedy selection, and a BO ensemble using random initialization. Here, our results demonstrate superior performance of the developed BODE approach. In noise-free scenarios, BODE notably reduces incorrect aleatoric uncertainty and significantly enhances predictive accuracy. Under conditions of 5% and 10% Gaussian noise, BODE adaptively quantifies uncertainty proportional to data noise, achieving up to an 80% reduction in root mean square error compared to baseline methods and producing well-calibrated prediction intervals.

Bayesian optimization↗
NASA NTRSDate not supplied

Advanced Metal Separation (AMS) via Marangoni Effect-driven Separation

For practical, economical, and sustainable human missions to the Moon, processing of lunar regolith and metal oxides is necessary for localized production of oxygen (O2) and metals that can be repurposed as rocket propellant, life support O2, and in-space manufactured parts. The primary candidates for in-situ resource utilization (ISRU) regolith processing are carbothermic reductions and molten regolith electrolysis (MRE) which both produce oxygen and a reduced, deoxygenated metallic slag. A metal separation technique is still required to capture and purify metals from the deoxygenated slag for reuse in areas such as metallic part fabrication, structural or design frameworks, and metal catalyst production. The Marangoni Effect is best explained as mass transfer along an interface driven by surface tension gradients induced by temperature and concentration differences. Due to the temperature gradients, molten metal can be transported in this fashion to create thin films. This method can transport molten metal in the absence of gravity or mechanical motion and greatly increases the available surface area for a vacuum fractional separation process. In this study, a novel vacuum furnace induces the Marangoni Effect to fractionally decompose and separate valuable metals (e.g., aluminum) from thin films of molten metal via vacuum distillation. The proposed work will examine lunar regolith, metal-based recycling feedstocks, and metallic slags produced from the carbothermal reduction of regolith. Data and observations from this study could produce a simple yet effective method of refining lunar derived metals by taking advantage of the high vacuum environment present on the lunar surface.

ISRU↗
NASA NTRS2001

Integrated Component-based Data Acquisition Systems for Aerospace Test Facilities

The Multi-Instrument Integrated Data Acquisition System (MIIDAS), developed by the NASA Langley Research Center, uses commercial off the shelf (COTS) products, integrated with custom software, to provide a broad range of capabilities at a low cost throughout the system s entire life cycle. MIIDAS combines data acquisition capabilities with online and post-test data reduction computations. COTS products lower purchase and maintenance costs by reducing the level of effort required to meet system requirements. Object-oriented methods are used to enhance modularity, encourage reusability, and to promote adaptability, reducing software development costs. Using only COTS products and custom software supported on multiple platforms reduces the cost of porting the system to other platforms. The post-test data reduction capabilities of MIIDAS have been installed at four aerospace testing facilities at NASA Langley Research Center. The systems installed at these facilities provide a common user interface, reducing the training time required for personnel that work across multiple facilities. The techniques employed by MIIDAS enable NASA to build a system with a lower initial purchase price and reduced sustaining maintenance costs. With MIIDAS, NASA has built a highly flexible next generation data acquisition and reduction system for aerospace test facilities that meets customer expectations.

Ross, Richard W.↗
NASA NTRS1973

The figure of Mars and its effect on radar-ranging.

Formulae are given which indicate the errors made in computing the radar-range to a planet when one assumes the planet to have a spherical surface, centered at the center of mass. These formulae are evaluated for Mars, using an ellipsoidal fit of the martian surface obtained from the occultation measurements of the Mariner 9 spacecraft. The secularlike part of the errors amounts to about 4 km in range over the past 3 oppositions, due mainly to the flattening of 0.006 and to a 3 km southerly displacement of the center of figure from the center of mass. The method of solution is given in the appendix along with a method for applying the ellipsoidal model to the reduction of radar-range data.

Standish, E. M., Jr.↗
NASA NTRS2000

Initial OSSE Sky Survey Observation: Deep Survey in the Virgo Region

The OSSE Virgo survey covered about 1% of the sky, using eight weeks of observing time on the Compton Gamma Ray Observatory, and was performed in conjunction with the EGRET and COMPTEL instruments on CGRO. A fundamental aspect of the survey was the development of data reduction techniques for the scanning data from the OSSE detectors. A direct algebraic approach for spatially deconvolving the data was developed. For the survey work, no background observations isolated from the survey region were acquired, unlike normal discrete source observations. The survey, reduction method covers the mapped region with a grid of pixels, and estimates the flux from each pixel by least-squares agreement with the data, assuming positivity constraints. This technique has led to the detection of compact sources at hard X-ray energies within the Virgo region, and the OSSE survey studies have been extended to the south galactic pole and north Ecliptic pole, with similar analysis techniques.

Cameron, Robert↗
NASA NTRSDate not supplied

Mitigating the Impacts of Measurement Error in the Quesst Mission Community Noise Study

Beginning in 2025, the NASA Quesst mission will conduct a series of community response tests involving flyovers of the X-59 aircraft at select localities across the United States. Several waves of a longitudinal survey will be administered over approximately one month of testing in order to capture perceptual responses to low-amplitude sonic booms, or “sonic thumps”. Simultaneously, noise exposure levels will be estimated by fusing model-based predictions with measurements taken from a sparse network of monitors in the region. As one of the aims of the study is to produce a dose-response curve, a regression model relating perceptual response to noise exposure levels, it is important to acknowledge the potential attenuation bias that results from measurement error in the estimated noise exposure levels. In this presentation we review and compare several methods for dealing with measurement error in generalized linear mixed models. The methods are demonstrated on simulated data and real data collected during past NASA risk reduction studies.

measurement error↗
DOE OSTI2025

FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration

Federated learning (FL) is a distributed machine learning approach that enables multiple local clients and a central server to collaboratively train a model while keeping the data on their own devices. First-order methods, particularly those incorporating variance reduction techniques, are the most widely used FL algorithms due to their simple implementation and stable performance. However, these methods tend to be slow and require a large number of communication rounds to reach the global minimizer. We propose FedOSAA, a novel approach that preserves the simplicity of first-order methods while achieving the rapid convergence typically associated with second-order methods. Our approach applies one Anderson acceleration (AA) step following classical local updates based on first-order methods with variance reduction, such as FedSVRG and SCAFFOLD, during local training. This AA step is able to leverage curvature information from the history points and gives a new update that approximates the Newton-GMRES direction, thereby significantly improving the convergence. We establish a local linear convergence rate to the global minimizer of FedOSAA for smooth and strongly convex loss functions. Numerical comparisons show that FedOSAA substantially improves the communication and computation efficiency of the original first-order methods, achieving performance comparable to second-order methods like GIANT.

Feng, Xue [University of California, Davis]↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 5: Utah FORGE Well 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 4: Utah FORGE Well 78B-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 3: Utah FORGE Well 56-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Analysis of Rig Parameter Data Using Drilling Process Modeling Constraints, Volume 1: Summary of Utah FORGE Wells 16A(78)-32, 56-32, 78B-32 and 16B(78)-32

Drill rig parameter measurements are routinely used during deep well construction to monitor and guide drilling conditions for improved performance and reduced costs. While insightful into the drilling process, these measurements are of reduced value without a standard to aid in data evaluation and decision making. In the main body of this work (Volume 1), a method is demonstrated whereby rock reduction model constraints are used to interpret drilling response parameters; the method could be applied in real-time to improve decision-making in the field and to further discern technology performance during post-drilling evaluations. Drilling parameters are evaluated using laboratory-validated rock reduction models for predicting the phenomenological response of drag bits (Detournay and Defourny, 1992) in computational algorithms. The method presented has applicability to development of advanced analytics on future geothermal wells using real-time electronic data recording for improved performance and reduced drilling costs. A drilling cost model is also used to show the tradeoff between rate of penetration and bit life and the influence on interval drilling costs. Details of the bit specifications and performance are cataloged in an independent volume, documented under separate cover, for each of the four wells, and include Volume 2: Utah FORGE 16A(78)-32; Volume 3: Utah FORGE 56-32; Volume 4: Utah FORGE 78B-32 and Volume 5: Utah FORGE 16B(78)-32.

15 GEOTHERMAL ENERGY↗