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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1931

Resistance of plates and pipes at high Reynolds numbers

It was learned that the law of resistance for high R values does not follow the simple powers, and that the powers, which can be obtained approximately for the velocity distribution, gradually change. Since, moreover, very important investigations have recently been made on the resistance of plates at very high R values, it seemed of interest to apply the above line of reasoning to the new general law of resistance. For this purpose, the resistance and velocity distribution along the plate must always be equal to the values of the pipe flow at the corresponding Reynolds number. We made two kinds of calculations, of which the one given here is the simpler and more practical and also agrees better with the experimental results.

Schiller, L↗
NASA NTRS2006

Lunar e-Library: Putting Space History to Work

As NASA plans and implements the Vision for Space Exploration, managers, engineers, and scientists need historically important information that is readily available and easily accessed. The Lunar e-Library - a searchable collection of 1100 electronic (.PDF) documents - makes it easy to find critical technical data and lessons learned and put space history knowledge in action. The Lunar e-Library, a DVD knowledge database, was developed by NASA to shorten research time and put knowledge at users' fingertips. Funded by NASA's Space Environments and Effects (SEE) Program headquartered at Marshall Space Flight Center (MSFC) and the MSFC Materials and Processes Laboratory, the goal of the Lunar e- Library effort was to identify key lessons learned from Apollo and other lunar programs and missions and to provide technical information from those programs in an easy-to-use format. The SEE Program began distributing the Lunar e-Library knowledge database in 2006. This paper describes the Lunar e-Library development process (including a description of the databases and resources used to acquire the documents) and the contents of the DVD product, demonstrates its usefulness with focused searches, and provides information on how to obtain this free resource.

McMahan, Tracy A.↗
NASA NTRS2019

The Lunar Geophysical Network Mission

Overarching Principles: Must be better than Apollo (coverage, duration, instrument performance); Learn from the Apollo experience. Lunar Geophysical Network (LGN) New Frontiers (NF)-class mission, as part of the NF-5 call. “This mission consists of several identical landers distributed across the lunar surface, each carrying geophysical instrumentation. The primary science objectives are to characterize the Moon’s internal structure, seismic activity, global heat flow budget, bulk composition, & magnetic field.” Global distribution of multiple stations. Each station should contain a seismometer, heat flow probe, electromagnetic sounder, laser retroreflector (lunar nearside). Each station must be long-lived (e.g., approximately10 years)to allow other stations (from other countries?) to be integrated with the anchor nodes to form the International Lunar Network. Why LGN? Planetary Science: Moon represents an end-member in planetary evolution (large small body, small rocky planet); Primary planetary differentiation preserved; Key to understanding terrestrial planet initial differentiation. Lunar Science: Heat flow probes yield crustal heat budget estimates; Combined with EMS (ElectroMagnetic Sounding), the temperature profile of the deep interior can be modeled along with mineralogy; Seismic and LLR (Lunar Laser Ranging) data also yield structure and compositional information of the lunar interior; High fidelity data from LGN would enhance the usefulness of the GRAIL (Gravity Recovery and Interior Laboratory) and SELENE (Selenological and Engineering Explorer) gravity data. Human Exploration: LGN must be established prior to renewed human lunar activity - we do not know the exact locations or causes of the shallow moonquakes (SMQs) - the largest magnitude seismic events recorded by Apollo (1 event per year of magnitude greater than or equal to 5); Establishing surface infrastructure near SMQ epicenters must be avoided.

Neal, C. R.↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM: Preprint

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2023

Enhancement of operational Flexibility of Power Plants Using IN740

Headers are crucial components within diverse industries, especially in the energy sector, as they enable the efficient transfer of fluids. The selection of materials for headers is determined by their specific applications; for example, Grade 91 and Grade 92 steels are commonly utilized. In our research, we recommend the adoption of INCONEL alloy 740 due to its exceptional robustness and heat-resistant characteristics. One critical parameter in header systems is the heat transfer coefficient, which directly influences the efficiency of heat exchange processes and can thereby impact the structural integrity of the headers. This coefficient is closely linked with factors such as the Nusselt number, which is influenced by fluid flow characteristics and thermal properties. The flow direction within headers, whether unidirectional or multidirectional—significantly affects the overall dynamics of heat transfer. This research focuses on exploring the behavior of headers, a specific pipeline system component, using ANSYS simulation software. The study aims to predict heat transfer and mechanical behavior within headers under various conditions through steady-state and transient simulations (parts 1 and 2 of the report). Key parameters such as heat transfer coefficient, velocity, and temperature are examined with the goal of optimizing header design. Part 3 of the report addresses the critical yet underexplored relationship between pressure drop and heat transfer coefficients in the transient flow regime within headers, specifically focusing on smooth horizontal circular tubes. Limited experimental work has been conducted in this area, prompting the need for comprehensive analysis. By leveraging machine learning techniques, this research aims to establish a correlation between pressure drop and heat transfer across various flow conditions, including laminar, transient, quasi-turbulent, and turbulent regimes. The data utilized for this analysis were meticulously gathered from existing literature, capturing simultaneous measurements of pressure drops and heat transfer. Part 4: In the context of power plants, header pipes are essential components that significantly influence system performance by facilitating the collection and distribution of steam. This report highlights the critical role of header pipes in enhancing reliability, efficiency, and overall power plant performance. A key aspect of this investigation is shape optimization, which aims to maximize performance while minimizing material usage. By focusing on shape optimization, this research contributes to improved efficiency and a reduced environmental footprint for power plant installations. The methodology developed in this study emphasizes optimizing header shapes to decrease reliance on expensive alloy materials and lower maintenance costs. Furthermore, a case study was conducted using a header from an operational power plant to validate the proposed optimization techniques. Finally, part 5 addresses the challenges associated with flexible operations in boiler systems, the study outlines several strategies for enhancing the durability and reliability of steam headers. Key approaches include material selection, which involves utilizing advanced materials with superior high-temperature properties and enhanced fatigue resistance to extend the lifespan of steam headers. Design modifications are also recommended, focusing on implementing changes that mitigate thermal stress and cyclic loading to reduce the likelihood of failures under varying operating conditions. Additionally, the study emphasizes the importance of regular inspection and monitoring by establishing rigorous protocols to detect early signs of damage, allowing for timely maintenance and minimizing the risks of catastrophic failures. Furthermore, operational guidelines are developed to minimize the frequency and severity of thermal transients, ensuring stable and efficient operations. Collectively, these strategies provide a robust framework for overcoming the unique challenges posed by flexible operation in boiler systems, ultimately contributing to improved reliability and performance in power plant operations. Through this comprehensive approach, the study not only enhances the understanding of pressure drop and heat transfer relationships but also promotes advancements in design and operation that will benefit the entire power generation industry.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2008

Services, Perspective and Directions of the Space Physics Data Facility

The multi-mission data and orbit services of NASA's Space Physics Data Facility (SPDF) project offer unique capabilities supporting science of the Heliophysics Great Observatory and that are highly complementary to other services now evolving in the international heliophysics data environment. The VSPO (Virtual Space Physics Observatory) service is an active portal to a wide rage of distributed data sources. CDAWeb (Coordinated Data Analysis Web) offers plots, listings and file downloads for current data from many missions across the boundaries of missions and instrument types. CDAWeb now includes extensive new data from STEREO and THEMIS, plus new ROCSAT IPEI data, the latest data from all four TIMED instruments and high-resolution data from all DE-2 experiments. SSCWeb, Helioweb and out 3D Animated Orbit Viewer (TIPSOD) provide position data and identification of spacecraft and ground conjunctions. OMNI Web, with its new extension to 1- and 5-minute resolution, provides interplanetary parameters at the Earth's bow shock. SPDF maintains NASA's CDF (Common Data Format) standard and a range of associated tools including format translation services. These capabilities are all now available through web services based APIs, one element in SPDF's ongoing work to enable heliophysics community development of Virtual discipline Observatories (e.g. VITMO). We will demonstrate out latest data and capabilities, review the lessons we continue to learn in what science users need and value in this class of services, and discuss out current thinking to the future role and appropriate focus of the SPDF effort in the evolving and increasingly distributed heliophysics data environment.

McGuire, Robert E.↗
DOE OSTI2021

Approximating Nash Equilibrium in Day-ahead Electricity Market Bidding with Multi-agent Deep Reinforcement Learning

In this paper, a day-ahead electricity market bidding problem with multiple strategic generation company (GEN-CO) bidders is studied. The problem is formulated as a Markov game model, where GENCO bidders interact with each other todevelop their optimal day-ahead bidding strategies. Considering unobservable information in the problem, a model-free and data-driven approach, known as multi-agent deep deterministic policy gradient (MADDPG), is applied for approximating the Nash equilibrium (NE) in the above Markov game. The MADDPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks. The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested caseand a congested case. Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient (DDPG) demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains. In addition, the comparison with a conventional model-based method shows that the MADDPG algorithm has higher computational efficiency, which is feasible for real-world applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems

Security-constrained unit commitment (SCUC) is a fundamental problem in power systems and electricity markets. In practical settings, SCUC is repeatedly solved via mixed-integer linear programming (MIP), sometimes multiple times per day, with only minor changes in input data. In this work, we propose a number of machine learning techniques to effectively extract information from previously solved instances in order to significantly improve the computational performance of MIP solvers when solving similar instances in the future. Based on statistical data, we predict redundant constraints in the formulation, good initial feasible solutions, and affine subspaces where the optimal solution is likely to lie, leading to a significant reduction in problem size. Computational results on a diverse set of realistic and large-scale instances show that using the proposed techniques, SCUC can be solved on average 4.3 times faster with optimality guarantees and 10.2 times faster without optimality guarantees, with no observed reduction in solution quality. Out-of-distribution experiments provide evidence that the method is somewhat robust against data-set shift. Summary of Contribution. The paper describes a novel computational method, based on a combination of mixed-integer linear programming (MILP) and machine learning (ML), to solve a challenging and fundamental optimization problem in the energy sector. The method advances the state-of-the-art, not only for this particular problem, but also, more generally, in solving discrete optimization problems via ML. We expect that the techniques presented can be readily used by practitioners in the energy sector and adapted, by researchers in other fields, to other challenging operations research problems that are solved routinely.

Machine Learning↗
DOE OSTI2023

Model-Free Probabilistic Forecasting of Nodal Voltages in Distribution Systems

As the penetration of distributed energy resources (DERs) into distribution systems increases, so does the interest in forecasting relevant system variables to help mitigate the associated challenges. One such challenge is the more frequent occurrence of excessive voltages in distribution systems with higher shares of DERs. Accurate and reliable estimates together with forecasts of system states (i.e., nodal voltages) will therefore play a key role in improving the utilization of these variable and uncertain sources while mitigating potential operational risks. Whilst recent literature has explored machine learning (ML) methods for voltage estimation and their extrapolation for a short-time period into the future, few have taken uncertainty quantification into account, and these methods have not yet been translated into operations. This paper discusses the advantages offered by probabilistic voltage forecasts and proposes a non-parametric Bayesian method suitable for forecasting nodal voltages at short-term time horizons while accounting for uncertainties in load and distributed photovoltaic (PV) generation. We demonstrate the value of the proposed Gaussian process (GP) model for a case study using historical forecasts and observation data.

distribution system↗
NASA NTRS2022

Microstructure Segmentation With Deep Learning Encoders Pre-Trained on a Large Microscopy Dataset

This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.

machine learning↗
NASA NTRS2002

Novel Advancements in Internet-Based Real Time Data Technologies

AZ Technology has been working with MSFC Ground Systems Department to find ways to make it easier for remote experimenters (RPI's) to monitor their International Space Station (ISS) payloads in real-time from anywhere using standard/familiar devices. AZ Technology was awarded an SBIR Phase I grant to research the technologies behind and advancements of distributing live ISS data across the Internet. That research resulted in a product called "EZStream" which is in use on several ISS-related projects. Although the initial implementation is geared toward ISS, the architecture and lessons learned are applicable to other space-related programs. This paper presents the high-level architecture and components that make up EZStream. A combination of commercial-off-the-shelf (COTS) and custom components were used and their interaction will be discussed. The server is powered by Apache's Jakarta-Tomcat web server/servlet engine. User accounts are maintained in a My SQL database. Both Tomcat and MySQL are Open Source products. When used for ISS, EZStream pulls the live data directly from NASA's Telescience Resource Kit (TReK) API. TReK parses the ISS data stream into individual measurement parameters and performs on-the- fly engineering unit conversion and range checking before passing the data to EZStream for distribution. TReK is provided by NASA at no charge to ISS experimenters. By using a combination of well established Open Source, NASA-supplied. and AZ Technology-developed components, operations using EZStream are robust and economical. Security over the Internet is a major concern on most space programs. This paper describes how EZStream provides for secure connection to and transmission of space- related data over the public Internet. Display pages that show sensitive data can be placed under access control by EZStream. Users are required to login before being allowed to pull up those web pages. To enhance security, the EZStream client/server data transmissions can be encrypted to preclude interception. EZStream was developed to make use of a host of standard platforms and protocols. Each are discussed in detail in this paper. The I3ZStream server is written as Java Servlets. This allows different platforms (i.e. Windows, Unix, Linux . Mac) to host the server portion. The EZStream client component is written in two different flavors: JavaBean and ActiveX. The JavaBean component is used to develop Java Applet displays. The ActiveX component is used for developing ActiveX-based displays. Remote user devices will be covered including web browsers on PC#s and scaled-down displays for PDA's and smart cell phones. As mentioned. the interaction between EZStream (web/data server) and TReK (data source) will be covered as related to ISS. EZStream is being enhanced to receive and parse binary data stream directly. This makes EZStream beneficial to both the ISS International Partners and non-NASA applications (i.e. factory floor monitoring). The options for developing client-side display web pages will be addressed along with the development of tools to allow creation of display web pages by non-programmers.

Myers, Gerry↗
DOE OSTI2025

LATTE: open-source, high-performance traveltime computation, tomography and source location in acoustic and elastic media

Traveltime-based tomography and source location are fundamental approaches for imaging subsurface structures and understanding the spatiotemporal distribution of seismicity from local to global scales. We present an open-source, high-performance framework integrating eikonal equation solvers and adjoint-state theory for traveltime computation, velocity tomography, source location and joint tomography-location in 2-D/3-D acoustic and elastic media. We introduce novel regularization schemes based on total generalized p-variation, structural similarity and multitask machine learning to enhance the fidelity and interpretability of inverted models and source locations. Key features of our implementation also include the ability to leverage both absolute-difference and double-difference traveltime misfits for high-fidelity velocity tomography and source parameter estimation; support for traveltime computation and inversion in diverse 2-D/3-D scenarios with arbitrary source and receiver distributions; and a perturbation-based optimal step-size estimation method to reduce computational costs. In addition, our implementation employs shared-memory and distributed-memory parallelization to provide an efficient solution for traveltime computation, tomography, and source location. In conclusion, we validate the efficacy and accuracy of our approach through multiple synthetic data examples.

58 GEOSCIENCES↗
DOE OSTI2026

Data-Driven Voltage Regulation of Distribution Grid Using Nonlinear Autoregressive Model with Exogenous Inputs (NARX)

This article proposes data-driven control via a nonlinear autoregressive model with exogenous inputs (NARX) for real-time voltage regulation of a modified feeder using reactive power sources. Traditional voltage control strategies rely on rule-based heuristics or optimization techniques, which often require detailed system models and extensive computational resources. The NARX-based controller learns system dynamics from historical data and predicts optimal reactive power dispatch in real-time for voltage correction. The proposed approach is evaluated on a power system feeder model under varying load and network conditions. Simulation results demonstrate that the NARX-based controller achieves improved voltage regulation, offering higher adaptability to system fluctuations. This study highlights the potential of data-driven control for enhancing the reliability of power distribution networks.

Donge, Vrushabh [ORNL] (ORCID:0000000306062803)↗
NASA NTRS2004

Onboard planning for geological investigations using a rover team

This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The Multi-Rover Integrated Science Understanding System (MISUS) combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers.

scheduling↗
DOE OSTI2020

Moving the Mission Forward with Renewable Energy

Presentation gives an overview of proven examples of renewable energy technologies. Learn how to properly utilize renewable energy technologies; research, select, fund, and deploy renewable technologies; best operate and maintain the federal government’s fleet of on-site solar photovoltaic (PV) systems; and learn best practices to detect and address performance issues with a PV system.

distributed energy↗
DOE OSTI2017

Precision and Accuracy in PDV and VISAR

This is a technical report discussing our current level of understanding of a wide and varying distribution of uncertainties in velocity results from Photonic Doppler Velocimetry in its application to gas gun experiments. Using propagation of errors methods with statistical averaging of photon number fluctuation in the detected photocurrent and subsequent addition of electronic recording noise, we learn that the velocity uncertainty in VISAR can be written in closed form. For PDV, the non-linear frequency transform and peak fitting methods employed make propagation of errors estimates notoriously more difficult to write down in closed form expect in the limit of constant velocity and low time resolution (large analysis-window width). An alternative method of error propagation in PDV is to use Monte Carlo methods with a simulation of the time domain signal based on results from the spectral domain. A key problem for Monte Carlo estimation for an experiment is a correct estimate of that portion of the time-domain noise associated with the peak-fitting region-of-interesting in the spectral domain. Using short-time Fourier transformation spectral analysis and working with the phase dependent real and imaginary parts allows removal of amplitude-noise cross terms that invariably show up when working with correlation-based methods or FFT power spectra. Estimation of the noise associated with a given spectral region of interest is then possible. At this level of progress, we learn that Monte Carlo trials with random recording noise and initial (uncontrolled) phase yields velocity uncertainties that are not as large as those observed. In a search for additional noise sources, a speckleinterference modulation contribution with off axis rays was investigated, and was found to add a velocity variation beyond that from the recording noise (due to random interference between off axis rays), but in our experiments the speckle modulation precision was not as important as the recording noise precision. But from these investigations we do appreciate that the velocity-uncertainty itself has a wide distribution of values that varies with signal-amplitude modulation (is not a single value). To provide a rough rule of thumb for the velocity uncertainty, we computed the average of the relative standard deviation distributions from 60 recorded traces (with distributions of uncertainties roughly between 0.1 % to 1 % in each trace) and found a mean of the distribution of uncertainties for our experiments is not better than 0.4 % at an analysis window width of 5 ns (although for brief intervals it can be as good as 0.1 %). Further imagination and testing may be needed to reveal other possible hydrodynamics-related sources of velocity error in PDV.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Building a Wide-Area File Transfer Performance Predictor: An Empirical Study

Wide-area data transfer is central to geographically distributed scientific workflows. Faster delivery of data is important for these workflows. Predictability is equally (or even more) important. With the goal of providing a reasonably accurate estimate of data transfer time to improve resource allocation & scheduling for workflows and enable end-to-end data transfer optimization, we apply machine learning methods to develop predictive models for data transfer times over a variety of wide area networks. To build and evaluate these models, we use 201,388 transfers, involving 759 million files totaling 9 PB transferred, over 115 heavily used source-destination pairs (“edges”) between 135 unique endpoints. We evaluate the models for different retraining frequencies and different window size of history data. In the best case, the resulting models have a median prediction error of ≤21% for 50% of the edges, and ≤32% for 75% of the edges. We present a detailed analysis of these results that provides insights into the cause of some of the high errors. We envision that the performance predictor will be informative for scheduling geo-distributed workflows. The insights also suggest obvious directions for both further analysis and transfer service optimization.

Liu, Zhengchun↗