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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2022

A computationally efficient algorithm for computing convex hull prices

Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource's capabilities, they create challenges for market clearing processes. For example, system operators may be required to execute side payments to participants whose costs are not covered through energy sales as determined via traditional locational marginal pricing schemes. Convex hull pricing minimizes this and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. Computing convex hull prices involves solving either a large-scale linear program or the Lagrangian dual of the corresponding non-convex scheduling problem. Further, the former approach requires explicit descriptions of market participants' convex hulls. While linear programs for computing convex hull prices are large, their structure is naturally decomposable by generators. Here, in this work, we propose and empirically analyze a Benders decomposition approach to computing convex hull prices that leverages recent advances in convex hull formulations for thermal generating units. We demonstrate across a large set of test instances that our decomposition approach only requires modest computational effort, obtaining solutions at least an order of magnitude faster than the equivalent large-scale linear programming approach. Overall, we provide a computationally feasible method for computing convex hull prices for industrial scale market clearing problems, enabling the possibility of practical adoption of this advanced pricing mechanism.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Ultrahigh rate capability of manganese based olivine cathodes enabled by interfacial electron transport enhancement

In this study, manganese-based Olivine is a promising cathode candidate with high energy and low cost for Li-ion batteries (LIBs). Its rate capability and cyclability challenges still remain even with nano-size and carbon coating. Herein, an ultrahigh rate performance is achieved by introducing an affinitive conductor to enhance the interfacial electron transport of LiMn 0.7 Fe 0.3 PO 4 via Li 3 V 2 (PO 4 ) 3 . It is found that the Li 3 V 2 (PO 4 ) 3 facilitates sp 2 hybridization to form a highly conductive carbon coating during the carbonized process. The composite 0.9LiMn 0.7 -Fe 0.3 PO 4 .0.1Li 3 V 2 (PO 4 ) 3 can deliver a capacity of 90.9 mAh g -1 and power density of 11444 W kg -1 at 50 C-rate. Both in situ X-ray diffraction and conductive-atomic force microscopy are conducted to understand the synergetic effect between LiMn 0.7 Fe 0.3 PO 4 and Li 3 V 2 (PO 4 ) 3 . The results suggest that the interfacial electron transfer between LiMn 0.2 Fe 0.3 PO 4 particles and the electron conducting medium, such as binder/carbon black composite, is greatly improved so that the highly Li + conductive nature of olivine materials can be fully unleashed. This work demonstrates the importance of efficient interfacial electron transfer to the active cathode particles, and opens up a new venue for the rational design of high-energy and high-power batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Physicochemical Gas–Solid Sorption Properties of Geologic Materials Using Inverse Gas Chromatography

The goal of this study was to determine the physicochemical properties of a variety of geologic materials using inverse gas chromatography (IGC) by varying probe gas selection, temperature, carrier gas flow rate, and humidity. This is accomplished by measuring the level of interaction between the materials of interest and known probe gases. Identifying a material’s physicochemical characteristics can help provide a better understanding of the transport of gaseous compounds in different geologic materials or between different geological layers under various conditions. Our research in this study focused on measuring the enthalpy (heat) of adsorption, Henry’s constant, and diffusion coefficients of a suite of geologic materials, including two soil types (sandy clay-loam and loam), quartz sand, salt, and bentonite clay, with various particle sizes. The reproducibility of IGC measurements for geologic materials, which are inherently heterogeneous, was also assessed in comparison to the reproducibility for more homogeneous synthetic materials. This involved determining the variability of physicochemical measurements obtained from different IGC approaches, instruments, and researchers. For the investigated IGC-determined parameters, the need for standardization became apparent, including the need for application-relevant reference materials. The inherent physical and chemical heterogeneities of soil and many geologic materials can make the prediction of sorption properties difficult. Characterizing the properties of individual organic and inorganic components can help elucidate the primary factors influencing sorption interactions in more complex mixtures. This research examined the capabilities and potential challenges of characterizing the gas sorption properties of geologic materials using IGC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Deep compressed seismic learning for fast location and moment tensor inferences with natural and induced seismicity

Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Same-sided successive-shock HED instability experiments

Inertial confinement fusion (ICF) and high-energy density (HED) physics experiments experience complicated forcing for instability growth and mix due to the ubiquitous presence of multiple shocks interacting with perturbations on multiple material interfaces. One common driver of instability growth is successive shocks from the same direction. However, there is a severe lack of analytic work and modeling validation for same-sided successive shocks since they are extremely difficult to achieve with conventional (non-HED) drivers. Successive shocks access a large instability parameter space; idealized fluid theory [K. O. Mikaelian, Phys. Rev. A 31, 410 (1985)] predicts 15 different interface evolution scenarios for a sinusoidal perturbation. Growth becomes more complex for multi-mode, compressible HED systems. The Mshock campaign is the first experiment in any fluid regime to probe a wide portion of successive shock parameter space. This is enabled by our development of a hybrid direct/indirect drive platform capable of creating independently controllable successive shocks on the National Ignition Facility. These experiments have delivered the first data capable of rigorously challenging our models and their ability to accurately capture Richtmyer–Meshkov growth under successive shocks. Single-mode and two-mode experiments have successfully demonstrated the ability to access and control the various growth scenarios of the shocked interface, including re-inversion, freeze out, and continued growth. Simulations and theoretical modeling are shown to accurately capture the experimental observations in the linear growth phase, giving us confidence in our ICF/HED design codes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Instrumentation and experimental procedures for robust collection of X-ray diffraction data from protein crystals across physiological temperatures

Traditional X-ray diffraction data collected at cryo-temperatures have delivered invaluable insights into the three-dimensional structures of proteins, providing the backbone of structure–function studies. While cryo-cooling mitigates radiation damage, cryo-temperatures can alter protein conformational ensembles and solvent structure. Furthermore, conformational ensembles underlie protein function and energetics, and recent advances in room-temperature X-ray crystallography have delivered conformational heterogeneity information that can be directly related to biological function. Given this capability, the next challenge is to develop a robust and broadly applicable method to collect single-crystal X-ray diffraction data at and above room temperature. This challenge is addressed herein. The approach described provides complete diffraction data sets with total collection times as short as ~5 s from single protein crystals, dramatically increasing the quantity of data that can be collected within allocated synchrotron beam time. Its applicability was demonstrated by collecting 1.09–1.54 Å resolution data over a temperature range of 293–363 K for proteinase K, thaumatin and lysozyme crystals at BL14-1 at the Stanford Synchrotron Radiation Lightsource. Here, the analyses presented here indicate that the diffraction data are of high quality and do not suffer from excessive dehydration or radiation damage.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of Quaternary Pumped Storage Hydropower on Frequency Response of U.S. Western Interconnection with High Renewable Penetrations

As renewable penetration increases in the United States, maintaining stability and reliability of low-inertia power grid by providing sufficient frequency control capability becomes a challenge. Advanced pumped storage hydro technologies (APSH) will be expected to play an important role for future grid as not only an energy supplier, but also as an ancillary services provider. This paper studies the impact of using quaternary pumped storage hydropower (Q-PSH), as one of the newly proposed APSH technology, to provide primary frequency response. To quantify the impact of Q-PSH on frequency response of the U.S. Western Interconnection, a user-defined dynamic model of Q-PSH is developed on the GE Positive Sequence Load Flow (PSLF) platform and is implemented in a set of detailed U.S. Western Electricity Coordination Council (WECC) planning cases in which renewable penetration levels are 20%, 40%, 60% and 80%. Simulation results show that Q-PSH can help improve frequency nadir and settling frequency comparing to the conventional PSH.

frequency response↗
DOE OSTI2025

xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Digital Agriculture↗
DOE OSTI2020

CASL Annual Report (FY2018)

The Consortium for Advanced Simulation of Light Water Reactors (CASL) was established in July 2010 as the first US Department of Energy (DOE) Energy Innovation Hub. The consortium was initially funded for five years to develop advanced modeling and simulation (M&S) tools that can be used to analyze issues associated with the operation of US commercial light water reactors (LWRs). During this period, the program focused primarily on capabilities to solve challenges related to pressurized water reactor (PWR) operation. In January 2015, the program was extended for an additional five years with the goal of finalizing development and deployment of CASL tools for PWR analysis and extending the program’s tools for use in small modular reactor (SMR) operation and boiling water reactor (BWR) analyses. In February 2018, the CASL program was authorized to receive additional funds to focus on the use of advanced M&S tools in a regulatory environment.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Science & Technology Review - July 2021 Issue

In the early weeks of the COVID-19 pandemic, Lawrence Livermore researchers mobilized to address emerging challenges and develop capabilities for detecting the SARS-CoV-2 virus and fighting COVID-19, the diseased caused by the virus. As described in the article beginning on p. 4, the Laboratory’s Innovation and Partnerships Office provided technology transfer expertise to deploy existing, in-development, and new technologies to help detect SARS-CoV-2 and treat COVID-19.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Automating the uplink process for planetary missions

The dual challenges of increased capability along with lower costs are addressed with regard to flight operations. A program has been formulated to take advantage of advances in software and hardware in order to design, and prototype where indicated, a set of tools which can be applied to the building of a more productive uplink process for NASA missions that are envisaged for the the next decade. Techniques such as object-oriented programming, expert systems, natural-language parsing, interactive graphics, and parallel processing are employed in the quest for increased productivity. The paper is organized hierarchically with the level of 'goal', 'objectives' and 'tasks'. A brief review of the uplink process is included.

Mclaughlin, William I.↗
NASA NTRS1988

The Systems Autonomy Demonstration Project - Catalyst for Space Station advanced automation

The Systems Autonomy Demonstration Project (SADP) was initiated by NASA to address the advanced automation needs for the Space Station program. The application of advanced automation to the Space Station's operations management system (OMS) is discussed. The SADP's future goals and objectives are discussed with respect to OMS functional requirements, design, and desired evolutionary capabilities. Major technical challenges facing the designers, developers, and users of the OMS are identified in order to guide the definition of objectives, plans, and scenarios for future SADP demonstrations, and to focus the efforts on the supporting research.

Healey, Kathleen J.↗
NASA NTRS1992

Structures technology applications for the National AeroSpace Plane

The National AeroSpace Plane (NASP) presents a unique set of very complex structural problems that challenge our computational capabilities. Complex analyses are required in the conceptual design phase to achieve sufficient accuracy to address the extreme load conditions and to adequately evaluate vehicle weight. The computational capability must be available to perform these analyses in a rapid manner to accommodate the design process.

Little, T. E.↗
NASA NTRS1994

Evaluation of a liquid cooling garment as a component of the Launch and Entry Suit (LES)

The LES is a partial pressure suit and a component of the shuttle life support system used during launch and reentry. The LES relies on gas ventilation with cabin air to provide cooling. There are conditions during nominal launch and reentry, landing, and post-landing phases when cabin temperature is elevated. Under these conditions, gas cooling may result in some discomfort and some decrement in orthostatic tolerance. There are emergency conditions involving loss of cabin ECS capability that would challenge crew thermal tolerance. The results of a series of tests are presented. These tests were conducted to assess the effectiveness of a liquid-cooled garment in alleviating thermal discomfort, orthostatic intolerance, and thermal intolerance during simulated mission phases.

Waligora, J.↗
NASA NTRS1994

Scientific analysis of satellite ranging data

A network of satellite laser ranging (SLR) tracking systems with continuously improving accuracies is challenging the modelling capabilities of analysts worldwide. Various data analysis techniques have yielded many advances in the development of orbit, instrument and Earth models. The direct measurement of the distance to the satellite provided by the laser ranges has given us a simple metric which links the results obtained by diverse approaches. Different groups have used SLR data, often in combination with observations from other space geodetic techniques, to improve models of the static geopotential, the solid Earth, ocean tides, and atmospheric drag models for low Earth satellites. Radiation pressure models and other non-conservative forces for satellite orbits above the atmosphere have been developed to exploit the full accuracy of the latest SLR instruments. SLR is the baseline tracking system for the altimeter missions TOPEX/Poseidon, and ERS-1 and will play an important role in providing the reference frame for locating the geocentric position of the ocean surface, in providing an unchanging range standard for altimeter calibration, and for improving the geoid models to separate gravitational from ocean circulation signals seen in the sea surface. However, even with the many improvements in the models used to support the orbital analysis of laser observations, there remain systematic effects which limit the full exploitation of SLR accuracy today.

Smith, David E.↗