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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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Power plants use transformers for supplying generated electricity to the grid. As these transformers age and degrade, they produce specific gases. These gases can be monitored and used to predict when maintenance should be completed. This code uses four complementary techniques to predict when maintenance should be performed based on gas concentrations within the transformer.
In an effort to transform future space power system architectures and operations, the Advanced Modular Power Systems (AMPS) project is currently developing an interface standard for power electronic modules to enable modular architectures for space power systems. Each architecture would consist of one or more modular electronic units (MEU) that would comprise of a tailored combination of modules. The standard currently comprises modules enabling switchgear, power conversion, and data interfacing. The objective of the AMPS project is to enable the modularity and interchangeability of different architectures using these standardized modules in hopes of reducing complexity and cost, increasing inherent redundancy and reliability, and minimizing power system redesign for future programs. A key enabler to these future modular architectures will be the thermal management system, which has to contend with acquiring heat from the electronics modules at the card level and then transporting that heat to the radiator of the larger system. The focus of this paper, therefore, will be the thermal design and analysis of multiple AMPS power electronics modules and the development of their thermal-vacuum test.
This article proposes a multiport control method to enable partial power processing (PPP) in a medium-voltage (MV) multiport solid-state transformer (SST). MV multiport SSTs are promising in integrating low-voltage DC sources or loads such as solar photovoltaic, energy storage, and electric vehicles into smart grids without bulky line-frequency transformers. Compared to voltage-source SST, current-source (CS) SST features single-stage isolated bidirectional AC/AC, AC/DC, or DC/DC conversion using an inductive DC link. For a multiport CS SST, it is revealed in this article that the PPP capability can be enabled through the proposed control without extra hardware, different from the case of voltage-source converters where special hardware architecture is required for the PPP. With the PPP, most power exchange between LV ports is processed by only a fraction of the entire conversion stage, leading to reduced DC-link current, volume, loss, and improved efficiency. The proposed multiport PPP control scheme is analyzed to verify the advantages across a wide voltage and power range against conventional full power processing (FPP) multiport control, using the soft-switching solid-state transformer (S4T) with reduced conduction loss as an example. Comparative experimental results based on a SiC three-port S4T prototype verify the effectiveness of the proposed PPP scheme against the FPP scheme under both steady state and dynamic conditions. Here, the DC-link current reduction is measured to be more than 36%. Significantly, the proposed multiport PPP control scheme is generic and applicable to any hard-switching or soft-switching CS SSTs without extra hardware.
The goal of this project is to develop packaging technologies for making high-temperature, high-density, and low-profile wide-bandgap (WBG) power electronics modules for electric drives. These modules are aimed at enabling the DOE’s University Consortium to reach its 2025 inverter targets of ≥ 100 kW/L and ≤ 2.7 $/kW. The specific objectives are to: design and fabricate SiC half-bridge power modules with double-sided cooling and parasitic inductances < 5 nH, heat flux density > 400 W/cm 2 , and working junction temperature of 200 o C; design, fabricate, and deliver a gate driver with double-sided cooled modules for the construction of a 100 kW/L inverter at Oak Ridge National Lab; and design and prototype intelligent gate drivers with integrated current sensor and a low-profile DC-DC power supply with air-core transformer for testing power modules at 200 o C junction temperature. We followed an iterative technical approach of design, simulation, fabrication, and testing of various versions of modules, current sensors, and power supply. The state-of-the-art silicon carbide devices rated at 1.2 kV and 149 A were packaged by sintered-silver bonding on an aluminum nitride direct-bond-copper substrate for high thermal conductivity, high working temperature, and high joint reliability. Porous silver posts were used to interconnect the device’s source pads to the other direct-bond-copper substrate for low mechanical stresses, ease of manufacturing, and double-sided cooling. A current sensor based on package parasitic inductance was developed to measure switching current. A dynamic feedback scheme was developed to compensate the effect of parasitic resistance and temperature variation. A constant-current class-E dc-dc converter with air-core transformer was developed. Air-core transformer was used due to the unavailability of magnetic core at high temperatures. Gate driver and power supply were integrated with the double-side cooled, high temperature SiC power modules for testing the modules at 200 o C junction temperature. Double-pulse and continuous testing of the integrated technologies validated the design and fabrication of the three component technologies. Throughout the project, we overcame the challenge for design verification caused by low prototyping yield, which then helped train the graduate students, the future workforce, to learn the engineering know-how for low-cost manufacturing of reliable products. Below is a summary of the major accomplishments of this project: development of a prototyping process for fabricating double-side cooled (1200 V, 149 A) SiC phase-leg modules capable of working to 200 o C Tj; simulation and experimental verification of the improvement of thermo-mechanical reliability of the double-side cooled SiC phase-leg module by using rigid encapsulant; design and experimental validation of a current sensor based on package parasitic inductance and a compensation solution for eliminating the effect of parasitic resistance; design and experimental validation of a low-profile power supply with six-output air-core transformer for gate driver; functional demonstration of a SiC phase-leg module integrated with its gate driver, current sensor, and an air-core power supply at 200 o C Tj in a double-pulse switching test setup and Buck converter continuous test setup; successful completion of six PhD and two MS students who are or will work at Apple Inc., Tesla Inc., Wolfspeed Inc., Microchip Inc., Monolithic Power Systems Inc., and LG Magna Inc.
Taking advantage of the similarities that exist among all waveform-based non-destructive evaluation (NDE) methods, a common software platform has been developed containing multiple- signal and image-processing techniques for waveforms and images. The NASA NDE Signal and Image Processing software has been developed using the latest versions of LabVIEW, and its associated Advanced Signal Processing and Vision Toolkits. The software is useable on a PC with Windows XP and Windows Vista. The software has been designed with a commercial grade interface in which two main windows, Waveform Window and Image Window, are displayed if the user chooses a waveform file to display. Within these two main windows, most actions are chosen through logically conceived run-time menus. The Waveform Window has plots for both the raw time-domain waves and their frequency- domain transformations (fast Fourier transform and power spectral density). The Image Window shows the C-scan image formed from information of the time-domain waveform (such as peak amplitude) or its frequency-domain transformation at each scan location. The user also has the ability to open an image, or series of images, or a simple set of X-Y paired data set in text format. Each of the Waveform and Image Windows contains menus from which to perform many user actions. An option exists to use raw waves obtained directly from scan, or waves after deconvolution if system wave response is provided. Two types of deconvolution, time-based subtraction or inverse-filter, can be performed to arrive at a deconvolved wave set. Additionally, the menu on the Waveform Window allows preprocessing of waveforms prior to image formation, scaling and display of waveforms, formation of different types of images (including non-standard types such as velocity), gating of portions of waves prior to image formation, and several other miscellaneous and specialized operations. The menu available on the Image Window allows many further image processing and analysis operations, some of which are found in commercially-available image-processing software programs (such as Adobe Photoshop), and some that are not (removing outliers, Bscan information, region-of-interest analysis, line profiles, and precision feature measurements).
Radioisotope power systems (RPS) have been essential to the U.S. exploration of outer space. RPS have two primary uses: electrical power and thermal power. To provide electrical power, the RPS uses the heat produced by the natural decay of a radioisotope (e.g., plutonium-238 in U.S. RPS) to drive a converter (e.g., thermoelectric elements or Stirling linear alternator). As a thermal power source the heat is conducted to whatever component on the spacecraft needs to be kept warm; this heat can be produced by a radioisotope heater unit (RHU) or by using the excess heat of a radioisotope thermoelectric generator (RTG). As of 2010, the U.S. has launched 41 RTGs on 26 space systems. These space systems have ranged from navigational satellites to challenging outer planet missions such as Pioneer 10/11, Voyager 1/2, Galileo, Ulysses, Cassini and the New Horizons mission to Pluto. In the fall of 2011, NASA plans to launch the Mars Science Laboratory (MSL) that will employ the new Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) as the principal power source. Hundreds of radioisotope heater units (RHUs) have been launched to provide warmth to Apollo 11, used to provide heating of critical components in a seismic experiment package, Pioneer 10/11, Voyager 1/2, Galileo, Cassini, Mars Pathfinder, MER rovers, etc. to provide temperature control to critical spacecraft electronics and other mechanical devices such as propulsion system propellant valves. A radioisotope (electrical) power source or system (RPS) consists of three basic elements: (1) the radioisotope heat source that provides the thermal power, (2) the converter that transforms the thermal power into electrical power and (3) the heat rejection radiator. Figure 1 illustrates the basic features of an RPS. The idea of a radioisotope power source follows closely after the early investigations of radioactivity by researchers such as Henri Becquerel (1852-1908), Marie Curie (1867-1935), Pierre Curie (1859-1906) and R. J. Strut. Almost 100 years ago, in 1913, English physicist H. G. J. Moseley (1887-1915) constructed the first nuclear battery using a vacuum flask and 20 mCi of radium (Corliss and Harvey, 1964, Proceedings of the Royal Society, 1913). After World War II, serious interest in radioisotope power systems in the U.S. was sparked by studies of space satellites such as North American Aviation s 1947 report on nuclear space power and the RAND Corporation s 1949 report on radioisotope power. (Greenfield, 1947, Gendler and Kock, 1949). Radioisotopes were also considered in early studies of nuclear-powered aircraft (Corliss and Harvey, 1964). In 1951, the U.S. Atomic Energy Commission (AEC) signed several contracts to study a 1-kWe space power plant using reactors or radioisotopes. Several of these studies, which were completed in 1952, recommended the use of RPS. (Corliss and Harvey, 1964). In 1954, the RAND Corporation issued the summary report of the Project Feedback military satellite study in which radioisotope power was considered (Lipp and Salter, 1954, RAND). Paralleling these studies, in 1954, K. C. Jordan and J. H. Birden of the AEC s Mound Laboratory conceived and built the first RTG using chromel-constantan thermocouples and a polonium-210 (210Po or Po-210) radioisotope heat source (see Figure 2). While the power produced (1.8 mWe) was low by today s standards, this first RTG showed the feasibility of RPS. A second thermal battery was built with more Po-210, producing 9.4 mWe. Jordan and Birden concluded that the Po-210 thermal battery would have about ten times the energy of ordinary dry cells of the same mass (Jordan and Birden, 1954). The heat source consisted of a 1-cm-diameter sphere of 57 Ci (1.8 Wt) of Po-210 inside a capsule of nickel-coated cold-rolled steel all inside a container of Lucite. The thermocouples were silver-soldered chromel-constantan. The thermal battery produced 1.8 mWe.
This presentation focuses on the challenge of integrating AI-driven data centers with the power grid at scale. It examines the AI data center capacity challenge and the role of new Medium Voltage Direct Current (MVDC) and other grid-enhancing technologies in enabling efficient and reliable power delivery. The session will highlight the National Laboratory of the Rockies' ARIES capabilities and planning tools, along with collaborative examples involving Verrus, Compass, and Schneider through the Agora test bed for grid-friendly data center evaluations, and ON. Energy for UPS evaluation. It will showcase the NLR Stable Grid Platform for studying oscillations caused by large-scale data centers, along with planning tools to assess grid security and reliability. Additionally, the presentation covers reconductoring strategies to increase grid capacity and explores innovative data center architectures, including the Advanced DC Architectures with Power-electronic Transformers (ADAPT) platform, which enables testing of complete DC architectures for data centers.
Two algorithms are developed for calculating power spectrums from the autocorrelation function when there are missing data points in the time series. Both methods use an average sampling interval to compute lagged products. One method, the correlation function power spectrum, takes the discrete Fourier transform of the lagged products directly to obtain the spectrum, while the other, the modified Blackman-Tukey power spectrum, takes the Fourier transform of the mean lagged products. Both techniques require fewer calculations than other procedures since only 50% to 80% of the maximum lags need be calculated. The algorithms are compared with the Fourier transform power spectrum and two least squares procedures (all for an arbitrary data spacing). Examples are given showing recovery of frequency components from simulated periodic data where portions of the time series are missing and random noise has been added to both the time points and to values of the function. In addition the methods are compared using real data. All procedures performed equally well in detecting periodicities in the data.
A thermoelectrically temperature controlled quartz crystal microbalance (QCM) system was developed for the measurement of ion thrustor generated mercury contamination on spacecraft. Meaningful flux rate measurements dictated an accurately held sensing crystal temperature despite spacecraft surface temperature variations from -35 C to +60 C over the flight temperature range. An electronic control unit was developed with magentic amplifier transformer secondary power supply, thermal control electronics, crystal temperature analog conditioning and a multiplexed 16 bit frequency encoder.
Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.
Rapid increases in distributed energy resources on distribution systems are prompting research efforts to improve and evaluate electric power distribution algorithms; however, there is a shortage of realistic, large-scale, U.S.-style test systems for the evaluation of such advanced algorithms. Some available tools to build large-scale test systems are of European style, and their application to studies focused on the United States might not be desirable given very different characteristics between the European and U.S. distribution designs. Motivated by this need, this paper develops detailed algorithms to build large-scale U.S. distribution systems and incorporates them in a new Reference Network Model, RNM-US. The approach starts with information from street maps and a catalog with electric equipment that includes power lines, transformers, voltage regulators, capacitors, and switching devices. The paper presents the algorithms through an illustrative case study of the different steps that comprise the process of building a synthetic distribution grid. Finally, the paper presents a medium- and a large-scale data set covering 10 million electrical nodes and 120,000 km of power lines, demonstrating the applicability of the proposed method to build very large-scale synthetic distribution systems.
Method and apparatus is presented for producing a phase-modulated waveform having a high degree of linearity between the modulating signal and the phase of the modulated carrier signal. Two signals representing finite odd and even power series transformations of the modulating signal are produced and multiplied with two quadrature components of the input carrier signal, respectively. One of the multiplied signals is subtracted from the other and the resulting signal is hard-limited to produce a phase-modulated output signal. The means for producing the two signals representing the odd and even power series of the modulating signal includes means for varying the coefficients of the two power series. By means of an existing computer program, the coefficients of the two power series are selected such that there is an extremely high degree of linearity between the modulating signal and the phase of the modulated carrier signal.
This report covers key lessons learned for the rapid decarbonization of power systems, emphasizing best practices in planning, building, and operating power systems. This report is the result of a collaborative effort among various Clean Energy Ministerial (CEM) workstreams and partner initiatives: 21st Century Power Partnership (21CPP); Carbon Capture, Utilization and Storage (CCUS); Global Power System Transformation Consortium (G-PST), a partner to CEM; Industrial Deep Decarbonization Initiative (IDDI); International Energy Agency Digital Demand-Driven Electricity Networks Initiative (IEA 3DEN), a partner to CEM; International Smart Grid Action Network (ISGAN); Long Term Scenarios for the Energy Transition (LTES); Nuclear Innovation: Clean Energy Future (NICE Future); Super-Efficient Equipment and Appliance Deployment (SEAD). The contents are not intended to be comprehensive of all power sector topics, and there may be overlap between content in each section due to the nature of this first-of-its-kind collaborative effort to deliver unified messaging on power sector decarbonization to energy ministers. This work is intended to complement other work at the Clean Energy Ministerial and offers options for consideration, not specific policy recommendations.
The purpose of this project was to develop a new breed of Power Conversion Systems (PCS) for PV integration that is enabled by the newly developed 4-Quadrant Single Die SiC Power Semiconductor Switches (4-QPS) or also referred to as “Bidirectional FET (BIDFET)”. This work includes semiconductor die development, advanced packaging, converter design, development, and testing of 4-QPS enabled hardware prototypes at 1 kW (for single phase residential application) and 10 kW (for three phase commercial application). The BiDirectional Field-Effect Transistor (BiDFET) can enable circuit topologies requiring four-quadrant switches, that were earlier designed using discrete combinations of MOSFETs, IGBTs, GaN HEMTs, and PiN diodes. The monolithic nature of the BiDFET allows lower device count, smaller switch volume, lower inductance, and simpler packaging, and hence more reliable and commercially viable implementation in power electronics converters. The matrix converter topologies, now feasible using BiDFETs, can eliminate the bulky and unreliable dc link capacitors or inductors required for conventional voltage-source or current-source converters in ac–ac and ac–dc applications. The 1.2 kV BiDFET has the potential to disrupt all the applications utilizing 1.2 kV switches, including electric vehicle (EV) drivetrain, bidirectional EV chargers, industrial motor drives, solid-state transformers, datacenter power supplies, elevator drives, dc microgrids, energy storage grid integration, solid-state breakers, etc.
Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.
This project develops a Bayesian optimization approach to extracting insights from Z Machine experimental data to determine if and how these insights can be used to extrapolate to a larger facility. The primary goal is to address the scientific challenge of informing how confidently experimental conditions can be predicted on a next generation facility, the design of which requires the reliable extrapolation of current high energy density technologies to regimes yet unobserved, except by costly high-fidelity computational models. Maximizing the use of presently available data and understanding how it informs future endeavors is critically important to enable transformative pulsed power and the science of extreme conditions. We explore a Bayesian optimization approach to experimental design which combines information theory, experimental data, and computational modeling to explore how information gain can be maximized.
The transformation of the power system to include more distributed energy resources (DER) implies an increase in the number of inverter-based resources deployed on the grid. Envisioning future scenarios, this paper presents a small-signal stability analysis for a power grid comprising synchronous generators and inverter-based resources. Three types of inverter control are considered: grid following, droop-controlled grid forming, and virtual oscillator control grid forming. Although small-signal stability of power systems is a widely studied topic, systematic analysis of mixed machine-inverter systems with detailed control models at various inverter penetration levels are limited. This paper addresses the gap with numerical simulations tailored to the IEEE 39-bus system. Results show the system may become unstable at high inverter penetration level of grid-following inverters, and grid-forming inverter control can potentially improve system stability, thereby enabling very high level of DERs.
Abstract Supercritical CO2 (sCO2) power cycles designed for thermodynamic efficiencies above 50% push component designs into pressure and temperature combinations that approach the limits of American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code and ASME Piping Design Codes with Turbine inlet conditions near 250 bar and 715 °C. These design conditions which limit available materials to high nickel alloys are compounded by the high fluid densities of sCO2 at these conditions which result in very compact machinery at the 10 MWe class which are currently being developed for the DOE supercritical transformational electric power (STEP) program. Design of the STEP Turbine is complete with integrated lessons learned from a previous 1 MWth subscale demonstration of a high temperature 10 MWe Turbine designed under the DOE SunShot Program. Fabrication of the STEP Turbine is nearing completion with all housings and pressure containing components in hand. This paper provides a detailed look at design intentions and limitations for the pressurized sections of the main pressure casing and the fabrication of key turbine components. Additional discussion is provided regarding postfabrication inspection and acceptance of the casing for use after issues with casing welds were identified late in the fabrication process.