Switching Transition Control to Improve Efficiency of a DC/DC Power Electronic System
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42 ENGINEERING↗
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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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Review of Ultrafast Switching Power Modules: Trends, Challenges, and Technical Solutions
Benefiting from the superior properties of wide-bandgap semiconductor materials, wide-bandgap power devices demonstrate exceptional switching performance, enabling more efficient and compact power electronics systems. However, ultrafast switching poses challenges to the reliability of the system in terms of severe oscillations and voltage overshoot, electromagnetic interference, and increased risk of partial discharge. By developing advanced power module packaging for fast-switching power devices, the above-mentioned challenges can be mitigated at the packaging level, enabling the full utilization of the fast-switching capability of wide-bandgap devices. Meanwhile, such technology also lays the groundwork for packaging next-generation power devices with even higher blocking voltage and faster switching speed. In this paper, a comprehensive review of ultrafast switching power modules has been made, including the benefits and status of ultrafast switching power modules, challenges brought by ultrafast switching, and promising technologies to address these challenges. In addition, future development trends and research gaps are also discussed in this paper. Furthermore, this review can serve as a reference for future wide-bandgap power module packaging design.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters
The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Solving the sample size problem for resource selection functions
Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.
Street, Garrett M.↗
Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters
The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Submetering Resources: Commercial Buildings
This fact sheet summarizes resources available to commercial organizations to help them make the business case for and implement energy submetering.
Commercial, Energy Data, Submetering, Energy Effic↗
Semantic Interoperability to Enable Smart, Grid-Interactive Efficient Buildings
Achieving a widespread transition to grid-interactive, efficient buildings (GEBs) depends critically on there being sufficient interoperability among connected building systems. While many critical elements already exist at the technical interoperability level (TCP/IP, BACnet, etc.), a lack of interoperability in the semantic level hinders streamlined integration of interdependent applications. Semantics refers to expressing information about “things” in a way that can be consistently understood by applications. Key components of formalized semantics include identifying what a “thing” is (its “type”), defining general information about that “thing” (its characteristics or properties), and defining the appropriate relationships of that “thing” to other “things” (its function or role in a larger system). Although this might seem initially trivial, the success of smart building applications is highly dependent on maintaining consistent self-descriptive notions of the “things”. Without semantic interoperability, it is technically difficult, labor-intensive, and cost-prohibitive to enable three key objectives of GEBs: optimizing performance, automatically identifying and diagnosing faults, and delivering grid services. Industry, academia, and standards bodies have invested effort in developing information models to facilitate semantic interoperability, however, they have not been widely adopted across the U.S. commercial building portfolio. This paper will present a pathway to drive semantic interoperability through a three-pronged approach to be led by the DOE Building Technologies Office in partnership with NIST and multiple national laboratories comprising: 1) industry engagement and coordination across existing efforts; 2) a semantic interoperability standard that empowers building owners to identify and require interoperable attributes when procuring equipment and applications; 3) tools to assist in implementation and a test framework to verify compliance of products with semantic interoperability specifications. This approach is designed to accelerate the timeline for adoption of semantic interoperability specifications. The intent is to reduce soft costs associated with implementing advanced controls, fault detection and diagnostics, and other smart building technologies and use cases as a necessary step in achieving an energy efficient smart grid future.
30 DIRECT ENERGY CONVERSION↗
Low Carbon Technology Strategies: Large Office
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon large office buildings within their existing building portfolios. Large offices are typically over 50,000 square feet and often include complex heating and cooling systems.
analytics↗
Low Carbon Technology Strategies: Small Office
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon small office buildings within their existing building portfolios. Small offices are typically less than 50,000 square feet and often use packaged rooftop units for heating, cooling, and ventilation.
analytics↗
Low Carbon Technology Strategies: Supermarket
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon supermarkets within their existing building portfolios. Supermarkets include built-up refrigeration systems and refrigerated display cases and often use packaged rooftop units for heating, cooling, and ventilation.
analytics↗
Low Carbon Technology Strategies: Midrise Apartment
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon midrise apartment buildings within their existing building portfolios. Midrise apartments may use packaged rooftop units for heating, cooling, and ventilation or central plant systems for larger buildings.
analytics↗
Low Carbon Technology Strategies: Secondary School
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon secondary schools within their existing building portfolios. Secondary schools often include complex heating and cooling systems or packaged rooftop units and can include specialty equipment for gymnasiums, pools, and buses.
analytics↗
Low Carbon Technology Strategies: Primary School
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon primary schools within their existing building portfolios. Primary schools often use packaged rooftop units for heating, cooling, and ventilation.
analytics↗
Low Carbon Technology Strategies: Outpatient Healthcare
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon outpatient healthcare buildings within their existing building portfolios. Outpatient healthcare includes diagnostic and treatment facilities for outpatient care, and these buildings often use packaged rooftop units for heating, cooling, and ventilation.
analytics↗
Low Carbon Technology Strategies: Hospital
This document includes steps that building owners and operators can implement to achieve smart, healthy, and low-carbon hospitals within their existing building portfolios. Hospitals typically include complex heating and cooling systems and specialty medical equipment.
analytics↗