A trade off study of metal and composite turbines using fluid-structure interaction modeling.
Abstract not provided.
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Abstract not provided.
The Energy & Environmental Research Center (EERC) is leading the Williston Basin Carbon Ore, Rare Earth, and Critical Minerals (CORE-CM) Initiative, an initiative to drive the expansion and transformation of coal and coal-based resource usage within the Williston Basin to produce rare-earth elements (REEs), critical minerals (CMs), and nonfuel carbon-based products (NFCBPs). This presentation describes the program, initial results, and potential benefits.
This presentation highlights developments in Project PARETO’s ongoing produced water exchange tools and makes audience members aware of planned pilot studies in the near future. It is geared towards requesting volunteers to partake in noncommittal pilots to test the software tools and provide feedback to aid in continued development.
This project is intended to support the development of new traction drive systems that meet the targets of 100 kW/L for power electronics and 50 kW/L for electric machines with reliable operation to 300,000 miles. To meet these goals, new designs must be identified that make use of state-of-the-art and next-generation electronic materials and design methods. Designs must exploit synergies between components, for example converters designed for high-frequency switching using wide band gap (WBG) devices and ceramic capacitors. This project included: (1) a survey of available technologies; (2) investigating new technologies, that for example, reduce volume of thermal management or magnetic components; (3) the development of computer aided design tools that consider the converter volume, reliability, and electrical performance; (4) exercising the design software to evaluate performance gaps and predict the impact of certain technologies and design approaches, i.e. GaN semiconductors, ceramic capacitors, ceramic thermal management components, and select topologies; (5) building and testing hardware prototypes to validate models and concepts. The design tools enable co-optimization of the power module and passive elements and provide some design guidance. At the end of the project, new advanced computing methods, such as machine learning approaches, were considered.
Explore the source record for details and available documents.
This poster is a presentation at the DOE EERE VTO AMR for 2024
To be effective at maintaining indoor air conditions, a building heating, ventilation, and air conditioning (HVAC) system needs to be controlled in some fashion. The control system needs to sense the temperature of a space, feeding it to a control system, and manipulate the HVAC equipment to maintain desired temperatures for the occupants. Within the portfolio of buildings under the jurisdiction, custody, or control of the General Services Administration (GSA), approximately 20% have HVAC systems that are operated by pneumatic control systems. These buildings are under the same energy conservation mandates as all federal facilities. Unfortunately, the major drawback to pneumatic systems is that they are not capable of implementing automated energy-saving control strategies. They can’t automatically change the thermostat setting at night and on weekends in the same way as more modern control systems such as direct digital controls (DDCs). Converting a building from a pneumatic control system to DDCs is typically cost prohibitive.
The Ko’olauloa community resilience hub has affordable microgrid options to meet its resilience requirements. Renewables and battery are insufficient without very high cost. Either diesel generation or a hydrogen battery are needed to assure availability and keep cost down.
Explore the source record for details and available documents.
The National Reactor Innovation Center (NRIC) is a national program that was established as part of the Nuclear Energy Innovation Capabilities Act (NEICA). NRIC’s mission is to accelerate the demonstration and deployment of advanced nuclear energy through its mission to inspire stakeholders and the public, empower innovators, and deliver successful outcomes through efficient coordination of partners and resources. NRIC is designed to bridge the gap between research, development, and the marketplace to help convert some of the nation’s most promising advanced nuclear reactors into commercial applications. The NRIC Demonstration of Microreactor Experiments (DOME) facility, formerly known as Experimental Breeder Reactor II (EBR II), located at the Materials and Fuels Complex (MFC) at the Idaho National Laboratory (INL) is intended to allow industrial and other partners the opportunity to test Advanced Microreactors up to 20MW thermal power. The 75-ton capacity polar crane located in the EBR II facility was rendered inoperable to support planned facility demolition in 2015; small holes were flame cut in the girders; hoists and cables were removed; oil was drained from gear boxes; trolley drive, and then filled with absorbent; and the electrical and control umbilical’s were disconnected, removed and disposed. Subsequently, the decision was made to convert the EBR II facility into the DOME test bed.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Challenging combinatorial optimization problems are ubiquitous in science and engineering. Several quantum methods for optimization have recently been developed, in different settings including both exact and approximate solvers. Addressing this field of research, this manuscript has three distinct purposes. First, we present an intuitive method for synthesizing and analyzing discrete (i.e., integer-based) optimization problems, wherein the problem and corresponding algorithmic primitives are expressed using a discrete quantum intermediate representation (DQIR) that is encoding-independent. This compact representation often allows for more efficient problem compilation, automated analyses of different encoding choices, easier interpretability, more complex runtime procedures, and richer programmability, as compared to previous approaches, which we demonstrate with a number of examples. Second, we perform numerical studies comparing several qubit encodings; the results exhibit a number of preliminary trends that help guide the choice of encoding for a particular set of hardware and a particular problem and algorithm. Our study includes problems related to graph coloring, the traveling salesperson problem, factory/machine scheduling, financial portfolio rebalancing, and integer linear programming. Third, we design low-depth graph-derived partial mixers (GDPMs) up to 16-level quantum variables, demonstrating that compact (binary) encodings are more amenable to QAOA than previously understood. We expect this toolkit of programming abstractions and low-level building blocks to aid in designing quantum algorithms for discrete combinatorial problems.
A detailed understanding of the various factors affecting the trends in gross-indicated thermal efficiency with changes in key operating parameters has been carried out, applied to a one-liter displacement single-cylinder boosted Low-Temperature Gasoline Combustion (LTGC) engine. This work systematically investigates how the supplied fuel energy splits into the following four energy pathways: gross-indicated thermal efficiency, combustion inefficiency, heat transfer and exhaust losses, and how this split changes with operating conditions. Additional analysis is performed to determine the influence of variations in the ratio of specific heat capacities (γ) and the effective expansion ratio, related to the combustion-phasing retard (CA50), on the energy split. Heat transfer and exhaust losses are computed using multiple standard cycle analysis techniques. Furthermore, the various methods are evaluated in order to validate the trends.
The majority of anthropogenic CO 2 emissions are attributable to urban areas. While the emissions from urban electricity generation often occur in locations remote from consumption, many of the other emissions occur within the city limits. Evaluating the effectiveness of strategies for controlling these emissions depends on our ability to observe urban CO 2 emissions and attribute them to specific activities. Cost-effective strategies for doing so have yet to be described. Here we characterize the ability of a prototype measurement network, modeled after the Berkeley Atmospheric CO 2 Observation Network (BEACO 2 N) in California's Bay Area, in combination with an inverse model based on the coupled Weather Research and Forecasting/Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) to improve our understanding of urban emissions. The pseudo-measurement network includes 34 sites at roughly 2 km spacing covering an area of roughly 400 km 2 . The model uses an hourly 1 × 1 km 2 emission inventory and 1 × 1 km 2 meteorological calculations. We perform an ensemble of Bayesian atmospheric inversions to sample the combined effects of uncertainties of the pseudo-measurements and the model. We vary the estimates of the combined uncertainty of the pseudo-observations and model over a range of 20 to 0.005 ppm and vary the number of sites from 1 to 34. We use these inversions to develop statistical models that estimate the efficacy of the combined model–observing system in reducing uncertainty in CO 2 emissions. We examine uncertainty in estimated CO 2 fluxes on the urban scale, as well as for sources embedded within the city such as a line source (e.g., a highway) or a point source (e.g., emissions from the stacks of small industrial facilities). Using our inversion framework, we find that a dense network with moderate precision is the preferred setup for estimating area, line, and point sources from a combined uncertainty and cost perspective. The dense network considered here (modeled after the BEACO 2 N network with an assumed mismatch error of 1 ppm at an hourly temporal resolution) could estimate weekly CO 2 emissions from an urban region with less than 5 % error, given our characterization of the combined observation and model uncertainty.
Stakeholders of CSP and non-CSP high-intensity broadband flux measurements were surveyed and interviewed to obtain flux sensor design and calibration requirements. Existing sensor technologies and existing calibration facilities were then compared against this standard. Stakeholders require a flux sensor designed for >5,000 kW/m2 flux measurements, >1,000 life cycles, <500 ms response time, >60-minute exposure at maximum flux, and <5% measurement uncertainty. Stakeholders also require a sensor with minimal cost, short procurement lead time, and a high-intensity broadband flux calibration. Commercial CSP stakeholders primarily rely on infrared (IR) temperature measurements of receiver equipment to control CSP plant process operation, whereas CSP research and development (R&D) and non-CSP stakeholders rely on accurate flux gauge measurements for a variety of applications. It was determined that existing flux sensor technologies and calibration facilities do not comprehensively meet stakeholder needs. This study suggests a more robust circular foil gauge with a high-intensity solar flux calibration comprehensively meets stakeholder flux measurement needs. Improved circular foil gauge designs and an improved flux sensor calibration facility are discussed.
The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Planning for Major Energy Disruptions in the Southeast" focuses on strategies for entities in the Southeast to quickly recover from impacts to energy infrastructure caused by extreme events, such as severe weather and natural disasters. This presentation focuses on the tradeoffs and relative costs of different strategies to improve outcomes from these extreme events. The workshop is on May 21, 2026.
Phase II work is meant to develop an advanced understanding of how production of a renewable diesel blendstock from lignocellulosic biomass feedstocks via aerobic respiration at different production scales affects emissions of criteria pollutants and greenhouse gases (GHGs), local and regional air quality. The results could inform decisions by stakeholders (e.g., industry, community, air quality planners, investors) regarding relationships and tradeoffs between multiple environmental impacts and the challenges and opportunities for the design and deployment of a sustainable biofuel supply chain.