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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Decomposing sources of value for electricity and negative emissions technologies in net-zero power systems

Deep decarbonization of the US power system would require rapid deployment of variable renewable energy (VRE) resources, which are projected to provide a substantial share of electricity generation at the time of net-zero emissions. However, the exact share of generation met by VRE and the roles of other technologies in supplying key electricity services—energy and firm capacity—remain uncertain. This study employs a detailed model of the US power sector to decompose the provision and value of electricity services, including negative emissions, by technology across a range of deep decarbonization scenarios. Results indicate that while technology deployment and the share of services provided by each technology vary significantly depending on future technological and market conditions, the value composition and future roles of individual technologies remain consistent. These findings offer guidance for research and development priorities and provide insights to inform electricity policy and planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Applying a science‐based systems perspective to dispel misconceptions about climate effects of forest bioenergy

Abstract The scientific literature contains contrasting findings about the climate effects of forest bioenergy, partly due to the wide diversity of bioenergy systems and associated contexts, but also due to differences in assessment methods. The climate effects of bioenergy must be accurately assessed to inform policy‐making, but the complexity of bioenergy systems and associated land, industry and energy systems raises challenges for assessment. We examine misconceptions about climate effects of forest bioenergy and discuss important considerations in assessing these effects and devising measures to incentivize sustainable bioenergy as a component of climate policy. The temporal and spatial system boundary and the reference (counterfactual) scenarios are key methodology choices that strongly influence results. Focussing on carbon balances of individual forest stands and comparing emissions at the point of combustion neglect system‐level interactions that influence the climate effects of forest bioenergy. We highlight the need for a systems approach, in assessing options and developing policy for forest bioenergy that: (1) considers the whole life cycle of bioenergy systems, including effects of the associated forest management and harvesting on landscape carbon balances; (2) identifies how forest bioenergy can best be deployed to support energy system transformation required to achieve climate goals; and (3) incentivizes those forest bioenergy systems that augment the mitigation value of the forest sector as a whole. Emphasis on short‐term emissions reduction targets can lead to decisions that make medium‐ to long‐term climate goals more difficult to achieve. The most important climate change mitigation measure is the transformation of energy, industry and transport systems so that fossil carbon remains underground. Narrow perspectives obscure the significant role that bioenergy can play by displacing fossil fuels now, and supporting energy system transition. Greater transparency and consistency is needed in greenhouse gas reporting and accounting related to bioenergy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Adaptive control of wave energy converters

A wave energy capture system deployed in water converts mechanical motion induced by waves in the water to electrical energy. A controller of the wave energy capture system receives input regarding real-time wave conditions in a vicinity of the wave energy capture system. The controller applies a control model to the received input to select a value of a control parameter for the wave energy capture system, where the control model includes a model that has been trained using machine learning to take wave condition data as input and to output control parameter values selected based on the wave condition data in order to increase an amount of energy captured by the wave energy capture system. The controller implements the selected value of the control parameter on the wave energy capture system.

Orona, Alexander J.↗
DOE OSTI2023

AI-Based EMT Dynamic Model of PV Systems

Several electromagnetic transient (EMT) dynamic modeling methods are available to model systems like photovoltaic (PV) plants, wind power plants, variable-speed drives, among others. The methods include: (a) physics-based models and (b) data-driven models. The physics-based dynamic models may include high-fidelity switched system model and average-value model that both require the control algorithms included in the models. However, manufacturers typically prefer to provide black-box models to avoid disclosing proprietary. One of the solutions to prevent disclosing control algorithms is the use of data-driven dynamic EMT models of PV systems. In this paper, data-driven dynamic EMT model based on artificial intelligence (AI) algorithms are presented. The AI algorithms evaluated include convolutional neural networks, recurrent neural networks, and nonlinear auto-regressive exogenous model. Automation in generating data and training these models is also discussed in this paper. The results generated by the best AI algorithms have been observed to be greater than 95 % accurate.

Debnath, Suman↗
DOE OSTI2024

Photovoltaic inverter-based quantification of snow conditions and power loss

Snow is a significant challenge for photovoltaic (PV) systems at northern latitudes, where the pace of deployment is rapid but snow-related power losses can exceed 30% of annual production. Accurate snow-related power loss estimation methods for utility-scale sites can support snow mitigation strategies, inform resource planning and validate predictive snow-loss models. This study builds on our previous work on inverter-based detection of snow, and its implications for utility-scale power production, by validating the accuracy of our snow-loss method across different PV sites and system designs and highlighting its value in bringing greater visibility to PV plant operations in winter. Our estimation method is both novel and scalable, requiring only standard monitoring data to correlate snow-related losses with meteorological data. As demonstrated here, our validation method involved three main steps: 1) estimation of performance losses for multiple systems by comparing measured inverter data to modeled data; 2) application of a detection framework to identify which performance losses are snow-related; and 3) comparison of snow-related losses among three utility-scale sites differing in tilt angle. Results show that utility-scale systems at higher tilt angles consistently shed snow more quickly/completely than their lower-tilt counterparts. Further, monthly and seasonal snow losses are inversely and non-linearly correlated with tilt angle when normalized for cumulative snowfall. These results are consistent with the findings of previous studies and support the broad applicability of this method to fixed-tilt utility-scale PV systems around the world that routinely experience snow-related performance losses.

Cooper, Emma C. (ORCID:0000000190554098)↗
DOE OSTI2021

An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Commuter preferences for a first-mile/last-mile microtransit service in the United States

Transportation system models rely heavily upon value of time (VOT) estimates to predict customer behavior. Accurate VOT estimates are particularly vital for planning new services such as on-demand ride hailing or microtransit because customers’ sensitivity to wait time, walk time, and route detour time affects their likelihood of selecting these modes. If an incorrect VOT is assumed during service planning, then ridership will be depressed because of a mismatch between their preferences and how the system is designed. In this paper, we report on the measurement of VOT for microtransit, a shared first-mile/last-mile mobility service, obtained using stated preference microdata from four U.S. cities. Here, we found a median in-vehicle VOT for microtransit of $\$$18.63 (95% CI: $\$$13.39–$\$$24.46) and an access VOT of $\$$75.38 (95% CI: $\$$59.22–$\$$94.96). The former is practically equal to the VOT we found for respondents’ current modes ($\$$20.24, 95% CI: $\$$13.71–$\$$26.94). We also found that men, younger riders, the highly educated, and transit riders are more likely to be interested in microtransit. Since the disutility of time spent on microtransit is not higher than that of other modes, we believe this new service has the potential to attract riders, and particularly if the system is designed with low waiting and walking times.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Best Practices Guide for Energy-Efficient Data Center Design

This guide provides an overview of best practices for energy-efficient data center design which spans the categories of information technology (IT) systems and their environmental conditions, data center air management, cooling and electrical systems, and heat recovery. IT system energy efficiency and environmental conditions are presented first because measures taken in these areas have a cascading effect of secondary energy savings for the mechanical and electrical systems. This guide concludes with a section on metrics and benchmarking values by which a data center and its systems energy efficiency can be evaluated. No design guide can offer “the most energy-efficient” data center design but the guidelines that follow offer suggestions that provide efficiency benefits for a wide variety of data center scenarios.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Value-Added Process Intensification in the Supply Chain

The project goal is to assess opportunities for value-adds in the supply chain through designing a feedstock logistics systems that uses moisture during the residence time in storage to protect and increase feedstock value.

09 BIOMASS FUELS↗
DOE OSTI2021

Local and global decision fusion for cyber-physical system abnormality detection

Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.

Abbaszadeh, Masoud↗
DOE OSTI2024

Direct Air Capture and Utilization System (DACUS): FEED Study of CarbonCapture Inc. DAC and CarbonCure Utilization Technologies Using United States Steel’s Gary Works Plant Waste Heat

The effects of climate change are drastically perceived through the alarming rates of severe events such as flooding, hurricanes, wildfires, global temperature rise, sea-level rise, and ocean acidification etc., which has prompted the nations across the globe to control & reduce greenhouse gas (GHG) emissions, by massive adoption of electric vehicles, switching to renewal energy sources and promoting sustainable green products. However, recent climate studies have shown that such efforts will not be enough to keep the global temperature rising under 1.5° Celsius by the year 2050, which lead to the development of multiple Direct Air Capture (DAC) technologies. A DAC system extracts CO2 directly from the atmosphere, which is either stored underground for long term or utilized in other processes. The DACUS proposed at U.S. Steel Corporation’s Gary Works Plant in Gary, Indiana, will include an innovative DAC technology from CarbonCapture Inc. (CCI), which will be collocated and powered by waste heat from the steel plant to capture CO2 from ambient air, and CarbonCure CO2 utilization technology- which is a commercial technology that is already installed and in use by early adopters in more than 150 concrete mixing plants across North America and Asia. The University of Illinois at Urbana Champaign spearheaded this Front-End Engineering Design (FEED) study project, which was funded by US Department of Energy (DOE), aimed at designing a state-of-the art DACU system that can remove minimum of 5,000 tonne/year net CO2 from air (based on cradle-to-gate LCA) and converting the captured CO2 into low carbon intensity products. The off takers of the low Carbon product produced by the DACUS have also been identified near the steel plant in the Indiana, Illinois & Wisconsin Region. This creates a full CO2 value chain and provides a means to assess the impact of this holistic approach on job creation, regional economic impact, and environmental justice. DACUS is a promising new technology for reducing greenhouse gas in the atmosphere. Operation costs are minimized by locating CCI’s innovative DAC collector at United States Steel's Gary Works site where waste heat is utilized. Carbon emissions are minimized in CO2 transportation. The captured CO2 will be permanently sequestered in concrete used as construction material. This DOE funded FEED study demonstrates a full CO2 value chain for a DACU system, supported by an industrial facility. If this project were constructed, it would have a very positive and lasting impact on both the environment and the community.

42 ENGINEERING↗
DOE OSTI2021

PV Fleet Performance Data Initiative: Performance Index-Based Analysis

In this analysis, we report on 250 PV systems throughout the US, comprising 157 MWdc of system capacity and over 10,000 monthly PI values based on high-frequency (subhourly) energy data and satellite-based resource data. The distribution of PI values is analyzed, and multiple causes of underperformance are assessed, including first-year startup issues, snowfall and inverter downtime. An initial distribution of raw monthly PI values was collected with mean measured / modeled performance of PI = 0.935. After correcting for the three identifiable loss factors mentioned above, an average monthly performance of PI = 0.994 was obtained, with a distribution closely following a Gumbel Extreme Value distribution. In particular, inverter availability was found to contribute a system energy loss of 2.3% on average, except in the first six months of operation when availability losses are closer to 8%. Other startup issues beyond inverter downtime such as partial string outage contributed an additional 5% underperformance in the first year of operation across the fleet. Winter performance was also found to be 5%-10% below summer performance on average, likely due to snowfall. A simple linear relationship was found between snow loss and monthly snowfall accumulation in cm, indicating between 6% - 40% loss in months with nonzero snowfall, depending on climate.

14 SOLAR ENERGY↗
DOE OSTI2021

Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review

The goal of the circular economy (CE) is to transition from today's take-make-waste linear pattern of production and consumption to a circular system in which the societal value of products, materials, and resources is maximized over time. Yet circularity in and of itself does not ensure social, economic, and environmental performance (i.e., sustainability). Sustainability of CE strategies needs to be measured against their linear counterparts to identify and avoid strategies that increase circularity yet lead to unintended externalities. The state of the practice in quantitatively comparing sustainability impacts of circular to linear systems is one of experimentation with various extant methods developed in other fields and now applied here. While the proliferation of circularity metrics has received considerable attention, to-date, there is no critical review of the methods and combinations of methods that underlie those metrics and that specifically quantify sustainability impacts of circular strategies. Our critical review herein analyzes identified methods according to six criteria: temporal resolution, scope, data requirements, data granularity, capacity for measuring material efficiency potentials, and sustainability completeness. Results suggest that the industrial ecology and complex systems science fields could prove complementary when assessing the sustainability of the transition to a CE. Both fields include quantitative methods differing primarily with regard to their inclusion of temporal aspects and material efficiency potentials. Moreover, operations research methods such as multiple-criteria decision-making (MCDM) may alleviate the common contradictions which often exist between circularity metrics. This review concludes by suggesting guidelines for selecting quantitative methods most appropriate to a particular research question and making the argument that while there are a variety of existing methods, additional research is needed to combine existing methods and develop a more holistic approach for assessing sustainability impacts of CE strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Demonstration of Decentralized Physics-Driven Learning

In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain, neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine-learning tasks from local evolution rules. However, to date, such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here, we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this method, our system effectively trains itself, optimizing its resistance values without the use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. Here, we demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks, where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Automated Calibration of Parallel and Distributed Computing Simulators: A Case Study

Many parallel and distributed computing research results are obtained in simulation, using simulators that mimic real-world executions on some target system. Each such simulator is configured by picking values for parameters that define the behavior of the underlying simulation models it implements. The main concern for a simulator is accuracy: simulated behaviors should be as close as possible to those observed in the real-world target system. This requires that values for each of the simulator's parameters be carefully picked, or “calibrated,” based on ground-truth real-world executions. Examining the current state of the art shows that simulator calibration, at least in the field of parallel and distributed computing, is often undocumented (and thus perhaps often not performed) and, when documented, is described as a labor-intensive, manual process. In this work we evaluate the benefit of automating simulation calibration using simple algorithms. Specifically, we use a real-world case study from the field of High Energy Physics and compare automated calibration to calibration performed by a domain scientist. Our main finding is that automated calibration is on par with or significantly outperforms the calibration performed by the domain scientist. Furthermore, automated calibration makes it straightforward to operate desirable tradeoffs between simulation accuracy and simulation speed.

Mc donald, Jesse↗
DOE OSTI2025

Supporting Special Values in ZFP

This white paper outlines potential approaches to supporting special values in the ZFP numerical compressor without breaking backwards compatibility. Other than infinities and NaNs, special values are often used to indicate the absence of data, where no value is defined, for example by designating finite but extreme “fill values” as special. Such fill values are commonly used in earth system science, among other applications, but if left as is during compression lead to artifacts and loss of precision in nearby true values. Multiple candidate solutions that would allow ZFP to recognize special values are here proposed. Until such support is available, we also sketch available workarounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Evaluating the Impacts of Coordination for Cascaded Hydropower Plants on the Mid-Columbia

The Mid-Columbia region in the U.S. Pacific Northwest includes seven large, cascaded hydropower plants with a combined nameplate capacity of nearly 14 GW. These plants have several different operators, and there is currently limited coordination among the operators. We develop a detailed model of the Mid-Columbia system that captures both river flow constraints and power system operations. We then place this detailed model within a production cost model of a 2030 system of the Western Interconnection to evaluate how coordination of the Mid-Columbia hydropower plants might impact operations throughout the interconnection. We find that coordination of the Mid-Columbia plants leads to more generation from the Mid-Columbia plants and increased revenues for the Mid-Columbia operators. This benefit is achieved primarily through better utilization of the storage reservoirs and decreased spill through the cascade. We also evaluate the impacts of look-ahead on production costs and find that longer look-ahead values result in lower system-wide production costs.

13 HYDRO ENERGY↗
DOE OSTI2020

Effective viscosities in a hydrodynamically expanding boost-invariant QCD plasma

Background: The near-equilibrium properties of a QCD plasma can be encoded into transport coefficients such as bulk and shear viscosity. In QCD, the ratio of these transport coefficients to entropy density, ζ/$\textit{s}$ and η/$\textit{s}$, depends nontrivially on the plasma's temperature. This is unlike in conformal systems where they take constant values such as η/$\textit{s}$ = 1/(4π). Purpose: In this work, we show that in a (0 + 1)D boost-invariant fluid with no transverse expansion, a temperature-dependent ζ/$\textit{s(T)}$ or η/$\textit{s(T)}$ can be described by an equivalent effective viscosity $\langleζ/s\rangle_{\text{eff}}$ or $\langleη/s\rangle_{\text{eff}}$. This effective viscosity combines the actual temperature-dependent ζ/$\textit{s(T)}$ or η/$\textit{s(T)}$ with the temperature profile of the fluid. Further, we extend the concept of effective viscosity in systems with transverse expansion and discuss how effective viscosities can be used to identify families of ζ/$\textit{s(T)}$ and η /$\textit{s(T)}$ that lead to similar hydrodynamic evolution. Methods: The Navier-Stokes relativistic hydrodynamic equations are used to provide a first definition of effective viscosity, in (0 + 1)D and (1 + 1)D. In the (0 + 1)D case, the analysis is extended to Israel-Stewart-type second-order hydrodynamics to clarify the effect of higher-order hydrodynamics corrections on the effective viscosity. Results: In a boost-invariant fluid with no transverse expansion [(0 + 1)D], the effective viscosity is expressed as a simple integral of ζ/$\textit{s(T)}$ or η/$\textit{s(T)}$ over temperature, with a weight determined by the speed of sound of the fluid. The result is general for any equation of state with a moderate temperature dependence of the speed of sound, including the QCD equation of state. This definition of effective viscosity can be used to identify infinite families of ζ/$\textit{s(T)}$ or η/$\textit{s(T)}$ that produce essentially indistinguishable temperature profiles. In a boost-invariant cylindrical system [(1 + 1)D], a similar definition of effective viscosity is obtained in terms of characteristic trajectories in time and transverse direction. This leads to an infinite number of constraints on an infinite functional space for ζ/$\textit{s(T)}$ and η/$\textit{s(T)}$. Realistic examples are presented by using a finite number of constraints on a finite functional space. Conclusions: The definition of effective viscosity in a (0 + 1)D system clarifies how infinite families of ζ/$\textit{s(T)}$ and η/$\textit{s(T)}$ can result in nearly identical hydrodynamic temperature profiles. By extending the study to a boost-invariant cylindrical [(1 + 1)D] fluid, we identify an approximate but more general definition of effective viscosity that highlights the potential and limits of the concept of effective viscosity in fluids with limited symmetries.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗