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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

IEA Wind Task 32 and Task 37: Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering

Lidar-assisted control is a promising technology for reducing the levelized cost of energy from wind turbines, but quantifying its impact at the overall system level requires sophisticated systems engineering analysis and optimization frameworks. The joint workshop on Optimizing Wind Turbines with Lidar-Assisted Control Using Systems Engineering was held by the International Energy Agency Wind Task 32 (Lidar) and Task 37 (Systems Engineering) in October 2019 to address this challenge. This paper summarizes the outcome of the workshop and presents a road map for further research. The most promising applications of lidar-assisted control identified at the workshop and discussed here include 1) increasing annual energy production, 2) decreasing capital expenditure costs by reducing design loads, 3) extending turbine lifetime by reducing operating loads, and 4) enabling wind turbine class upgrades. For each application, we review the state of the art and highlight remaining research needs. Finally, we discuss strategies for addressing these research needs by conducting high-fidelity systems engineering optimizations.

17 WIND ENERGY↗
DOE OSTI2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Genetic and Structural Analysis of SARS-CoV-2 Spike Protein for Universal Epitope Selection

Evaluation of immunogenic epitopes for universal vaccine development in the face of ongoing SARS-CoV-2 evolution remains a challenge. Herein, we investigate the genetic and structural conservation of an immunogenically relevant epitope (C662–C671) of spike (S) protein across SARS-CoV-2 variants to determine its potential utility as a broad-spectrum vaccine candidate against coronavirus diseases. Comparative sequence analysis, structural assessment, and molecular dynamics simulations of C662–C671 epitope were performed. Mathematical tools were employed to determine its mutational cost. We found that the amino acid sequence of C662–C671 epitope is entirely conserved across the observed major variants of SARS-CoV-2 in addition to SARS-CoV. Its conformation and accessibility are predicted to be conserved, even in the highly mutated Omicron variant. Costly mutational rate in the context of energy expenditure in genome replication and translation can explain this strict conservation. These observations may herald an approach to developing vaccine candidates for universal protection against emergent variants of coronavirus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Geometrical-Based Generative Adversarial Network to Enhance Digital Rock Image Quality

X-ray microcomputed tomography (micro-CT) is a common tool for the study of porous media structures and properties. High-quality micro-CT data are required to accurately capture pore structures. Acquiring high-quality micro-CT data, however, is not always possible, owing to application limitations and experimental constraints. Therefore, we propose a geometrical-based generative adversarial network (GAN) to rapidly restore noisy micro-CT images to their clean counterparts. The training data and related ground-truth (GT) data are scanned for 7 min and 9.5 h, respectively. To evaluate the performance of the geometrical-based GAN, a 6003 voxel image that has never been used for training is reconstructed and compared with the corresponding GT image. Histogram matching and linear normalization are implemented to adjust the histogram of the reconstructed image to that of the GT image. A watershed-based segmentation method is then applied to delineate pore and solid phases. Lastly, we measure the Minkowski functionals and petrophysical properties, including absolute permeability, pore size distribution, drainage capillary pressure-saturation curve, and imbibition relative permeability, to estimate the physical accuracy of the denoised image. The results show that the proposed geometrical-based GAN can accurately restore noisy micro-CT data. By reducing the scanning time from 9.5 h to 7 min, the expenditure of collecting micro-CT can be decreased significantly. This is particularly important for applications where time-lapse images of a dynamic process are required, high-throughput imaging is necessary for real-time data analysis, or where the quantification of large sample volumes is required.

58 GEOSCIENCES↗
DOE OSTI2023

In situ X-ray area detector flat-field correction at an operating photon energy without flat illumination

Flat-field calibration of X-ray area detectors is a challenge due to the inability to generate an X-ray flat-field at the selected photon energy the beamline operates at, which has a strong influence on the measurement behavior of the detector. A method is presented in which a simulated flat-field correction is calculated without flat-field measurements. Instead, a series of quick scattering measurements from an amorphous scatterer is used to calculate a flat-field response. The ability to rapidly obtain a flat-field response allows for recalibration of an X-ray detector as needed without significant expenditure of either time or effort. Area detectors on the beamlines used, such as the Pilatus 2M CdTe, PE XRD1621 and Varex XRD 4343CT, were found to have detector responses that drift slightly over timescales of several weeks or after exposure to high photon flux, suggesting the need to more frequently recalibrate with a new flat-field correction map.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Computing with a Chemical Reservoir

Contemporary computation is expensive, with large language models and artificial intelligence becoming more common in daily life. However, high-performance computing is reaching the limits in speed and energy expenditure, and domain science requires ever-increasing computational capacity, with simulations and data analysis pipelines ever-growing in complexity. As we progress towards post-exascale computation, with the associated high energy costs, new methods of energy-conscious computation are required. Novel analog and hybrid digital-analog systems can overcome these challenges, and chemical reactions offer a promising avenue. Computers based on chemistry can provide compact desktop devices with immense computational power. These devices are readily scalable by considering greater reaction systems or vessels, meeting the high-performance requirements for scientific workflows. In this article, we present ChemComp, a compilation pipeline for the conversion of ordinary differential equations into implementable chemical reactions. We then demonstrate the solving capabilities of ChemComp by emulating a potential chemical reservoir device. We leverage the multi-layer intermediate representation (MLIR) compiler framework to implement an expressive chemical reaction abstraction and propose a path for chemical reaction networks (CRNs) to represent mathematical problems effectively. Combined, we demonstrate a potential workflow that can harness chemistry’s computing power to create energy-efficient, high-performance computation systems for contemporary computing needs.

artificial intelligence↗
DOE OSTI2022

Techno-Economic Implications of Electrical Machine Scaling for Wave Energy Converters

The sizing of an electrical machine for a Wave Energy Converter (WEC) can have a substantial impact on the overall sizing, cost, and rating of the device. An electrical generator is typically part of the power take-off system, which is the mechanism by which the energy absorbed by the prime mover is transformed into usable electrical energy. For practically all WECs, the rate of change of actuation is predominantly determined by the wave resource (i.e., the wave height and frequency), and devices will see a sinusoidal varying velocity according to the wave conditions. The same can then be said for both directly and indirectly coupled power take-offs with electrical generators. This techno-economic study investigates electrical machine scaling and associated cost implications through core machine design theory, manufacturer data, supporting literature, and the Reference Model Project sponsored by the U.S. Department of Energy. The Reference Model Project was a partnered effort to develop open-source marine energy point designs as reference models to benchmark marine energy technology performance and costs, methods for design and analysis of marine energy technologies, estimations for capital costs, operational costs, and levelized cost of energy. The results from this study show torque is directly related to (1) the physical size of the machine required to increase the air-gap sheer stresses, (2)the amount of active material, (3) the support structure, (4) bearing size and rating, and (5) offshore cable rating, all of which have a significant effect on overall system costs in terms of both capital and operational expenditures. This paper aims to be a critical benchmark in helping determine an "optimal" nameplate rating for wave energy devices and their associated power take-offs. With an optimized rating and sizing process, WEC costs can be reduced and overall performance can be improved.

cables↗
DOE OSTI2024

Towards the Flexibility of HVDC-Interconnected Systems: A Novel Emergency Freqeuncy Response Model

In this paper, we propose a novel multi-time scale emergency frequency response model by unlocking the flexibility of High Voltage Direct Current (HVDC) systems. Unlike assigning power ramping rates for FACTS to regulate frequency in traditional methods, this paper designs a step-change electromagnetic power frequency response (EPFR) scheme, by leveraging the temporal over/under DC voltage capability of HVDC. Wherein the Kullback-Leibler Divergence is adopted to convexify the modified swing equation after integrating the step-change power. Further, to avoid the complicated differential equations, we equivalently reformulate the duration limits of DC voltage deviation into the HVDC decreasing power ramping rates, which participate in the system primary frequency response. Finally, the new steady-state operation level of HVDC is involved with the secondary frequency response. As a result, an improved three-level algorithm is developed to solve the model, wherein the instant step-change, primary, and secondary frequency response are coordinated together. After applying the proposed frequency response scheme on the test system, the EPFR is validated to effectively provide the most instantaneous supports when faced with bulk power loss due to extreme contingencies, and the resilience is ensured within acceptable expenditures.

Jiang, Sufan↗
DOE OSTI2020

Technoeconomic Analysis of Photovoltaics Module Manufacturing with Thin Silicon Wafers

Reducing silicon usage by adopting thinner wafers can significantly reduce capital expenditure (capex) and cost, and thus accelerate the growth of manufacturing capacity and deployment. In this work, we evaluated potential benefits of thin Si wafers for current and future PV modules. We apply a technoeconomic framework that couples bottom-up cost model, and a cash-flow growth model to analyze PV modules with thin wafers. First, we show that, comparing the current PERC with 160 um thick wafers to the high-efficiency concept with 50 um thin wafers, the capex is reduced from 0.39 to 0.2 $/(W/year) while the cost is from 0.32 to 0.2 $/W. Second, the potential of accelerated deployment is analyzed for the highefficiency thin wafer concept. We found the significant advantages of higher growth rate (~20% relative) and higher deployment plateau (~3 times) than the current PERC modules. modules with thin wafers. First, we show that, comparing the current PERC with 160 um thick wafers to the high-efficiency concept with 50 um thin wafers, the capex is reduced from 0.39 to 0.2 $/(W/year) while the cost is from 0.32 to 0.2 $/W. Second, the potential of accelerated deployment is analyzed for the highefficiency thin wafer concept. We found the significant advantages of higher growth rate (~20% relative) and higher deployment plateau (~3 times) than the current PERC modules.

14 SOLAR ENERGY↗
DOE OSTI2022

Six Machine-Learning Methods for Predicting Hospital-Stay Duration for Patients with Sepsis: A Comparative Study

Sepsis is a life-threatening medical condition that, if not treated promptly, can result in tissue damage, organ failure, and death. According to the Centers for Disease Control, about 270,000 individuals die of sepsis in the US each year. Further, sepsis expenditures accounted for 13% of total US hospital costs in 2013, totaling more than $24 billion. Our project objectives were to determine if Machine Learning algorithms could reliably predict hospital stay duration for patients with sepsis. The data set we used has been de-identified and is freely available through the BupaR package. The data includes 1050 cases, 15214 events, and 16 types of actions related to sepsis patient care. First, we used process mining to determine how long each patient was in the hospital. Using BupaR’s functions, we created several process model graphs. These process models depict the movement of patients at a hospital and provide duration data for each patent case. Second, we identified outlier data and created two dataset versions: one with and one without outliers. We then applied the following analysis methods: Linear Regression, Random Forest, K-Nearest Neighbors, Neural Networks, XGBoost, and lightGBM. We compared the model validations for the six machine learning models using the same data-splitting method. We found that the XGBoost model had the best prediction accuracy of 73.9 percent for cases with outliers, and 79 percent for cases without outliers. We also found that the lightGBM model had the lowest mean absolute error between prediction and actual duration in days with 3.66 days for the case with outliers, and 2.4 days for the case without outliers. These two models outperformed the other four models. This work will be enhanced in the future by exploring new prediction algorithms and comparing them with the results of this study.

Chen, Lingtao↗
DOE OSTI2021

A general theory of avian migratory connectivity

Birds exhibit a remarkable array of seasonal migrations. Despite much research describing migratory behaviour, the underlying forces driving how a species’ breeding and wintering populations redistribute each year, that is, migratory connectivity, remain largely unknown. Here, we test the hypothesis that birds migrate in a way that minimises energy expenditure while considering intraspecific competition for energy acquisition, by developing a modelling framework that simulates an optimal redistribution of individuals between breeding and wintering areas. Using 25 species across the Americas, we find that the model accurately predicts empirical migration patterns, and thus offers a general explanation for migratory connectivity based on first ecological and energetic principles. Our model provides a strong basis for exploring additional processes underlying the ecology and evolution of migration, but also a framework for predicting how migration impacts local adaptation across seasons and how environmental change may affect population dynamics in migratory species.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Oxygen Storage Incorporated Into Net Power and the Allam–Fetvedt Oxy-Fuel sCO2 Power Cycle—Techno-Economic Analysis

Abstract With the planned future reliance on variable renewable energy, the ability to store energy for prolonged time periods will be required to reduce the disruption of market fluctuations. This paper presents a method to analyze a hybrid liquid-oxygen (LOx) storage/direct-fired supercritical carbon dioxide (sCO2) power cycle and optimize the economic performance over a diverse range of scenarios. The system utilizes a modified version of the NET Power process to produce energy when energy demand exceeds the supply while displacing much of the cost of the air separation unit (ASU) energy requirements through cryogenic storage of oxygen. The model uses marginal cost of energy data to determine the optimal times to charge and discharge the system over a given scenario. The model then applies ramp rates and other time-dependent factors to generate an economic model for the system without storage considerations. The size of the storage system is then applied to create a realistic model of the plant operation. From the real plant operation model, the amount of energy charged and discharged, the capital expenditures (CAPEX) of each system, energy costs and revenue and other parameters can be calculated. The economic parameters are then combined to calculate the net present value (NPV) of the system for the given scenario. The model was then run through the SMPSO genetic algorithm in Python for a variety of geographic regions and large-scale scenarios (high solar penetration) to maximize the NPV based on multiple parameters for each subsystem. The LOx storage requirements will also be discussed.

Engineering↗
DOE OSTI2020

A Decision Support Tool for Distributed Solar and Storage Investments: A Case Study in Austin, TX

This study builds a decision support tool to evaluate when it is a good economic decision (least cost with minimum discomfort) for the residential customer to invest in distributed energy resources (DERs) based on different electricity rate structures, DER ownership frameworks, and DER rebates offered by electric utilities. Here, the tool is demonstrated using empirical electricity consumption data from Pecan Street Inc. (a non-profit entity based on Austin, Texas), residential rates from Austin Energy (the municipal electric utility in Austin, Texas), DER ownership costs from various nationwide pilot programs, and incentives offered by electric utilities in the United States. Results show that for constant electricity rates, the overall expenditure is least when the customer owns solar panels without storage, while for time-varying pricing structures, the least expensive scenario is one where the customer does not own any DERs. As the capital costs for DERs decline, utilities incentivize customer ownership of DERs, and more residential customers face the decision of whether to invest in DERs, this study aims to be a key tool in aiding that decision-making process.

14 SOLAR ENERGY↗
DOE OSTI2023

Policy-relevant differences between secondhand and thirdhand smoke: strengthening protections from involuntary exposure to tobacco smoke pollutants

Starting in the 1970s, individuals, businesses and the public have increasingly benefited from policies prohibiting smoking indoors, saving thousands of lives and billions of dollars in healthcare expenditures. Smokefree policies to protect against secondhand smoke exposure, however, do not fully protect the public from the persistent and toxic chemical residues from tobacco smoke (also known as thirdhand smoke) that linger in indoor environments for years after smoking stops. Nor do these policies address the economic costs that individuals, businesses and the public bear in their attempts to remediate this toxic residue. We discuss policy-relevant differences between secondhand smoke and thirdhand smoke exposure: persistent pollutant reservoirs, pollutant transport, routes of exposure, the time gap between initial cause and effect, and remediation and disposal. We examine four policy considerations to better protect the public from involuntary exposure to tobacco smoke pollutants from all sources. We call for (a) redefining smokefree as free of tobacco smoke pollutants from secondhandandthirdhand smoke; (b) eliminating exemptions to comprehensive smoking bans; (c) identifying indoor environments with significant thirdhand smoke reservoirs; and (d) remediating thirdhand smoke. We use the case of California as an example of how secondhand smoke-protective laws may be strengthened to encompass thirdhand smoke protections. The health risks and economic costs of thirdhand smoke require that smokefree policies, environmental protections, real estate and rental disclosure policies, tenant protections, and consumer protection laws be strengthened to ensure that the public is fully protected from and informed about the risks of thirdhand smoke exposure.

Public, Environmental & Occupational Health↗
DOE OSTI2023

A Three-Dimensional, Thermodynamically and Variationally Consistent, Fully Coupled, Electro-Chemo-Thermo-Mechanical Model of Solid-State Batteries

Here we present a theoretical framework for high fidelity modeling of three-dimensional electro-chemo-thermo-mechanical coupled systems such as solid-state batteries. The framework provides a useful means of utilizing molecular level information to predict the overall performance of the system at the continuum level. The central statement of the theoretical framework is a thermodynamically consistent inf-sup problem. The total potential of the coupled system consists of the Helmholtz free energy, the electrical kinetic potential, the chemical potential, the Fourier potential, the chemical reaction potential, and the external power expenditure. All governing equations derive from variational principles. We also show that the resulting initial-boundary-value problem (IBVP) is consistent with existing, widely adopted equations for modeling batteries. Moreover, the IBVP can be naturally discretized using the (variational) finite element method to simulate practical solid-state batteries with arbitrary geometries. Several examples illustrate multiphysics coupling between pairs of the physical field equations, culminating in a comprehensive three-dimensional model of charge-discharge in a solid state battery.

25 ENERGY STORAGE↗
DOE OSTI2020

TEAL

TEAL is a financial performance calculator plugin for the RAVEN code, framework, resolving around the computation of Net Present Value and associated financial metrics. TEAL can make use of inflation rates, taxation, escalation factors, capital expenditure economy of scale scaling factors. The unique feature of TEAL is the capability to be linked with RAVEN external models and build corresponding cash flows using the variables computed by those external models. In addition to be able to use the capability to generate cash flows derived from complex physical models generated by RAVEN, another distinctive feature of TEAL is the capability to provide financial risk/probabilistic metrics that can empower RAVEN to perform optimization/analysis driven by financial risk augmentations. Optimization, robust optimization, parametric studies, large parallel simulations, sensitivity analysis, data mining, etc. are just some of the capabilities that can be leveraged.

Alfonsi, Andrea↗
DOE OSTI2021

SSRS (Stochastic Soaring Raptor Simulator)

SSRS (Stochastic Soaring Raptor Simulator) is a generalizable, probabilistic, and predictive tool for wind energy developers, ecologists, wildlife managers and industry consultants to estimate the potential for soaring raptors to interact with operating wind turbines, without the need for site-specific data collection. Rapid expansion of wind energy development across the world has exposed the risk of turbine collisions for birds and bats. The risk to obligate soaring raptors such as golden eagles is of particular concern due to their small population and influence on ecological balance. Golden eagles rely heavily on updrafts to subsidize their flight, putting them in direct conflict with operational wind turbines that utilize the same wind resource. Understanding the behavior of soaring raptors with varying atmospheric conditions is crucial for predicting and mitigating the risk of turbine collision. This software contains a predictive movement model that simulates individual flight paths of golden eagles during updraft-subsidized long-distance flight, including migration. For a given set of atmospheric conditions, the model simulates thousands of eagles at turbine-scale spatial resolution (50m) to produce a relative presence density map. The simulated eagles rely on updrafts to pursue uninterrupted directional flight with minimal energy expenditure, following fluid-flow principles. The simulator includes a stochastic model of eagle behavior and a systematic method of accounting for spatiotemporal variations in atmospheric conditions. This framework only requires publicly available atmospheric data to estimate orographic and thermal updrafts, ensuring general usability.

Sandhu, Rimple↗
DOE OSTI2023

Digital Twin Technology (“Morpheus”) for Optimized Building Operations [SWR-22-74]

The electrification of buildings is an important step to reducing greenhouse gas emissions across all industries. The management of increasingly electrified buildings is a complex pursuit, and there remains a need for cost-effective software capable of handling the computational burden required of such complexity. Through a partnership with Dallas Fort Worth (DFW) Airport, researchers at NREL have developed a digital twin modeling framework to optimize building operations, called Morpheus. Pairing predictive control with automatic fault detection and diagnostics, Morpheus decreases energy expenditures, costs, and faults for large facilities. Additionally, Morpheus employs artificial intelligence to continuously improve its performance using information provided by sensor systems, human experts with deep industry domain knowledge, and even from other similar machines or fleets of machines. Coupling this novel energy-management software with other digital twins, such as NREL’s Athena software for mobility operations, enables robust decision-making for asset and space management. The implementation of Morpheus at DFW has resulted in significantly improved HVAC system operations and reduced both peak power and overall energy consumption. This enhanced functionality comes at a more affordable price than previously developed digital twins and can be customized for other facilities’ geometries to provide optimal, individualized control of a facility’s energy consumption.

Chinde, Venkatesh↗