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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Gradient-Descent-Based Learning Gain for Backstepping Controller and Disturbance Observer of Nonlinear Systems

This paper proposes a gradient-descent-based learning (GL) gain for backstepping controller and disturbance observer (DOB) of nonlinear system. The proposed method consists of the GL gain update law, controller, and DOB. The GL gain update law is proposed to adapt the control gain and DOB gain according to the direction that minimizes the cost function. The mathematical analysis reveals that the GL gain always has a positive sign and upper bound. The controller is designed via a backstepping procedure to track the desired output with GL control gain. The DOB is designed to estimate the unknown external disturbance with the GL DOB gain. Because the control and DOB gains are simultaneously tuned to achieve improved performance, the time consumption for tuning can be reduced. In addition, the peaking phenomenon can be avoided initially by a small initial value of GL gains. The stability of the closed-loop system is guaranteed using the input-to-state stability property. The performance of the proposed method was validated via simulations and experiments using a DC motor.

42 ENGINEERING↗
DOE OSTI2023

LA100 Equity Strategies. Chapter 11: Truck Electrification for Improved Air Quality and Health

The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. This report focuses on truck electrification as a means to improve air quality and health in traffic and air quality disadvantaged communities. It also identifies potential strategies to more equitably distribute air quality benefits from electrification of trucks, defined here as heavy-duty vehicles over 8,500 pounds (lbs) gross vehicle weight. Specifically, NREL analyzed 1) baseline air pollutant emissions, 2) emissions reductions associated with incremental increases in electrification of three types of heavy-duty trucks in 2035, and 3) resultant changes to air pollutant concentrations for selected census tracts along major roadways in disadvantaged and non-disadvantaged communities for comparison. In addition, NREL analyzed the impact of estimated pollutant concentrations on several health effects and the distribution of those health effects by disadvantaged community status. NREL's analysis is complemented by a University of California Los Angeles (UCLA) analysis of air quality benefits from transportation electrification, which included light-duty vehicles (Chapter 15) and evaluated regional air-quality changes across Los Angeles. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Introduction to Evolve Central Appalachia

The Evolve-CAPP project is developing and implementing strategies to enable the Central Appalachian basin to realize its full economic potential for producing high-value, non-fuel, carbon-ore (CO) products, rare earth elements (REE), and critical minerals (CM). The Basinal Assessment of CORE-CM Resources is a state-of-the-art geological model and comprehensive database that will be used to identify, locate, and quantify in-situ resources in the basin. Restrictive aspects of resource recovery, such as ownership and legal restrictions, are being considered in estimating recoverable resources. A gap analysis of missing or unavailable data that could not be fully integrated in the initial assessment due to budget, access, or other constraints during the assessment effort is planned. A characterization and data acquisition plan to fully characterize the basin’s CORE-CM potential will address gaps in data. The plan will be developed with input from project team members and relevant stakeholders, including land and mineral holding companies and coal operators, who maintain sampling programs as part of their normal operations.

Bishop, Richard↗
DOE OSTI2024

Cloud condensation nuclei concentrations derived from the CAMS reanalysis

Determining number concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, and a process key for aerosol–cloud interactions (ACI). However, due to sparse coverage of in situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source as well as temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN numbers is one of the goals for quantifying ACI processes and achieving uncertainty-reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMSRA are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatiotemporal quantification of CCN numbers. The derived CCN dataset captures the general trend and spatial and temporal distribution of total CCN number concentrations and CCN from different aerosol species. A brief evaluation with ground-based in situ measurements demonstrates the improvement of the modelled CCN over the sole use of AOD as a proxy for CCN as the overall correlation coefficient improved from 0.37 to 0.71. However, we find the modelled CCN from CAMSRA to be generally high biased and find a particular erroneous overestimation at one heavily polluted site which emphasises the need for further validation. The CCN dataset (https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1, Block, 2023), which is now freely available to users, features 3-D CCN number concentrations of global coverage for various supersaturations and aerosol species covering the years 2003–2021 with daily frequency. This dataset is one of its kind as it offers lots of opportunities to be used for evaluation in models and in ACI studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Influence of Shear Strength Assumptions on BISON Debonding Simulations

Accurately predicting the thermomechanical response of buffer–IPyC debonding in TRISO fuel particles requires reliable mechanical property inputs for each coating layer, particularly the normal and shear strengths that influence interlayer delamination and stress concentrations. Micro tensile testing of AGR-2 fuel particles provided experimentally measured normal strengths for the buffer, IPyC, and buffer–IPyC interface; however, shear strength was not measured. As a result, BISON simulations of interface debonding must rely on assumed shear strength values, typically estimated as 20–40% of the measured ultimate tensile strength. This study evaluates how these assumed shear strength values influence cohesive zone model (CZM) predictions of buffer–IPyC separation in AGR 2 TRISO particles. Using micro tensile data from three AGR 2 compacts (2 1 3, 5 1 3, and 6 3 3), BISON simulations were performed with multiple shear strength assumptions to quantify their effect on radial and tangential stress evolution, debonding, and gap propagation. The results show that shear strength is a high sensitivity parameter: increasing the assumed shear strength significantly alters the stress distribution at the buffer–IPyC junction, shifts the predicted debonding location, and changes the extent of partial gap formation. While normal strength controls the initiation of interface separation, shear strength strongly influences the mode mixity of the failure process and the resulting stress concentrations transmitted to the IPyC and SiC layers. These findings highlight a critical gap in current TRISO mechanical characterization. Without experimentally measured shear strength, BISON simulations must rely on approximations that introduce uncertainty into predictions of coating layer integrity and fission product barrier performance. Future fuel qualification campaigns should therefore consider measurement of shear strength at the interlayer interfaces to reduce model uncertainty and improve the fidelity of TRISO fuel performance simulations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses

Physics-based models typically require an in-depth understanding of a phenomenon and assumptions of the underlying process(es), which are often hard to obtain in practice, whereas data-driven machine learning models learn the structure and patterns in the training data without any prior theoretical assumptions and then use inference to develop useful predictions. A novel machine learning-based algorithm has been previously developed for the prediction of black carbon mass absorption cross-section (MAC BC ) and applied to a variety of different atmospheric environments. In contrast to light scattering theories which require assumptions about the underlying physics, this algorithm uses time-series data of aerosol properties to estimate the temporally-varying MAC BC at 870 nm. Here, we analyze our algorithm and discuss the influence of aerosol optical properties (such as Ångström exponents and single scattering albedo) and chemical composition on the model outputs and the associated accuracy. Additionally, we conduct sensitivity analyses on our models to understand how the predictions change in response to different sets of input variables. Our support vector machine (SVM) for regression model is the least sensitive to variations in the input variables, although all models tend to exhibit a degradation to their accuracy when scattering Ångström exponents are less than one.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Ensemble Kalman inversion of induced polarization data

SUMMARY This paper explores the applicability of ensemble Kalman inversion (EKI) with level-set parametrization for solving geophysical inverse problems. In particular, we focus on its extension to induced polarization (IP) data with uncertainty quantification. IP data may provide rich information on characteristics of geological materials due to its sensitivity to characteristics of the pore–grain interface. In many IP studies, different geological units are juxtaposed and the goal is to delineate these units and obtain estimates of unit properties with uncertainty bounds. Conventional inversion of IP data does not resolve well sharp interfaces and tends to reduce and smooth resistivity variations, while not readily providing uncertainty estimates. Recently, it has been shown for DC resistivity that EKI is an efficient solver for inverse problems which provides uncertainty quantification, and its combination with level set parametrization can delineate arbitrary interfaces well. In this contribution, we demonstrate the extension of EKI to IP data using a sequential approach, where the mean field obtained from DC resistivity inversion is used as input for a separate phase angle inversion. We illustrate our workflow using a series of synthetic and field examples. Variations with uncertainty bounds in both DC resistivity and phase angles are recovered by EKI, which provides useful information for hydrogeological site characterization. Although phase angles are less well-resolved than DC resistivity, partly due to their smaller range and higher percentage data errors, it complements DC resistivity for site characterization. Overall, EKI with level set parametrization provides a practical approach forward for efficient hydrogeophysical imaging under uncertainty.

Geochemistry & Geophysics↗
DOE OSTI2021

Network Based Estimation of Wind Farm Power and Velocity Data Under Changing Wind Direction

This paper describes an estimation algorithm for velocity and power output signals in a wind farm under changing wind direction. A graph-theoretic definition describes the wind farm as a collection of nodes (turbines) and time-varying weighted edges (inter-turbine wake propagation) that change as a function of incoming wind direction. The velocity at each turbine is determined through a discrete input-output model. Changes in wind direction serve as the input and the output is defined in terms of a time-varying weighted adjacency matrix that depends on the time-delay of information propagation between turbines. These delays, which are defined in terms of the advection speed of the wind and the distance between the turbines, capture the delayed effect of wind direction changes on the inter-connectivity of the graph as the wind conditions at the farm inlet propagate through the turbine array. An event-based update framework is employed to capture time-dependent topology changes due to shifts in wind direction. Simulation results for dynamically changing wind inlet directions to a circular wind farm are compared to predictions from both the static and dynamic versions of the FLOw Redirection and Induction in Steady State (FLORIS) model. The approach is shown to enable real-time tracking of dynamic changes to wind farm power output within a framework that can be easily integrated into real-time, horizon-based, control strategies that typically do not account for wind direction changes.

distributed↗
DOE OSTI2021

Achievable Rate and Energy Efficiency Analysis of Multiuser Relay-Aided Massive MIMO Downlink

In this article, the achievable sum rate and the energy efficiency (EE) are investigated for a multiuser relay-aided massive multiple-input–multiple-output (MIMO) downlink. Low-resolution digital-to-analog converters (DACs) are equipped at both the base station (BS) and the relay station (RS), and the amplify-and-forward protocol is adopted at the RS. Under the Rician fading channel, closed-form approximate expressions for the achievable sum rate are derived with perfect and imperfect channel state information. Furthermore, a more general power law is extracted to save transmit power without reducing the achievable sum rate, and a local optimal power allocation scheme is proposed to improve the channel capacity of active users. Then, the tradeoff between the achievable sum rate and the EE is discussed. The numerical results show that, due to the quantization noise, the transmit power has a limited increase in the achievable rate. In addition, it is more valuable to improve the DAC quantization bit at the RS when the number of BS antennas is larger than that of RS antennas. In addition, under strong line-of-sight channels or high pilot transmit power conditions, the channel estimation accuracy is higher, and the best tradeoff between the achievable sum rate and the EE can be obtained when the DAC quantization bit is 4.

25 ENERGY STORAGE↗
DOE OSTI2023

Parametric analysis on optimized design of hybrid solar power plants

There is increasing interest in utility-scale solar power plants with storage which can flexibly dispatch renewable energy to the grid. However, plant design possesses many degrees of freedom and non-obvious trade-offs in performance. Software tools can estimate or optimize the performance of a specific plant configuration under market and weather conditions of interest; the associated cost parameters and operating assumptions strongly influence estimates of plant performance and decisions regarding optimal sizing. We employ the National Renewable Energy Laboratory's Hybrid Optimization and Performance Platform, which incorporates optimal dispatch when evaluating plant performance, and investigate the sensitivity to weather and market conditions, operating limitations, and the presence of a capacity-based incentive. We demonstrate changes in plant performance and optimal sizing with respect to these inputs and discuss implications. Here results show that PV-with-battery designs are more profitable under our assumptions, but that designs including a concentrated solar power (CSP) system produce significantly greater annual energy; and that CSP-with-thermal energy storage designs maximizing the benefit-to-cost ratio have an input-dependent linear relationship between the CSP field solar multiple and the hours of storage as the project budget varies.

14 SOLAR ENERGY↗
DOE OSTI2022

The underappreciated role of anthropogenic sources in atmospheric soluble iron flux to the Southern Ocean

The atmospheric deposition of soluble (bioaccessible) iron enhances ocean primary productivity and subsequent atmospheric CO 2 sequestration in iron-limited ocean basins, especially the Southern Ocean. While anthropogenic sources have been recently suggested to be important in some northern hemisphere oceans, the role in the Southern Ocean remains ambiguous. By comparing multiple model simulations with the new aircraft observations for anthropogenic iron, we show that anthropogenic soluble iron deposition flux to the Southern Ocean could be underestimated by more than a factor of ten in previous modeling estimates. Our improved estimate for the anthropogenic iron budget enhances its contribution on the soluble iron deposition in the Southern Ocean from about 10% to 60%, implying a dominant role of anthropogenic sources. We predict that anthropogenic soluble iron deposition in the Southern Ocean is reduced substantially (30-90%) by the year 2100, and plays a major role in the future evolution of atmospheric soluble iron inputs to the Southern Ocean.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Modeling Impedance Caused by Ohmic Losses in High Surface Area Carbons in Polymer Electrolyte Fuel Cells

An analytical model for a blocking porous electrode is derived and used to predict how ohmic resistance inside micropores that host catalyst nanoparticles affects impedance. This paper revisits a widely used approach for estimating ionic conductivities of catalyst layers in fuel cells. An interfacial term associated with resistance in micropores is derived and added to the classical expression for the impedance of a blocking porous electrode at low frequency. The revised model predicts a physically realistic rise in the real component of the impedance at low frequency when reasonable pore dimensions and conductivities are used as inputs.

08 HYDROGEN↗
DOE OSTI2024

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Abstract Robust quantification of predictive uncertainty is a critical addition needed for machine learning applied to weather and climate problems to improve the understanding of what is driving prediction sensitivity. Ensembles of machine learning models provide predictive uncertainty estimates in a conceptually simple way but require multiple models for training and prediction, increasing computational cost and latency. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but does not account for epistemic uncertainty. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainties with one model. This study compares the uncertainty derived from evidential neural networks to that obtained from ensembles. Through applications of the classification of winter precipitation type and regression of surface-layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. To encourage broader adoption of evidential deep learning, we have developed a new Python package, Machine Integration and Learning for Earth Systems (MILES) group Generalized Uncertainty for Earth System Science (GUESS) (MILES-GUESS) ( https://github.com/ai2es/miles-guess ), that enables users to train and evaluate both evidential and ensemble deep learning. Significance Statement This study demonstrates a new technique, evidential deep learning, for robust and computationally efficient uncertainty quantification in modeling the Earth system. The method integrates probabilistic principles into deep neural networks, enabling the estimation of both aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations using a single model. Our analyses reveal how decomposing these uncertainties provides valuable insights into reliability, accuracy, and model shortcomings. We show that the approach can rival standard methods in classification and regression tasks within atmospheric science while offering practical advantages such as computational efficiency. With further advances, evidential networks have the potential to enhance risk assessment and decision-making across meteorology by improving uncertainty quantification, a longstanding challenge. This work establishes a strong foundation and motivation for the broader adoption of evidential learning, where properly quantifying uncertainties is critical yet lacking.

Schreck, John S.↗
DOE OSTI2021

Software Quality Assurance for EBR-II Fuels Irradiation and Physics Database (FIPD)

The Fuels Irradiation and Physics Database (FIPD) is an ongoing DOE project on archival of the EBR-II metal-alloy fuel irradiation experiments. As part of its use in support of license applications, the Quality Assurance Program Plan (QAPP) was drafted and endorsed by NRC in an effort to demonstrate its compliance with regulatory expectations. Software Quality Assurance (SQA) for the physics portion of FIPD is intended to qualify the calculated quantities such as fuel and cladding temperatures, neutron fluence and axially varying burnup estimates for irradiated fuel elements. This report covers the initial evaluation of SQA status of three neutron physics and thermo-fluid codes (REBUS, RCT and SE2RCT) that form the basis of calculated quantities for as-irradiated characteristics of the tested metallic fuel elements. The report also introduces an SQA plan to address the identified deficiencies. The REBUS, RCT, and SE2RCT codes are all part of the Argonne Reactor Code (ARC) code system. There is considerable knowledge and experience on REBUS and RCT but relatively less on SE2RCT. During FY2021, efforts focused on an assessment of how the data in the EBR-II Physics and Analysis DataBase (PADB) is generated with SE2RCT and used in FIPD. Additional tasks included considerations of uncertainties for power estimates in REBUS and RCT calculations and their impact on the combined RCT methodology. The RCT software usage in FIPD was assessed this year and the input/output details studied. A “requirements” document was created that identifies the key features of the RCT software being used in FIPD that need to have SQA documentation. A brief discussion on the history of RCT and its input is included in this report along with the basic SQA roadmap laid out in the requirements document. The SE2RCT software usage in FIPD is still being studied noting that there is no current manual. As part of the work done this year, two bugs were identified in the SE2RCT software which have a minor impact on the accuracy of the results it produces. No requirements document has been created, but one identified feature of SE2RCT being used that needs verification was its fuel pin temperature calculation. The work completed this year confirms that the approximations which will be included in the software verification report for SE2RCT are accurate. In addition to software quality assurance work for RCT and SE2RCT, an automated verification framework is proposed to simplify the software quality assurance process. The purpose of this framework is to streamline code verification and documentation while minimizing repetitive tasks for code developers and reviewers. The reduction of repeated input (between reference solution, software, and documentation input) throughout the SQA process reduces potential for human errors during the preparation of the supporting software quality records. The automation of the verification and documentation process proposed for this project leverages the existing verification structure already in place for the SAS4A/SASSYS-1 code.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

RectifHyd Version 2.0: Historical and counterfactual-climatological hydropower monthly generation totals for CONUS plants, 1980 – 2019

This dataset will contain the following files: - RectifHydV2.zip – the RectifHydV2 dataset, split into two files: o RectifHydV2_Actual_MWh.csv: Estimated actual monthly net generation from 590 Hydropower Plants (>10MW nameplate) in CONUS, 1980 – 2019 o RectifHydV2_Counterfactual_MWh.csv: Counterfactual (climate-only) monthly net generation from 590 Hydropower Plants (>10MW nameplate) in CONUS, 1980 – 2019 - RectifHydV2_code.zip: Full data processing pipeline, coded using R {targets} framework. This is a snapshot release (v1.0) of the code repository stored at https://code.ornl.gov/turnersw/rectifhydv2 - RectifHydV2_inputs.zip: Complete set of input data used to create RectifHydV2, organized for direct entry into “/data/” directory of the RectifHydV2 reproducible data pipeline - RectifHydV2_misc.zip: - RectifHydV2_dailyRelease.csv

13 HYDRO ENERGY↗
DOE OSTI2020

Technology Development for Dry Storage of Aluminum-Clad Spent Nuclear Fuel - 20490

A candidate disposition pathway for the > 13 MTHM of aluminum-clad spent nuclear fuel (ASNF), owned and managed by the U.S. Department of Energy, is the drying and placement of the SNF into sealed-canister dry storage, with the ASNF-in- canisters 'road-ready' for transportation to and final direct disposal in a repository waste package. Technical information gaps in fuel drying, and fuel dry storage behavior, have previously challenged the declaration of technology readiness for drying and placement of this fuel into the DOE Standard Canister design for > 50 years of safe dry storage. The principal technical information gaps included: i) characterization and thermal dehydration behavior of aluminum (oxy)hydroxide films attendant on the cladding due to film formation during reactor operation and during post-discharge up to long-term wet storage histories; and ii) G-value data to enable estimation of the radiolytic gas generation from the cladding with its (oxy)hydroxide films. Thus, the oxide films on the ASNF challenged the safety of a sealed storage canister with thermal and radiolytic decomposition of the waters on the films that can lead to corrosion, pressurization, and flammability issues. These gaps in the technical information base have largely been closed. This paper discusses the investigations at the Idaho National Laboratory (INL) and the Savannah River National laboratory (SRNL), and outlines the pending technology development work for input to an engineering design to enable a road-ready dry storage system for ASNF. The ASNF inventory considered for road-ready dry storage is stored at the Savannah River Site (SRS) and at the Idaho Nuclear Technology and Engineering Center (INTEC) at the INL. The ASNF inventory in the SRS L Basin is from foreign and domestic research reactors (FRR and DRR), and is diverse in terms of design, irradiation, and post-reactor-discharge storage conditions; these factors yield a range of characteristics of cladding oxide films on ASNF. Mixed aluminum (oxy)hydroxide (boehmite and bayerite/gibbsite) films, non-uniform in thicknesses up to a maximum local thickness bounded by 25 μm, were observed on ASNF materials removed from wet storage in the L Basin and in non-sealed dry storage at the SRS following reactor service and a long-term (up to 40+ years) interim storage history. The ASNF inventory at INTEC, in both wet and vented dry storage, is predominantly from the Advanced Test Reactor (ATR), but it also includes DRR and FRR fuel. To address a profound behavior of these films, radiolysis testing of aluminum specimens with mixed type boehmite/bayerite oxide films was performed to develop basic data on G-values for production of radiolytic hydrogen under dry storage conditions with nominal relative humidity, temperature, and cover gas. Modeling and simulation of canister internal environments with postulated inventories of oxide films provides estimation of the evolution of the conditions of the canister loaded with ASNF. Simultaneous Thermal Analysis (TGA/DSC) of hydrated oxide powders, and laboratory-scale aluminum specimens with a bayerite film (∼10 μm) using TGA methods, inform drying time/temperature conditions to be used for the ASNF. A demonstration project is recommended for Verification and Validation of the drying and storage of the ASNF. Remaining major tasks leading up to the hot demonstration include scale-up radiolysis testing and scale-up drying testing. Engineering design with the information from the technology program will establish the safety basis and enable long-term (> 50 years) dry storage compatibility with ASNF in the DOE Standard Canister pending its transportation to and disposal in a repository. This full capability would show the ASNF-in-canister storage system to be road-ready. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

A semblance measure for model comparison

Algorithmic and computational advances have made it possible that geophysical survey and earth model design can be aided by many systematic trial inverse-modelling runs with synthetic data. Such may, for example, come up in machine-learning approaches. Automated image appraisal pertaining to such applications will involve common statistical tests for goodness-of-data fit as a primary evaluation method. However, solution non-uniqueness may render multiple images equivalent in terms of their data fit, requiring secondary categorizers. A logical choice for classifying synthetic-imaging results quantifies the goodness of model fit where a known reference model replaces the observational input. The task of model intercomparison in terms of measuring the resemblance to the reference model poses challenges to common distance-based metrics like root mean square error and mean absolute error. First, distance-based metrics can introduce spurious contributions when smooth models with fuzzy target contours are to be compared against a sharp reference. Second, large differences due to parameter-estimation overshoots can dominate distance metrics. Here, we propose a remedy that is referred to as semblance and is based on the idea of logistic functions, where a binary-dependent variable adds non-zero or zero accumulation terms for the, respectively, passing or failing of preset target thresholds. This classifying approach is amenable to an objective where model feature recognition is primary. Numerical comparisons to distance-based metrics provide evidence for the advantages of the semblance in view of this objective. Geophysical imaging in conjunction with machine-learning is seen as a benefitting upcoming application area.

58 GEOSCIENCES↗
DOE OSTI2021

U.S.-China Clean Energy Research Center Building Energy Efficiency (CERC-BEE) Open-Source Retrofit Targeting Tool (CRADA FP00007338 Final Report)

To increase the cost-saving energy and carbon dioxide (CO 2 ) emissions reductions in buildings and portfolios at the scale and speed necessary to limit climate change, researchers at LBNL and Johnson Controls (JCI) developed the Building Efficiency Targeting Tool for Energy Retrofits (BETTER). BETTER is a software tool that consists of three components: (1) the BETTER analytical engine source code (which was developed with intellectual property provided by JCI under CRADA FP00007338); (2) the BETTER web application, developed by LBNL and McQuillen Interactive Pty. Ltd; and (3) the BETTER application programming interface (API), also developed by LBNL and McQuillen Interactive Pty. Ltd. BETTER enables building and portfolio owners, managers, and service providers worldwide to quickly, easily identify cost-saving energy efficiency retrofits in existing buildings and portfolios without expensive site visits or complex modeling. With minimal data input, the tool benchmarks a building’s electric and fossil energy usage against peers; quantifies energy, cost and greenhouse gas (GHG) emission reduction potentials at the building and portfolio levels; and recommends energy efficiency measures to decarbonize and electrify buildings and portfolios, targeting specific energy savings levels. No other tool so comprehensively analyzes buildings and portfolios with such ease. If fully implemented, it is estimated that BETTER could help reduce emissions equivalent to planting 1.3 billion trees globally by 2030. Moreover, an additional 50-75% of embodied GHG emissions could be avoided in each case where BETTER results in a building being retrofitted instead of demolished and replaced, providing substantial additional decarbonization benefits for the buildings sector. BETTER has garnered multiple awards and avid interest from investors. In 2020, it earned a R&D 100 Award for innovation and a LBNL Director’s Award for Technology Transfer. In 2021, BETTER was named an EarthX E-Capital Summit Climate Tech Prize semi-finalist

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗