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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Control of airborne infectious disease in buildings: Evidence and research priorities

The evolution of SARS-CoV-2 virus has resulted in variants likely to be more readily transmitted through respiratory aerosols, underscoring the increased potential for indoor environmental controls to mitigate risk. Use of tight-fitting face masks to trap infectious aerosol in exhaled breath and reduce inhalation exposure to contaminated air is of critical importance for disease control. Administrative controls including the regulation of occupancy and interpersonal spacing are also important, while presenting social and economic challenges. Indoor engineering controls including ventilation, exhaust, air flow control, filtration, and disinfection by germicidal ultraviolet irradiation can reduce reliance on stringent occupancy restrictions. However, the effects of controls-individually and in combination-on reducing infectious aerosol transfer indoors remain to be clearly characterized to the extent needed to support widespread implementation by building operators. We review aerobiologic and epidemiologic evidence of indoor environmental controls against transmission and present a quantitative aerosol transfer scenario illustrating relative differences in exposure at close-interactive, room, and building scales. We identify an overarching need for investment to implement building controls and evaluate their effectiveness on infection in well-characterized and real-world settings, supported by specific, methodological advances. Finally, improved understanding of engineering control effectiveness guides implementation at scale while considering occupant comfort, operational challenges, and energy costs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Unlocking the Value of Deep Energy Retrofits

This publication is based on an extensive study conducted on behalf of the New York State Energy Research and Development Authority (NYSERDA) and the U S Department of Energy (DOE) to explore financial and risk products to accelerate the implementation of deep energy retrofit (DER) solutions to reduce costs These financial products are intended to speed market development via three fundamental themes: reallocating risk from building owners and lenders to insurers, quantifying and monetizing previously unrecognized value associated with DERs, and providing building owners and lenders with confidence in the performance of building systems Findings are based on a comprehensive analysis and characterization of 100+ existing DER case studies, and more than 40 qualitative interviews with industry experts, including insurers, researchers, building owners, and policymakers In addition, a quantitative model of overall project economics with baseline, high, and low cases was developed This data informed the analysis that resulted in three recommended financial product solutions, which were evaluated for their potential to raise projected cash flows and finance DERs: 1) Building System Performance and Energy Savings Guarantee; 2) Trade Credit Insurance; 3) Ancillary Revenue Contracts. This report identifies many value streams associated with DERs and introduces these three potential financial products, which aim to reallocate risks and reduce barriers to the adoption of advanced envelope DERs.

ancillary renenue contracts↗
DOE OSTI2020

Evaluation of a visual interpretation method for tau-PET with 18 F-flortaucipir

Introduction: Positron emission tomography targeting tau (tau-PET) is a promising diagnostic tool for the identification of Alzheimer's disease (AD). Currently available data rely on quantitative measures, and a visual interpretation method, critical for clinical translation, is needed. Methods: In this work, we developed a visual interpretation method for 18 F-flortaucipir tau-PET and tested it on 274 individuals (cognitively normal controls, patients with mild cognitive impairment [MCI], AD dementia, and non-AD diagnoses). Two readers interpreted 18 F-flortaucipir PET using two complementary indices: a global visual score and a visual distribution pattern. Results: Global visual scores were reliable, correlated with global cortical 18 F-flortaucipir standardized uptake value ratio (SUVR) and were associated with clinical diagnosis and amyloid status. The AD-like 18 F-flortaucipir pattern had good sensitivity and specificity to identify amyloid-positive patients with AD dementia or MCI. Discussion: This 18 F-flortaucipir visual rating scheme is associated with SUVR quantification, clinical diagnosis, and amyloid status, and constitutes a promising approach to tau measurement in clinical settings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

How good are learning-based control v.s. model-based control for load shifting? Investigations on a single zone building energy system

Both model predictive control (MPC) and deep reinforcement learning control (DRL) have been presented as a way to approximate the true optimality of a dynamic programming problem, and these two have shown significant operational cost saving potentials for building energy systems. Furthermore, there is still a lack of in-depth quantitative studies on their approximation levels to the true optimality, especially in the building energy domain. To fill in the gap, this paper provides a numerical framework that enables the evaluation of the optimality levels of different controllers for building energy systems. This framework is then used to comprehensively compare the optimal control performance of both MPC and DRL controllers with given computation budgets for a single zone fan coil unit system. Note the optimality is estimated based on a user-specific selection of trade-off weights among energy costs, thermal comfort and control slew rates. Compared with the best optimality we can find through expensive optimization simulations, the best DRL agent can maximally approximate the optimality by 96.54%, which outperforms the best MPC whose optimality level is 90.11%. However, due to the stochasticity, the DRL agent is only expected to approximate the optimality by 90.42%, which is almost equivalent to the best MPC. Except for Proximal Policy Optimization (PPO), all DRL agents can have a better approximation to the optimality than the best MPC, and are expected to have better approximation than the MPC with a prediction horizon of 32 steps (15 min per step). In terms of reducing energy cost and thermal discomfort, MPC can outperform the rule-based control (RBC) by 18.47%–25.44%. DRL can be expected to outperform RBC by 18.95%–25.65% ,and the best DRL control policy can outperform RBC by 20.29%–29.72%. Although the comparison of the optimality level is performed in a perfect setting, e.g., MPC assumes perfect models, and DRL assumes a perfect offline training process and online deployment process, this can shed insight on their capabilities of approximating to the original dynamic programming problem.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Numerical Simulation of Light to Heat Conversion by Plasmonic Nanoheaters

Plasmonic nanoparticles are widely recognized as photothermal conversion agents, i.e., nanotransducers or nanoheaters. Translation of these materials into practical applications requires quantitative analyses of their photothermal conversion efficiencies (η). However, the value of η obtained for different materials is dramatically influenced by the experimental setup and method of calculation. Here, we evaluate the most common methods for estimating η (Roper’s and Wang’s) and compare these with numerical estimates using the simulation software ANSYS. Experiments were performed with colloidal gold nanorod solutions suspended in a hanging droplet irradiated by an 808 nm diode laser and monitored by a thermal camera. The ANSYS simulations accounted for both heating and evaporation, providing η values consistent with the Wang method but higher than the Roper approach. This study details methods for estimating the photothermal efficiency and finds ANSYS to be a robust tool where experimental constraints complicate traditional methods.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Expanding the genetic engineering toolbox for the metabolically flexible acetogen Eubacterium limosum

Abstract Acetogenic bacteria are an increasingly popular choice for producing fuels and chemicals from single carbon (C1) substrates. Eubacterium limosum is a promising acetogen with several native advantages, including the ability to catabolize a wide repertoire of C1 feedstocks and the ability to grow well on agar plates. However, despite its promise as a strain for synthetic biology and metabolic engineering, there are insufficient engineering tools and molecular biology knowledge to leverage its native strengths for these applications. To capitalize on the natural advantages of this organism, here we extended its limited engineering toolbox. We evaluated the copy number of three common plasmid origins of replication and devised a method of controlling copy number and heterologous gene expression level by modulating antibiotic concentration. We further quantitatively assessed the strength and regulatory tightness of a panel of promoters, developing a series of well-characterized vectors for gene expression at varying levels. In addition, we developed a black/white colorimetric genetic reporter assay and leveraged the high oxygen tolerance of E. limosum to develop a simple and rapid transformation protocol that enables benchtop transformation. Finally, we developed two new antibiotic selection markers—doubling the number available for this organism. These developments will enable enhanced metabolic engineering and synthetic biology work with E. limosum.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Bayesian approach to evaluation of soil biogeochemical models

Abstract. To make predictions about the carbon cycling consequences of rising global surface temperatures, Earth system scientists rely on mathematical soil biogeochemical models (SBMs). However, it is not clear which models have better predictive accuracy, and a rigorous quantitative approach for comparing and validating the predictions has yet to be established. In this study, we present a Bayesian approach to SBM comparison that can be incorporated into a statistical model selection framework. We compared the fits of linear and nonlinear SBMs to soil respiration data compiled in a recent meta-analysis of soil warming field experiments. Fit quality was quantified using Bayesian goodness-of-fit metrics, including the widely applicable information criterion (WAIC) and leave-one-out cross validation (LOO). We found that the linear model generally outperformed the nonlinear model at fitting the meta-analysis data set. Both WAIC and LOO computed higher overfitting risk and effective numbers of parameters for the nonlinear model compared to the linear model, conditional on the data set. Goodness of fit for both models generally improved when they were initialized with lower and more realistic steady-state soil organic carbon densities. Still, testing whether linear models offer definitively superior predictive performance over nonlinear models on a global scale will require comparisons with additional site-specific data sets of suitable size and dimensionality. Such comparisons can build upon the approach defined in this study to make more rigorous statistical determinations about model accuracy while leveraging emerging data sets, such as those from long-term ecological research experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Quantitative Separation of Unknown Organic–Metal Complexes by Liquid Chromatography–Inductively Coupled Plasma-Mass Spectrometry

Dissolved organic matter (DOM) is widely recognized to control the solubility and reactivity of trace metals in the environment. However, the mechanisms that govern metal-DOM complexation remain elusive, primarily due to the analytical challenge of fractionating and quantifying metal–organic species within the complex mixture of organic compounds that comprise DOM. Here, we describe a quantitative method for fractionation and element-specific detection of organic–metal complexes using liquid chromatography with online inductively coupled plasma mass spectrometry (LC–ICP-MS). The method implements a post-column compensation gradient to stabilize ICP–MS elemental response across the LC solvent gradient, thereby overcoming a major barrier to achieving quantitative accuracy with LC–ICP-MS. With external calibration and internal standard correction, the method yields concentrations of organic–metal complexes that were consistently within 6% of their true values, regardless of the complex’s elution time. We used the method to evaluate the effects of four stationary phases (C18, phenyl, amide, and pentafluoroylphenyl propyl) on the recovery and separation of environmentally relevant trace metals (Mn, Fe, Co, Ni, Cu, Zn, Cd, and Pb) in Suwannee River Fulvic Acid and Suwannee River Natural Organic Matter. The C18, amide, and phenyl phases generally yielded optimal metal recoveries (>75% for all metals except Pb), with the phenyl phase separating polar species to a greater extent than C18 or amide. We also fractionated organic-bound Fe, Cu, and Ni in oxidized and reduced soils, revealing divergent metal-DOM speciation across soil redox environments. Finally, by enabling quantitative fractionation of DOM-bound metals, our method offers a means for advancing a mechanistic understanding of metal–organic complexation throughout the environment.

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

Experimental evaluation of shear modulus scaling of dynamic strength at extreme pressures

Recent progress in the development of dynamic strength experimental platforms is allowing for unprecedented insight into the assumptions used to construct constitutive models operating in extreme conditions. In this work, we make a quantitative assessment of how tantalum strength scales with its shear modulus to pressures of hundreds of gigapascals through a cross-platform examination of three dynamic strength experiments. Specifically, we make use of Split–Hopkinson pressure bar and Richtmyer–Meshkov instability experiments to assess the low-pressure strain and strain rate dependence. Concurrent examination of magnetically driven ramp-release experiments up to pressures of 350GPa allows us to examine the pressure dependence. In this work, using a modern description of the shear modulus, validated against both ab initio theory and experimental measurements, we then assess how the experimentally measured pressure dependence scales with shear modulus. Furthermore, we find that the common assumption of scaling strength linearly with the shear modulus is too soft at high pressures and offer discussion as to how descriptions of slip mediated plasticity could result in an alternative scaling that is consistent with the data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Identification and mapping of quantitative trait loci for Fusarium head blight resistance in a synthetic hexaploid × hard red spring wheat population

Abstract Fusarium head blight (FHB), caused byFusarium graminearumSchwabe, is one of the most devastating diseases in wheat (Triticum aestivumL.). The synthetic hexaploid wheat line Largo was developed from a cross between the durum wheat [T. turgidumssp.durum(Desf.) Husn.] variety Langdon and theAegilops tauschiiCosson accession PI 268210, and it was previously found to have a moderate level of FHB resistance. This study was conducted to identify quantitative trait loci (QTL) associated with FHB resistance using a population of 188 recombinant inbred lines (RILs) from a cross between Largo and the susceptible wheat line ND495. The RILs were evaluated for Type II resistance in two greenhouse and two field environments. The disease severity and 90K single‐nucleotide polymorphism marker data were used for QTL analysis, which revealed six QTL on chromosomes 1D, 2D, 5B, and 7D. Four QTL (QFhb.rwg‐1D,QFhb.rwg‐5B,QFhb.rwg‐7D.1, andQFhb.rwg‐7D.3) from Largo had minor effects, whereas two QTL (QFhb.rwg‐2DandQFhb.rwg‐7D.2) from ND495 showed large effects on FHB resistance. The result suggested that ND495 may possess suppressor or susceptibility gene(s) suppressing or masking FHB resistance controlled by the resistance QTL. Among these QTL, four coincided with previously reported QTL, includingFhb9, and two (QFhb.rwg‐1DandQFhb.rwg‐7D.1) are likely novel QTL. From the six QTL regions, 10 Kompetitive allele‐specific PCR markers were developed and validated for marker‐assisted selection. The QTL detected from the resistant and susceptible parents enhance our understanding of FHB resistance expression and provide new resources for improving FHB resistance in wheat.

Genetics & Heredity↗
DOE OSTI2025

A mathematical approach to using the forgetting curve to evaluate experience and training factors in human reliability analysis

Traditional human reliability analysis (HRA) methods have difficulty dealing with the dynamic nature of factors such as time and rely on static and expert-judgment-based assessments of performance-shaping factors (PSFs) across limited levels. In this study, we introduce a mathematical approach for dynamically evaluating the experience and training PSF. Our proposed method integrates the psychological concept of the “forgetting curve” to evaluate how PSFs are impacted by the number of trainings and the time elapsed since training. To confirm the validity of the model, we provide experimental data fitted by identifying the quantitative relationship between training and human performance. This research enables dynamic and objective assessments, thus reducing reliance on subjective expert judgment and improving the accuracy of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Performance of three-dimensional printed nasopharyngeal swabs for COVID-19 testing

At the start of the COVID-19 pandemic, the US faced nationwide shortages of nasopharyngeal swabs due to both overwhelmed supply chains and an increase in demand. To address this shortfall, multiple 3D printed swabs were ultimately produced and sold for COVID-19 testing. In this work, we present a framework for mechanical and functional bench-testing of nasopharyngeal swabs using standard and widely available material testing equipment. Using this framework, we offer a comprehensive, quantitative comparison of the 3D printed swabs to benchmark their performance against traditional flocked swabs. The test protocols were designed to emulate the clinical use of the nasopharyngeal swabs and to evaluate potential failure modes. Overall, the 3D printed swabs performed comparably to, or outperformed, the traditional swabs in all mechanical tests. While traditional swabs outperformed some of the new 3D printed swabs in terms of sample uptake and retention, similar amounts of RNA were recovered from both 3D printed and traditional swabs.

3D printing↗
DOE OSTI2021

Model-Based Ultrasonic Signal Processing for the Nondestructive Evaluation of Additive Manufacturing Components

Ultrasonic testing (UT) for nondestructive evaluation (NDE) is a critical entity necessary to resolve both the quality and precision questions of complex parts evolving from the innovative additive manufacturing (AM) process. This modality provides the essential quantitative information for acceptance and potential flaw detectionof the part under investigation. A primary ingredient in UT besides the required precision robotic hardware for theacquisition of high quality measurement data is the underlying signal processing. It is here that much of the system performance capability resides. In this report,we discuss the basic steps in UT signal processing along with current and future capabilities that must be achieved in order to satisfy the critical demands created by the AM process.

42 ENGINEERING↗
DOE OSTI2025

Southwest Regional Partnership on Carbon Sequestration: Phase III (Final Scientific/Technical Report)

The Southwest Regional Partnership on Carbon Sequestration (SWP) is one of 7 regional partnerships formed in 2003 under the U.S. Department of Energy’s (DOE) Regional Carbon Sequestration Partnerships (RCSPs) initiative. The overall purpose of the initiative was to help determine and implement the technology, infrastructure, and regulations most appropriate to promote carbon storage in different regions of the country. Covering Arizona, Colorado, New Mexico, Oklahoma, Utah, and parts of Texas, Wyoming, and Kansas, the SWP evaluated regional carbon storage and utilization potential and focused on technologies and sites that could complement the region’s strong position in energy production. The project progressed through three phases: • Phase I (2003–2005): Characterized regional geologic formations and CO 2 sources, assessed sequestration potential, and identified pilot test sites. • Phase II (2005–2013): Conducted small-scale field tests to validate sequestration methods, including geologic and terrestrial projects. • Phase III (2008–2022): Demonstrated large-scale CO 2 injection at a commercial oil field to test monitoring, verification, and long-term storage strategies. This report covers Phase III. The final project site, the Farnsworth Unit (FWU) in Texas, provided real-world testing of reservoir characterization, monitoring, and risk evaluation tools and processes that could be used in any commercial scale carbon capture, utilization, and storage (CCUS) project. Extensive data collection and analysis helped refine best practices for reservoir characterization, injection monitoring, and storage verification. The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Key lessons learned include the importance of robust data management, strategic site selection, regulatory navigation, and effective industry collaboration. The project’s findings will inform ongoing and future carbon storage initiatives. Task 1 (Regional Characterization) • The SWP continued to participate in national outreach efforts and NATCARB. • The SWP evaluated multiple potential sites before selecting the FWU as the primary field test location. Task 2 (Public Outreach and Education) • The SWP contributed to national databases, DOE best practice manuals, and regional geological assessments to support future sequestration efforts. Task 3 (Permitting and Regulatory Compliance) • The SWP ensured compliance with federal and state regulations, including National Environmental Policy Act (NEPA) requirements. • The SWP obtained all necessary permits for drilling, injection, and monitoring activities. Task 4 (Site Characterization and Planning) • The SWP developed work plans for four key activities: characterization, simulation, monitoring and verification, and risk evaluation. • The SWP collected and synthesized legacy data from multiple sources to build initial static geological models and dynamic reservoir models demonstrating project feasibility. • The SWP conducted an initial risk evaluation and developed mitigation plans. Task 5 (Field Operations and Data Collection) • The SWP drilled, logged, and cored three characterization wells to gather critical subsurface data. • The SWP conducted multiple geophysical surveys, including 3D seismic, crosswell seismic, and vertical seismic profiling, to improve reservoir characterization. Task 6 (Monitoring and Verification) • The SWP performed extensive geological characterization using data from characterization wells and seismic surveys. • The SWP established a surface monitoring network to track CO 2 flux in soil gas, groundwater chemistry, and near-surface atmospheric CO 2 levels. • The SWP built and refined reservoir models to study the effects of relative permeability on simulation behavior and improve calibration with experimental data. Task 7 (Risk Assessment and Model Refinement) • The SWP conducted multiple studies to evaluate reservoir integrity, predict CO 2 plume behavior and improve predictive modeling capabilities. • The SWP refined geological models and used them to enhance the accuracy of simulation models. • The SWP continued quantitative risk assessment of top-ranked risks and strengthened the link between qualitative and quantitative risk methodologies.

02 PETROLEUM↗
DOE OSTI2026

Space Cooling: Recovery, Reuse, Recycling and Supply Chain Impacts

The global cooling sector has undergone tremendous growth in recent years, and cooling demand is projected to double or triple by 2050. Surging air conditioning demand will continue to be driven by increased populations, incomes and occurrences of extreme heat and humidity events. Cooling equipment growth has important pollution and material implications as they are made up of critical metals and precious raw materials - including ferrous metals, copper, aluminum and printed circuit boards composed of copper, silver, gold and palladium - that generate significant electronic waste at end-of-life. Air conditioning equipment also contains fluorinated gas (F-gas) refrigerants, which are potent gases that are thousands of times more heat-trapping than carbon dioxide (CO2). While recovery, reuse, and recycling strategies have been assessed for cold chain and refrigeration, there is currently limited analysis on how these strategies could be adopted for the global space cooling industry and potential supply chain implications. This paper aims to address this research gap by qualitatively evaluating product and material recovery, reuse and recycling frameworks from both demand and supply-side perspectives and with the support of cooling-specific case studies. It also focuses on quantitatively assessing potential energy, emissions and resource benefits of such strategies for space cooling equipment. This paper first analyzes how existing recovery, reuse and recycling frameworks can be applied to space cooling, with emphasis on the demand-side enablers (e.g., innovative business models, supporting policies and regulations) and changes in supply-side production network throughout the supply chain (e.g., design, production and distribution, end-of-life recovery) needed to overcome existing barriers. It will present case studies of innovative business models for space cooling technologies, including reuse and recycling, and how effective refrigerant reclamation and recovery programs have been operationalized. Lastly, the paper will highlight global modeling results of energy, emissions and material recovery from scenario analysis of selected strategies for air conditioners. The findings of this paper are intended to inform the development of product and material recovery, reuse and recycling strategies for a rapidly growing stock of space conditioning equipment by addressing existing organizational, economic and regulatory barriers and potential supply-chain bottlenecks.

Khanna, Nina↗
DOE OSTI2025

A cost and community perspective on the barriers to microbiome data reuse

Microbiome research is becoming a mature field with a wealth of data amassed from diverse ecosystems, yet the ability to fully leverage multi-omics data for reuse remains challenging. To provide a view into researchers’ behavior and attitudes towards data reuse, we surveyed over 700 microbiome researchers to evaluate data sharing and reuse challenges. We found that many researchers are impeded by difficulties with metadata records, challenges with processing and bioinformatics, and problems with data repository submissions. We also explored the cost constraints of data reuse at each step of the data reuse process to better understand “pain points” and to provide a more quantitative perspective from sixteen active researchers. The bioinformatics and data processing step was estimated to be the most time consuming, which aligns with some of the most frequently reported challenges from the community survey. From these two approaches, we present evidence-based recommendations for how to address data sharing and reuse challenges with concrete actions for future work.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Impact of Urban Representation on Simulation of Hurricane Rainfall

Abstract Taking the examples of Hurricane Florence (2018) over the Carolinas and Hurricane Harvey (2017) over the Texas Gulf Coast, the study attempts to understand the performance of slab, single‐layer Urban Canopy Model (UCM), and Building Environment Parameterization (BEP) in simulating hurricane rainfall using the Weather Research and Forecasting (WRF) model. The WRF model simulations showed that for an intense, large‐scale event such as a hurricane, the model quantitative precipitation forecast over the urban domain was sensitive to the model urban physics. The spatial and temporal verification using the modified Kling‐Gupta efficiency and Method for Object based Diagnostic and Evaluation in Time Domain suggests that UCM performance is superior to the BEP scheme. Additionally, using the BEP urban physics scheme over UCM for landfalling hurricane rainfall simulations has helped simulate heavy rainfall hotspots.

Geology↗