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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Epidemiology of Coccidioidomycosis (Valley fever) and the Disease Ecology of Coccidioides spp. in New Mexico (2006–2023)

Coccidioidomycosis (Valley fever), caused by Coccidioides spp., is a fungal infection endemic to semi-arid regions of the Americas. Despite 80 years of disease recognition in New Mexico, there is limited disease awareness. We incorporated clinical, epidemiological, and ecological datasets to summarize the knowledge of Valley fever in New Mexico. We analyzed 1541 human cases from 2006 to 2023. On average, 86 cases were reported each year (4.1 cases per 100,000 population per year). The highest levels of incidence were in southwestern New Mexico. American Indian or Alaska Natives in New Mexico had a 1.9 times higher incidence rate of coccidioidomycosis than White people, and among age groups, older populations in New Mexico had the highest incidence rates. We analyzed 300 soil samples near Las Cruces, New Mexico, for the presence of Coccidioides and reported the first known positive soil samples collected from the state, the majority of which were from grassland-dominated sites and from animal burrows. Sequence analyses in clinical specimens, wild animals, and soil samples confirmed that Coccidioides posadasii is the main causative species of coccidioidomycosis in New Mexico. Environmental surveillance validated that locally acquired infections could occur in, but are not limited to, Catron, Doña Ana, Sierra, and Socorro Counties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Dust Storms, Valley Fever, and Public Awareness

We discuss several issues raised by Comrie (2021, https://doi.org/10.1029/2021GH000504), which uses a crowdsourced data set to study dust storms and coccidioidomycosis (Valley fever). There is inconsistency in the term “dust storm” used by science communities. The dust data from National Oceanic and Atmospheric Administration Storm Events Database are from diverse sources, unsuitable for assessing dust-coccidioidomycosis relationships. Population exposure to dust or Coccidioides needs to consider the frequency, magnitude, and duration of dust events. Given abundant evidence that dust storms are a viable driver to transport pathogens, it is in best public interest to advocate dust storms may put people at risk for contracting Valley fever.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The FEWSION for Community Resilience (F4R) Process: Building Local Technical and Social Capacity for Critical Supply Chain Resilience

Local business leaders, policy makers, elected officials, city planners, emergency managers, and private citizens are responsible for, and deeply affected by, the performance of critical supply chains and related infrastructures. At the center of critical supply chains is the food-energy-water nexus (FEW); a nexus that is key to a community’s wellbeing, resilience, and sustainability. In the 21st century, managing a local FEW nexus requires accurate data describing the function and structure of a community’s supply chains. However, data is not enough; we need data-informed conversation and technical and social capacity building among local stakeholders to utilize the data effectively. There are some resources available at the mesoscale and for food, energy, or water, but many communities lack the data and tools needed to understand connections and bridge the gaps between these scales and systems. As a result, we currently lack the capacity to manage these systems in small and medium sized communities where the vast majority of people, decisions, and problems reside. This study develops and validates a participatory citizen science process for FEW nexus capacity building and data-driven problem solving in small communities at the grassroots level. The FEWSION for Community Resilience (F4R) process applies a Public Participation in Scientific Research (PPSR) framework to map supply chain data for a community’s FEW nexus, to identify the social network that manages the nexus, and then to generate a data-informed conversation among stakeholders. F4R was piloted and co-developed with participants over a 2-year study, using a design-based research process to make evidence-based adjustments as needed. Results show that the F4R model was successful at improving volunteers’ awareness about nexus and supply chain issues, at creating a network of connections and communication with stakeholders across state, regional, and local organizations, and in facilitating data-informed discussion about improvements to the system. In this paper we describe the design and implementation of F4R and discuss four recommendations for the successful application of the F4R model in other communities: 1) embed opportunities for co-created PPSR, 2) build social capital, 3) integrate active learning strategies with user-friendly digital tools, and 4) adopt existing materials and structure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Urban Energy and Climate: Prospects for a Sustainable Transition

With the continuous migration of people towards metropolitan areas in search of employment, the demands for core services and energy, coupled with an increasing awareness of the impact of climate change, have placed the management and planning of global urban energy under a lot of pressure. Trends toward urban energy service transformations that offer greater affordability, reliability, efficiency and adaptability provide hope for a global sustainable future. At the same time, there are also limits to these transitions, as well as risks involved. For example, on one end of the spectrum, our urban energy future includes land use sprawl, high fossil fuel use, pollution, and unhealthy urban conditions. On the other side of this transition spectrum is more energy choices, and healthier, more livable cities, along with less energy use and fewer greenhouse gas emissions. What the future might hold for transforming the world's cities depends upon an understanding of the risks of current trajectories and the opportunities for and limitations to developing sustainable urban energy systems. This edited volume brings together leading experts on the prospects and challenges of urban energy innovation and on related-economic, social and environmental sustainability transitions. The focus of the volume is on multidisciplinary reviews, research informing technologies and policies for sustainability, and analytical insights addressing rapid urbanization and changes across a diverse typology of global cities. The volume will include an overview of the current state of urban energy systems. It will also document and evaluate urban energy prospects for a sustainable, resilient future.

drivers of change↗
DOE OSTI2020

An agent-based approach to study the diffusion rate and the effect of policies on joint placement of photovoltaic panels and green roof under climate change uncertainty

As two of the highest trending green technologies, photovoltaic panels and green roofs are proven to be effective practices for energy generation and energy saving. The achievable impact from the widespread installation of such technologies is, however, not clearly established. This is mainly because the degree of this impact highly depends on the inherently uncertain environmental and climate factors, as well as the unknown adoption rates of these technologies, which in turn depend on different characteristics of decision makers and interactions among them. To that end, this study aims to investigate the diffusion rate of these green technologies under uncertainties caused by climate change, characteristics of adopters, and their interactions. An integrated framework is developed to capture the interplay between financial and attitudinal aspects, as well as the uncertainties due to both the stochastic nature of system parameters and the interactions among agents involving human beings. Specifically, this framework consists of a integer programming model to optimize the green roof and/or photovoltaic panel installation settings for a given building under climate change uncertainty, and an agent-based model to factor in the role of human behavior and interactions. A case study for the city of Knoxville, TN, is presented to evaluate the effects of different policies on the diffusion rate of the green technologies of interest. The results show that the affordability of green technologies and public awareness are the key drivers of the adoption of these technologies, which highlight the important role of the decision makers in impacting the diffusion rate.

14 SOLAR ENERGY↗
DOE OSTI2025

Advancing ocean monitoring and knowledge for societal benefit: the urgency to expand Argo to OneArgo by 2030

The ocean plays an essential role in regulating Earth’s climate, influencing weather conditions, providing sustenance for large populations, moderating anthropogenic climate change, encompassing massive biodiversity, and sustaining the global economy. Human activities are changing the oceans, stressing ocean health, threatening the critical services the ocean provides to society, with significant consequences for human well-being and safety, and economic prosperity. Effective and sustainable monitoring of the physical, biogeochemical state and ecosystem structure of the ocean, to enable climate adaptation, carbon management and sustainable marine resource management is urgently needed. The Argo program, a cornerstone of the Global Ocean Observing System (GOOS), has revolutionized ocean observation by providing real-time, freely accessible global temperature and salinity data of the upper 2,000m of the ocean (Core Argo) using cost-effective simple robotics. For the past 25 years, Argo data have underpinned many ocean, climate and weather forecasting services, playing a fundamental role in safeguarding goods and lives. Argo data have enabled clearer assessments of ocean warming, sea level change and underlying driving processes, as well as scientific breakthroughs while supporting public awareness and education. Building on Argo’s success, OneArgo aims to greatly expand Argo’s capabilities by 2030, expanding to full-ocean depth, collecting biogeochemical parameters, and observing the rapidly changing polar regions. Providing a synergistic subsurface and global extension to several key space-based Earth Observation missions and GOOS components, OneArgo will enable biogeochemical and ecosystem forecasting and new long-term climate predictions for which the deep ocean is a key component. Driving forward a revolution in our understanding of marine ecosystems and the poorly-measured polar and deep oceans, OneArgo will be instrumental to assess sea level change, ocean carbon fluxes, acidification and deoxygenation. Emerging OneArgo applications include new views of ocean mixing, ocean bathymetry and sediment transport, and ecosystem resilience assessment. Implementing OneArgo requires about $100 million annually, a significant increase compared to present Argo funding. OneArgo is a strategic and cost-effective investment which will provide decision-makers, in both government and industry, with the critical knowledge needed to navigate the present and future environmental challenges, and safeguard both the ocean and human wellbeing for generations to come.

ARGO↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Learning from learning machines: improving the predictive power of energy-water-land nexus models with insights from complex measured and simulated data

Focal Area(s): Insights gleaned from complex data (observed and simulated) using AI; Predictive modeling through the use of AI techniques and AI-derived model components, including physics- and knowledge-informed models; Energy-water-land nexus and integrated energy systems – models of MultiSector dynamics. Science Challenge: Scientific communities are in need of tools for the computational integration of physics-based models, experimental data, and empirical/observational studies across a broad range of temporal and spatial scales to explore and meet EESSD grand challenges. We require AI algorithms for the discovery of process-drivers in the Earth-energy-human system and to link them with complex measured and simulated data. We aim to understand the energy-water-land nexus, particularly under extreme forcing scenarios and rare events, leveraging scale-aware AI process models, including probabilistic uncertainties, and benefiting from the emerging 5G-enabled landscape-scale sensing and edge computing capabilities. These models are needed to identify instabilities and tipping points that manifest extreme system behaviors with consequences for integrated energy systems and the environment. This work requires fundamental advances in uncertainty quantification, in particular, to identify and model unlikely but catastrophic outliers. Specifically, we need models that are interpretable to domain scientists and, essentially, explainable to public and private stake holders.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Coupling Noah-Multiparameterization land-surface Model with Energy Research and Forecasting Model

The Energy Research and Forecasting (ERF) model is a high-performance atmospheric model built on the AMReX adaptive mesh refinement (AMR) framework, enabling efficient simulations on heterogeneous computing platforms that combine multicore processors with hardware accelerators. To support land–atmosphere interactions within ERF’s AMR-based environment, a land-surface model must be capable of operating directly on hierarchically refined meshes. In this work, we present a methodology for coupling the Fortran-based Noah-Multiparameterization (Noah-MP) land-surface model with ERF’s C++ codebase. Rather than rewriting Noah-MP, we construct a Fortran–C interoperability layer using CodeScribe, a tool that leverages large language models (LLMs) to automate the generation of interface code. CodeScribe applies structured prompting techniques to generate bindings that support efficient data exchange and function calls between ERF and Noah-MP. The coupling framework also incorporates AMR-aware data handling strategies, allowing NoahMP to operate seamlessly within ERF’s hierarchical mesh structure. This work provides a structured approach for integrating legacy Fortran models into modern C++-based modeling systems using LLM-assisted code generation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Artificial Intelligence for Natural Gas Utilities: A Primer

Modern natural gas utilities face numerous challenges and competing priorities from various stakeholders. Policymakers, customers, and advocacy groups want to see gas utilities improve performance on safety, reliability, resilience, affordability, and environmental stewardship. State utility regulators — public utility commissions — are responsible for overseeing utility performance, ensuring that ratepayer funds are being spent in the public interest, and aligning utility goals with public goals. The use of new technologies is critical to enabling cost-effective performance on these attributes. Artificial intelligence (AI) is a widely used term among utilities and regulators, but the term means different things to different stakeholders, and it is often used to describe data analytics approaches that fall short of the formal definition of AI, which is: “…the ability of a machine to receive inputs and produce a behavior or reaction similar to that of an intelligent human being.” AI (and related tools, techniques, and technologies) can help utilities solve current and emerging challenges. By combining customer and system data with analytical tools and technologies, AI can augment human decision-makers by assisting in identifying problems and events before they occur, enabling resources to be more efficiently directed across utility infrastructure. The intended primary audience for this primer is state regulators, although utilities and other stakeholders might also find it useful and relevant to improve their awareness of AI. The objectives of this primer are to: (a) offer a set of broadly applicable definitions for AI and related terms, allowing regulators, utilities, and other stakeholders to speak the same language; (b) discuss how AI is currently being implemented in the gas utility sector; and (c) understand the challenges affecting AI solutions and how tools might be implemented in the future. This primer fits within NARUC’s goals of providing impartial information to improve the ability of public utility commissions to regulate in the public interest. As such, this primer does not seek to recommend AI over any other investment, nor does it endorse any particular vendor, product, or approach. It does seek to prepare state regulators to oversee AI investments by sharing information about the current landscape of commercially available tools. To these ends, the primer is organized as follows: Section I discusses the current environment in which natural gas utilities operate and how AI, when thoughtfully designed and implemented, can enable utilities to achieve performance goals; Section II offers definitions of AI and related terms within the data analytics discipline; Section III provides three current opportunities for which AI can offer solutions: replacing aging gas distribution infrastructure, preventing excavator damage to gas distribution infrastructure, and improving energy efficiency programs. This section discusses each problem statement in detail. Second, Section III includes a discussion of how costs and benefits of investments to solve each problem are measured. And third, this section offers real-world examples of utility implementation of AI solutions; Section IV discusses challenges with implementing AI, both from the perspective of utilities and regulators; Section V suggests areas in which AI could feasibly be implemented in the near future; Finally, Section VI offers concluding thoughts and areas for further research.

03 NATURAL GAS↗
DOE OSTI2022

Coupling Warm Rain With an Eddy Diffusivity/Mass Flux Parameterization: 1. Model Description and Validation

A new version of the stochastic multiplume Jet Propulsion Laboratory Eddy-Diffusivity/Mass-Flux (JPL-EDMF) parameterization which consistently couples the simplified Khairoutdinov and Kogan (2000), https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2, warm phase cloud microphysical parameterization with the parameterization of cloud macrophysical and subgrid scale dynamical processes is described. The new parameterization combines the EDMF approach with an assumed shape of a joint probability density function of thermodynamic and kinematic variables which provide the basis for the computation of all parameterized processes. As far as we are aware this is the first attempt to consistently couple all of these parameterized processes in the EDMF framework. This paper is part one of a two paper series. Here, the JPL-EDMF parameterization is described and benchmark simulations of precipitating stratocumulus and cumulus convection are performed in a single-column-model framework. The parameterization results compare favorably to the reference large-eddy-simulation results. In the second part (Smalley et al., 2022, https://doi.org/10.1029/2021MS002729) the JPL-EDMF parameterization is validated for a wide range of observation-based scenarios covering the continuous transition from subtropical stratocumulus to cumulus convection derived from global reanalysis, and parameterization uncertainties are studied in detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Shelter Island, New York, Energy Reliability Assessment and Local Generation Options

Shelter Island's Green Options Committee (GOC), an all-volunteer committee tasked with considering all environmental conservation issues, requested technical assistance under the U.S. Department of Energy (DOE)-funded Energy Technology Innovation Partnership Project (ETIPP). The primary goals of this technical assistance project included: 1. Informing the GOC about energy use trends within the community as well as distributed solar, wind and storage opportunities; 2. Providing additional resources and technical feasibility information for geothermal, agrivoltaics and tidal energy; 3. Supporting the development of a community engagement plan; and 4. Supporting collaboration between the GOC and PSEG in order to find mutually beneficial follow-on projects. Assistance from NLR to help the GOC meet these overall project goals was provided through the following primary tasks: 1. Collaborate with the local utility in order to collect energy use data and inform the community on energy use patterns through a baseline assessment; 2. Technical analysis of distributed renewable and resiliency opportunities (solar, wind and storage) that best align with the community's energy priorities; 3. Provide high level feasibility support for future renewable energy scenarios that include agrivoltaic solutions, tidal energy, and geothermal projects on Shelter Island, and 4. Integrate all findings into a Community Outreach presentation to help the GOC engage with community stakeholders to build support and awareness of chosen resilience strategies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Closed-Loop Distribution System Restoration Tool for Natural Disaster Recovery. Final CRADA report

We propose to continue the development of the restoration algorithms and the tool’s user interface, both of which have been developed under the GMLC-0131 project. This project aims to develop a distribution restoration decision support tool that will assist utilities in performing distribution restoration after extreme weather events in an optimal and efficient manner. The tool will integrate the weather information/forecasts and system fragility assessment together with the field measurement data to improve the situational awareness and estimate the system damage status. Advanced optimization models will be leveraged to dispatch repair crews and associated resources. New resources (e.g., automatic switches and distributed generators (DGs)) enabled by distribution automation and smart grid development will be utilized to reconfigure distribution grids and pick up loads promptly to reduce the outage sizes and durations. The closed-loop feature of the tool will make the tool adaptive to the evolving weather events and varying restoration capabilities. In this CRADA, we will collaborate with S&C Electric Company (S&C) to refine ANL-1060 (07/17/2019) the restoration algorithms that can be potentially applicable for any utility systems and vendor products, including: 1) Customizable information from both utility users and vendor users; 2) User interface design.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Subglacial discharge effects on basal melting of a rotating, idealized ice shelf

When subglacial meltwater is discharged into the ocean at the grounding line, it acts as a source of buoyancy, enhancing flow speeds along the ice base that result in higher basal melt rates. The effects of subglacial discharge have been well studied in the context of a Greenland-like, vertical calving front, where Earth's rotation can be neglected. Here we study these effects in the context of Antarctic ice shelves, where rotation is important. We use a numerical model to simulate ocean circulation and basal melting beneath an idealized three-dimensional ice shelf and vary the rate and distribution of subglacial discharge. For channelized discharge, we find that in the rotating case, total melt-flux anomaly increases with two-thirds power of the discharge, in contrast to existing non-rotating results for which the melt-flux anomaly increases with one-third power of the discharge. The higher melt-flux anomaly with discharge is attributed to a more extensive area of the ice-shelf base being exposed to direct high melting by the rising plume as it is deflected due to Earth's rotation and its path is prolonged. For distributed discharge, we find that in both the rotating and the non-rotating cases, the melt-flux anomaly increases with two-thirds power of the discharge. Furthermore, in the rotating case, the addition of channelized, subglacial discharge can produce either a higher or a lower ice-shelf basal melt-flux anomaly than the equivalent amount of distributed discharge, depending on its location along the grounding line relative to the directionality of the Coriolis force. This contrasts with previous results from non-rotating, vertical ice-cliff simulations, where distributed discharge was always found to be more efficient than channelized discharge at enhancing the terminus-averaged melt rate. The implication, based on our idealized simulations, is that melt-rate parameterizations attempting to include subglacial discharge effects that are not geometry- and rotation-aware may produce total melt-flux anomalies that are off by a factor of 2 or more.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Effects of Pollen on Hydrometeors and Precipitation in a Convective System

Anemophilous (wind-driven) pollen is one type of primary biological aerosol particle, which can rupture under high humidity conditions and form smaller sub-pollen particles (SPPs). Both pollen and SPPs can reach the upper troposphere under convective conditions, acting as cloud condensation nuclei (CCN) and ice nucleating particles (INPs), thus influencing cloud formation and precipitation. However, the impacts of these biological aerosols on cold cloud formation and local climate remain unclear as there are large uncertainties on their emission flux and ice nucleating abilities. Here, we incorporate pollen emission and rupture processes in the Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations and update the Morrison microphysics scheme within WRF-Chem using aerosol-aware INP parameterizations to account for pollen in addition to other anthropogenic and biogenic aerosol. INP parameterizations for pollen and SPP are derived from laboratory experiments. When including pollen rupture rates as observed in a series of chamber studies, SPP concentrations increase, leading to an increase of cloud ice and water by up to 50% and potentially extending the duration of the convective system. Among all simulated hydrometeors, graupel and raindrops exhibit the largest enhancements from the inclusion of SPPs, with intensifying precipitation at the backside of the convective system and a greater spatial extent. Sensitivity simulations indicate that SPPs have a greater effect on cloud microphysical processes than whole pollen grains, and further observational evidence is needed to constrain these processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Controversy and consensus: common ground and best practices for life cycle assessment of emerging technologies

This manuscript explores six controversial topics regarding LCA of emerging technologies: (1) appropriate use of LCA, (2) uncertainty assessment, (3) comparison with incumbents, (4) adopting standards, (5) system scale-up, and (6) stakeholder engagement. These topics encompass key issues vigorously debated during a series of workshop-style discussions convened by the LCA of Emerging Technologies Research Network (currently hosted by ACLCA). This paper represents the main points of support and opposition for a declarative resolution representing each topic, along with points of consensus, held amongst research network of LCA practitioners and experts. These debates and associated open questions are intended to build awareness amongst practitioners and decision-makers of the common challenges associated with assessing emerging technologies, while fostering evidence-based and context-informed discussions that are both transparent and impactful for the broader community.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Predicting Frigid Mixed-Phase Clouds for Pristine Coastal Antarctica

Supercooled water is common in the clouds near coastal Antarctica and occasionally occurs at temperatures at or below -30°C. Yet the ice physics in most regional and global numerical models will glaciate out these clouds. This presents a challenge for the simulation of highly supercooled clouds that were observed at McMurdo, Antarctica during the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) project during 2015–2017. The polar optimized version of the Weather Research and Forecasting model (Polar WRF) with the recently developed two-moment P3 microphysics scheme was used to simulate observed supercooled liquid water cases during March and November 2016. Nudging of the simulations to observed rawinsonde profiles and Antarctic automatic weather station observations provided increased realism and much greater cloud water amounts. Furthermore, sensitivity tests that adjust the ice physics for extremely low ice nucleating particle (INP) concentrations decrease cloud ice and increases the cloud liquid water closer to observed amounts. In these tests, a liquid layer near cloud top is simulated, in agreement with observations. Accurate representation of INP concentrations appears to be critical for the simulation of coastal Antarctic clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

An intercomparison of wall fluxes in a turbulent thermal convection chamber: Direct numerical simulations and wall-modeled large-eddy simulations enhanced by machine learning

Thermal convection in a closed chamber is driven by a warm bottom, a cold top, and side walls at various temperatures. Although wall fluxes are the source of convection energy, accurately modeling these fluxes (i.e., the wall model) is challenging. In large-eddy simulations (LESs), many wall models are traditionally derived from the canonical boundary layer, which may be unsuitable for thermal convection bounded by both horizontal and vertical walls. This study conducts a model intercomparison of dry convection in a cubic-meter chamber using three direct numerical simulations (DNSs) and four LESs with different wall models. The LESs employ traditional wall models, a new wall model employing physics-aware neural networks, and a refined grid near the walls. The experiment involves four cases with varying sidewall temperatures. Our results show that LESs capture the main flow features and the trends of mean fluxes. The physics-aware neural networks and refined wall grids can improve the temporally averaged local fluxes when the large-scale circulation has a preferred direction. Even without the local improvement of wall fluxes, the LES flow quantities (temperature and velocities) can still largely match those in DNSs, provided the mean flux largely matches the DNSs. Additionally, DNSs reveal that a variation in corner treatments has minimal impacts on the flow quantities away from corners. Finally, LESs underestimate the mean fluxes of the entire wall due to their inability to resolve corner regions, but their mean flux away from the corner can better match DNS.

54 ENVIRONMENTAL SCIENCES↗