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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Comparison of Some RANS Solvers

We will take a look at solving the Reynolds-averaged Navier-Stokes (RANS) equations that are encountered in the context of wind farm performance simulations and optimizations. We will compare some of the more popular ways to solve these equations with a focus on using iterative solvers for the linear solve. We will compare their performance and reliability to a direct solve as we scale the problem both by adding more and parallel resources and by increasing the size of the domain, both in two and three dimensions. There are many strategies that can be applied to solving the RANS equations, some are very efficient, while others are very insensitive to, for example, the Reynolds number. The first contender we will consider is the Pressure-Convection-Diffusion (PCD) preconditioner. Early results suggest that PCD is indeed a very efficient solver, in particular in two dimensions, as long as the Reynold's number remains small. Next we will try to reorder our degrees of freedom such that we can use GMRES with ILU for our linear solve. Another popular choice we will consider for solving the RANS equations is SIMPLE (and its derivatives). For all of our implementations we make use of either FEniCS or Firedrake, basing our work on both existing implementations of some of these solvers while also writing new extensions for others.

CFD↗
DOE OSTI2021

Sex ratio of Western Bluebirds ( Sialia mexicana ) is mediated by phenology and clutch size

Mothers may produce more of one sex to maximize their fitness if there are differences in the cost of producing each sex or there are differences in their relative reproductive value. Breeding date and clutch size are known to influence offspring sex ratios in birds through sex differences in dispersal, social behaviours, differential mortality and available food resources. We tested whether breeding date, clutch size and drought conditions influenced offspring sex ratios in a sexually size-monomorphic species, the Western Bluebird Sialia mexicana, by interrogating a 21-year dataset. After controlling for differential mortality, we found that hatch dates late in the breeding season were associated with the production of more females, suggesting that the value of producing males declines as the breeding season progresses. When clutch size was taken into account, small clutches yielded significantly more females late in the breeding season than in the early and middle parts of the breeding season, which produced significantly more males. Here, large clutches early in the season tended to produce more females, although this was not significant. Drought severity was not correlated with sex ratio adjustment. We propose and discuss several explanations for these patterns, including male offspring, but not female offspring, acting as helpers, increased female nestling provisioning late in the breeding season, differences in food abundance and egg-laying order. Future work will help to uncover the mechanisms leading to these patterns. Identifying patterns and mechanisms of sex ratio skew from long-term datasets is important for informing predictions regarding life-history trade-offs in wildlife populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

MPC solution for optimal load shifting for buildings with ON/OFF staged packaged units: Experimental demonstration, and lessons learned

Small and medium-sized commercial buildings (SMCB) are significant demand response resources, and it is important to develop grid-responsive control algorithms that exploit those resources and create financial benefits for building owners and HVAC service providers. Furthermore, unlike large-sized commercial buildings, there is an opportunity to have universally applicable control solutions for many SMCBs since those buildings have a consistent HVAC system configuration: SMCBs are commonly served by multiple-staged air conditioning units controlled by their own thermostats. Despite the demand response potential and scalability, however, very few control solutions are available for SMCBs. Typical model predictive control (MPC) and heuristic control approaches for cooling load shifting that lower thermostat setpoints before an electric price jump are suitable mainly for large-sized commercial buildings where a continuous capacity modulation is possible, e.g., via dampers in variable air volume terminal units. However, those approaches can cause undesired, high peaks for SMCBs due to the nature of ON/OFF unit staging and narrow thermostat deadbands. This could discourage the use of advanced grid-responsive controls for SMCBs due to the concern of high demand charges, and has to be resolved. This paper presents a MPC solution that overcomes this challenge. It has a hierarchical MPC structure where an upper level MPC is responsible for electrical load shifting in response to an electric price signal while a lower level MPC is responsible for coordinating compressor stages to eliminate unnecessary peaks and follows the setpoints determined by the upper level MPC. In this work, two one-month, comprehensive laboratory tests have been carried out to demonstrate load shifting and cost savings for the algorithm. Interesting trade-offs between energy efficiency and load flexibility were observed and are discussed, and lessons learned for applying MPCs for SMCBs are also presented.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Decarbonizing Building Thermal Systems: A How-to Guide for Heat Pump Systems and Beyond

Buildings account for a substantial portion of carbon emissions, primarily due to the widespread use of fossil fuels in heating systems. Decarbonization of heating is essential to meet climate targets and reduce the environmental impact of buildings. Heat pumps are capable of leveraging renewable energy sources and can provide heating and cooling in an energy-efficient manner. By leveraging heat pump technology, buildings can significantly reduce their carbon footprint, minimize energy consumption, and decrease their reliance on fossil fuels. The design and construction community plays a pivotal role in facilitating the transition to heat pump systems for heating and cooling. However, this transition requires specialized knowledge and expertise. This resource was developed for architects, engineers, and contractors in response to an industry need for a comprehensive technical resource that guides them through the intricacies of heat pump system design, installation, and maintenance. This resource provides detailed information on system sizing, selection of appropriate heat systems, heat sources, and integration with existing building systems. Moreover, it emphasizes best practices for ensuring operational efficiency, system longevity, and reliability. The development of this recourse was a collaborative effort between NREL/DOE Better Buildings Design and Construction Allies and ASHRAE Task Force for Building Decarbonization. This resource is composed of two complimentary portions that will be completed and released on separate time frames. The first portion will be completed and released in 2023, while the second portion will be released in 2024.

building thermal systems↗
DOE OSTI2024

Ultrafast jet classification at the HL-LHC

Abstract Three machine learning models are used to perform jet origin classification. These models are optimized for deployment on a field-programmable gate array device. In this context, we demonstrate how latency and resource consumption scale with the input size and choice of algorithm. Moreover, the models proposed here are designed to work on the type of data and under the foreseen conditions at the CERN large hadron collider during its high-luminosity phase. Through quantization-aware training and efficient synthetization for a specific field programmable gate array, we show that O ( 100 ) ns inference of complex architectures such as Deep Sets and Interaction Networks is feasible at a relatively low computational resource cost.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Seamless Wireless Communication Platform for Internet of Things Applications

The rapid growth of the Internet of Things (IoT) devices resulted in the proliferation of wireless technologies to cater to their increasing data rate requirements and support multiple applications. However, such ever-increasing wireless technologies present numerous challenges such as incompatible wireless standards, increased energy consumption, and insecure communication. The traditional gateways proposed in the literature suffers from limitations such as computational complexity, resource requirements, increased cost, and device size. We vision an era of seamless wireless communication to alleviate the aforementioned challenges in IoT applications. through three inter-dependent functionalities namely detection and identification of wireless technologies, energy-efficient transmit power control, and secure end-to-end communication. To prove the concept, a new gateway is proposed to achieve these three functionalities with only physical layer measurements so that the different communication protocols in the higher layers can be avoided. Novel schemes are conceptualized for resource-limited seamless IoT applications. Moreover, the conceptual seamless IoT platform is validated through software-based computer simulation and software-defined radio-based testbed implementation. Finally, the preliminary analysis demonstrates that the proposed platform has great potential in advancing seamless IoT applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Addressing the split incentive challenge for rooftop solar PV and battery energy storage in multifamily rental buildings (CRADA 638 Final Report)

This project advances the understanding of how roof solar PV systems and battery energy storage systems (BESS) can be effectively deployed in multifamily residential buildings, a sector that has historically faced barriers due to misaligned incentives between landlords and tenants. By leveraging high-resolution building stock data and simulation tools, the research demonstrates how energy consumption patterns vary across building types, climates, and occupant characteristics, and how these variations influence the optimal sizing and operation of distributed energy resources. A key contribution is the development of a publicly accessible, web-based tool named RESIDE (Residential Energy Systems & Infrastructure Data Evaluation) that allows users to explore building energy use and evaluate solar and battery configurations without requiring specialized expertise. This significantly lowers the barrier to entry for stakeholders such as property owners, utilities, and policymakers. From a technical perspective, the project shows that integrating rooftop solar PV with battery energy storage can substantially reduce electricity costs and peak demand through strategies such as energy arbitrage and peak shaving. The modeling framework incorporates real-world constraints, including time-of-use electricity pricing and battery degradation, providing realistic and actionable insights. Economically, the results indicate that properly sized systems can deliver meaningful cost savings, improving the feasibility of energy investments in multifamily housing. More broadly, the project benefits the public by supporting the transition to affordable and reliable energy, particularly in rental multifamily housing where adoption has traditionally lagged.

14 SOLAR ENERGY↗
DOE OSTI2023

Lipoprotein subfraction patterns throughout gestation in The Gambia: changes in subfraction composition and their relationships with infant birth weights

Lipoprotein subfraction concentrations have been shown to change as gestation progresses in resource-rich settings. The objective of the current study was to evaluate the impact of pregnancy on different-sized lipoprotein particle concentrations and compositions in a resource-poor setting. Samples were collected from pregnant women in rural Gambia at enrollment (8–20 weeks), 20 weeks, and 30 weeks of gestation. Concentrations of different-sized high-density, low-density, and triglyceride-rich lipoprotein particles (HDL, LDL, and TRL, respectively) were measured by nuclear magnetic resonance in 126 pooled plasma samples from a subset of women. HDL was isolated and the HDL proteome evaluated using mass spectroscopy. Subfraction concentrations from women in The Gambia were also compared to concentrations in women in the U.S. in mid gestation. Total lipoprotein particles and all-sized TRL, LDL, and HDL particle concentrations increased during gestation, with the exception of medium-sized LDL and HDL particles which decreased. Subfraction concentrations were not associated with infant birth weights, though relationships were found between some lipoprotein subfraction concentrations in women with normal versus low birth weight infants (< 2500 kg). HDL’s proteome also changed during gestation, showing enrichment in proteins associated with metal ion binding, hemostasis, lipid metabolism, protease inhibitors, proteolysis, and complement activation. Compared to women in the U.S., Gambian women had lower large- and small-sized LDL and HDL concentrations, but similar medium-sized LDL and HDL concentrations. Most lipoprotein subfraction concentrations increase throughout pregnancy in Gambian women and are lower in Gambian vs U.S. women, the exception being medium-sized LDL and HDL particle concentrations which decrease during gestation and are similar in both cohorts of women. The proteomes of HDL also change in ways to support gestation. These changes warrant further study to determine how a lack of change or different changes could impact negative pregnancy outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Abiotic selection of microbial genome size in the global ocean

Abstract Strong purifying selection is considered a major evolutionary force behind small microbial genomes in the resource-poor photic ocean. However, very little is currently known about how the size of prokaryotic genomes evolves in the global ocean and whether patterns reflect shifts in resource availability in the epipelagic and relatively stable deep-sea environmental conditions. Using 364 marine microbial metagenomes, we investigate how the average genome size of uncultured planktonic prokaryotes varies across the tropical and polar oceans to the hadal realm. We find that genome size is highest in the perennially cold polar ocean, reflecting elongation of coding genes and gene dosage effects due to duplications in the interior ocean microbiome. Moreover, the rate of change in genome size due to temperature is 16-fold higher than with depth up to 200 m. Our results demonstrate how environmental factors can influence marine microbial genome size selection and ecological strategies of the microbiome.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Climatic factors and human population changes in Eurasia between the Last Glacial Maximum and the early Holocene

Archaeological records document a significant expansion of populations from the Last Glacial Maximum (LGM, ~23–19 ka) to the early Holocene (EH, ~9 ka) in Eurasia, which is often attributed to the influence of orbital-scale climate changes. Yet, information remains limited concerning the climatic factor(s) which were responsible for conditioning demographic patterns. Here, in this work, we present results from an improved Minimalist Terrestrial Resource Model (MTRM), forced by a transient climate simulation from the LGM to the EH. Simulated potential hunter-gatherer population densities and spatial distributions across Eurasia are supported by observed archaeological sites in Europe and China. In the low latitudes, potential population size change was predominantly controlled by precipitation and its strong influence on plant and animal resources. In the middle-high latitudes, temperature was the dominant driver in influencing potential population size change and animal resource availability. Different regional responses of potential populations to climate change across Eurasia - owing to variations in available food resources between the LGM and EH - provide a better understanding of human dispersal during the Late Pleistocene.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Barren Plateaus Preclude Learning Scramblers

Scrambling processes, which rapidly spread entanglement through many-body quantum systems, are difficult to investigate using standard techniques, but are relevant to quantum chaos and thermalization. In this Letter, we ask if quantum machine learning (QML) could be used to investigate such processes. In this study we prove a no-go theorem for learning an unknown scrambling process with QML, showing that it is highly probable for any variational Ansatz to have a barren plateau landscape, i.e., cost gradients that vanish exponentially in the system size. This implies that the required resources scale exponentially even when strategies to avoid such scaling (e.g., from Ansatz -based barren plateaus or no-free-lunch theorems) are employed. Furthermore, we numerically and analytically extend our results to approximate scramblers. Hence, our work places generic limits on the learnability of unitaries when lacking prior information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

VOLTTRON/volttron-pnnl-aems

The Autonomous Energy Management Software (AEMS) system will continuously optimize the operations of the distributed energy resources in the small and medium size commercial building by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. Initially, AEMS system will manage rooftop air conditioners and heat pumps but it can be extended in the future to manage, hot water heaters, storage (battery and thermal), electric vehicle charging and monitoring solar photovoltaic. AEMS support both energy efficiency and grid service features.

Bleeker, Amelia [Pacific Northwest National Labora↗
DOE OSTI2023

Multi-Unit Dwelling Plug-in EV Charging Innovation Pilots (Final Report)

Nearly one-third of residences in the U.S. are multi-unit dwellings (MUDs), e.g., apartments and condominiums, and MUDs with five or more units account for approximately 45% of rental households. While 80% of EV charging takes place at home, less than 5% of home charging takes place at MUDs. With public electric vehicle (EV) charging still underdeveloped, lack of access to reliable home charging is a major barrier to EV adoption for MUD residents. Challenges to siting electric vehicle supply equipment (EVSE) at MUDs include the high upfront cost of EVSE installation, physical and/or electrical infrastructure constraints, a lack of clear incentives for property managers to invest in installing EV charging for tenants, and a limited number of EV charging service providers that offer solutions adapted to the unique needs of MUDs. Through award DE-EE0008473 from the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE), Center for Sustainable Energy (CSE), Energetics, and Forth, along with a diverse team of partners, led a three-year project to address barriers to EV charging at MUDs by developing an online toolkit geared toward residents, homeowner associations (HOAs), and property managers. The project, referred to as Vehicle Charging Innovations – Multi-Unit Dwellings (VCI-MUD project), engaged stakeholders across the country to identify real and perceived barriers to EV charging at MUDs and explored innovative technologies that attempt to alleviate the identified barriers. Over the course of three and a half years, the project conducted 60 interviews with industry stakeholders, compiled findings in an easy-to-use toolkit, and disseminated the toolkit across national, regional, state, and local channels. Key findings and outcomes of the VCI-MUD project include: Identifying six primary barriers to the installation of EV charging at MUDs; Developing five fact sheets and eight case studies highlighting innovative charging solutions to address barriers, including real-world operational and financial data; Developing a user-friendly, online empowerment toolkit with five important points for “making your pitch” to support EV charging to assist residents, property managers, building managers, and HOAs explore options for MUD charging. The VCI-MUD online toolkit was designed with replicability in mind. It includes general tools and guides to evaluate EV charging demand, gauge readiness for EV charging installation, and develop actionable plans. The fact sheets and case studies highlight the diversity of emerging MUD EV charging solutions, featuring different geographic and structural installation scenarios and providing interested parties with a menu of options, rather than prescribing a one-size-fits-all solution. The following resources are included in the toolkit to provide MUD stakeholders with all the information needed to navigate EV charging installation at their MUD location: Empowerment Toolkit – Easy-to-read FAQ overview, stakeholder roles and responsibilities, and additional resources for MUD EV charging installations; Charging Basics – Glossary of terms and descriptions of charging features, installation and operating expenses; EV Charging Survey Templates – Resident-to-Resident, Property Manager-to-Resident and HOA-to-Resident template letters with pre-populated template questions; Technology Selection Tool – Charging barrier fact sheets and case study examples; Installation Checklist – Submittal document requirements for EVSE installations; MUD Building Self-Evaluation Survey – Self-guided evaluation of potential barriers to EV charging installation at MUD locations; Curbside Resources – Case studies and fact sheets for curbside charging options; Find a Certified Electrician – Approved list of certified EV charging installers; “Right-to-Charge” State Legal References – Legal requirements for charging equipment at MUD locations. The online toolkit was promoted extensively in the final six months of the project and will continue to be disseminated by the Clean Cities Coalitions and other partners after the end of the VCI-MUD project to encourage, support, and demonstrate viable solutions for vehicle charging infrastructure in MUDs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

American-Made Solar Prize: Edgeli Enables DER Integration (CRADA 615) (Final Report)

The purpose of this project was to demonstrate how granular time series data and automated data transformation, and impact assessment tools could speed interconnection approvals for distributed energy resource projects of various types and sizes. Types included community solar, rooftop solar, and EV charging projects. Using software routines to automate the transformation of data (e.g. GIS) to a network database and power flow model then applying scenarios to create hourly (8760) hosting capacity values and voltage and thermal impacts for specific projects, we were able to demonstrate the feasibility of quickly assembling and analyzing key utility data sets for interconnection purposes. The outcomes of this effort will become the foundation for future work that will enhance and encapsulate the software components developed as part of this project, into web services (e.g. APIs) that can be integrated into queue management systems and automate interconnection screening processes.

14 SOLAR ENERGY↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
DOE OSTI2023

Elephants Sharing the Highway: Studying TCP Fairness in Large Transfers over High Throughput Links

Escalating bandwidth demand strains high-performance data networks, posing potential performance risks. TCP congestion control algorithms enhance reliability and optimize bandwidth usage. Network performance is influenced by factors such as AQM algorithms and router buffer size. In the context of constrained network resources, understanding how TCP flows share networks and the resulting performance impact is essential. This paper introduces insights into TCP fairness and performance involving a comparison of TCP CUBIC, Reno, Hamilton, and BBR versions 1 and 2 across real-world networks supporting high bandwidths of up to 25 Gbps. The research explores TCP behaviors with AQM algorithms like FIFO, FQ_CODEL, and RED, alongside diverse buffer sizes. Notably, findings reveal that manipulating buffers and queuing methods yields contrasting outcomes based on bandwidth. BBRv2 emerges as a superior fair algorithm, pivotal for swift transfers, particularly in scientific data scenarios. These results provide crucial guidance for future network design, ensuring equitable performance optimization.

Kiran, Mariam↗