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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 OSTI2024

End-Use Savings Shapes Measure Documentation: Heat Pump Rooftop Units With Exhaust Air Energy Recovery

This technical report documents the Advanced Rooftop Unit Control End Use Savings Shapes (EUSS) measure, including providing a description of the technology, modeling methods, and the energy savings calculated from applying the measure to applicable across the US commercial building stock using ComStock to quantify its potential. This measure replaces gas furnace and electric resistance rooftop units with high-efficiency variable speed heat pump rooftop units (HP-RTU) that include exhaust air heat or energy recovery. The measure uses the same assumptions and technology as the variable speed HP-RTU measure from EUSS 2023 release 1 but adds energy recovery to precondition outdoor ventilation air to reduce HVAC loads. The HP-RTU compressor lockout temperature is modeled as 0 degrees Fahrenheit; below this temperature, the heat pump is set to shut off. The unit is sized based on the design cooling loads, with backup electric resistance heating addressing any remaining loads including heating hours below the compressor lockout temperature when there is no heat pump heating.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Regional environmental controllers influence continental scale soil carbon stocks and future carbon dynamics

Abstract Understanding the influence of environmental factors on soil organic carbon (SOC) is critical for quantifying and reducing the uncertainty in carbon climate feedback projections under changing environmental conditions. We explored the effect of climatic variables, land cover types, topographic attributes, soil types and bedrock geology on SOC stocks of top 1 m depth across conterminous United States (US) ecoregions. Using 4559 soil profile observations and high-resolution data of environmental factors, we identified dominant environmental controllers of SOC stocks in 21 US ecoregions using geographically weighted regression. We used projected climatic data of SSP126 and SSP585 scenarios from GFDL-ESM 4 Earth System Model of Coupled Model Intercomparison Project phase 6 to predict SOC stock changes across continental US between 2030 and 2100. Both baseline and predicted changes in SOC stocks were compared with SOC stocks represented in GFDL-ESM4 projections. Among 56 environmental predictors, we found 12 as dominant controllers across all ecoregions. The adjusted geospatial model with the 12 environmental controllers showed an R 2 of 0.48 in testing dataset. Higher precipitation and lower temperatures were associated with higher levels of SOC stocks in majority of ecoregions. Changes in land cover types (vegetation properties) was important in drier ecosystem as North American deserts, whereas soil types and topography were more important in American prairies. Wetlands of the Everglades was highly sensitive to projected temperature changes. The SOC stocks did not change under SSP126 until 2100, however SOC stocks decreased up to 21% under SSP585. Our results, based on environmental controllers of SOC stocks, help to predict impacts of changing environmental conditions on SOC stocks more reliably and may reduce uncertainties found in both, geospatial and Earth System Models. In addition, the description of different environmental controllers for US ecoregions can help to describe the scope and importance of global and local models.

58 GEOSCIENCES↗
DOE OSTI2020

ComStock™ [SWR-19-33 and SWR-20-32]

ComStock™ is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.

Horsey, Henry↗
DOE OSTI2024

Observational benchmarks inform representation of soil organic carbon dynamics in land surface models

Abstract. Representing soil organic carbon (SOC) dynamics in Earth system models (ESMs) is a key source of uncertainty in predicting carbon–climate feedbacks. Machine learning models can help identify dominant environmental controllers and establish their functional relationships with SOC stocks. The resulting knowledge can be integrated into ESMs to reduce uncertainty and improve predictions of SOC dynamics over space and time. In this study, we used a large number of SOC field observations (n=54 000), geospatial datasets of environmental factors (n=46), and two machine learning approaches (namely random forest, RF, and generalized additive modeling, GAM) to (1) identify dominant environmental controllers of global and biome-specific SOC stocks, (2) derive functional relationships between environmental controllers and SOC stocks, and (3) compare the identified environmental controllers and predictive relationships with those in models used in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). Our results showed that the diurnal temperature, drought index, cation exchange capacity, and precipitation were important observed environmental predictors of global SOC stocks. While the RF model identified 14 environmental factors that describe climatic, vegetation, and edaphic conditions as important predictors of global SOC stocks (R2=0.61, RMSE = 0.46 kg m−2), current ESMs oversimplify the relationships between environmental factors and SOC, with precipitation, temperature, and net primary productivity explaining > 96 % of the variability in ESM-modeled SOC stocks. Further, our study revealed notable disparities among the functional relationships between environmental factors and SOC stocks simulated by ESMs compared with observed relationships. To improve SOC representations in ESMs, it is imperative to incorporate additional environmental controls, such as the cation exchange capacity, and refine the functional relationships to align more closely with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A new bioenergy model that simulates the impacts of plant‐microbial interactions, soil carbon protection, and mechanistic tillage on soil carbon cycling

Abstract Advancing our predictive understanding of bioenergy systems is critical to design decision tools that can inform which feedstock to plant, where to plant it, and how to manage its production to provide both energy and ecosystem carbon (C) benefits. Here, we lay the foundation for that advancement by integrating recent developments in the science of belowground processes in shaping the C cycle into a new bioenergy model, FUN‐BioCROP (Fixation and Uptake of Nitrogen‐Bioenergy Carbon, Rhizosphere, Organisms, and Protection). We show that FUN‐BioCROP can approximate the historical trajectory of soil C dynamics as natural ecosystems were successively converted into intensive agriculture and bioenergy systems. This ability relies in part on a novel tillage representation that mechanistically models tillage as a process that increases microbial access to C. Importantly, the impacts of tillage and feedstock choice also influence FUN‐BioCROP simulations of warming responses with no‐till perennial feedstocks, miscanthus, and switchgrass, having more C that is unprotected and susceptible to warming than tilled annual feedstocks like corn–corn–soybean. However, this susceptibility to warming is balanced by a greater potential for increases in belowground C allocation to enhance soil C stocks in perennial systems. Collectively, our model results highlight the importance of belowground processes in evaluating the ecosystem C benefits of bioenergy production.

09 BIOMASS FUELS↗
DOE OSTI2021

Estimating the value of jointly optimized electric power generation and end use: a study of ISO-scale load shaping applied to the residential building stock

A generation-to-load simulation estimated the impact, in terms of production costs and CO2 emissions, attributable to the joint optimization of electric power generation and flexible end uses to support increasing penetrations of renewable energy. Newly conceived, evaluated, and foundational in developing a U.S. National Standard was a transaction-less yet continuous demand response system based on a day-ahead optimum load shape (OLS) designed to encourage Internet-connected devices to autonomously and voluntarily explore options to favour lowest cost generators - without requiring two-way communications, personally identifiable information, or customer opt-in. Boundary conditions used for model calibration included historical weather, residential building stock construction attributes, home appliance and device empirical operating schedules, prototypical power distribution feeder models, thermal generator heat rates, startup and ramping constraints, and fuel costs. Results of an hourly-based annual case study of Texas indicate a 1/3 reduction in production costs and a 1/5 reduction in CO2 emissions are possible.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Quantitative, trait-based microbial ecology to accurately model the impacts of nitrogen deposition on soil carbon cycling in the Anthropocene (Final Technical Report)

Atmospheric nitrogen pollution has altered fundamental soil processes, challenging our understanding of the extent to which soils will continue to sequester carbon and slow the pace of future environmental change. While most evidence shows that increases in soil nitrogen have enhanced carbon storage in temperate forests, it remains unclear whether these effects will persist as nitrogen pollution continues to decline. Moreover, there remains uncertainty as to why some forests gain more carbon in response to nitrogen pollution than others. At the heart of this knowledge gap is a failure to link nitrogen–induced shifts in microbial biodiversity with gains or losses in their functional ability to decompose, assimilate, and ultimately stabilize soil carbon. Given that this uncertainty impedes the ability of predictive models to project future soil carbon stocks, there is a critical need to determine how key microbial traits drive soil carbon responses to nitrogen pollution. As such the goals of this project were to: (1) Quantify variation in taxon–specific and community–level microbial traits across gradients in microbial community composition, mycorrhizal symbioses, and nitrogen availability. (2) Integrate empirical data into a novel predictive framework that enhances our ability to project the regional soil carbon consequences of historical nitrogen pollution in temperate forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Future Hurricanes Will Increase Palm Abundance and Decrease Aboveground Biomass in a Tropical Forest

Hurricanes are expected to intensify throughout the 21st century, yet the impact of frequent major hurricanes on tropical ecosystems remains unknown. To investigate tropical forest damage and recovery under different hurricane regimes, we generate a suite of scenarios based on Coupled Model Intercomparison Project Phase 6 climate projections and increased hurricane recurrence and intensity for the Luquillo Experimental Forest, Puerto Rico. We then use the Ecosystem Demography model to predict changes in carbon stocks, forest structure and composition. In this work, our results indicate that frequent hurricane disturbances in the future would decrease the overall aboveground biomass, decrease the dominance of late-successional species, but increase the dominance of palm species. Warmer climates with increased CO 2 would have little effect on the functional-type composition but increase the aboveground biomass. However, the predicted climate and CO 2 fertilization effects would not compensate for the biomass loss due to more frequent severe-hurricane disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Land Use Change Alters Soil Organic Carbon: Constrained Global Patterns and Predictors

Abstract Land use change (LUC) alters the global carbon (C) stock, but our estimation of the alteration remains uncertain and is a major impediment to predicting the global C cycle. The uncertainty is partly due to the limited number and geographical bias of observations, and limited exploration of its predictors. Here we generated a comprehensive global database of 5,980 observations from 790 articles. The number of sites evaluated is at least seven times larger than in previous meta‐analyses. Our constrained estimates of different LUC's effects on soil organic C (SOC) and their variations across global climates reveal underestimation/overestimation in previous estimates. Converting forests and grasslands to croplands reduced SOC by 24.5% ± 1.53% (−11.03 ± 1.06 Mg ha −1 ) and 22.7% ± 1.22% (−8.09 ± 0.67 Mg ha −1 ), while 28.0% ± 1.56% (4.46 ± 0.42 Mg ha −1 ) and 33.5% ± 1.68% (5.8 ± 0.38 Mg ha −1 ) increases, respectively, were obtained in the reverse processes. Converting forests to grasslands decreased SOC by 2.1% ± 1.22% (−1.13 ± 0.44 Mg ha −1 ), while the reverse process increased SOC by 18.6% ± 1.73% (3.31 ± 0.51 Mg ha −1 ). Modeled relative importance of 10 drivers of LUC's impact on SOC revealed that higher initial SOC (iSOC) does not solely determine SOC loss in SOC‐negative LUC scenarios as previously proposed. Across four decades, reconverting croplands to forests and grasslands recovered only 49.5% (6.1 ± 0.51 Mg ha −1 ) and 75.3% (7.0 ± 0.38 Mg ha −1 ) of the iSOC, respectively, indicating the need for protecting C‐rich ecosystems. Our global data set advances information on LUC's effect on SOC and can be valuable to constrain Earth system models to reliably estimate global SOC stocks and plan climate change mitigation strategies.

Environmental Sciences & Ecology↗
DOE OSTI2022

End-Use Savings Shapes: Public Dataset Release for Residential Round 1 [Slides]

The End-Use Load Profiles project created a public database of 900,000 individual building end-use load profiles. Load profiles were modeled to represent the U.S. building stock as it was in 2018, as nearly as possible based on the best available data. The End-Use Savings Shapes follow-on project adds measure impact profiles for energy efficiency and electrification packages to the public dataset. This presentation details the public dataset release on September 20, 2022.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Italian prototype building models for urban scale building performance simulation

Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. Finally, the developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Residential Building Stock Characterization in Palm Beach County, Florida

This building stock characterizations is intended to help Palm Beach County (PBC) Office of Resilience (OOR) and municipalities composing the Municipal Resilience Partnership prioritize building energy efficiency investments to reduce energy costs and increase resilience for the county's most vulnerable residents. The analysis utilizes NREL’s ResStock model to characterize PBC’s residential building stock, including building type, renter/owner status, size (square footage), age of buildings (vintage), HVAC system types, and, for multi-family buildings, number of units. Building energy efficiency, weatherization, and electrification upgrade packages were assessed for approximate cost, customer bill-savings, and emissions reductions potential and findings will help guide OOR financial assistance program design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Quantifying the Known Unknown: Including Marine Sources of Greenhouse Gases in Climate Modeling

Researchers have recently estimated that Arctic submarine permafrost currently traps 60 billion tons of methane and contains 560 billion tons of organic carbon in seafloor sediments and soil, a giant pool of carbon with potentially large feedbacks on the climate system. Unlike terrestrial permafrost, the submarine permafrost system has remained a “known unknown” because of the difficulty in acquiring samples and measurements. Consequently, this potentially large carbon stock never yet considered in global climate models or policy discussions, represents a real wildcard in our understanding of Earth’s climate. This report summarizes our group’s effort at developing a numerical modeling framework designed to produce a first-of-its-kind estimate of Arctic methane gas releases from the marine sediments to the water column, and potentially to the atmosphere, where positive climate feedback may occur. Newly developed modeling capability supported by the Laboratory Directed Research and Development (LDRD) program at Sandia National Laboratories now gives us the ability to probabilistically map gas distribution and quantity in the seabed by using a hybrid approach of geospatial machine learning, and predictive numerical thermodynamic ensemble modeling. The novelty in this approach is its ability to produce maps of useful data in regions that are only sparsely sampled, a common challenge in the Arctic, and a major obstacle to progress in the past. By applying this model to the circum-Arctic continental shelves and integrating the flux of free gas from in situ methanogenesis and dissociating gas hydrates from the sediment column under climate forcing, we can provide the most reliable estimate of a spatially and temporally varying source term for greenhouse gas flux that can be used by global oceanographic circulation and Earth system models (such as DOE’s E3SM). The result will allow us to finally tackle the wildcard of the submarine permafrost carbon system, and better inform us about the severity of future national security threats that sustained climate change poses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

CityBES v2021

City Buildings, Energy, and Sustainability (CityBES) is a web-based data and computing platform, focusing on energy modeling and analysis of a city's building stock to support district or city-scale building energy efficiency programs. CityBES uses an international open data standard, CityGML, to represent and exchange 3D city models. CityBES employs EnergyPlus to simulate building energy use and savings from energy efficient retrofits. Other CityBES features include energy benchmarking, district heating and cooling system modeling, rooftop PV analysis, building performance visualization, heat resilience modeling, as well as urban scale mapping of microclimate and heat vulnerability at census tract level. Different from other tools, CityBES uses integrated open and standard 3D city building data and models each individual building using EnergyPlus. CityBES can be used by urban planners, city energy managers, building owners, utilities, energy consultants and researchers.

Hong, Tianzhen↗
DOE OSTI2022

Multi-modelling predictions show high uncertainty of required carbon input changes to reach a 4‰ target

Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4‰ at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 ± 1.2 Mg C ha -1 year -1 to increase SOC stocks by 4‰ per year over 30 years, compared to the C input in the control treatments (multi-model median ± median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Here, our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition.

4 per 1000 initiative↗
DOE OSTI2023

Component-Level Analysis of Heating and Cooling Loads in the U.S. Residential Building Stock

The residential building sector accounts for a substantial portion of total energy consumption in the United States and offers a significant opportunity for energy reduction and decarbonization through improvements in energy efficiency. Heating and air conditioning are the primary contributors to residential energy usage and electricity system peak demand. However, due to the diversity of the housing stock and the complexity of factors affecting heating and cooling demand, identifying the relative contributions to heating and cooling loads poses challenges. To address this, we applied the ResStock analysis tool to simulate 550,000 building energy models, providing statistical representation of residential buildings in the contiguous United States. We introduced outputs that quantified the heating and cooling influence of different components of a home, such as air leakage, envelope components (ceilings, walls, windows, foundations), internal heat gains from people, lighting, plug loads, and duct losses and gains. Leveraging the granularity of ResStock, we present a dataset to enable deeper understanding of the contributors to heating and cooling loads as a function of housing characteristics such as location, envelope efficiency, and building type. This work aims to support prioritization of research and development and informed decision-making for residential building decarbonization.

building simulation↗
DOE OSTI2024

Chicago Energy Efficiency Planning and Analysis and Integrated Retrofit Strategy Validation in Single-Family Homes

In June 2020, the project team comprised of Elevate and the National Renewable Energy Laboratory (NREL), with advisory roles from the City of Chicago and Commonwealth Edison, initiated a three-year project with the U.S. Department of Energy to inform communitywide energy planning at a local scale and validate advanced retrofit packages that achieve at least 50% energy savings in existing homes. The project team has characterized Chicago’s single-family and 2-4 unit housing stock and energy use using the ResStock™ modeling tool, and with that information developed advanced whole home energy retrofit packages for single family and 2-4 unit homes that can reduce energy use by at least 50% and support climate, air quality, and other goals. This report presents an analysis of the technical potential of applying these retrofit packages at a City-wide scale, and a roadmap for rapidly scaling up retrofit programs to improve Chicago’s housing stock and target investments in the neighborhoods that need it most. The main goals of the analysis are to: 1) develop a Chicago-specific single-family and 2-4 unit building retrofit prioritization strategy; 2) identify opportunities for beneficial electrification in communities and home types that stand to benefit most; and 3) assess costs and savings potential for retrofit investments at a household and city scale, in terms of energy use, bill impacts, carbon emissions, and other outcomes.

14 SOLAR ENERGY↗