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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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This case study describes how Healthcare Realty partnered with a renewable energy advisor to establish its solar program and make progress towards its energy and GHG emissions reduction goals; all of the projects in the pipeline will be financed through third-party ownership, via power purchase agreements or through a feed-in-tariff program.
Case study of a DOE 2021 Housing Innovation Award winning custom home in a marine climate that got a HERS -22 with PV, with 2,614 square feet, triple-pane windows, LED lighting, and heat recovery ventilator with a HEPA filter and CO2 sensor.
Case study of a DOE 2020 Housing Innovation Award winning custom home in a cold climate that got a HERS -2 with PV, with 3,357 square feet, 11-inch-thick insulated concrete form walls, 11.34 kilowatts of solar panels on the roof, and high-efficiency 98 AFUE gas furnace.
Case study of a DOE 2020 Housing Innovation Award winning custom home in a mixed-humid climate that got a HERS 9 with PV, with 2,227 square feet, an energy recovery ventilator tied to a high-efficiency central heat pump, and 2x6 advanced framed exterior walls.
Case study of a DOE 2020 Housing Innovation Award winning custom home in a mixed-humid climate that got a HERS 50 without PV, with 3,680 square feet, ENERGY STAR-labeled appliances, WaterSense-labeled fixtures, and 100% LED lighting.
Case study of a DOE 2021 Housing Innovation Award winning custom home in a mixed-humid climate that got a HERS 51 without PV, with 1,940 square feet, MERV 13 filters and low-emission paints, adhesives, and carpeting; plus a sealed, conditioned crawl space, and a high-efficiency gas furnace and heat pump.
Interactive mechanical theorem proving can provide high assurance of correct design, but it can also be a slow iterative process. Much time is spent determining why a proof of a conjecture is not forthcoming. In some cases, the conjecture is false and in others, the attempted proof is insufficient. In this case study, we use the SAL family of model checkers to generate a concrete counterexample to an unproven conjecture specified in the mechanical theorem prover, PVS. The focus of our case study is the ROBUS Interactive Consistency Protocol. We combine the use of a mechanical theorem prover and a model checker to expose a subtle flaw in the protocol that occurs under a particular scenario of faults and processor states. Uncovering the flaw allows us to mend the protocol and complete its general verification in PVS.
The need for faster, more efficient algorithms is an important aspect of scientific computing. Generally, scientists are only exposed to computational issues that arise in their field. Thus, collaboration between a numerical analyst and a scientist is becoming necessary for scientific computing. The purpose of this case study is to expose computer scientists to processes that an astronomer would use to obtain useful results from raw data. For example, astronomers are interested in determining the properties of galaxies and measuring changes in those properties as a function of time throughout cosmic history. To do so, they use certain models that are designed and refined over time via observations at different wavelengths of the light spectrum. The process of matching these models with observed data from studying celestial bodies is referred to as Spectral Energy Distribution (SED) fitting. In this case study, we learn how to perform the SED fit. This process requires knowledge of both the astronomical and computational issues involved when fitting flux, the total energy from a source as seen from Earth, to a set of physical templates. Once a fit is complete, one can classify the source and estimate a number of physical parameters. The goal is to demonstrate how the computer science skill set can be used in the scientific community and to possibly improve one or more of the computational aspects of this problem. The following provides some background on the astronomy issues, including information on the spectral energy distribution and related physical parameters, and the computational issues, including the fitting procedure..
In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based on human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM 2.5 ) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM 2.5 formation in the atmosphere using a novel reduced-complexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM 2.5 CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics.
As renewable energy technologies are increasingly adopted, they pose an opportunity to improve the sustainability and resilience of distributed grids, especially when their design and operation is coordinated as a hybrid power plant. When included in hybrid power plants, distributed wind turbines in particular have the potential to enhance the resilience of distributed grids in areas with good wind resource, due to their ability to provide more consistent generation and ancillary services as compared to photo-voltaic (PV) solar panels. Despite this benefit, U.S. distributed wind adoption is lower than other comparable renewable energy technologies. In this study, we seek to demonstrate how hybrid power plants that include distributed wind turbines can contribute to distribution grid resilience by meeting loads (especially critical loads) more consistently, increasing reserve capacity, and providing value to customers during outages. To demonstrate these contributions, we integrate three separate frameworks and apply them to a case study in a rural electric cooperative in Iowa. Through this case study, we simulate and compare hybrid power plant design and operation during two hazard events: a tornado that causes a 48-hour distribution outage and a winter weather event that causes a 6-hour generation outage. The inclusion of a hybrid power plant that leverages 1) increased battery duration and 2) advanced forecasting and dispatch strategies that reserve capacity leading up to a hazard event best reduce lost loads as well as diesel consumption that would otherwise be used to meet those loads during short- and long-duration hazard events. Depending on the hybrid power plant capacity and operation, we find that the outage mitigation value of a hybrid power plant (measured in value to customers to avoid an outage and avoided lost revenues for the utility) is significant in both hazard events; adding wind, solar, and battery assets to the existing system adds about $50-$100M in avoided lost load and at least $4-$8k in utility value in the tornado hazard event, and $570k-$2.2M in avoided lost load and at least $220-$650 in utility value in the winter hazard scenario. In both the tornado and winter hazard scenarios, optimizing the operation of the hybrid system for resilience can lend similar value as increasing battery duration by 5 MWh for the lower capacity systems considered.
Two case histories are presented. In one study the feasibility of consolidating dispatch center operations for small agencies is considered. System load measurements were taken and queueing analysis applied to determine numbers of personnel required for each separate agency and for a consolidated dispatch center. Functional requirements were developed and a cost model was designed to compare relative costs of various alternatives including continuation of the present system, consolidation of a manual system, and consolidated computer-aided dispatching. The second case history deals with the consideration of a multi-regional, intrastate radio frequency for improved interregional communications. Sample standards and specifications for radio equipment are provided.
Convective available potential energy (CAPE) is an important index for storm forecasting. Recent versions (v15.2 and v16) of the Global Forecast System (GFS) predict lower values of CAPE during summertime in the continental United States than analysis and observation. We conducted an evaluation of the GFS in simulating summertime CAPE using an example from the Unified Forecast System Case Study collection to investigate the factors that lead to the low CAPE bias in GFS. Specifically, we investigated the surface energy budget, soil properties, and near-surface and upper-level meteorological fields. Results show that the GFS simulates smaller surface latent heat flux and larger surface sensible heat flux than the observations. This can be attributed to the slightly drier-than-observed soil moisture in the GFS that comes from an offline global land data assimilation system. The lower simulated CAPE in GFS v16 is related to the early drop of surface net radiation with excessive boundary layer cloud after midday when compared with GFS v15.2. A moisture-budget analysis indicates that errors in the large-scale advection of water vapor does not contribute to the dry bias in the GFS at low levels. Common Community Physics Package single-column model (SCM) experiments suggest that with realistic initial vertical profiles, SCM simulations generate a larger CAPE than runs with GFS IC. SCM runs with an active LSM tend to produce smaller CAPE than that with prescribed surface fluxes. Note that the findings are only applicable to this case study. Including more warm-season cases would enhance the generalizability of our findings.
On-demand systems have increased in popularity in recent years, especially in rural and smaller-sized communities. This presentation provides a brief introduction to NREL's on-demand mobility research and an in-depth case study of the town of Fort Erie, Ontario. Fort Erie is a relatively sparsely populated region of 32,901 residents, spread across a land area of 166 square kilometers (64 square miles), for an average population density of 193 residents per square kilometer (500 per square mile). In October 2021, the town implemented a mobility-on-demand system integrated with smartphone software to replace its fixed-route community bus system, which consisted of four buses with three routes, each with a roughly 1-hour, one-way loop. The new service utilizes a fleet of six minivans, two of which are retrofitted with wheelchair-accessible ramps. The system may require that a passenger requesting a standard van walk up to 400 meters (a quarter mile) to their pickup location to optimize vehicle routing while providing origin-to-destination service. The on-demand system proved effective in providing service, eclipsing pre-pandemic ridership by 40%, decreasing greenhouse gas emissions per ride by 63%, and decreasing the cost to the town per ride by 29%. This presentation documents both the previous system and the new system in terms of routes, ridership, costs, fuel, and other notable system parameters. This work is part of an ongoing series of case studies on providing small communities with on-demand, right-sized vehicle service coupled with a smartphone application.
Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks. An understudied question in that community is: for whom do these defenses defend? This work considers common approaches to defending learned systems and how security defenses result in performance inequities across different sub-populations. We outline appropriate parity metrics for analysis and begin to answer this question through empirical results of the fairness implications of machine learning security methods. We find that many methods that have been proposed can cause direct harm, like false rejection and unequal benefits from robustness training. The framework we propose for measuring defense equality can be applied to robustly trained models, preprocessing-based defenses, and rejection methods. We identify a set of datasets with a user-centered application and a reasonable computational cost suitable for case studies in measuring the equality of defenses. In our case study of speech command recognition, we show how such adversarial training and augmentation have non-equal but complex protections for social subgroups across gender, accent, and age in relation to user coverage. We present a comparison of equality between two rejection-based defenses: randomized smoothing and neural rejection, finding randomized smoothing more equitable due to the sampling mechanism for minority groups. This represents the first work examining the disparity in the adversarial robustness in the speech domain and the fairness evaluation of rejection-based defenses.
Operation and maintenance (O&M) costs for wind turbines pose a risk to competitiveness and asset owners. With machine-learning technologies and digitalization rapidly maturing, the wind industry is actively investigating these new technologies to optimize O&M practices and reduce costs. This paper reviews recent work on machine-learning approaches to generator bearing failure prediction and presents a relevant real-world case study through a collaboration between the National Renewable Energy Laboratory and Envision Digital Corporation. In the case study, we evaluate the performance of representative machine-learning algorithms for predicting wind turbine generator bearing failures. Operational supervisory control and data acquisition data from one wind power plant was used to train and test the machine-learning models. The investigated data channels are chosen based on whether physically they reflect the failed generator bearing conditions and the component historical usage, including both environmental and operational conditions. Benefits and drawbacks of different methods are identified.
A cirrus parameter retrieval methodology and the results of its application over the cirrus Intensive Field Observation (IFO) area using data from the Geostationary Operational Environmental Satellite (GOES) taken during the FIRE cirrus case study days, October 27-28, 1986. An impirical cloud bidirectional reflectance model derived from another IFO analysis was combined with a theoretical ice crystal cloud albedo model to estimate visible cloud optical thickness, which was used to derive the cloud infrared emittance. Cloud altitude was adjusted based on the derived emittance and observed temperatures. The approach developed here produced a very reasonable picture of cirrus cloud fields. It was estimated that the cloud-top and cloud-center heights are derived with a precision of about 0.6 km, except in very broken cloud conditions. Cirrus cloud thicknesses were also estimated and it is found that the derived results are comparable to other case study observations.
This article introduces an experimental methodology to identify proxy indicators that are conceptually consistent with the processes of Climate Gentrification (“CG”), in which a change in demand preferences among consumers and investors drives the increased consumption for real estate, in part, on lower measures of physical risk from climate change. Evaluated through case studies in the state of Florida, this article builds on the integration of multiple datasets concerning rental properties, evictions, and socioeconomic data, as well as environmental risk indices to build a Climate Gentrification Risk Index (CGRI). In the Miami case study, we find that the CGRI identifies a hotly contested neighborhood that is already known to be in a state of transition consistent with the processes of CG. In the Tampa case, the index highlights a district that exhibits strong metrics for the future accelerated occurrence of CG. Our findings suggest that transitional land uses and flexible zoning in low-exposure areas are key elements for attracting new development consistent with CG and offer insight into the challenges that local governments face understanding the types and rates of change that may be catalyzed in the broader urban processes of public and private sector climate adaptation in the built environment.
This paper is Part II of a two‐part paper that documents the Climate Model version 4X (CM4X) hierarchy of coupled climate models developed at the Geophysical Fluid Dynamics Laboratory. Part I of this paper is presented in Griffies et al. (2025a, https://doi.org/10.1029/2024MS004861 ). Here we present a suite of case studies that examine ocean and sea ice features that are targeted for further research, which include sea level, eastern boundary upwelling, Arctic and Southern Ocean sea ice, Southern Ocean circulation, and North Atlantic circulation. The case studies are based on experiments that follow the protocol of version 6 from the Coupled Model Intercomparison Project. The analysis reveals a systematic improvement in the simulation fidelity of CM4X relative to its CM4.0 predecessor, as well as an improvement when refining the ocean/sea ice horizontal grid spacing from the 0.25° of CM4X‐p25 to the 0.125° of CM4X‐p125. Even so, there remain many outstanding biases, thus pointing to the need for further grid refinements, enhancements to numerical methods, and/or advances in parameterizations, each of which target long‐standing model biases and limitations.