SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Case study of a DOE 2018 Housing Innovation Award winning multifamily home in the cold climate that met the DOE Zero Energy Ready Home criteria and achieved a HERS 42 without PV or HERS -8 with PV.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a mixed-humid climate that got a HERS 47 without PV, with 2x6 advanced framed walls, an unvented conditioned crawl space, an insulated attic, and a central heat pump with fresh air intake.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a mixed humid climate that got a HERS 14 with PV, 6.5-inch SIPs, an insulated vented attic, a geothermal heat pump, and an ERV.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a cold climate that got a HERS 2 with PV, with double stud walls, a daylight basement with interior rigid foam, an attic insulated along roof line with R-80 dense-packed fiberglass, an air-to-water CO2 heat pump for radiant floor heat and hot water, and a ductless mini-split heat pump for cooling.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a cold climate that got a HERS 26 with PV, with 2x4 staggered studs on 2x6 plates, an insulated basement, a vented R-60 attic, and a gas furnace.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a hot humid climate that got a HERS 5 with PV, with insulated concrete block walls, a spray foam-insulated attic, and a geothermal heat pump with fresh air intake.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a marine climate that got a HERS -9 with PV, and is a zero energy, zero water, zero sewer home with 100% on-site rain water harvesting, an air-to-water CO2 heat pump, and passive solar design.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a cold climate that got a HERS 31 without PV, with 11-inch thick ICF walls, a geothermal heat pump with backup tankless water heater for heating, hot water, and air conditioning.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a cold climate that got a HERS -11 with PV, with ICF walls, a spray foam-insulated unvented roof, and a ground-source heat pump.
Case study of a DOE 2016 Housing Innovation Award winning production home in a cold climate that got a HERS -5 with PV, with ICF walls, , a spray foam-insulated unvented roof, and a ground-source heat pump.
Case study of a DOE 2016 Housing Innovation Award winning custom home in a marine climate that got a HERS -2 with PV, with SIPs taped at all exterior joints, an ICF foundation walls, and an air-to-water heat pump with in-floor radiant hydronic heat.
Case study of a DOE 2016 Housing Innovation Award winning affordable home in a cold climate that got a HERS -12 with PV, with pre-cast concrete wall panels with exterior rigid foam, a SIPs attic, and an air source mini-split heat pump with continuous exhaust fans.
Case study of a DOE 2016 Housing Innovation Award winning production home in a cold climate that got a HERS 28 with PV, with 2x4 double-wall framing, an insulated basement, an R-50 vented attic, a central heat pump, and a tankless water heater.
Case study of a DOE 2021 Housing Innovation Award winning custom home in a mixed-humid climate that got a HERS 34 with PV, with 1,872 square feet, ENERGY STAR appliances and lighting, high-efficiency HVAC, along with 1.9-kW of photovoltaic panels.
The purpose of this case study was to evaluate the Compliance Officer/Coordinator (CO/CC) training program using Phillips’ approach to training evaluation. INL Learning Performance Consultants (LPCs) applied the ROI Methodology to measure the effectiveness of the CO/CC training program. INL CO/CCs participated in focus groups to provide feedback on their learning, enablers and barriers to applying what they learned, and opportunities to improve the CO/CC learning experience. With collaboration from the CO/CC training program owner, the LPCs demonstrated the business impact and return on investment (ROI) of the CO/CC training program. Included are training program recommendations and lessons learned using the ROI Methodology.
Highlights: • We combine System Dynamics model and Geographic Information System to analyze the energy-environment-economy (3E) system both temporally and spatially, which explicitly explore the interaction of economics, energy, and environment and effects of the key influencing factors. • Beijing is selected as a case study. • Alternative scenarios, e.g., current, technology, energy and environment scenarios are explored and compared. • Simulation results shows that, current scenario is not sustainable; technology scenario is applicable to economic growth; environment scenario maintains a balanced path of development for long term stability. Economic development, accompanying with environmental damage and energy depletion, becomes essential nowadays. There is a complicated and comprehensive interaction between economics, environment and energy. Understanding the operating mechanism of Energy-Environment-Economy model (3E) and its key factors is the inherent part in dealing with the issue. In this paper, we combine System Dynamics model and Geographic Information System to analyze the energy-environment-economy (3E) system both temporally and spatially, which explicitly explore the interaction of economics, energy, and environment and effects of the key influencing factors. Beijing is selected as a case study to verify our SD-GIS model. Alternative scenarios, e.g., current, technology, energy and environment scenarios are explored and compared. Simulation results shows that, current scenario is not sustainable; technology scenario is applicable to economic growth; environment scenario maintains a balanced path of development for long term stability. Policy-making insights are given based on our results and analysis.
Scientists often predict physical outcomes, e.g., experimental results, with the assistance of computer codes that, at their best, only coarsely approximate reality. Coarse predictions are challenging in critical part due to the multitude of seemingly arbitrary yet consequential decisions that must be made such as choice of relevant data, calibration of code parameters, and construction of empirical discrepancy forms. In this paper, we present a case study in the context of inertial confinement fusion (ICF) implosion experiments where extrapolative predictions are needed with quantified uncertainties. The purpose of this case study is to reflect relevant statistical methods, as applied to ICF model fitting and prediction, to document the numerous decisions that must be made in the prediction pipeline, to extend a complex example in extrapolation to the uncertainty quantification (UQ) community, and to reflect on the challenges we encountered supporting extrapolations with imperfect models and thereby recommend several future research directions. We conclude with a discussion about the UQ community's role in less than ideal predictive scenarios like our ICF exercise.
This report summarizes analysis conducted to support a case study under the Feedstock-Conversion Interface Consortium (FCIC) focused on techno-economic analysis (TEA) modeling to quantify the economic implications of biomass hydrolysate substrate variability on fermentation performance and resultant biorefinery fuel yields. It is known that fermentation inhibitors or byproducts, as may either come from constituents in the biomass feedstock or imparted through biorefinery processing operations, can detrimentally impact fermentation rates and yields. However, it is often difficult to isolate fermentation impacts to individual components given the complex nature of biomass varying simultaneously in multiple attributes from one lot to another. For this study, we worked with FCIC researchers to obtain data on key fermentation performance metrics, namely productivity rates and conversion yields, across a number of material attribute species previously selected by the researchers as "critical" attributes which may be found in hydrolysate used as microbial carbon sources during fermentation. These data were run through TEA models to estimate resultant impacts on biorefinery economics, reported as minimum fuel selling price (MFSP). Overall, the trends in MFSP closely followed fuel yields, in turn tied to fermentation process yields and selectivity, with minimal economic impact from variations in fermentation productivity.