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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows

In the present work, a general formulation is proposed to implement the contact angle boundary conditions for the second-order Phase-Field models, which is applicable to N-phase (N ≥ 2) moving contact line problems. To remedy the issue of mass change due to the contact angle boundary condition, a source term or Lagrange multiplier is added to the original second-order Phase-Field models, which is determined by the consistent and conservative volume distribution algorithm so that the summation of the order parameters and the consistency of reduction are not influenced. To physically couple the proposed formulation to the hydrodynamics, especially for large-density-ratio problems, the consistent formulation is employed. The reduction-consistent conservative Allen-Cahn models are chosen as examples to illustrate the application of the proposed formulation. The numerical scheme that preserves the consistency and conservation of the proposed formulation is employed to demonstrate its effectiveness. Results produced by the proposed formulation are in good agreement with the exact and/or asymptotic solutions. The proposed method captures complex dynamics of moving contact line problems having large density ratios.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Is turning down the sun a good proxy for stratospheric sulfate geoengineering?

Deliberately blocking out a small portion of the incoming solar radiation would cool the climate. One such approach would be injecting SO2 into the stratosphere, which would produce sulfate aerosols that would remain in the atmosphere for 1–3 years, reflecting part of the incoming shortwave radiation. The cooling produced by the aerosols can offset the warming produced by increased greenhouse gas (GHG) concentrations, but it would also affect the climate differently, leading to residual differences compared to a climate not affected by either. Many climate model simulations of geoengineering have used a uniform reduction of the incoming solar radiation as a proxy for stratospheric aerosols, both because many models are not designed to adequately capture relevant stratospheric aerosol processes, and because a solar reduction has often been assumed to capture the most important differences between how stratospheric aerosols and GHG would affect the climate. Here we show that dimming the sun does not produce the same surface climate effects as simulating aerosols in the stratosphere. By more closely matching the spatial pattern of solar reduction to that of the aerosols, some improvements in this idealized representation are possible, with further improvements if the stratospheric heating produced by the aerosols is included. This is relevant both for our understanding of the physical mechanisms driving the changes observed in stratospheric-sulfate geoengineering simulations, and in terms of the relevance of impact assessments that use a uniform solar dimming.

Visioni, Daniele↗
DOE OSTI2020

Energy Consumption and Cost Reduction of Future Light-Duty Vehicles through Advanced Vehicle Technologies: A Modeling Simulation Study Through 2050

The U.S. Department of Energy’s (DOE’s) Vehicle Technologies Office (VTO) and Hydrogen and Fuel Cell Technologies Office (HFTO) aim to develop sustainable, affordable and efficient technologies for transportation of goods and people. Translating investments in advanced transportation component technologies and powertrains to estimate vehicle-level fuel savings potential is critical for understanding DOE’s impact. In this work, we simulated technologies funded by VTO and HFTO for light duty vehicles. The simulations were performed across: Multiple powertrain configurations (i.e., conventional, power-split, extended-range electric vehicle, battery electric drive, and fuel-cell vehicles), Vehicle classes (i.e., compact car, midsize car, small sport utility vehicle [SUV], midsize SUV, and pickup trucks); and Fuels (i.e., gasoline, diesel, hydrogen, and battery electricity). These various technologies are assessed for six different timeframes: laboratory years 2015, 2020, 2025, 2030, and 2045. A delay of 5 years is assumed between laboratory year and model year (year technology is introduced into production). Finally, uncertainties are included for both technology performance and cost aspects by considering two cases: Low case, aligned with DOE technology manager estimates of expected original equipment manufacturer (OEM) improvements based on regulations, business as usual; and High case, aligned with aggressive technology advancements based on R&D targets developed through support by VTO & HFTO. These scenarios are not intended as predictions of future performances. The energy and cost impact of different technologies were estimated using Autonomie (www.autonomie.net), Argonne vehicle system simulation tool. Autonomie is a state-of-the-art vehicle system simulation tool used to assess the energy consumption, performance and cost of multiple advanced vehicle technologies across classes (from light to heavy duty), powertrains (from conventional to HEVs, FCEVs, PHEVs and BEVs), components and control strategies. Autonomie is packaged with a complete set of vehicle models for a wide range of vehicle classes, powertrain configurations and component technologies, including vehicle level and component level controls. These controls were developed and calibrated using dynamometer test data. Autonomie has been used to support a wide range of studies including analyzing various component technologies, sizing powertrains components for different vehicle requirements, comparing the benefits of powertrain configurations, optimizing both heuristic and route based vehicle energy control and predicting transportation energy use when paired with a traffic modeling tool such as POLARIS. This report documents the assumptions and estimates the vehicle-level energy consumption benefits and associated technology costs for the various types of light duty vehicles. All details of vehicle assumptions and simulation results are available in the spreadsheets accompanying this report.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Diagnosing nuclear power plant pipe wall thinning due to flow accelerated corrosion using a passive, thermal non-destructive evaluation method: Feasibility assessment via numerical experiments

Flow accelerated corrosion (FAC) in nuclear power plant pipes is one of the leading causes of accidents, fatalities, damage and outages. Current FAC identification methods employ expensive sensing technology and are “active” methods, where the response of the piping system to an externally-generated thermal, mechanical or optical excitation must be measured. As a result, these techniques require a disruptive and time-consuming setup. Here we propose a method that utilizes pipe surface temperature measurements to passively monitor for FAC-induced pipe wall thinning without the need for expensive equipment or post-installation setup time. This diagnostic method utilizes a simulation data-driven diagnostic model to estimate the amount of thickness reduction in a pipe based on changes in measured steady-state pipe temperatures. In order to reduce the computational burden of generating large, simulation-based datasets, the behavior of the insulation of the pipe was modeled using a suitably calibrated heat transfer boundary parameter. Additionally, global sensitivity analysis was performed to determine system parameter(s), such as the temperature of water flowing inside the pipe, which significantly affect the steady state pipe wall temperature and could cause errors in diagnosis. Two diagnostic models, one using only the change in steady-state temperature as an indicator for FAC-induced pipe wall thinning and the other using water temperature as an additional diagnostic model input were evaluated for their ability to estimate thickness reductions in a pipe using simulated pipe wall temperature data. For the numerical experiments conducted in this work, both models estimated wall thickness with errors within 0.5 mm, indicating that the proposed technique can potentially be used as a low-cost, first-pass method for FAC monitoring.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

U.S. Solar Photovoltaic System and Energy Storage Cost Benchmarks: Q1 2021

Based on our bottom-up modeling, the Q1 2021 PV and energy storage cost benchmarks are: $\$2.65$ per watt DC (WDC) (or $\$3.05$/WAC) for residential PV systems, 1.56/WDC (or $\$1.79$/WAC) for commercial rooftop PV systems, $\$1.64$/WDC (or $\$1.88$/WAC) for commercial ground-mount PV systems, $\$0.83$/WDC (or $\$1.13$/WAC) for fixed-tilt utility-scale PV systems, $\$0.89$/WDC (or $\$1.20$/WAC) for one-axis-tracking utility-scale PV systems, $\$30,326$-$\$33,618$ for a 7.15-kWDC residential PV system with 5 kW/12.5 kWh nameplate of storage, $\$2.04$ - $\$2.10$ million for a 1-MWDC commercial ground-mount PV system colocated with 600 kW/2.4 MWhusable of storage, $\$166$ - $\$167$ million for a 100-MWDC one-axis tracker PV system colocated with 60 MW/240 MWhusable of storage. Between 2020 and 2021, there were 3.3% ($\$0.0$9/W), 10.7% ($\$0.19$/W), and 12.3% ($\$0.13$/W) reductions (in 2020 USD) in the residential, commercial rooftop, and utility-scale (one-axis) PV system cost benchmarks respectively. Balance of system (BOS) costs have either increased or remained flat across sectors, year-on-year, unlike in previous benchmark reports, which generally have reported declining BOS costs. The increase in BOS cost has been offset by a 17% reduction in module cost. Overall, modeled PV installed costs across the three sectors have declined compared to our Q1 2020 system costs.

14 SOLAR ENERGY↗
DOE OSTI2013

The Hydrological Impact of Geoengineering in the Geoengineering Model Intercomparison Project (GeoMIP)

Abstract: The hydrologic impact of enhancing Earth’s albedo due to solar radiation management (SRM) is investigated using simulations from 12 models contributing to the Geoengineering Model Intercomparison Project (GeoMIP). An artificial experiment is investigated, where global mean temperature is preserved at pre-industrial conditions, while atmospheric carbon dioxide concentrations are quadrupled. The associated reduction of downwelling surface solar radiation in a high CO2 environment leads to a reduction of global evaporation of 10% and 4% and precipitation of 6.1% and 6.3% over land and ocean, respectively. An initial reduction of latent heat flux at the surface is largely driven by reduced evapotranspiration over land with instantly increasing CO2 concentrations in both experiments. A warming surface associated with the transient adjustment in the 4xCO2 experiment further generates an increase of global precipitation, with considerable regional changes, such as a significant precipitation reduction of 7% for the North American summer monsoon. Reduced global precipitation persists in the geoengineered experiment where temperatures are stabilized, with considerable regional rainfall deficits. Precipitation reductions that are consistent in sign across models are identified in the geoengineered experiment over monsoonal land regions of East Asia (6%), North America (7%), South America (6%) and South Africa (5%). In contrast to the 4xCO2 experiment, where the frequency of months with heavy precipitation intensity is increased by over 50%, it is reduced by up to 20% in the geoengineering scenario . The reduction in heavy precipitation is more pronounced over land than over the ocean, and accompanies a stronger reduction in evaporation over land. For northern mid-latitudes, maximum precipitation reduction over land ranges from 1 to 16% for individual models. For 45-65°N, the frequency of median to high intensity precipitation in summer is strongly reduced. These changes in precipitation in both total amount and frequency of extremes, point to a considerable weakening of the hydrological cycle in a geoengineered world.

Earth's albedo↗
DOE OSTI2019

State-variable model of porous electrode batteries 1-D electrochemical/thermal behavior

Compared to full-order numerical models, the state-variable model (SVM) achieves multiple orders of reduction in computation time for simulation of electrochemical/thermal transport and reaction processes occurring inside lithium-ion and similar batteries with porous electrode construction. The model efficiently describes Li diffusion dynamics in active material and electrolyte phases, charge transfer kinetics, charge balance, and electron transport processes. The model requires a separate pre-processing step to identify SVM parameters.

Kim, Gi-Heon↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2007

Space Radiation Cancer Risks

Space radiation presents major challenges to astronauts on the International Space Station and for future missions to the Earth s moon or Mars. Methods used to project risks on Earth need to be modified because of the large uncertainties in projecting cancer risks from space radiation, and thus impact safety factors. We describe NASA s unique approach to radiation safety that applies uncertainty based criteria within the occupational health program for astronauts: The two terrestrial criteria of a point estimate of maximum acceptable level of risk and application of the principle of As Low As Reasonably Achievable (ALARA) are supplemented by a third requirement that protects against risk projection uncertainties using the upper 95% confidence level (CL) in the radiation cancer projection model. NASA s acceptable level of risk for ISS and their new lunar program have been set at the point-estimate of a 3-percent risk of exposure induced death (REID). Tissue-averaged organ dose-equivalents are combined with age at exposure and gender-dependent risk coefficients to project the cumulative occupational radiation risks incurred by astronauts. The 95% CL criteria in practice is a stronger criterion than ALARA, but not an absolute cut-off as is applied to a point projection of a 3% REID. We describe the most recent astronaut dose limits, and present a historical review of astronaut organ doses estimates from the Mercury through the current ISS program, and future projections for lunar and Mars missions. NASA s 95% CL criteria is linked to a vibrant ground based radiobiology program investigating the radiobiology of high-energy protons and heavy ions. The near-term goal of research is new knowledge leading to the reduction of uncertainties in projection models. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. The current model for projecting space radiation cancer risk relies on the three assumptions of linearity, additivity, and scaling along with the use of population averages. We describe uncertainty estimates for this model, and new experimental data that sheds light on the accuracy of the underlying assumptions. These methods make it possible to express risk management objectives in terms of quantitative metrics, i.e., the number of days in space without exceeding a given risk level within well defined confidence limits. The resulting methodology is applied to several human space exploration mission scenarios including lunar station, deep space outpost, and a Mars mission. Factors that dominate risk projection uncertainties and application of this approach to assess candidate mitigation approaches are described.

Cucinotta, Francis A.↗
DOE OSTI2023

Modeling the U.S. Western Electric Interconnection to Understand the Consequences of Hydrometeorological Extremes and Options for Risk Mitigation

Electricity grid operators around the world face a dual challenge; withstanding increasingly severe weather and the longer term impacts of climate change, while simultaneously decarbonizing. Extreme weather events such as heat waves and droughts are rising in both severity and frequency, which is threatening the reliability of electricity grids through increased demand, generation capacity losses, and equipment failures. Consequently, incorporating hydrometeorological stressors into computational power systems analysis is becoming an even more critical tool in long term planning and short term operations. However, there is a general lack of open-source customizable grid simulation software capable of exhaustively stress testing the grid under hydrometeorological uncertainty, and/or examining potential risk mitigation pathways. A related, persistent challenge for power system modelers is striking an appropriate balance between model fidelity (e.g. spatial scale and time resolution) and computational tractability (wall clock run-time). In this study, we are proposing a solution to this problem with open-source software that allows users to seamlessly customize the scale and track the accuracy of grid operations models. Our approach allows users to search over numerous model parameters (network topology, mathematical formulation, economic hurdle rates, and transmission line scaling) to identify model instantiations that accommodate experimental design. Further, we use this approach to demonstrate the importance of including extreme weather events in model validation and model selection. Focusing on the occurrence of heatwaves and droughts in the U.S. Western Interconnection, we examine role of extreme events in balancing tradeoffs between model fidelity and run-time at the model design stage.

Economics↗
DOE OSTI2024

Modeling diurnal and annual ethylene generation from solar-driven electrochemical CO 2 reduction devices

Integrated solar fuels devices for CO 2 reduction (CO 2 R) are a promising technology class towards reducing carbon emissions. Designing integrated CO 2 R solar fuels devices requires careful co-design of electrochemical and photovoltaic components as well as consideration of the diurnal and seasonal effects of solar irradiance, temperature, and other meteorological factors expected for ‘on-sun’ deployment. Using a photovoltaic-electrochemical (PV-EC) platform, we developed a temperature and potential-dependent diurnal and annual model using experimentally-determined CO 2 R performance of Cu-based electrocatalysts, local meteorological data from the National Solar Radiation Database (NSRD), and modeled performance of commercial c-Si PVs. Here we simulated gaseous diurnal product outputs with and without the effects of ambient temperature. From these outputs, we observed seasonal variation in gaseous product generation, with up to two-fold increases in ethylene productivity between the Winter and Summer, analyzed the consequences of dynamic cloud coverage, and identified periods where device cooling/heating mechanisms could be implemented to maximize ethylene generation. Finally, we modeled the annual ethylene generation for a scaled 1 MW solar farm at three different locations (Beijing, CN; Sydney, AUS; Barstow, CA) to determine the consequences of local meteorological climates on PV-EC CO 2 R product output, recording a maximum ethylene output of 18.5 tonne per year at Barstow. Overall, this model presents a critical tool for streamlining the translation of experimental solar-driven electrochemical research to real-world implementation.

14 SOLAR ENERGY↗
DOE OSTI2014

Assessing image quality and dose reduction of a new x-ray computed tomography iterative reconstruction algorithm using model observers

Purpose: A number of different techniques have been developed to reduce radiation dose in x-ray computed tomography (CT) imaging. In this paper, the authors will compare task-based measures of image quality of CT images reconstructed by two algorithms: conventional filtered back projection (FBP), and a new iterative reconstruction algorithm (IR). Methods: To assess image quality, the authors used the performance of a channelized Hotelling observer acting on reconstructed image slices. The selected channels are dense difference Gaussian channels (DDOG).A body phantom and a head phantom were imaged 50 times at different dose levels to obtain the data needed to assess image quality. The phantoms consisted of uniform backgrounds with low contrast signals embedded at various locations. The tasks the observer model performed included (1) detection of a signal of known location and shape, and (2) detection and localization of a signal of known shape. The employed DDOG channels are based on the response of the human visual system. Performance was assessed using the areas under ROC curves and areas under localization ROC curves. Results: For signal known exactly (SKE) and location unknown/signal shape known tasks with circular signals of different sizes and contrasts, the authors’ task-based measures showed that a FBP equivalent image quality can be achieved at lower dose levels using the IR algorithm. For the SKE case, the range of dose reduction is 50%–67% (head phantom) and 68%–82% (body phantom). For the study of location unknown/signal shape known, the dose reduction range can be reached at 67%–75% for head phantom and 67%–77% for body phantom case. These results suggest that the IR images at lower dose settings can reach the same image quality when compared to full dose conventional FBP images. Conclusions: The work presented provides an objective way to quantitatively assess the image quality of a newly introduced CT IR algorithm. The performance of the model observers using the IR images was always higher than that seen using the FBP images in the authors’ SKE and SKE location unknown detection tasks. To achieve a FBP-equivalent image quality in CT systems, the authors can lower the radiation dose by using this IR image reconstruction algorithm. Further studies are warranted using clinical data and human observer to validate these results for more complicated and realistic tasks.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Radiation dose rate distributions of spent fuel dry casks estimated with MAVRIC based on detailed geometry and continuous-energy models

This study presents a detailed comparison of the dose rate distributions of a dry TN–32 fuel cask with two geometry models and two cross-sectional datasets. The accuracies of radiation dose rate estimation and computational efficiencies of each geometry model with two cross-sectional data-sets are compared. The use of automated variance reduction techniques can significantly improve the computational efficiency of such a realistic, deep penetration problem that involves radiation transport from different volumetric sources, thereby eliciting only a small statistical error. Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) is a computational sequence within the SCALE 6.2 code package based on consistent adjoint driven importance sampling (CADIS), a type of automated variance reduction technique. Homogenous and full fuel assembly models are built herein, and two nuclear cross-section libraries (V7–200N47G and continuous energy) are applied in this work. Based on the detailed comparisons, we found that neutron dose rate estimation is more dependent on geometry modeling than on cross-section data. For neutron-induced gamma rays, the dose rate distribution depends on both the spatial self-shielding effect and the cross-section library. The primary gamma rays respectively contribute to the total dose rate by ~91% and ~ 99% on side and top surfaces, and the dose rate accuracy is more dependent on the cross-section library than on geometry modeling. In terms of the computation efficiency and efforts spent on geometry modeling, the homogenous fuel assembly model with the MG library can produce an acceptable dose rate distribution, but the detailed fuel assembly model with the continuous-energy library is required for more precise dose rate estimation.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2014

Catalytic Tar Reduction for Assistance in Thermal Conversion of Space Waste for Energy Production

The Trash to Gas (TtG) project investigates technologies for converting waste generated during spaceflight into various resources. One of these technologies was gasification, which employed a downdraft reactor designed and manufactured at NASA's Kennedy Space Center (KSC) for the conversion of simulated space trash to carbon dioxide. The carbon dioxide would then be converted to methane for propulsion and water for life support systems. A minor byproduct of gasification includes large hydrocarbons, also known as tars. Tars are unwanted byproducts that add contamination to the product stream, clog the reactor and cause complications in analysis instrumentation. The objective of this research was to perform reduction studies of a mock tar using select catalysts and choose the most effective for primary treatment within the KSC downdraft gasification reactor. Because the KSC reactor is operated at temperatures below typical gasification reactors, this study evaluates catalyst performance below recommended catalytic operating temperatures. The tar reduction experimentation was observed by passing a model tar vapor stream over the catalysts at similar conditions to that of the KSC reactor. Reduction in tar was determined using gas chromatography. Tar reduction efficiency and catalyst performances were evaluated at different temperatures.

energy conversion↗
DOE OSTI2013

IMAGING DISCOVERY OF THE DEBRIS DISK AROUND HIP 79977

We present Subaru/HiCIAO H-band high-contrast images of the debris disk around HIP 79977, whose presence was recently inferred from an infrared excess. Our images resolve the disk for the first time, allowing characterization of its shape, size, and dust grain properties. We use angular differential imaging (ADI) to reveal the disk geometry in unpolarized light out to a radius of {approx}2'', as well as polarized differential imaging to measure the degree of scattering polarization out to {approx}1.''5. In order to strike a favorable balance between suppression of the stellar halo and conservation of disk flux, we explore the application of principal component analysis to both ADI and reference star subtraction. This allows accurate forward modeling of the effects of data reduction on simulated disk images, and thus direct comparison with the imaged disk. The resulting best-fit values and well-fitting intervals for the model parameters are a surface brightness power-law slope of S{sub out} = -3.2[ - 3.6, -2.9], an inclination of i = 84 Degree-Sign [81 Degree-Sign , 86 Degree-Sign ], a high Henyey-Greenstein forward-scattering parameter of g = 0.45[0.35, 0.60], and a non-significant disk-star offset of u = 3.0[ - 1.5, 7.5] AU = 24[ - 13, 61] mas along the line of nodes. Furthermore, the tangential linear polarization along the disk rises from {approx}10% at 0.''5 to {approx}45% at 1.''5. These measurements paint a consistent picture of a disk of dust grains produced by collisional cascades and blown out to larger radii by stellar radiation pressure.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Modeling Diurnal and Annual Ethylene Generation from Solar-Driven Electrochemical CO 2 Reduction Devices

Integrated solar fuels devices for CO 2 reduction (CO 2 R) are a promising technology class towards achieving net-negative carbon emissions. Designing integrated CO 2 R solar fuels devices requires careful co-design of electrochemical and photovoltaic components as well as consideration of the diurnal and seasonal effects of solar irradiance, temperature, and other meteorological factors expected for ‘on-sun’ deployment. Here, using a photovoltaic-electrochemical (PV-EC) platform, we developed a temperature and potential-dependent diurnal and annual model using experimental CO 2 R performance of Cu-based electrocatalysts, local meteorological data from the National Solar Radiation Database (NSRD), and modeled performance of commercial c-Si PVs. We simulated diurnal product outputs with and without the effects of ambient temperature to determine gaseous product temperature sensitivity. From these outputs, we observed seasonal variation in gaseous product generation, with up to two-fold increases in ethylene productivity between the Winter and Summer, analyzed the consequences of dynamic cloud coverage, and identified periods where device cooling/heating mechanisms could be implemented to maximize ethylene generation. Finally, we modeled the annual ethylene generation for a scaled 1 MW solar farm at three different locations (Beijing, CN; Sydney, AUS; Barstow, CA) to determine the consequences of local meteorological climates on PV-EC CO 2 R product output, recording a maximum ethylene output of 18.5 tonne/yr at Barstow. Overall, this model presents a critical tool for streamlining the translation of experimental solar-driven electrochemical research to real-world implementation.

Yap, Kyra M. K.↗