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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

U.S. Light-Duty Vehicle Air Conditioning Fuel Use and Impact of Solar/Thermal Control Technologies

To reduce fuel consumption and carbon dioxide (CO2) emissions from mobile air conditioning (A/C) systems, 'U.S. Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards' identified solar/thermal technologies such as solar control glazings, solar reflective paint, and active and passive cabin ventilation in an off-cycle credit menu. National Renewable Energy Laboratory (NREL) researchers developed a sophisticated analysis process to calculate U.S. light-duty A/C fuel use that was used to assess the impact of these technologies, leveraging thermal and vehicle simulation analysis tools developed under previous U.S. Department of Energy projects. Representative U.S. light-duty driving behaviors and weighting factors including time-of-day of travel, trip duration, and time between trips were characterized and integrated into the analysis. In addition, U.S. weather conditions weighted based on light-duty vehicle registrations were generated and used for the analysis. Three representative vehicle types for the light-duty fleet were selected based on registration data containing vehicle size information. These key inputs were used to support a weighted parametric analysis that quantified vehicle thermal load, vehicle accessory load, and vehicle fuel use. This analysis estimates that 7.6 billion gallons of fuel a year are used in the United States for vehicle A/C, equivalent to 6.1% of the total national light-duty vehicle fuel use. This equates to 30.0 gallons/year per vehicle or 23.5 grams CO 2 per mile (g/mi) for an average U.S. light-duty vehicle. A/C is a significant contributor to national fuel use; therefore, technologies that reduce A/C loads have an opportunity to reduce fuel consumption, reduce imported oil, and improve energy security. Implementing solar control glass reduced the CO 2 emissions by 2.0 g/mi and the reduction due to solar reflective paint was 0.8 g/mi. The active and passive ventilation strategies as defined in this analysis only reduced emissions by 0.1 and 0.2 g/mi, respectively. This analysis shows the potential to reduce operational costs, A/C fuel use, and CO 2 emissions by implementing advanced vehicle climate control technologies including the solar/thermal technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Assessing Geospatial and Seasonal Influences on Energy and Cost-Efficiency of Drayage Trucks

The electrification of heavy-duty vehicles is a critical pathway toward improved energy efficiency in the freight sector. The current battery electric truck technology poses several challenges to commercial vehicle operations, such as limited driving range, sensitivity to climate conditions, and long recharging times. Estimating the energy consumption of heavy-duty electric trucks is crucial to assessing the feasibility of fleet electrification and its impact on the electric grid. This article focuses on developing a model-based simulation approach to predict and analyze the energy consumption of electric trucks by considering the impact of weather and geographical conditions on vehicle road load and auxiliary components power consumption, as well as the impact these factors have on driving range. Specifically, drayage trucks employed in logistics around maritime ports are used as a case study, with consideration of seasonal climate variations and geographical characteristics at different locations. The article includes results for three major container ports within the United States, providing region-specific insights into the energy requirements and driving range of the electric drayage trucks in these regions, which will inform decision-makers in integrating electric trucks into the existing drayage operations and plan investments for electric grid development.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2013

In-Use and Vehicle Dynamometer Evaluation and Comparison of Class 7 Hybrid Electric and Conventional Diesel Delivery Trucks

This study compared fuel economy and emissions between heavy-duty hybrid electric vehicles (HEVs) and equivalent conventional diesel vehicles. In-use field data were collected from daily fleet operations carried out at a FedEx facility in California on six HEV and six conventional 2010 Freightliner M2-106 straight box trucks. Field data collection primarily focused on route assessment and vehicle fuel consumption over a six-month period. Chassis dynamometer testing was also carried out on one conventional vehicle and one HEV to determine differences in fuel consumption and emissions. Route data from the field study was analyzed to determine the selection of dynamometer test cycles. From this analysis, the New York Composite (NYComp), Hybrid Truck Users Forum Class 6 (HTUF 6), and California Air Resource Board (CARB) Heavy Heavy-Duty Diesel Truck (HHDDT) drive cycles were chosen. The HEV showed 31% better fuel economy on the NYComp cycle, 25% better on the HTUF 6 cycle and 4% worse on the CARB HHDDT cycle when compared to the conventional vehicle. The in-use field data indicates that the HEVs had around 16% better fuel economy than the conventional vehicles. Dynamometer testing also showed that the HEV generally emitted higher levels of nitric oxides than the conventional vehicle over the drive cycles, up to 77% higher on the NYComp cycle (though this may at least in part be attributed to the different engine certification levels in the vehicles tested). The conventional vehicle was found to accelerate up to freeway speeds over ten seconds faster than the HEV.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Sleeper Cab Climate Control Load Reduction for Long-Haul Truck Rest Period Idling

Annual fuel use for long-haul truck rest period idling is estimated at 667 million gallons in the United States. The U.S. Department of Energy’s National Renewable Energy Laboratory’s CoolCab project aims to reduce heating, ventilating, and air conditioning (HVAC) loads and resulting fuel use from rest period idling by working closely with industry to design efficient long-haul truck climate control systems while maintaining occupant comfort. Enhancing the thermal performance of cab/sleepers will enable smaller, lighter, and more cost-effective idle reduction solutions. In order for candidate idle reduction technologies to be implemented at the original equipment manufacturer and fleet level, their effectiveness must be quantified. To address this need, a number of promising candidate technologies were evaluated through experimentation and modeling to determine their effectiveness in reducing rest period HVAC loads. For this study, load reduction strategies were grouped into the focus areas of solar envelope, occupant environment, and conductive pathways. The technologies selected for a complete-cab package of technologies were “ultra-white” paint, advanced insulation, and advanced curtains. To measure the impact of these technologies, a nationally-averaged solar-weighted reflectivity long-haul truck paint color was determined and applied to the baseline test vehicle. Using the complete-cab package of technologies, electrical energy consumption for long-haul truck daytime rest period air conditioning was reduced by at least 35% for summer weather conditions in Colorado. The National Renewable Energy Laboratory's CoolCalc model was then used to extrapolate the performance of the thermal load reduction technologies nationally for 161 major U.S. cities using typical weather conditions for each location over an entire year.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Evaluating the Impact of Road Grade on Simulated Commercial Vehicle Fuel Economy Using Real-World Drive Cycles

Commercial vehicle fuel economy is known to vary significantly with both positive and negative road grade. Medium- and heavy-duty vehicles operating at highway speeds require incrementally larger amounts of energy to pull heavy payloads up inclines as road grade increases. Non-hybrid vehicles are then unable to recapture energy on descent and lose energy through friction braking. While the on-road effects of road grade are well understood, the majority of standard commercial vehicle drive cycles feature no climb or descent requirements. Additionally, existing literature offers a limited number of sources that attempt to estimate the on-road energy implications of road grade in the medium- and heavy-duty space. This study uses real-world commercial vehicle drive cycles from the National Renewable Energy Laboratory's Fleet DNA database to simulate the effects of road grade on fuel economy across a range of vocations, operating conditions, and locations. Drive-cycles are matched with vocation-specific vehicle models and simulated with and without grade. Fuel use due to grade is presented, and variation in fuel consumption due to drive cycle and vehicle characteristics is explored through graphical and statistical comparison. The results of this study suggest that road grade accounts for 1%-9% of fuel use in commercial vehicles on average and up to 40% on select routes.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2015

Quantitative Effects of Vehicle Parameters on Fuel Consumption for Heavy-Duty Vehicle

The National Renewable Energy Laboratory's (NREL's) Fleet Test and Evaluations team recently conducted chassis dynamometer tests of a class 8 conventional regional delivery truck over the Heavy Heavy-Duty Diesel Truck (HHDDT), West Virginia University City (WVU City), and Composite International Truck Local and Commuter Cycle (CILCC) drive cycles. A quantitative study was conducted by analyzing the impacts of various factors on fuel consumption (FC) and fuel economy (FE) by modeling and simulating the truck using NREL's Future Automotive Systems Technology Simulator (FASTSim). Factors used in this study included vehicle weight, and the coefficients of rolling resistance and aerodynamic drag. The simulation results from a single parametric study revealed that FC was approximately a linear function of the weight, coefficient of aerodynamic drag, and rolling resistance over various drive cycles. Among these parameters, the truck weight had the largest effect on FC. The study of the impact of two technologies on FE suggested that, depending on the circumstances, it may be more cost effective to reduce one parameter (such as coefficient of aerodynamic drag) to increase fuel economy, or it may be more beneficial to reduce another (such as the coefficient of rolling resistance). It also provided a convenient way to estimate FE by interpolating within the parameter values and extrapolating outside of them. The simulation results indicated that the FC could be reduced from 38.70 L/100 km, 50.72 L/100 km, and 38.42 L/100 km in the baseline truck to 26.78 L/100 km, 43.14 L/100 km and 29.84 L/100 km over the HHDDT, WVU City and CILCC drive cycles, respectively, when the U.S. Department of Energy's three targeted new technologies were applied simultaneously.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Application of High Performance Computing for Simulating Cycle-to-Cycle Variation in Dual-Fuel Combustion Engines

Interest in operational cost reduction is driving engine manufacturers to consider lower-cost fuel substitution in heavy-duty diesel engines. These dual-fuel (DF) engines could be operated either in diesel-only mode or operated with premixed natural gas (NG) ignited by a pilot flame of compression-ignited direct-injected diesel fuel. One promising application is that of large-bore, medium-speed engines such as those used in locomotives. With realistic natural gas substitution levels in the fleet of locomotives currently in service, such fuel substitution could result in billions of dollars of savings annually in the US alone. However, under certain conditions, dual-fuel operation can result in increased cycle-to-cycle variability (CCV) during combustion, resulting in variations in cylinder pressure and work extraction. In certain situations, the CCV of dual-fuel operation can be notably higher than that of diesel-only combustion under similar operating conditions. Excessive CCV can limit the NG substitution rate and operating range of a dual-fuel engine by increasing emissions and reducing engine stability, reliability and fuel efficiency via incomplete natural-gas combustion. Running multiple engine cycles in series to simulate CCV can be quite time consuming. Hence innovative modelling techniques and large computing resources are needed to investigate the factors affecting CCV in dual-fuel engines. This paper discusses the use of the High Performance Computing resource Titan, at the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, to investigate cycle-to-cycle combustion variability of a dual-fuel engine. The CONVERGE CFD software was used to simulate multiple, parallel single cycles of dual-fuel combustion with perturbed operating parameters and boundary conditions. These perturbations are imposed according to a sparse grids sampling of the parameter space. The sampling scheme chosen is similar to a design of experiments method but uses functions designed to minimize the number of samples required to achieve a desired degree of accuracy. The perturbed input parameters were connected to engine performance output over a large parameter space. Additionally, the same methodology was used to perform a set of diesel-only simulations at similar operating conditions to assess the numerical capability of capturing relatively low combustion variation. Lastly, this technique is expected to be useful to understand and predict combustion stability limits (knock and misfire) which are important for designing dual-fuel engines with improved operability and reliability.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2017

Decomposing Fuel Economy and Greenhouse Gas Regulatory Standards in the Energy Conversion Efficiency and Tractive Energy Domain

The three foundational elements that determine mobile source energy use and tailpipe carbon dioxide (CO2) emissions are the tractive energy requirements of the vehicle, the on-cycle energy conversion efficiency of the propulsion system, and the energy source. The tractive energy requirements are determined by the vehicle's mass, aerodynamic drag, tire rolling resistance, and parasitic drag. Oncycle energy conversion of the propulsion system is dictated by the tractive efficiency, non-tractive energy use, kinetic energy recovery, and parasitic losses. The energy source determines the mobile source CO2 emissions. For current vehicles, tractive energy requirements and overall energy conversion efficiency are readily available from the decomposition of test data. For future applications, plausible levels of mass reduction, aerodynamic drag improvements, and tire rolling resistance can be transposed into the tractive energy domain. Similarly, by combining thermodynamic, mechanical efficiency, and kinetic energy recovery fundamentals with logical proxies, achievable levels of energy conversion efficiency can be established to allow for the evaluation of future powertrain requirements. Combining the plausible levels of tractive energy and on-cycle efficiency provides a means to compute sustainable vehicle and propulsion system scenarios that can achieve future regulations. Using these principles, the regulations established in the United States (U.S.) for fuel consumption and CO2 emissions are evaluated. Fleet-level scenarios are generated and compared to the technology deployment assumptions made during rule-making. When compared to the rule-making assumptions, the results indicate that a greater level of advanced vehicle and propulsion system technology deployment will be required to achieve the model year 2025 U.S. standards for fuel economy and CO2 emissions.

CAFE↗
DOE OSTI2019

Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming

This paper presents a methodology to optimize the blending of charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be completely driven in all-electric mode. The PHEV used in this investigation is the second-generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan an optimal mode scheme in highly dynamic road conditions with minimal computational resources. On-road performance of the optimizer paired with automated CD and CS mode selection is evaluated on a fleet of four instrumented Chevrolet Volts in a variety of driving scenarios. Here, the results indicate variable energy savings depending on the drive route and initial battery SOC with potential ranging between 2 to 12% and less than 7 seconds initial optimization for a 24-mile drive cycle.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Potential Impacts of High-Octane Fuel Introduction in a Naturally Aspirated, Port Fuel-Injected Legacy Vehicle

In recent years there has been an increased interest in raising the octane level of gasoline to enable higher compression ratios (CR) in spark-ignition engines to improve vehicle fuel efficiency. A number of studies have examined opportunities to increase efficiency in future vehicles, but potential impacts on the legacy fleet have not received as much attention. In this study, our effort focused on experimental studies on an engine using high-octane fuels without changing the engine’s CR. Spark timing was advanced until maximum torque was reached or knock was encountered for each engine condition, using each individual fuel to maximize engine efficiency. Knock-limited conditions occurred as the output brake mean effective pressure (BMEP) neared the maximum attainable output at a given engine speed. Increasing research octane numbers generally enabled knock-free operation under a greater number of operating conditions. Vehicle modeling using Autonomie was used to project vehicle energy use, fuel economy, and tailpipe CO 2 emissions for the Urban Dynamometer Driving Schedule (UDDS), the Highway Fuel Economy Test (HWFET), and the US06 cycle. Results show that decreases in energy consumption of up to 2% for a small SUV are possible through the use of a 97 RON fuel compared to a baseline using 91 RON fuel, provided that the formulation of the fuel does not cause unanticipated operational issues such as lower maximum BMEP. Greater improvements using high-octane fuels are possible if the CR is increased, but there is no opportunity to increase the CR in legacy vehicles. Thus, these vehicles realize an improvement from increased octane rating in accordance with their ability to spark advance to take advantage of a fuel with a higher octane rating. For the modeled vehicle, improvements of up to 2% in volumetric fuel economy may be possible through the use of a 97 RON fuel with the largest gains expected on the US06 cycle. Fuel economy impacts are strongly coupled to the heating value of the fuels in addition to changes in engine efficiency. Similarly, decreases in tailpipe CO 2 emissions are also achievable. However, simultaneous improvements in energy consumption, fuel economy, and tailpipe CO 2 emissions are not guaranteed and are dependent upon fuel formulation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Decision Tree Regression to Identify Representative Road Sections for Evaluating Performance of Connected and Automated Class 8 Tractors

Currently, connected and autonomous vehicle (CAV) technology is being developed for Class 8 tractor trucks aimed at improved safety and fuel economy and reduced CO2 emissions. Despite extensive efforts conducted across the world, the reported efficiency gains were varied from different research groups, raising concerns about the fidelity of models, the performance of control, and the effectiveness of the experimental validation. One root cause for this variation stems from the fact that the efficiency gain obtained from the CAV is sensitive to real-world conditions, including surrounding traffic and road grade. This study presents an approach aimed at identifying representative public road sections and facilitating CAV research from this perspective. By employing the decision tree regression (DTR) method to the Fleet DNA database, the most representative road sections can be identified. High-level metrics and detailed information of the derived road sections are also illustrated and discussed, which demonstrate their representativeness and the effectiveness of the approach. Meanwhile, the capability of this approach can be easily extended by integrating specific constraints into the DTR algorithm. As an example, a specific representative road section with an aggressive road grade profile was also provided via this approach.

27 ARPA - Advanced Research Projects Agency-Energy↗
DOE OSTI2021

Advancing Platooning with ADAS Control Integration and Assessment Test Results

The application of cooperative adaptive cruise control (CACC) to heavy-duty trucks known as truck platooning has shown fuel economy improvements over test track ideal driving conditions. However, there are limited test data available to assess the performance of CACC under real-world driving conditions. As part of the Cummins-led U.S. Department of Energy Funding Opportunity Announcement award project, truck platooning with CACC has been tested under real-world driving conditions and the results are presented in this paper. First, real-world driving conditions are characterized with the National Renewable Energy Laboratory’s Fleet DNA database to define the test factors. The key test factors impacting long-haul truck fuel economy were identified as terrain and highway traffic with and without advanced driver-assistance systems (ADAS). Track and on-highway testing guided by SAE J1321 procedures were conducted to assess truck platooning operation under the characterized real-world driving conditions. On-highway testing is done on a route in Indiana representing operation of long-haul Class 8 trucks in the United States. The road includes low-, medium-, and high-grade segments. The highway test results of a two-truck platooning configuration indicate considerable fuel-saving reduction comparing to the test track data collected under ideal driving conditions. The test data indicate that platooning could lead to increases in fuel consumption during traffic or high-grade portions of the route, causing reduction of the overall fuel saving on the road comparing to test track results. However, integration of ADAS features on the lead truck during on-road tests leads to significant improvement of fuel saving for both trucks in CACC operation.

ADAS↗
DOE OSTI2023

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Assessing the Impact of Mirror Technology on Driver Perception and Safety: Traditional vs. Camera-Based Systems

Camera-based mirror systems (CBMS) are being adopted by commercial fleets based on the potential improvements to operational efficiency through improved aerodynamics, resulting in better fuel economy, improved maneuverability, and the potential improvement for overall safety. Until CBMS are widely adopted it will be expected that drivers will be required to adapt to both conventional glass mirrors and CBMS which could have potential impact on the safety and performance of the driver when moving between vehicles with and without CBMS. To understand the potential impact to driver perception and safety, along with other human factors related to CBMS, laboratory testing was performed to understand the impact of CBMS and conventional glass mirrors. Drivers were subjected to various, nominal driving scenarios using a truck equipped with conventional glass mirrors, CBMS, and both glass mirrors and CBMS, to observe the differences in metrics such as head and eye movement, reaction time, and perception of distance. The finds from this study will serve as the baseline measurements for future research regarding off-nominal driving scenarios and hardware failures of CBMS, as well as inform potential future policy regarding CBMS for the use in commercial vehicles in lieu of conventional glass mirrors.

Siekmann, Adam [ORNL] (ORCID:0000000284653935)↗
DOE OSTI2016

Introduction: Observations and modeling of the Green Ocean Amazon (GoAmazon2014/5)

The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment was carried out in the environs of Manaus, Brazil, in the central region of the Amazon basin for 2 years from 1 January 2014 through 31 December 2015. The experiment focused on the complex interactions among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other. The objective was to understand and quantify these linked processes, first under natural conditions to obtain a baseline and second when altered by the effects of human activities. To this end, the pollution plume from the Manaus metropolis, superimposed on the background conditions of the central Amazon basin, served as a natural laboratory. The present paper, as the introduction to the special issue of GoAmazon2014/5, presents the context and motivation of the GoAmazon2014/5 Experiment. The nine research sites, including the characteristics and instrumentation of each site, are presented. The sites range from time point zero (T0) upwind of the pollution, to T1 in the midst of the pollution, to T2 just downwind of the pollution, to T3 furthest downwind of the pollution (70 km). In addition to the ground sites, a low-altitude G-159 Gulfstream I (G-1) observed the atmospheric boundary layer and low clouds, and a high-altitude Gulfstream G550 (HALO) operated in the free troposphere. During the 2-year experiment, two Intensive Operating Periods (IOP1 and IOP2) also took place that included additional specialized research instrumentation at the ground sites as well as flights of the two aircraft. GoAmazon2014/5 IOP1 was carried out from 1 February to 31 March 2014 in the wet season. GoAmazon2014/5 IOP2 was conducted from 15 August to 15 October 2014 in the dry season. In addition, the G-1 aircraft flew during both IOP1 and IOP2, and the HALO aircraft flew during IOP2. In the context of the Amazon basin, the two IOPs also correspond to the clean and biomass burning seasons, respectively. The Manaus plume is present year-round, and it is transported by prevailing northeasterly and easterly winds in the wet and dry seasons, respectively. This introduction also organizes information relevant to many papers in the special issue. Information is provided on the vehicle fleet, power plants, and industrial activities of Manaus. The mesoscale and synoptic meteorologies relevant to the two IOPs are presented. Regional and long-range transport of emissions during the two IOPs is discussed based on satellite observations across South America and Africa. Fire locations throughout the airshed are detailed. In conjunction with the context and motivation of GoAmazon2014/5 as presented in this introduction, research articles including thematic overview articles are anticipated in this special issue to describe the detailed results and findings of the GoAmazon2014/5 Experiment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

The Open-source Data Inventory for Anthropogenic CO 2 , version 2016 (ODIAC2016): a global monthly fossil fuel CO 2 gridded emissions data product for tracer transport simulations and surface flux inversions

The Open-source Data Inventory for Anthropogenic CO 2 (ODIAC) is a global high-spatial-resolution gridded emissions data product that distributes carbon dioxide (CO 2 ) emissions from fossil fuel combustion. The emissions spatial distributions are estimated at a 1 × 1 km spatial resolution over land using power plant profiles (emissions intensity and geographical location) and satellite-observed nighttime lights. This work describes the year 2016 version of the ODIAC emissions data product (ODIAC2016) and presents analyses that help guide data users, especially for atmospheric CO 2 tracer transport simulations and flux inversion analysis. Since the original publication in 2011, we have made modifications to our emissions modeling framework in order to deliver a comprehensive global gridded emissions data product. Major changes from the 2011 publication are (1) the use of emissions estimates made by the Carbon Dioxide Information Analysis Center (CDIAC) at the Oak Ridge National Laboratory (ORNL) by fuel type (solid, liquid, gas, cement manufacturing, gas flaring, and international aviation and marine bunkers); (2) the use of multiple spatial emissions proxies by fuel type such as (a) nighttime light data specific to gas flaring and (b) ship/aircraft fleet tracks; and (3) the inclusion of emissions temporal variations. Using global fuel consumption data, we extrapolated the CDIAC emissions estimates for the recent years and produced the ODIAC2016 emissions data product that covers 2000–2015. Our emissions data can be viewed as an extended version of CDIAC gridded emissions data product, which should allow data users to impose global fossil fuel emissions in a more comprehensive manner than the original CDIAC product. Our new emissions modeling framework allows us to produce future versions of the ODIAC emissions data product with a timely update. Such capability has become more significant given the CDIAC/ORNL's shutdown. The ODIAC data product could play an important role in supporting carbon cycle science, especially modeling studies with space-based CO 2 data collected in near real time by ongoing carbon observing missions such as the Japanese Greenhouse gases Observing SATellite (GOSAT), NASA's Orbiting Carbon Observatory-2 (OCO-2), and upcoming future missions. The ODIAC emissions data product including the latest version of the ODIAC emissions data (ODIAC2017, 2000–2016) is distributed from http://db.cger.nies.go.jp/dataset/ODIAC/ with a DOI (https://doi.org/10.17595/20170411.001).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

sdt (Solar Data Tools) [SWR-25-130]

Solar Data Tools (sdt) is an open-source Python library for analyzing PV power (and irradiance) time-series data. It was developed to enable analysis of unlabeled PV data, i.e. with no model, no meteorological data, and no performance index required, by taking a statistical signal processing approach in the algorithms used in the package’s main data processing pipeline. Solar Data Tools empowers PV system fleet owners or operators to analyze system performance a hundred times faster even when they only have access to the most basic data stream—power output of the system.

Meyers-Im, Bennet [National Laboratory of the Rock↗
DOE OSTI2022

Addressing Function Allocation for the Digital Transformation of Existing Nuclear Power Plants

The existing nuclear power plants in the United States (U.S.) have a vital role in providing carbon-free electricity. For the existing nuclear power plant fleet to remain economically viable, a significant digital transformation that fundamentally changes the way in which these plants are operated, maintained, and supported ought to be seriously considered. Safe and reliable automation is needed. This work describes important considerations and challenges that come with function allocation for the adoption of new automation at existing nuclear power plants. Specifically, this work reviews the state-of-the-art in function allocation guidance and highlights how it can be used within the U.S. nuclear industry. An objective of this work is to present the current challenges and proposed approaches to the human factors community to support future research and development that ultimately supports the effective use of function allocation in the digital transformation of existing nuclear power plants.

99 GENERAL AND MISCELLANEOUS↗

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