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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 OSTI2024

ResStock Dataset 2024.1 Documentation

Public ResStock datasets provide credible, relevant, and accessible information on energy use and related non-energy metrics to a variety of stakeholders in the residential buildings space. The current public datasets include baseline building characteristics, timeseries (15-minute) energy consumption, and timeseries carbon emissions for the baseline (existing) U.S. housing stock and the U.S. housing stock with 10 "what-if" energy measure packages applied. This report documents a new public ResStock dataset to complement and build upon the existing public datasets. This dataset is specifically intended to be a resource for state and local decision-makers considering options for energy retrofits for their housing stock to reduce carbon emissions, energy use, and/or utility bills. These data consist of housing stock characteristics and modeled full-year energy consumption, carbon emission, energy bill, and energy burden data for the baseline U.S. housing stock as well as the U.S. housing stock with 260 "what-if" energy measure packages applied. These measure packages include measures related to the building envelope, appliances, pools and spas, lighting, water heating, and HVAC (including efficiency improvements and fuel switching with equipment at a range of performance levels) in a variety of combinations. This report provides methodology information on the generation of this dataset and serves as a key part of the dataset's public documentation.

buildings↗
DOE OSTI2022

The cost savings potential of controlling solar thermal collectors with storage for time-of-use electricity rates

Homes and commercial buildings account for roughly 40% of U.S. energy consumption. A significant portion of this energy consumption, and therefore utility cost, comes from domestic hot-water use. Solar collectors such as flat plate collectors, evacuated tubes, and combined photovoltaic/thermal panels, are renewable options that collect thermal energy and could help reduce utility costs. Hot water is generated by pumping a working fluid through the solar collector, where the fluid captures thermal energy via absorbed solar radiation incident on the collector surface. The collection of heat coincides with the availability of solar radiation, but the best time to use this thermal energy depends on the utility rate at a given time, which often changes throughout the day. In this work, we implement and simulate a control strategy using Transient System Simulation Tool (TRNSYS) that reduces utility costs by controlling when the system stores and uses thermal energy based on solar availability and time-of-use electricity rates. Our modeled results show that the control strategy can reduce utility costs associated with using hot water between 3% and 30%. Here, we also show how the on-peak utility rates, the size and type of the solar collectors, and the location's climate impact the results.

14 SOLAR ENERGY↗
NASA NTRS1974

Transportation vehicle energy intensities. A joint DOT/NASA reference paper

A compilation of data on the energy consumption of air and ground vehicles is presented. The ratio BTU/ASM, British Thermal Units/Available Seat Mile, is used to express vehicle energy intensiveness, and related to the energy consumed directly in producing seat-mile or ton-mile productivity. Data is presented on passenger and freight vehicles which are in current use or which are about to enter service, and advanced vehicles which may be operational in the 1980's and beyond. For the advanced vehicles, an estimate is given of the date of initial operational service, and the performance characteristics. Other key considerations in interpreting energy intensiveness for a given mode are discussed, such as: load factors, operations, overhead energy consumption, and energy investments in new structure and equipment.

Mascy, A. C.↗
DOE OSTI2025

Development of an Energy-Efficient and High-Productivity Ammonia Recovery and Removal Process Using Resin-Wafer Electrodeionization

As efforts to develop various energy resources, ammonia energy is emerging as a promising carbon-free fuel alternative. Recovering high-concentration ammonia and ammonium from wastewater potentially offers significant environmental and economic benefits. However, research on recovery technologies for industries such as semiconductors remains limited. This study develops an efficient, energy-saving ammonia recovery technology using Resin Wafer Electrodeionization (RW-EDI), specifically for the semiconductor industry. RW-EDI shows promise for recovering ammonia from high-concentration wastewater. By optimizing parameters, such as voltage and initial concentration, a balance between productivity and energy consumption is achieved. Results indicate that ammonium and fluoride ion transport kinetics are similar, with minimal competition and selectivity values between 0.95 and 1.08. As the initial ammonia/ammonium concentration increases from 500 to 8000 ppm, reaction rate constants and the overall mass transfer coefficient decrease. Increasing the voltage can enhance mass transfer and eliminate barriers. Ion transport primarily occurs in the RW solid phase, accounting for 93.89% of the total current. Additionally, RW-EDI shows superior specific energy consumption within a concentration range of 500 ppm to 8000 ppm, outperforming technologies like electrodialysis by reducing specific energy consumption from 8−15 kWh/kg-NH 4 + to 1.2−2.5 kWh/kg-NH 4 + . This study highlights RW-EDI’s potential for ammonia recovery, providing valuable insights for future applications in wastewater treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Incorporating two-dimensional conduction modeling techniques into an energy simulation program: The EnergyPlus radiant system example

Due to the inherent complexity of multi-dimensional solutions for transient conduction in a multilayered building element, most heat balance-based algorithms assume that conduction occurs only in one dimension—through the building element between the inside and outside environments. Usually, since the length and width of the elements are much greater than the thickness of the element, this is a valid assumption. However, there are some cases such as hydronic radiant slabs where this assumption may not lead to an accurate estimate of the energy consumption of the system. In a hydronic radiant slab, water tubes are embedded in a slab, and hot or chilled water is circulated to meet any heating or cooling loads. The energy consumption of such a system will depend highly on the temperature of the water being sent to the slab (i.e., produced by the boiler or chiller). The temperature of the water that must be sent to the slab will depend on the spacing of the tubes, the multilayer construction of the building element, and the boundary conditions to which the construction is exposed. Thus, to accurately predict the energy consumption of such a system, a transient two-dimensional heat conduction model must be integrated with a heat balance-based simulation. This paper demonstrates the potential loss of accuracy associated with the one-dimensional radiant system models by providing a comparison of one- and two-dimensional solutions using EnergyPlus.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Single-column cryogenic air separation: Enabling efficient oxygen production with rapid startup and low capital costs—application to low-carbon fossil-fuel plants

The rapid integration of intermittent renewable sources into the electricity grid is driving the need for more flexible, low-carbon fossil-fuel plants with lower capital costs. This then drives the need to improve the cryogenic air separation unit (ASU). To address this changing landscape, we explore a Praxair single-column ASU (PSC-ASU) design with the goal of reducing costs and improving flexibility, compared to a conventional double-column ASU. The PSC-ASU incorporates partial air condensation and air pre-separation in the bottom reboiler with a phase separator as well as N 2 -enriched vapor condensation in the upper reboiler to decrease energy consumption, as compared to Linde’s single-column ASU. All three of the above-mentioned ASU designs are simulated in Aspen Plus and analyzed. An economic analysis is applied to evaluate the relative cost savings of the PSC-ASU compared to the double-column ASU. Results suggest that the specific energy consumption of the PSC-ASU is significantly lower than that of Linde’s single-column ASU due to a drastically improved oxygen recovery rate. Although this improved oxygen recovery rate is still lower than that of the double-column ASU, the required pressure ratio of the main air compressor is 21% lower than that of the double-column ASU. As a result, the specific energy consumption of the PSC-ASU is only 1.9% greater than that of the double-column ASU for producing 95.1 mol% O 2 . However, the PSC-ASU reduces the hourly capital cost by 19% due to the elimination of a high-pressure column. This would effectively decrease the total hourly cost of the ASU, and thus the total hourly cost of low-carbon, fossil-fuel power plants that require oxygen.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

BuildingsBench: A Benchmark for Universal Building Load Forecasting [SWR-23-51]

The residential and commercial building stock in the United States is responsible for a significant percentage of energy consumption and greenhouse gas emissions. Electrification of end-uses, as well as decarbonizing the electrical grid through renewable energy sources such as solar and wind, constitutes the pathway to zero-emission buildings. Forecasting day-ahead building energy consumption is an integral part of this solution. Currently, specialized forecasting models are hand-made for each individual building, which is time-consuming, expensive, and leads to duplicated efforts. BuildingsBench is a Python software framework for training and comparing generalized machine learning models for universal building load forecasting. This challenge tasks a single foundational model to generalize its forecasts for a wide variety of buildings, across geographic regions, building types, weather patterns, and more. This software provide code for pre-training such models and subsequently evaluating their performance on a suite of hundreds of diverse real and synthetic buildings. BuildingsBench is a platform for: - Large-scale pretraining with the synthetic Buildings-900K dataset for short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock and is extracted from the NREL End-Use Load Profiles database. - Benchmarking on two tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. We provide an index-based PyTorch Dataset for large-scale pretraining, easy data loading for multiple real building energy consumption datasets as PyTorch Tensors or Pandas DataFrames, simple (persistence) to advanced (transformer) baselines, metrics management, and more.

Emami, Patrick↗
DOE OSTI2020

FINAL TECHNICAL AND ECONOMIC FEASIBILITY STUDY ON THE APPLICATION OF A HEAT INTEGRATED POST-COMBUSTION CO2 CAPTURE SYSTEM WITH HITACHI ADVANCED SOLVENT INTO EXISTING COAL-FIRED POWER PLANT

This report contains the results of a techno-economic assessment (TEA) conducted of a heat integrated post-combustion CO2 capture process with Hitachi advanced solvent for retrofit into an existing coal-fired power plant (but treated as greenfield plant on cost analysis). The process has been developed by the University of Kentucky Center for Applied Energy (UK CAER). EPRI was chiefly responsible for this analysis, with significant input from WorleyParsons, Hitachi Power Systems America (Hitachi) and UK CAER. The project also involves the design, fabrication, installation, testing, and analyses of a slipstream facility located at L&GE-KU’s E.W. Brown Generating Station to demonstrate the UK CAER carbon capture system that could utilize heat integration with the main power plant. The design, start-up, and baseline of the pilot system was performed with a generic 30 wt% MEA solvent to obtain data for direct comparison with the DOE/NETL Reference Case followed by testing Hitachi’s proprietary solvent H3-1. In this techno-economic analysis, two cases utilizing the UK CAER process are compared, using different approach temperatures and solvent, against the DOE/NETL Reference Case (Case 10). The results are shown comparing the energy demand for post-combustion CO2 capture and the net higher heating value (HHV) efficiency of the power plant integrated with the post-combustion capture (PCC) plant. A levelized cost of electricity (LCOE) assessment was performed showing the costs of the options presented in the study. The key factors contributing to the reduction of LCOE were identified as CO2 partial pressure increase at the flue gas inlet, thermal integration of the process, and performance of the Hitachi H3-1 solvent. Recent UK CAER process pilot-scale testing data and process simualtion data showed that the packing heights of absorber and stripper columns were significantly oversized in the prelimanary TEA (Task 2 of this project) and thus updated in this final TEA for the H3-1 case only. In addition, the solvent make-up cost for H3-1 was updated based on lattest test results. Finally, a heat integration with the main power plant was applied in this final TEA to increase overall energy effciency for both the MEA and H3-1 cases. Additonal reductions in capital and operational costs are expected but not taken into account here. Shorter columns result in reduced pressure drops, smaller blower head and pump hydraulic head requirements. An increase in overall energy efficiency resuls in a decreased size of the power plant, the CCS and a reduced parasitic steam requirement to the CCS. The net efficiency of the UK CAER integrated PC power plant with CO2 capture changes from 26.2% for the Reference Case 10 plant in 2010 revised DOE/NETL baseline report to 27.6% for the MEA options considered, and 29.1% for the options utilizing the Hitachi advanced solvent. The UK CAER Process + Hitachi case also produces an extra 30.9 MW of generation compared to the UK CAER Process + MEA case and total 60.9 MW more than DOE Case 10. LCOE ($/MWh) values are $172.08/MWh for the MEA option and $157.65/MWh for the Hitachi H3-1 solvent cases considered in comparison to $189.59/MWh in January 2012 dollar for the Reference Case 10. The UK CAER CCS process with MEA case lowers energy consumption for CO2 capture to 1340 Btu/lb-CO2 captured as compared to 1540 Btu/lb-CO2 in the Reference Case 10. The UK CAER CCS process with H3-1 case further lowers energy consumption for CO2 capture to 973 Btu/lb-CO2 captured, for an advantage of 36.8% less energy consumption than Case 10. The study also shows 38.1% less heat rejection associated with the carbon capture system from 3398 MBtu/hr (Case 10) to 2104 MBtu/hr for the UK CAER + MEA system. Heat rejection is reduced to 2464 MBtu/hr in the UK CAER + H3-1 case, for a 27.5 % decrease compared to Case 10. Modeling outputs show that in the UK CAER process, the cooling water that is 2-5°C cooler than conventional cooling tower water can be achieved for ambient conditions common to the midwest and other regions. The results from the techno-economic assessment show that the proposed technology can be investigated further as a viable alternative to conventional CO2 capture technology. The evaluation also shows the effect of the critical parameters on the LCOE, with the main variables being the approach temperature and CO2 partial pressure increase at the flue gas inlet. A summary of the key advantages of the UK CAER Process + H3-1 case for LCOE and other economic factors compared to the DOE Case 10 is as follows: • A lower variable operating cost by $1.56/MWh ($1.08MWh less than the UK CAER Process + MEA Case), a 11.7% reduction compared to the DOE Case 10 • A lower COE by $25.32MWh ($13.94/MWh lower than the UK CAER Process + MEA Case), a 16.9% reduction compared to the DOE Case 10 • A lower LCOE by $31.94/MWh ($17.51/MWh lower than the UK CAER Process + MEA Case), a 16.9% reduction compared to the DOE Case 10 • A lower cost of CO2 captured by $18.65/tonne CO2 ($9.44/tonne CO2 lower than the UK CAER Process + MEA Case), a 30.4% reduction compared to the DOE Case 10 • A lower cost of CO2 avoided by $34.95/tonne CO2 ($18.53 tonne CO2 lower than the UK CAER Process + MEA Case), a 38.7% reduction compared to the DOE Case 10

Bhown, Abhoyjit S.↗
DOE OSTI2021

Developing a Hybrid Electric Vehicle Eco-Cooperative Adaptive Cruise Control System at Signalized Intersections.

This study develops an eco-driving strategy for hybrid electric vehicles (HEVs) in the vicinity of signalized intersections, entitled HEV Eco-Cooperative Adaptive Cruise Control at Intersections (Eco-CACC-I). The proposed system computes real-time, energy-optimized vehicle trajectories using HEV vehicle dynamics and energy consumption models. In the proposed system, a simple HEV energy model is used to compute the instantaneous fuel consumption. This HEV energy model is selected since it is general, transferable, and can be easily used to compute instantaneous energy consumption levels for HEVs without the additional input of vehicle engine data or complicated power control strategies. In addition, a vehicle dynamics model is used to capture the relationship between speed, acceleration level, and tractive/resistance forces on vehicles. The energy-optimum problem is formulated as an optimization problem with constraints, which is solved using a moving-horizon dynamic programming approach. The proposed HEV Eco-CACC-I system was tested to evaluate its performance for various speed limits, roadway grades, and signal timings. Lastly, the proposed HEV controller was implemented in a microscopic traffic simulation software to test its network-wide performance. The test results from an arterial corridor with three signalized intersections demonstrate that the proposed system can effectively reduce stop-and-go traffic in the vicinity of signalized intersections producing savings of 7.4% in energy consumption, 5.8% in traffic delay and 23% vehicle stops, respectively.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Design Requirements and Software Specification for the Autonomous Energy Management Software System for Small Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems. Many of these buildings are small (<50,000 square feet), underserved, and use rooftop units for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the rooftop units heating and cooling set points, schedules, setbacks, and optimal start can result in 20 to 25 percent reduction in electricity consumption in small commercial buildings. In addition, improving demand flexibility of these buildings will result additional cost savings for the building owner. Therefore, the Department of Energy’s Building Technologies Office approved a project to address the needs for small commercial buildings. The project is led by Pacific Northwest National Laboratory (PNNL) with Intellimation LLC as the cooperative research and development agreement partner. The primary goal of the project is to develop and validate an autonomous energy management software (AEMS) system that will continuously optimize small commercial building operations by minimizing energy consumption and cost, while providing a solution for maximizing decarbonization benefits from electrification of buildings. The work will leverage the vast experience of PNNL research and development staff who have over two decades of experience in developing and successfully transferring software technologies to the private sector. This solution will be jointly developed with Intellimation, a company that plans to use it to scale their building energy efficiency (EE) and grid services offering. Widespread deployment of the AEMS system will improve the EE and demand flexibility of the building commercial building stock. It should also support cities and states in meeting their climate change mitigation goals. This document describes the various EE and grid service features of the AEMS system, infrastructure and data required to implement those features, and how the features should be automated. It also details how the various features will be tested and validated, including field validation. The document also details what flexibility the users have and how they will be able to leverage those capabilities exercise those. The intent is to create an AEMS system that would support scalable deployment, requires minimal configuration, and is easy to maintain over its expected lifespan. The initial alpha release of AEMS system is planned for March 2023, and the beta release is planned for the summer of 2023. The final release is planned for March 2024. Section 2 of the report documents the relevant building types that AEMS is suitable for. Section 3 documents EE features that will be supported. It will also include the data requirements, hardware requirements, implementation details, and how EE features will be tested and validated. Grid service features will be documented in section 4, including data requirements, hardware requirements, implementation details, and how the services will be tested and validated. Planned next steps are described in section 5.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Impact of refrigerant undercharge faults on building indoor conditions and HVAC system operation in residential Buildings: A simulation study

This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. Finally, this study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

Engineering↗
DOE OSTI2019

Life Cycle Analysis of Decentralized Preprocessing Systems for Fast Pyrolysis Biorefineries with Blended Feedstocks in the Southeastern United States

Blending biomass feedstock is an enticing approach to mitigate supply chain risks that are common challenges for large-scale biomass utilization. Understanding the potential environmental benefits of biofuels produced from blended biomass and identifying driving parameters are critical for the supply chain design. Herein, a cradle-to-gate life cycle analysis model for fast pyrolysis biorefineries converting blended feedstocks (pine residues and switchgrass) with traditional centralized and alternative decentralized preprocessing sites, so-called depots, is explained. Different scenarios are developed to investigate the impacts of parameters such as feedstock blending ratios, biorefinery and depot capacities, preprocessing technologies, and allocation methods. The life-cycle energy consumption and global warming potential (GWP) of biofuel production with depots vary between 0.7–1.1 MJ MJ -1 and 43.2–76.6 g CO 2 eq. MJ -1 , respectively. The results are driven by biorefinery processes and depot preprocesses. A decentralized design reduces the energy consumption of the biorefinery but increases the overall life-cycle energy and GWP. Such increases can be significantly mitigated by increasing switchgrass content as the energy consumption at the depot is driven largely by the higher moisture content of pine feedstocks. Allocation methods have a further impact on the results but do not change the major trends and overall conclusions.

09 BIOMASS FUELS↗
DOE OSTI2022

Particle scale impacts on deconstruction energy of pine residues

The goal of this Case Study was to quantify the impacts of variable moisture and ash on hammer mill throughput and energy consumption and on generation of fines that are not able to be fed to conversion, as compared to a status quo Base Case system. Also considered was convertible carbon content (minimum carbon specification) and maximum ash content and the delivered feedstock cost impacts of not being able to feed residue not meeting both specifications to the conversion reactor. Laboratory data on the impacts of input particle size and moisture content on the exit particle size were received from FCIC Subtask 5.2 from their single particle impact population balance modeling study. Additional throughput and energy consumption data were obtained from FCIC Subtask 5.2 for the same grinder with a 6 mm screen in place. These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the ash contents in the separated fines had not been analyzed in the laboratory at the time of the model runs, we chose to assume that the ash distributed proportionally with total mass into the overs and unders in the disk screen following grinding. Key takeaways from this Case Study are that it is significantly more cost effective to hammer mill the residue prior to drying, even though the grinder throughput is lower and energy consumption is higher versus drying first before grinding. An effect of dry grinding versus high moisture grinding is the production of higher amounts of fines during dry grinding, leading to significantly more of the ground feedstock being rejected by conversion for being below a minimum particle size. With wet grinding the system is still able to produce more preprocessed feedstock meeting the minimum particle size specification even though the instantaneous throughput is lower than for the case of grinding dry feedstock. Additionally, even without the higher fines production from dry grinding, the status quo would still be more costly than wet grinding because the material is rejected after the drying energy has already been input for the dry grinding case. Finally, significant reductions in drying energy are obtained by drying after grinding, and those reductions are of far greater magnitude than the grinding energy increase.

09 BIOMASS FUELS↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Cold Climate Air-Source Heat Pump Demonstration and Analysis: Experimental Study on Cold Temperature Performance

Space heating energy is the largest end use for U.S. residential buildings, accounting for nearly 45% of residential building energy consumption nationwide, and approximately 51% of space heating energy is consumed on-site by combusting natural gas (EIA 2020a). In Colorado, approximately 53% of the statewide residential building energy consumption goes to space heating, 76% of which is provided by natural gas (EIA 2020a). Heat pumps are an efficient, electric alternative space heating technology and have been proven viable for decades. However, historically air-source heat pumps (ASHPs) have been limited to temperate climates because of (1) subpar performance at extremely cold outdoor air temperatures, (2) the need for air conditioning in summer months, and (3) the availability of natural gas in colder climates. Relatively recent advances to cold climate ASHP technology, which typically relies on inverter-driven, variable-speed compressors and variable-speed fans, have significantly improved low-temperature heat pump performance, enabling the technology to potentially save energy for many homes in cold climates.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Understanding the Dissolution and Passivation of an Aluminum Electrode during Electrocoagulation of Groundwater Using Neutron and X-ray Reflectometry

An aluminum (Al)-based electrocoagulation (EC) system can effectively remove dissolved silica and hardness in groundwater. The effectiveness of Al-EC in terms of pollutant removal, Faradaic efficiency, and energy consumption depends on the interfacial electrolysis or passivation of the electrode in water. Thus, understanding the electrolysis reaction at the liquid/electrode interface during operation is important for sustainable EC deployment. Here, a continuous flow-through Al-EC system was tested with various groundwater simulants, i.e., chloride (Cl – )-based, sulfate (SO 4 2– )-based, and mixed solutions. High pollutant removal with low energy consumption was observed in Cl – -based groundwater treatment, while low pollutant removal with high energy consumption was observed in SO 4 2– -based groundwater. For example, the required energy per unit mass of Al dosing in SO 4 2– -based groundwater is three times higher than that in Cl – -based groundwater at 10 mA/cm 2 . However, increasing the Cl – concentration significantly reduces this energy demand. In SO 4 2– -based groundwater, the silicate removal efficiency drops from 85.1% to 24.0% compared to that for Cl – -based groundwater, while Mg 2+ and Ca 2+ removal efficiencies decrease to 0.6% from 15.8% and 5.7% from 44.8%, respectively. To better understand this EC performance, we used in situ neutron reflectometry (NR) to examine the interfacial dynamics of Al dissolution and passivation at a 100 nm scale occurring on the surface of the sacrificial Al electrodes during EC. Ex situ X-ray reflectometry (XRR) was also used to support the in situ NR results. Both NR and XRR results revealed that Al dissolution is influenced by the presence of Cl – in the simulants, while a passivating layer forms on the electrode in a SO 4 2– -based solution. In the Cl – -based solution, anodic Al dissolution occurred locally and inhomogeneously across the surface of the Al anode film, resulting in a localized thickness reduction over time. In the SO 4 2– -based solution, no apparent dissolution of the Al anode was identified. Instead, Al underwent oxidation, forming an amorphous Al 2 O 3 surface layer within the Al electrode film that increased in thickness over time. In the mixed solution, both anodic Al dissolution and surface Al 2 O 3 layer formation occurred, indicating that Al dissolution and surface Al 2 O 3 layer formation are attributable to the Cl – and SO 4 2– ions, respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗