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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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At least 145 records · Page 8

High Efficacy Validation of Hydride Mega Tanks at the ARIES Lab (HEVHY METAL)

The High Efficacy Validation of Hydride Mega Tanks at ARIES Lab (HEVHY METAL) project will advance materials-based hydrogen storage technologies by large-scale demonstration and identification of deployment pathways. This includes demonstrating how two metal hydride HY2MEGA subsystems are installed with megawatt-scale green hydrogen infrastructure; validating its performance via rates, capacities, and efficiencies; and investigating supply and demand side techno-economics.

ARIES↗

Enhanced Validation of Advanced Battery Supply Chains (EVALS) Overview

EVALS is a consortium funded by the Vehicle Technologies Office at DOE involving Idaho National Lab, Argonne National Lab, and NREL. The goal of EVALS is to fully develop a suite of tools that support domestic electric vehicle manufacturing through evaluation of domestic primary resources and acceleration of their path to domestic material and battery production. This talk will focus on describing the EVALS project and discussing initial results regarding domestic LiFePO4 precursor sourcing and impacts on the domestic manufacturing supply chain.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Higher Efficiency, Demand Flexible Refrigerator with On-Demand Micro-Vibrational De-icing Technology

Refrigerator technology has advanced significantly over the last couple of decades. Today’s refrigerators use only about 25% of the energy that was required to power models built in 1975. Even as they continually improve efficiency to meet standards, refrigerators have increased in size by almost 20%, added energy-consuming features such as through-the-door ice, and provide more benefits than ever before. However, a few challenges and technology gaps are preventing further improvement of the demand responsiveness and efficiency of the refrigerators. One of the major technology gaps in existing refrigerators is their outdated de-icing process. When the evaporator generates frost, an old-fashioned resistive heating element melts the ice. Most refrigerators have a timed defrost cycle, rather than an active system that could monitor the state of the frost. In these systems, not only is the precious electricity used at its least efficient form of conversion (direct conversion of electricity to heat), but also all the latent heat associated with the ice is wasted during the melting process. On top of that, the refrigerator needs to work harder to pull the temperature down after defrosting, and, last but not least, the food quality is severely impacted by the temperature swings during the defrost cycle. According to a study, the EU alone wastes 89 million tons of food in the supply chain every year. Any temperature swing during defrosting (about 6F according to Emerson for low-temperature cases) can negatively impact the shelf life of meat and other products for multiple days. All these issues can happen during the peak demand time of the electric grid. Unlike the conventional systems, the proposed novel advanced micro-vibrational deicing process uses no heat for defrosting. Instead, it uses the micro vibrations generated by a piezoelectric or vibration-generating module to mechanically break ice from the heat exchanger almost instantaneously. The project titled “Higher Efficiency, Demand Flexible Refrigerator with On-Demand Micro-Vibrational De-icing Technology, performed by Ultrasonic Technology Solutions, LLC (UTS) of Knoxville, TN, in collaboration with Emerson (now Copeland), represents the final phase of a multi-year effort funded under the U.S. Department of Energy’s Building Technologies Office (BTO) BENEFIT FOA 2020. Initiated on October 1, 2021, and completed after a nine-month no-cost extension ending September 30, 2025, this project aimed to develop and validate a novel micro-vibrational mechanical defrosting system, achieving more than 25% improvement in defrosting energy efficiency over conventional baseline defrosting technologies. Over sixteen quarters, the project advanced from fundamental ice-mechanical characterization and prototype development to full-scale system integration and validation. Initial efforts established project management infrastructure and characterized ice adhesion properties, followed by the design and fabrication of early aluminum-based prototypes for resonance frequency testing. Subsequent quarters saw rapid technical progression, including the identification of optimal piezoelectric and motor-based vibration mechanisms, the demonstration of effective de-icing over 6x6-inch aluminum surfaces. The team achieved its Go/No-Go milestone by exceeding the 25% energy-efficiency improvement target—reaching up to 3,340% under optimized conditions—and later confirmed that motor-driven systems offered superior performance and energy efficiency compared to piezoelectric alternatives. Continued refinement led to the development of amplifier systems on printed circuit boards, improved control and instrumentation hardware, and integration into full-scale heat exchanger (HX) prototypes at both UTS and Copeland facilities. Multiple vibration-mounting studies and frost-growth experiments guided mechanical optimization and noise-mitigation strategies, achieving a 17.5 dB reduction in sound pressure level and verifying robust mechanical performance. Advanced analyses, including modal and harmonic simulations, established a quantitative understanding of vibrational behavior and de-icing efficiency across >1000 cm² systems. The final project phase successfully demonstrated scalable integration within reach-in and chest freezer prototypes, confirmed >25% efficiency improvements in large-area systems, and completed a comprehensive business model and scale-up strategy identifying electric defrost systems as the primary beachhead market. The culmination of this DOE-supported effort establishes micro-vibrational defrosting as a viable, high-efficiency, low-noise, and demand-flexible de-icing technology, paving the way for commercial deployment and broader application in next-generation refrigeration systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Model Validation and Uncertainty Quantification on the KRUSTY Microreactor Design Using GRIFFIN Neutron Transport Code [Poster]

Argonne National Laboratory (ANL) and INL have developed a GRIFFIN steady state neutronics model for the multiphysics simulations of the Kilopower Reactor Using Sterling TechnologY (KRUSTY) microreactor in the Multiphysics Object Oriented Simulation Environment (MOOSE). The reliability of such deterministic neutronics models can be validated by comparing with computations from Monte Carlo codes (e.g. MCNP, SERPENT, OpenMC, Shift, etc). Furthermore, potential modeling/design improvements can be identified by incorporating uncertainty quantification (UQ), which can be performed by MOOSE’s Stochastic Tools Module (STM). KRUSTY is a prototype for a 5-kW thermal nuclear-powered space reactor. Its primary components consist of nuclear fuel, heat pipes, a control rod, a reflector, and the shielding. The fuel consists of 3 stacked U-7.65Mo cylinders with a hole in the center for the control rod. 8 liquid sodium heat pipes transfer fission energy from the solid fuel block to the Sterling power conversion system where the energy is extracted, and the cooled sodium flows back to the core via capillary action . The movable Boron Carbide control rod regulates the neutron population during startup or when a reactor temperature boost is needed . The beryllium oxide reflector is in 3 places in the reactor; it surrounds the core axially, it lies beneath the core on a platen, and it is present in the shim. The axial and lower reflectors rest on an adjustable stainless-steel platen that moves upward to cover the fuel and help the reactor reach criticality. Lastly, radial stainless steel surrounds the core offering protection from radiation exposure .

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Review of High‐Speed Digital Image Correlation: Advancements and Good Practices

This paper reviews the current state of the art in high‐speed (HS) and ultrahigh‐speed (UHS) digital image correlation (DIC) techniques, emphasizing their critical role in experimental research across various scientific domains. HS and UHS DIC have evolved significantly, driven by advancements in camera systems, image processing algorithms and experimental methodologies. These developments have opened new avenues for capturing and analysing dynamic events with unprecedented temporal and spatial detail, but not without introducing challenges such as optical distortions, motion blur and lighting issues that can affect measurement quality. This review advocates for standardized reporting practices in HS/UHS DIC methodologies to improve reproducibility and reliability across studies, drawing on guidelines from the International Digital Image Correlation Society (iDICs). Through a comprehensive analysis of over 150 articles, this review identifies key advancements in imaging technology and their application in six research domains: material characterization, test development, fracture mechanics, model validation, ballistic and explosive phenomena assessment and measurement uncertainties. Distinctions between two‐dimensional (2D) and stereo‐DIC applications are explored, offering insights into their practical implementation and trade‐offs. Good practices for HS/UHS DIC applications are proposed along with suggestions for future directions for this evolving field, highlighting the indispensable role of technological innovation in expanding the capabilities of optical metrology.

experimental mechanics↗

The ePIC dual-radiator RICH detector

The dual radiator Ring Imaging Cherenkov (dRICH) detector is required to provide continuous hadron identification from ≈3 GeV/c up to ≈50 GeV/c, and to supplement electron and positron identification from a few hundred MeV/c up to about 15 GeV/c, in the forward (ion-side) end-cap of the ePIC experiment. Such an extended momentum range imposes the use of two radiators, gas and aerogel. The common imaging system, that ensures compactness and cost-effectiveness, is based on SiPM sensors to work in a high non-uniform magnetic field. During the R&D phase, the dual radiator principle and the single component performance have been validated. A status overview of the project is presented. The design and technological choices are discussed together with the results obtained from laboratory characterization of the component demonstrators and beam tests of the evolving prototypes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

TEAMER Technical Support of Ramboll's for Numerical Modeling of WECs to Support OES Task 10: Cooperative Research and Development (Final Report)

National Technology & Engineering Solutions of Sandia, LLC (NTESS) in collaboration with the National Renewable Energy Laboratory (NREL), and with guidance from Ramboll, will perform fluid dynamics simulations to support of The Ocean Energy Systems (OES) Energy Technology Collaboration Program Task 10 Wave Energy Converters (WEC) Modelling Verification and Validation effort. Specific numerical simulations include the fixed device wave impingement studies to benchmark the performance of simulation techniques against physical testing results.

16 TIDAL AND WAVE POWER↗

Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters (CRADA 625) (Final Report)

This report is a final deliverable for project 81966: Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters (CRADA 625) Commercial heat pump water heater (CHPWH) systems can significantly improve the energy efficiency of service water heating (SWH), aka domestic hot water (DHW), in commercial and multifamily buildings but have not yet achieved widespread adoption due to pricing, complexity, and perceived risk factors. The goal of this project – Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial HPWHs (Scaling Up) – was to demonstrate and validate a holistic systems approach for CHPWHs that de-risks the technology, creating a reliable, cost-effective, and repeatable approach to CHPWH deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Prime Time for Model-Predictive Control? Assessing the Technical and Market Readiness of Advanced Controls in Buildings

Despite three decades of extensive research and field testing that have consistently validated the benefits of Model Predictive Control (MPC) in building applications, the technology has seen limited market adoption. This paper evaluates the readiness of MPC for widespread deployment, showcases recent demonstrations and field tests across diverse building types, including residential, small commercial, large commercial, and campus settings. Our results demonstrate that MPC can optimize system operations to achieve load shifting, minimize curtailment of on-site generation, and reduce energy costs by up to 80 %, while maintaining or improving occupant comfort. We also show that MPC can effectively control large assets, such as MW-sized thermal storage systems, and respond to dynamic pricing signals. However, achieving scale remains difficult due to labor-intensive workflows, reliance on a “PhD-in-the-loop” for MPC design and maintenance, susceptibility to fragile data infrastructure, and persistent workforce education and acceptance barriers. To bridge this gap, we outline a transition from bespoke, labor intensive prototypes toward streamlined, segment-targeted deployment strategies that leverage model templates, semantic tools, and generative AI. By automating control configuration and reducing engineering effort, these recommendations provide a pathway for transforming successful research demonstrations into scalable, market ready solutions for MPC-based controls.

Pritoni, Marco↗

Employing MACS/ViBRANT as a Surrogate MARVEL Reactor for Startup Reactivity Tuning and Supervisory Control Processes

Advanced nuclear reactors are a key part of the future of nuclear energy both in the United States and globally. They offer unique benefits for various energy-demanding applications, including use in remote locations, compact size, modular manufacturing, remote monitoring, low and/or variable power rating operation, and reliance on novel technologies to enhance operational safety. To achieve economic feasibility, advanced reactors must significantly reduce their workforces in comparison with the current fleet. Achieving this reduction will occur through reducing staff workloads using technology to achieve autonomous or semi-autonomous operations, demonstrated by comprehensive testing and validation activities. These operations will require both software and hardware platforms during the design and testing phases. While simulations are useful during the design phase, their performance can significantly deviate during actual deployment on hardware. This report presents the outcomes of a collaborative technical initiative between the U.S. Department of Energy (DOE) Microreactor Program (MRP) and Advanced Sensors and Instrumentation (ASI) Program. The collaboration utilized the Microreactor Automated Control System (MACS) hardware platform to bridge the gap between theoretical reactor design and actual startup and control operations. Two key use cases were investigated: facilitating the startup testing period and demonstrating supervisory control. The first use case details the key Microreactor Applications Research Validation and Evaluation (MARVEL) reactor startup physics testing activities conducted using the MACS platform. These activities included drum worth measurements, shutdown margin assessment, temperature feedback analysis, and scram time evaluation, as well as unique testing that would apply to the MARVEL reactor to demonstrate the testing methodologies in a low-risk environment. The MACS platform, serving as a surrogate representation of the MARVEL reactor, proved instrumental in performing these tests. The exercise revealed aspects that led to optimized processes, refined hardware design, and enhanced base software capabilities. By maturing methods and technologies in this manner, the initiative promises to reduce wasted time in the actual on-site reactor deployment effort, thereby saving significant time and resources. The second use case focuses on the development and implementation of supervisory control methods aimed at managing core tilt, which can result from asymmetrical operations or manufacturing imperfections in fuel rods or reactivity control devices. A key objective was to assess and compare the use of artificial intelligence (AI) for supervisory control. The effort aimed to define the role of supervisory control to enhance performance without risking control instability. This effort explored three distinct approaches: rules-based (RB) methods, optimization techniques, and reinforcement learning (RL) algorithms. Each approach was evaluated for its ease of implementation, its usability, and its effectiveness in responding to asymmetries in neutron flux. Comparative analysis of these approaches provided valuable insights into their applicability and effectiveness, offering a robust framework for advanced reactor operations. Together, these two use cases highlight the potential of hardware test beds to help streamline the design, operation, and control of advanced nuclear reactors. This collaborative effort underscores the importance of continued innovation and experimentation in achieving the next generation of safe, reliable, and economically viable nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles

Off-road vehicles, such as wheel loaders, excavators, and harvesters, are extensively utilized across a wide range of industries, including construction, agriculture, and mining. These machines have become indispensable in supporting the day-to-day operational needs of a nation, playing a critical role in various sectors' infrastructure and productivity. However, despite their utility, off-road vehicles are significant consumers of fossil fuels, resulting in substantial emissions that contribute to environmental degradation. This highlights the pressing need for research and technological advancements aimed at improving their energy efficiency and reducing their carbon footprint. There are, however, two primary challenges that must be addressed to achieve these goals. First, off-road vehicles typically perform both driving and working tasks simultaneously, which introduces a high level of complexity into their overall dynamic systems. Analysis the interactions between these functions is challenging. Second, research into off-road vehicles is inherently interdisciplinary, demanding expertise across several domains such as fluid power systems, vehicle dynamics, control theory, optimization techniques, and real-world implementation. Recognizing these challenges, we proposed the project titled "Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles" as a comprehensive solution to enhance fuel efficiency while simultaneously improving productivity. This project specifically focuses on autonomous off-road vehicles, with particular attention to wheel loaders, and seeks to develop novel methods to optimize energy consumption without sacrificing operational performance. The project integrates real-time control algorithms, vehicle dynamics modeling, and co-optimization of powertrain system and vehicle system to achieve these goals. Our optimization strategy dynamically co-optimizes critical parameters at both the powertrain and vehicle levels, including vehicle speed, working tool movements, powertrain dynamics, and engine operations in real-time. To streamline this optimization process, we developed a vehicle model that captures the key dynamics while significantly enhancing computational efficiency. This allows the system to intelligently minimize fuel consumption, all while maintaining or even improving productivity through real-time calculations during various off-road operations. To validate the effectiveness of this energy optimization method, we introduced a state-of-the-art Hardware-in-the-Loop (HIL) testbed. This reconfigurable testbed seamlessly integrates the actual engine with virtual models of the wheel loader's subsystems, allowing for accurate emulation of real-world operational loads and environments. By simulating these conditions, the HIL testbed enables us to evaluate the wheel loader’s performance under diverse working scenarios, ensuring the developed solution is applicable in real-world operations. This testbed proved to be instrumental in validating the optimization algorithms and demonstrating the system's practical effectiveness. During the evaluation and testing phase, we employed the HIL testbed to rigorously assess the energy savings and productivity improvements generated by the optimized system. The results were highly encouraging, revealing that the automated wheel loader achieved over 30% fuel savings compared to traditional, human-operated cycles, with comparable or even enhanced levels of productivity. The insights gained from this HIL-based testing provided critical validation of our approach and highlighted the potential for deploying these optimized autonomous technologies in real-world off-road vehicles.

33 ADVANCED PROPULSION SYSTEMS↗

NEXT Generation Energy Technologies for Connected and Automated On-Road Vehicles (NEXTCAR Phase I & II)

The Ohio State University’s ARPA-E NEXTCAR project was a multi-phase, multi-year research, development, and demonstration program focused on improving the energy efficiency of connected and automated vehicles (CAVs). The team developed and validated advanced vehicle motion and powertrain control algorithms that coordinate propulsion and automation systems to optimize energy use. Key technologies included Dynamic Skip Fire engine control, predictive eco-driving functions such as Eco-Approach and Departure (Eco-AND) and Eco-Adaptive Cruise Control (Eco-ACC), and powertrain-agnostic optimization frameworks for hybrid, plug-in hybrid, and battery electric vehicles. The project successfully demonstrated up to 30% energy-efficiency improvement during real-world testing at the Transportation Research Center and the American Center for Mobility. The outcomes provide a foundation for scalable, cost-effective deployment of energy-optimized CAV technologies across the automotive industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Real-Twin

Real-Twin is a unified, model-agnostic scenario generation tool designed to streamline and standardize the evaluation of emerging mobility technologies. It provides an end-to-end framework that includes robust workflows, integrated tools, and comprehensive metrics to generate, calibrate, and benchmark microscopic traffic simulation scenarios across multiple platforms. Key Features of Real-Twin include: - Unified Scenario Generation: generate transferable, simulation-ready scenarios from heterogeneous data sources using a consistent workflow. - Automated Calibration Workflow: bridges simulation and real-world data, minimizing manual effort and making traffic simulation more accessible to researchers and engineers. - Model-Agnostic Compatibility: supports SUMO, VISSIM, and AIMSUN for cross-platform scenario generation and benchmarking. Enables reliable comparisons and reproducibility across different simulation tools. - Consistent Scenarios across Different Simulators: generate comparable simulation scenarios across different microscopic traffic simulators, providing users the ability to conduct benchmarking and cross-validation that are crucial for ensuring the reliability and reproducibility of simulation results. - Emerging Technology Support: includes a scenario database and pipeline for studying autonomous vehicles (AVs), with planned extensions to CAVs, EVs, and other advanced technologies.

Wang, Chieh (Ross) [Oak Ridge National Laboratory ↗

OC6 Phase IV: Validation of CFD Models for Stiesdal TetraSpar Floating Offshore Wind Platform

ABSTRACT With only a few floating offshore wind turbine (FOWT) farms deployed anywhere in the world, FOWT technology is still in its infancy, building on a modicum of real‐world experience to advance the nascent industry. To support further development, engineers rely heavily on modeling tools to accurately portray the behavior of these complex systems under realistic environmental conditions. This reliance creates a need for verification and validation of such tools to improve reliability of load and dynamic response prediction and analysis capabilities of FOWT systems. The Offshore Code Comparison Collaboration, Continued with Correlation and unCertainty (OC6) project was created under the framework of the International Energy Agency to address this need and considers a three‐sided verification and validation between engineering level models, computational fluid dynamics (CFD), and experimental results. In this paper, a novel floating offshore wind platform, the Stiesdal TetraSpar, is simulated using CFD under the load conditions defined by Phase IV of the OC6 project. The comparison of these CFD results against the experimental results demonstrated the ability to predict the platform response to waves when imposing the measured wave signals as input. Although validation versus experiment was largely successful, the damping behavior was impacted by uncertainties likely originating from the mooring system and sensor umbilical cable. This extensive comparison effort with multiple CFD practitioners offers insight into best practices to achieve reliable results.

17 WIND ENERGY↗

Development and Validation of a Process Model and Open-Source Process Simulator for Microalgae-Based Tertiary Phosphorus Recovery

Microalgae-based tertiary wastewater treatment has the potential to meet stringent effluent phosphorus limits, with the added benefit of producing a marketable feedstock. However, the lack of validated mechanistic models and their implementation in process simulators have limited the adoption of this technology. In this study, an updated lumped pathway metabolic model (Phototrophic-Mixotrophic Process Model, PM 2 ), including both photoautotrophic and heterotrophic metabolisms of microalgae, was developed to predict effluent phosphorus concentration and biomass yield in response to dynamic influent and varying environmental conditions. The model was implemented in QSDsan – an open-source, Python-based design and simulation platform – for robust simulation under uncertainty. A global sensitivity analysis was performed to prioritize model parameters for calibration. The model was then calibrated and validated using batch experimental data and 45 days of continuous online monitoring data from a full-scale (568 m 3 ·d -1 ) microalgae-based tertiary wastewater treatment plant (EcoRecover process). In particular, along with dynamic influent composition, temperature and light intensity data with diel variation were provided as model inputs to reflect the microalgal behavior under day-night cycling. Overall, the QSDsan-based microalgae process simulator was able to predict effluent phosphorus within 0.02–0.04 mg-P·L -1 , while also capturing the general trends of state variables according to nutrient availability.

Lumped pathway metabolic model↗

Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components

This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Development and Characterization of Densified Biomass-plastic Blend for Entrained Flow Gasification (Final Technical Report)

Supported by the U.S. DOE NETL Award DE-FE0032043, this project was a collaborative effort. Project participants included the University of Kentucky Institute for Decarbonization and Energy Advancement (UK IDEA), Biosystems and Agricultural Engineering department (UK BAE), and Wabash Valley Resources, LLC. The goal of this final technical project report is to comprehensively summarize the work conducted on project DE-FE0032043. In accordance with the Statement of Project Objectives (SOPO), the University of Kentucky (UK) (Project Prime Recipient) has developed and studied a biomass/plastic fuel with a hydrophobic surface area less than 10 m 2 /m 3 that is suitable for oxygen-blown entrained flow gasification with slurry feed. The project involved the utilization of an existing thermogravimetric analysis (TGA)-mass spectrometer (MS), 1.5” drop tube furnace, 1 ton per day (TPD) coal gasifier, and high-pressure extruder operated at UK. The pilot-scale production of blended material was done at the Polymers Technology Center in Charlotte, North Carolina. Parametric testing and solid fuel blend slurry performance validation was completed using the UK entrained flow gasifier with multiple opposed burners to narrow the major near-term technical gaps that impede gasification of biomass and carbonaceous mixed wastes such as plastics in order to achieve net-negative CO 2 emissions. Project results validated the UK approach to address the major technical challenges on the biomass/plastic pretreatment and gasification. Previously, this has been limited in application to fluidized-type or moving bed-type gasifiers due to the high-water uptake of porous biomass containing hydroxyl groups during the conventional slurry preparation, resulting in a highly viscous, un-pumpable slurry. The biomass pretreatment with plastic developed for this project demonstrates advantages in cost and flexibility, which include: 1) the development of a blended solid fuel slurry with 55-60 wt% solids and comparable heating value to 100% coal-based water slurry; 2) the collection of gasification kinetic data and identification of preliminary operating conditions by performing thermogravimetric analysis, gasification experiments by using a 1.5” drop tube furnace; and finally 3) the demonstrated gasification of the blended solid fuel in the UK entrained flow gasifier with a long-lasting stable solid fuel blend slurry, dataset detailing operating conditions, and characterization of slag phase formation and solidification. The lab-scale data and experience obtained during this project encourages the development of technologies and commercial approaches to enable a hydrogen-based energy economy while achieving net-negative CO 2 emissions through gasification of coal, biomass, and carbonaceous mixed wastes such as plastics.

01 COAL, LIGNITE, AND PEAT↗