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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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The initial design of a solar domestic hot water system is considered. The system performance specification and detailed design drawings are included. The hot water systems consist of the following subsystems: collector, storage, control, transport, auxiliary energy, and government-furnished site data acquisition. The two systems are installed at Tempe, Arizona, and San Diego, California.
Phoenix Refrigeration Systems, Inc.'s heat pipe addition to the Phoenix 2000, a supermarket rooftop refrigeration/air conditioning system, resulted from the company's participation in a field test of heat pipes. Originally developed by NASA to control temperatures in space electronic systems, the heat pipe is a simple, effective, heat transfer system. It has been used successfully in candy storage facilities where it has provided significant energy savings. Additional data is expected to fully quantify the impact of the heat pipes on supermarket air conditioning systems.
This data was acquired as part of the Subsurface Science, Technology and Engineering Research, and Development (SubTER) Crosscut which is a collaboration across the Department of Energy offices involved in research activities in energy production/extraction, subsurface storage, and environmental remediation. The contents of this zip contains the ZTEM survey measured and processed results for the Mineral Mountains area, Utah. The data were acquired through subcontract to Geotech Inc of Aurora, ON, Canada and their narrative report is included. There are also two readme.txt file included that further describe the data.
Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. Furthermore, these outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO₂ emissions amounting to 7.9142×10 3 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.
Hydropower and pumped-storage hydropower (PSH) have played a key role in providing flexible, low-carbon electricity to the U.S. electricity system for over a century. As variable generation (VG) deployment increases the demand for flexible, dispatchable generation, it is important to use all available methods for understanding how hydropower and PSH interfaces with VG in a future low-carbon grid. Capacity expansion models (CEMs) of electricity systems are often used to study future electricity scenarios, but these tools often have difficulty representing site- and technology-details of hydropower and PSH due to limited spatial, temporal, or process resolution. This report demonstrates a set of model advancements to improve hydropower and PSH representations in CEMs and other models that consider hydropower and PSH's role in electricity systems. Model advancements include new data integration to define closed-loop PSH resource availability and cost, along with site-level parameterizations of existing PSH capacity and energy storage specifications. Shifting energy across seasons for both hydropower and PSH is explored to demonstrate the potential value of long-duration storage. New representations of hydropower upgrades offer opportunities to increase dispatchability, add pumps, or independently add capacity or energy depending on what is most valuable. New model structures and data are demonstrated individually and in combination to observe their impact on model results and provide initial insights into what is most important for analysts, electricity system planners, and hydropower decision-makers to consider when assessing future roles of hydropower. Improving flexibility of existing hydropower assets has the potential to reduce CO 2 emissions through complementing VG technologies and/or improve electricity system economics by reducing the need to deploy higher-cost systems. Large-scale energy storage is also shown to provide opportunities for balancing seasonal differences in VG, particularly solar photovoltaics (PV). Initial analysis with a new closed-loop PSH resource and cost dataset also demonstrate the potential for new PSH deployment to offer new grid flexibility opportunities. Future analysis and modeling of hydropower's role in the U.S. and other electricity systems could include methodological improvements based on those discussed here along with sensitivity analysis to better represent current and future potential hydropower and PSH flexibility. Improvements to geospatial and site-level data can further improve the understanding of how hydropower and PSH can be upgraded and operated in response to future electricity system needs. This work lays the groundwork to use advanced planning models to explore the range of roles hydropower and PSH can play in the grid of the future.
Climate change impacts on watersheds can potentially exacerbate water scarcity issues where water serves multiple purposes including hydropower. The long-term management of water and energy resources is still mostly approached in a siloed manner at different basins or watersheds, failing to consider the potential impacts that may concurrently affect many regions at once. There is a need for a large-scale hydropower modeling framework that can examine climate impacts across adjoining river basins and balancing authorities (BAs) and provide a periodic assessment at regional to national scales. Expanding from our prior assessment only for the United States (US) federal hydropower plants, we enhance and extend two regional hydropower models to cover over 85% of the total hydropower nameplate capacity and present the first contiguous US-wide assessment of future hydropower production under Coupled Model Intercomparison Project phase 6’s high-end Shared Socioeconomic Pathway 5-8.5 emission scenario using an uncertainty-aware multi-model ensemble approach. We present regional hydropower projections, using both BA regions and US Hydrologic Subregions (HUC4s), to consistently inform the energy and water communities for two future periods—the near-term (2020–2039) and the mid-term (2040–2059) relative to a historical baseline period (1980–2019). We find that the median projected changes in annual hydropower generation are typically positive—approximately 5% in the near-term, and 10% in the mid-term. However, since the risk of regional droughts is also projected to increase, future planning cannot overly rely on the ensemble median, as the potential of severe hydropower reductions could be overlooked. The assessment offers an ensemble of future hydropower generation projections, providing regional utilities and power system operators with consistent data to develop drought scenarios, design long duration storage and evaluate energy infrastructure reliability under intensified inter-annual and seasonal variability.
Using data samples collected with the BESIII detector operating at the BEPCII storage ring, the cross section of the inclusive process e + e − → η + X , normalized by the total cross section of e + e − → hadrons , is measured at eight center-of-mass energy points from 2.0000 to 3.6710 GeV. These are the first measurements with momentum dependence in this energy region. Our measurement shows a significant discrepancy compared to the existing fragmentation functions. To address this discrepancy, a new QCD analysis is performed at the next-to-next-to-leading order with hadron mass corrections and higher twist effects, which can explain both the established high-energy data and our measurements reasonably well. Published by the American Physical Society 2024
As chemistry expands to more complex and interdisciplinary areas, a new generation of diverse researchers must engage with science and learn effective cross-disciplinary collaboration and communication. To these ends, we designed and implemented In the Mix, a graduate student-led, two-day workshop for undergraduate students promoting collaborative science in the context of energy storage innovations. Here, the interactive workshop was designed for future and emerging researchers to gain hands-on experience with data science, computational chemistry, and electrochemistry techniques that are critical for developing materials for battery technologies. Participants also visited commercial renewable energy facilities to help them connect discovery-based research with industry and broader societal considerations. The workshop content and structure ensured that participants experienced the interrelatedness of the fields and understood the importance of collaborative research to yield scientific advances with real-world applications. An external team evaluated the workshop and participants’ perceptions of their experiences. While our research context was energy storage, the workshop goals and outcomes are applicable to other contexts. Interdisciplinary, experiential workshops are a key avenue to broadening participation in science and research, and the ideas presented here can be readily modified for other scientific contexts and/or incorporated as broader impact activities.
High-temperature reservoir thermal energy storage (HT-RTES) has the potential to become an indispensable component in achieving the goal of the net-zero carbon economy, given its capability to balance the intermittent nature of renewable energy generation. In this study, a machine-learning-assisted computational framework is presented to co-optimize the performance metrics of HT-RTES by combining physics-based simulation with stochastic hydrogeologic formation and thermal energy storage operation parameters, artificial neural network regression of the simulation data, and genetic algorithm-enabled multi-objective optimization. A doublet well configuration with a layered (aquitard-aquifer-aquitard) generic reservoir is simulated for cases of continuous operation and seasonal-cycle operation scenarios. Further, neural network-based surrogate models are developed for the two scenarios and applied to generate the Pareto fronts of the HT-RTES performance for four potential HT-RTES sites. The developed Pareto optimal solutions indicate the performance of HT-RTES is operation-scenario (i.e., fluid cycle) and reservoir-site dependent, and the performance metrics have competing effects for a given site and a given fluid cycle. The developed neural network models can be applied to identify suitable sites for HT-RTES, and the proposed framework sheds light on the design of resilient HT-RTES systems.
Lithium-ion batteries are widely used in applications from consumer electronic devices to stationary energy storage. Appropriate management of batteries is challenging due to limited data on their performance and materials degradation. Previous studies have focused on characterization of single cells under specific operating conditions. In the present work, commercial 18650 lithium-ion cells with LiNi x Mn y Co 1-x-y O 2 (NMC) and LiNi x Co y Al 1-x-y O 2 (NCA) positive electrodes were characterized by a wide range of electrochemical and materials techniques after cycling at 15, 25, or 35 °C to ∼80% capacity. The NCA cells exhibit weak temperature dependence in their cycle aging and materials degradation. The NMC cells exhibited increased capacity fade and materials degradation as ambient temperature decreased. All cells exhibited loss of lithium inventory as their primary degradation mode. However, the NCA cells only showed evidence of solid electrolyte interphase (SEI) growth whereas the NMC cells showed signs of Li plating at 15 °C, transitioning to SEI growth at 35 °C. The NMC cells displayed signs of loss of active material at the positive electrode at lower temperatures, suggesting that Li plating is correlated to additional processes that increase the rate of degradation. These results highlight the importance of avoiding broad generalizations about Li-ion battery temperature dependence.
PyBLM is a battery life model to predict the capacity fade of two home battery energy storage systems, manufactured by LG and Tesla. The battery life model is built from experimental test data, and accounts for calendar and cycling aging mechanisms as functions of cell environmental conditions and use. PyBLM is developed in Python and formatted to work in conjunction with another NREL software named TEMPEST (Thermo-Electric Model for Powering Energy Storage Technologies).
The needs and requirements for an electrochemical energy storage for deep space exploration is well explored. It is often understood that different mission sites and environmental conditions require different battery chemistries or technologies. Additionally, various engineering solutions are deployed to overcome specific chemical challenges. One often overlooked need is the “health” monitoring of an electrochemical storage system. The term generalized health monitoring, as envisioned in this work, refers to the monitoring of various aspects such as electrode health, electrolyte health, reaction pathway health, cooling system health, sensor health, and BMS health [1]. Generalized health monitoring allows mission leads, engineers, and scientists to incorporate flexibility in mission designs, make on-the-fly mission changes, and extend the duration of science missions. Moreover, it enables automation and data-driven decision-making without compromising safety and performance. Recently, our group developed a hierarchy of thermal reduced-order models (TROM) by combining a physics-based modeling approach and data-driven model reduction techniques applied to flight data [2]. The resulting TROMs were found to be not only accurate but also identifiable from the flight data. Consequently, the coefficient of variance of the model parameters is small over the course of hundreds of flights, allowing for monitoring the parameter evolution trajectories as the battery ages and degrades. These parameters constitute the metrics of the generalized health of a battery. Monitoring their evolution allows such models to be used for anomaly detection and prognostics, improving early detection of abnormal behavior and thus enabling timely maintenance, longer battery life, and enhanced battery safety. For this presentation, the practicality of the thermal model will be validated on a pack of 14cells under various topology configurations such as 1S14P, 2P7S, 7S2P, and 1P14S. It is well known that manufacturing and non-uniform aging lead to variability in the performance of a cell, which is exacerbated by cell balancing during active load. Additionally, in extreme scenarios, the paramount objective is to complete the mission, regardless of the stresses on the battery. Topology-induced balancing issues further stress the battery. The goal of this study is to determine if the noise (identifiability) in the reduced-order thermal model parameters is sensitive to topology, cell spacing, cooling strategy, and manufacturing or age variability. The variability in cells is considered by assuming a multimodal distribution for microscopic parameters of a cell (such as porosity, tortuosity, reaction kinetics, volumetric thermal conductivity, and volumetric heat capacity). The compounded effect of manufacturing variability, topological selection, cooling strategies, and cell balancing ages each cell in a battery differently. The study aims to clarify whether the challenge in extracting maximum information depends on the minimum number of sensors or models used for data extraction.
Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.
A survey of U.S. and international paper and pulp mills using thermal energy storage (TES) systems as a part of their production processes was conducted to obtain sufficient operating data to conduct a benefits analysis encompassing: (1) an energy conservation assessment, (2) an economic benefits analysis, and (3) an environmental impact assessment. An information dissemination plan was then proposed to effectively present the benefits of TES to the U.S. paper and pulp industry.
Abstract Protonic ceramic electrochemical cells (PCECs) offer promising paths for energy storage and conversion. Despite considerable achievements made, PCECs still face challenges such as physiochemical compatibility between componenets and suboptimal solid–solid contact at the interfaces between the electrolytes and electrodes. In this study, a novel approach is proposed that combines in situ electrochemical characterization of interfacial electrical sensor embedded PCECs and machine learning to quantify the contributions of different cell components to total degradation, as well as to predict the remaining useful life. The experimental results suggest that the overpotential induced by the oxygen electrode is 48% less than that of oxygen electrode/electrolyte interfacial contact for up to 1171 h. The data‐driven machine learning simulation predicts the RUL of up to 2132 h. The root cause of degradation is overpotential increase induced by oxygen electrode, which accounts for 82.9% of total cell degradation. The success of the failure diagnostic model is demonstrated by its consistency with degradation modes that do not manifest in electrolysis fade during early real operations. This synergistic approach provides valuable insights into practical failure diagnosis of PCECs and has the potential to revolutionize their development by enabling improved performance prediction and material selection for enhanced durability and efficiency.
Topics include: Bioreactors Drive Advances in Tissue Engineering; Tooling Techniques Enhance Medical Imaging; Ventilator Technologies Sustain Critically Injured Patients; Protein Innovations Advance Drug Treatments, Skin Care; Mass Analyzers Facilitate Research on Addiction; Frameworks Coordinate Scientific Data Management; Cameras Improve Navigation for Pilots, Drivers; Integrated Design Tools Reduce Risk, Cost; Advisory Systems Save Time, Fuel for Airlines; Modeling Programs Increase Aircraft Design Safety; Fly-by-Wire Systems Enable Safer, More Efficient Flight; Modified Fittings Enhance Industrial Safety; Simulation Tools Model Icing for Aircraft Design; Information Systems Coordinate Emergency Management; Imaging Systems Provide Maps for U.S. Soldiers; High-Pressure Systems Suppress Fires in Seconds; Alloy-Enhanced Fans Maintain Fresh Air in Tunnels; Control Algorithms Charge Batteries Faster; Software Programs Derive Measurements from Photographs; Retrofits Convert Gas Vehicles into Hybrids; NASA Missions Inspire Online Video Games; Monitors Track Vital Signs for Fitness and Safety; Thermal Components Boost Performance of HVAC Systems; World Wind Tools Reveal Environmental Change; Analyzers Measure Greenhouse Gasses, Airborne Pollutants; Remediation Technologies Eliminate Contaminants; Receivers Gather Data for Climate, Weather Prediction; Coating Processes Boost Performance of Solar Cells; Analyzers Provide Water Security in Space and on Earth; Catalyst Substrates Remove Contaminants, Produce Fuel; Rocket Engine Innovations Advance Clean Energy; Technologies Render Views of Earth for Virtual Navigation; Content Platforms Meet Data Storage, Retrieval Needs; Tools Ensure Reliability of Critical Software; Electronic Handbooks Simplify Process Management; Software Innovations Speed Scientific Computing; Controller Chips Preserve Microprocessor Function; Nanotube Production Devices Expand Research Capabilities; Custom Machines Advance Composite Manufacturing; Polyimide Foams Offer Superior Insulation; Beam Steering Devices Reduce Payload Weight; Models Support Energy-Saving Microwave Technologies; Materials Advance Chemical Propulsion Technology; and High-Temperature Coatings Offer Energy Savings.
For nuclear power to be flexible in a functioning Integrated Energy System (IES), excess produced heat must be stored or utilized during times of low power demand to ensure a load factor of 1 while load balancing. The Dynamic Energy Transport and Integration Laboratory (DETAIL) is one facility that is under development to emulate IES conditions on the engineering-scale, planned to conduct virtual real time operations with industry-scale facilities, and is currently testing thermal storage and high temperature electrolysis. As part of the study to develop a method to preprocess input signals or postprocess output signals between systems of different scales via Dynamical System Scaling (DSS), the current research is one of the continued efforts branching from the data projection activity conducted for the Thermal Energy Distribution System and currently engages the High Temperature Electrolysis (HTE) System in DETAIL. The HTE SOEC electrical, fluid, and thermal dynamics Figure of Merits (FOM) were identified, governing equations and closure relations were successfully scaled, and relations between FOM scaling ratios were determined. Setting the scaling objectives to reform existing data to project a data set that doubly accelerated the electrolysis process while preserving the produced amount of hydrogen was generated for the full transient. The calculated boundary conditions were inlet temperature, stack current, and inlet steam mass flow rate at 1470 K, 121.1 A, and 1.886 g/s, respectively. The research outcomes demonstrated an output signal postprocessing case accelerating the hydrogen production without changing geometry, number of cells, and partial pressures.
The report provides research summaries of two developed theories: data projection to enable multi-scale facility digital interactions and introducing the Evaluation Model Development and Assessment Process (EMDAP) approach to Integrated Energy Systems (IES) design. For the data projection, a two-system scaling case including interactions between thermal storage and hydrogen production is provided. The corresponding facilities are the thermal energy distribution system (TEDS) facility and high temperature steam electrolysis (HTSE) facility at the Dynamic Energy Transport and Integration Laboratory (DETAIL) in Idaho National Laboratory (INL). For the EMDAP adaption to system design, a modified process of the EMDAP was developed and applied to indirect two-tank heat storage system design. System phenomena were identified, ranked, and scaled to discover helpful key relations that would determine the ideal component geometry.