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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Near isothermal compressed air energy storage system in residential and commercial buildings: Techno-economic analysis

Electrical energy storage systems offer a wide range of options to efficiently manage our power supply infrastructure. Energy and environmental security via renewable resources and energy storage devices will also play a critical role in addressing the supply-demand challenges. A novel energy efficient storage system based on near isothermal compressed air energy storage concept, named as Ground-Level Integrated Diverse Energy Storage (GLIDES) is analyzed for integration with residential and commercial buildings. Additionally, the influence of different configurational aspects on key performance and cost attributes is presented in this study. GLIDES modules in the range of 0.5 kWh–15 kWh configurations were considered for residential buildings whereas 25 kW h–500 kW h modules were considered for commercial buildings. Detailed cost and performance analysis of the GLIDES module proved them to be an effective resource in lowering the peak energy demand and corresponding utility bills.

25 ENERGY STORAGE↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2020

Air compressor load forecasting using artificial neural network

Air compressor systems are responsible for approximately 10% of the electricity consumed in United States and European Union industry. As many researches have proven the effectiveness of using Artificial Neural Network inair compressor performance prediction, there is still a need to forecast the air compressor electrical load profile. The objective of this study is to predict compressed air systems’ electrical load profile, which is valuable to industry practitioners as well as software providers in developing better practice and tools for load managementand look-ahead scheduling programs. Two artificial neural networks, Two-Layer Feed-Forward Neural Networkand Long Short-Term Memory were used to predict an air compressors electrical load. Compressors with three different control mechanisms are evaluated with a total number of 11,874 observations. Here, the forecasts were validated using out-of-sample datasets with 5-fold cross-validation. Models produced average coefficient ofdetermination values from 0.24 to 0.94, average root-mean-square errors from 0.05 kW - 5.83 kW, and meanabsolute scaled errors from 0.20 to 1.33. The results indicate that both artificial neural networks yield goodresults for compressors using variable speed drive (average R 2 = 0.8 and no naïve forecasting), only the longshort-term memory model gives acceptable results for compressors using on/off control (average R 2 = 0.82 and no naïve forecasting), and no satisfactory results are obtained for load/unload type air compressors (models constituting active forecasting).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Aluminum-doped calcium manganite particles for solar thermochemical energy storage: Reactor design, particle characterization, and heat and mass transfer modeling

A two-step cycle is discussed for solar thermochemical energy storage based on particulate aluminum-doped calcium manganite reduction/oxidation reactions for direct integration into Air Brayton cycles. The two steps encompass 1) the storage of concentrated solar irradiation within endothermic reduction of aluminum-doped calcium manganite and 2) the delivery of heat to an Air-Brayton cycle via exothermic re-oxidation of oxygen-deficient aluminum-doped calcium magnanite. A 5 kW th scale solar thermochemical reactor operating under vacuum was designed, modeled, and optimized to thermally reduce a continuous, gravity-driven flow of aluminum-doped calcium manganite particles. The granular flows were characterized in a tilt-flow rig, and particle image velocimetry was used to determine flow properties via frictional and velocity scaling relationships. Flow properties were integrated into a detailed heat and mass transfer model of the solar thermochemical reactor. A reactor design with 31° inclination angle, 230 g/min of particles, and 5.2 kW th radiative input from the high-flux solar simulator was discovered to produce an outlet flow temperature of 1158 K, with stoichiometric deviations of 0.076 and a storage efficiency of 0.628 while avoiding particle overheating and promoting longer particle residence times.

14 SOLAR ENERGY↗
DOE OSTI2022

Electrifying New York City Ride-Hailing fleets: An examination of the need for public fast charging

This report assesses the scale of public fast charging needed to electrify approximately 20,000 vehicles across the yellow cab and for-hire segments in New York City. The analysis considers real-world trip data in conjunction with driver home locations, overnight charging access rates, driver schedules, and more. Outcomes indicate that the existing charging network in New York City is not adequate even in the most optimistic scenario; 1,054 150-kW ports are required when 15% of drivers have access to overnight charging, whereas 367 150-kW ports are needed when 100% of drivers have access. Results also indicate that although charging is demanded in areas nearby high trip demand, fast charging ports are also demanded in areas near driver residences as a supplement for home charging in scenarios with limited overnight charging access. These findings motivate investment into both overnight charging and public fast charging to meet the charging demands of ride-hailing fleets.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Enhanced grindability of bastnaesite ore by ex-situ CO 2 treatment under the partial pressure of 0-100 psi

High grinding energy consumption has long constrained the sustainable development of mineral processing. This study introduces an innovative technology that employs ex-situ CO 2 treatment to enhance the grindability of bastnaesite ore. The grinding aid effect was evaluated under CO 2 partial pressures ranging from 0 psi to 100 psi using particle size distribution and the Bond work index (BWI), while the underlying mechanism was elucidated with various characterization techniques including inductively coupled plasma (ICP), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and Brunauer-Emmett-Teller (BET). The optimal grinding aid effect was achieved at 100 psi partial pressure, 50% slurry concentration, and 3 h reaction duration. Correspondingly, the P80 of the grinding product decreased from 81.76 μm to 72.73 μm and the BWI of bastnaesite ore decreased from 6.96 kW·h/t to 6.30 kW·h/t, a reduction of 9.48%. The grinding aid effect primarily resulted from the transformation of sparingly soluble carbonates like calcite and dolomite into more soluble bicarbonates, which created substantial cracks and pores, thereby reducing the ore's hardness and improving its grindability. By significantly saving grinding energy consumption while delivering environmental benefits, this technology exhibits great promise for further optimization and widespread adoption.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2024

Highly silanized cellulose biocomposites for sustainable insulation materials

Microfibrillated lignocellulose networks, derived from agricultural byproducts, represent an environmentally friendly biogenic material production due to their abundant availability to circular bioeconomy and inherent carbon sink in life cycle analysis. Yet, its vulnerability to moisture and flammability, coupled with challenges in creating highly reinforced insulation materials, poses challenges for the carbon-zero green building sector. Here we address these challenges with a new concept of in-situ grafting polymerization of nanoporous silica in pre-formed lignocellulosic fiber networks. The seamlessly integrating nanoporous silica with cellulose through hydrogen bonding networks enabled us to prepare highly reinforced biogenic composites for green building insulations. A high reinforcement biocomposite with hierarchal arrangements of nanoporous silica within the cellulose network exhibits remarkable attributes. It boasts a thermal conductivity of 24.2 mW·m –1 ·K –1 , a flexural modulus of 942 MPa, and soundproofing with a 20.8 % noise reduction, as well as the fire resistance characterized by an extended time to ignition and a reduced peak heat release rate of 144 kW·m –2 at 35 kW·m –2 of incident radiant heat flux. Furthermore, it demonstrates a reduced water absorption capacity, dropping from 5.12 g·g –1 to 0.75 g·g –1 . Altogether, this study opens the new pathways towards sustainable carbon-zero building materials in the context of circular bioeconomy.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Thermal loading analysis of the ring injection dump for the Spallation Neutron Source facility

The ring injection dump (RID) is the largest beam dump in the Spallation Neutron Source facility at Oak Ridge National Laboratory and accepts a fraction of the beam from the ring that is not captured in the ring during injection. Thermo-fluid modeling of the RID components was performed during the initial design by using the software ANSYS CFX to characterize the power rating for the RID. From the calculations, the power rating was lowered from 200 kW to 150 kW, at 1.3 GeV beam energy due to concerns regarding the heating of the concrete structure. The Proton Power Upgrade project scope included reevaluating the power limit to the RID in anticipation that increasing it would provide more operational flexibility. The focus was on the shielding because it was the limiting factor, and data collected from thermocouples installed on the structure were used to benchmark the analysis. A three-stage process was adopted in the latest study. First, the already existing steady-state calculations were validated and extended with the latest Monte Carlo N-Particle source term calculations. Second, transient calculations were conducted to capture the dynamic state of the system in response to the energy dumped to the RID during the following operational period of the beam from 2005–2019 (14 years), and sensitivity analyses of crucial parameters were performed to benchmark the model with reasonable accuracy. Finally, an idealized transient cycle analysis was conducted with realistic duty factors to predict the temperature distribution at higher beam powers. Overall, the results confirmed the accuracy of the original steady state model but allowed the development of a new validated transient model with higher accuracy for future analyses.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Rate-induced aging effects on Parallel-Plate Avalanche Counter (PPAC) caused by heavy ion beams

The Facility for Rare Isotope Beams (FRIB) is one of the premier scientific user facilities for nuclear science with radioactive beams, capable of producing most (approximately 80%) of the isotopes expected to exist, from oxygen to uranium, at energies up to 200 MeV/u. With the increase in beam power from the present 10 kW to the planned 400 kW, FRIB experiments are about to enter a new era. An unprecedented rate capability as well as stable performance of all the planned instrumentation intended for beam diagnostics and beam tuning is required at the expected high beam intensities (> 1 MHz). A summary of aging phenomena at high heavy-ion beam rates observed in the Advanced Rare Isotope Separator (ARIS) detectors for beam diagnostics, including Parallel Plate Avalanche Counters (PPAC) and plastic scintillation for time-of-flight measurements, is discussed. Current research and development project to mitigate rate-induced aging are presented.

Aging effects↗
DOE OSTI2025

High efficiency RF sources developments

Calabazas Creek Research, Inc. (CCR) and its collaborators are developing high efficiency RF sources operating from a few hundred MHz to C-Band and power levels from tens to hundreds of kilowatts with the goal of providing MW-relevant sources. The efficiencies approach or exceed 80% with projected costs as low as $0.50/ Watt. Sources under development include magnetrons with phase and amplitude control, single and multi-beam klystrons, multi-beam power grid tubes, and multiple beam IOTs. A magnetron system achieved more than 80% efficiency with fast amplitude control using modulation of the phase locking signal. This would be a low cost, high efficiency RF source for superconducting accelerators. An L-Band, single beam klystron was built with simulated efficiency of 80%. The klystron has yet to be tested to confirm the simulation results. CCR is currently developing a multi-beam klystron to produce more than 200 kW CW at 80% efficiency. Also in development is a multiple beam triode to produce 200 kW CW from 300 MHz to approximately 1 GHz. Not only does the simulated efficiency exceed 75%, but it would be the lowest cost RF source in this frequency range. Finally, CCR recently concluded research for a multiple beam IOT at 700 MHz using third harmonic drive to boost efficiency toward 85%. Successful development and transition to production of these sources will significantly alter the cost/performance landscape for RF power generation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Development of an accelerator-based neutron source to prototype Mo-99 production, part I: A liquid LBE windowless target

In this article, Molybdenum-99 (Mo-99)’s decay product, technetium-99 (Tc-99 m), is one of the most critical isotopes for medical diagnostics. To provide U.S. domestic supply of Mo-99 without using high-enriched uranium (HEU), a subcritical uranium target assembly (UTA) is irradiated by an accelerator-based neutron source to create Mo-99 through fission. This study discusses the development of the accelerator-based neutron source. The high-energy electrons from the accelerator irradiate a liquid lead-bismuth eutectic (LBE) target to produce neutrons. Part I of this work focuses on numerical and experimental analysis towards the development of a liquid LBE windowless target. Unlike the existing windowless targets in literature, the current design creates a vertical free surface for a beam to irradiate. First, a hydrodynamic analysis of the LBE windowless target is performed. Simplified analytical calculations are assisted by 2D computational fluid dynamics (CFD) simulations to design the target, with the focus on eliminating recirculation zones and avoiding cavitation. With the optimized geometry, the experimental study is performed to investigate the flow hydrodynamics using liquid LBE. The experiments (1) compare pressure drop in the system to correlation predictions; (2) visualize the free surface liquid LBE flow from the beam view; (3) validate the LBE flow profile using temperature sensitive paint from the side view; and (4) validate the liquid LBE film thickness using gamma densitometer measurements. Second, the power handling capability of the designed windowless target is investigated. The divider plate in the current design is susceptible to overheating due to the thin LBE film in front. As LBE erosion and corrosion is likely to occur at an LBE velocity of 2.0 m/s and temperature above 500 °C, a power limit of 10 kW of beam power was established to prevent this corrosion from occurring, which is calculated by a Nusselt number correlation. The divider plate surface temperature at 10 kW agrees well with the 3D CFD simulation results. Part I demonstrates the fundamental physics in liquid LBE windowless target design and associated testing. A companion paper, Part II will demonstrate how to couple this windowless target into the Mo-99 production system, including an accelerator system operating under an ultra-high vacuum and the UTA cooled by water at room temperature.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Characteristics of the sub-micron ash aerosol generated during oxy-coal combustion at atmospheric and elevated pressures

This paper is concerned with the effect of pressure on the particle size distribution and the size-segregated composition of the sub-micron ash aerosol created during oxy-coal combustion under near practical self-sustaining combustion conditions. The problem is important because pressurized oxy-coal combustion has been proposed as one promising technology to minimize CO 2 emissions. Sub-micron ash plays a major role in ash deposition mechanisms, which, in turn, can control boiler performance. In this work, the same bituminous coal was burned at pressures of 1, 8 and 15 bar in O 2 /CO 2 environments. Here, tests employed a 100 kW (rated) oxy-fuel combustor (OFC) operated at atmospheric pressure (1 bar) and a 300 kW (rated) entrained-flow pressurized reactor (EFPR) at elevated pressures (8 and 15 bar). Although these tests were conducted under near practical combustion conditions, confounding effects of peak flame temperature variations were minimized for the 1 bar and 15 bar tests, allowing the role of elevated pressure to be isolated. For the EFPR tests, a specially designed sampling system was used to sample sub-micron ash aerosols from the pressurized combustor and is described in detail. Results showed that at the same peak temperature but higher pressure, the fractions of ash aerosol partitioned into the PM 0.6 and PM 1 size fractions were greatly diminished. Moreover, elevated pressures caused significant changes in the composition of the (size-segregated) sub-micron aerosol, especially in its alkali content, which increased significantly. Examination of fractions of each element that ended up in the sub-micron fume suggested that, at constant temperature, the effect of pressure on vaporization of semi-volatile metals was very different from that on the release into the gas phase of non-volatile metals and could not be explained by equilibrium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Environmental and economic optima of solar home systems design: A combined LCA and LCC approach

Here, we compare the economic and environmental optimal design of Solar Home Systems (SHSs) and explore the role of economic incentives (such as tariffs and technology costs) in approximating the two optima. To achieve that, we present a methodology for the environmental and economic evaluation of grid-connected SHSs: user-scale electric systems involving a photovoltaic (PV) power system and a battery energy storage system. The proposed methodology is based on a mixed integer linear programming (MILP) optimization, life cycle assessment and life cycle costing. This methodological framework is applied to a case study involving a typical SHS installation in Italy. The results of the environmental optimal design brought to the evaluation of a 3.25 kW PV assisted by 8.66 kWh of nickel cobalt manganese batteries, whereas the costs of the SHS are minimized by a small PV system (less than 1 kW). Results underline that the environmental optimal configurations rely on battery technologies, which entails a significant cost compared to the grid connection. In contrast, the economic optimal design solutions is less impactful than the grid mix both from an environmental and economic points of view. Thanks to a reduction of batteries and PV costs, the environmental impact of the economic optimal design is expected to decrease in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Evaluating system responses to electric vehicle charging infrastructure expansion through data-driven simulation

Understanding the system responses to electric vehicle (EV) charging infrastructure expansion, including vehicle charging needs, station utilization, and energy consumption, is critical for effective planning to meet growing charging demand without unnecessary resource investment. This study evaluates the system responses to EV charging infrastructure expansion, focusing on charging needs, station utilization, and energy consumption. Using trip data from the National Household Travel Survey and origin–destination patterns, we simulated trip chains in downtown Atlanta with 10 % EV penetration. We assessed 32 scenarios involving different charging port power levels and siting strategies. Furthermore, we found that higher-power ports were more sensitive to placement, with concentrated expansion boosting station utilization more than uniform expansion. Adding high-power ports did not always increase peak energy consumption; in some cases, a few 400 kW ports reduced overall consumption compared to 150 kW ports by enabling faster charging and higher vehicle turnover.

Electric vehicle↗
DOE OSTI2025

Reduced order modeling of a fluidized bed particle receiver for concentrating solar power with thermal energy storage

Oxide particles can serve as both the heat transfer and thermal energy storage (TES) media for next-generation concentrating solar power (CSP) plants where high-temperature TES enables dispatchable electricity from efficient power cycles with firing temperatures above 600 °C. Transferring heat to flowing particles at such high temperatures in a MW-scale central tower receiver remains a challenge for the CSP community. For indirect receivers with external walls to contain the particles, maintaining wall temperatures below the limits of structural metal alloys requires high heat transfer coefficients between the wall and the moving particle stream. Bubbling fluidization of downward-flowing particles can sustain high bed-wall heat transfer coefficients (> 1000 W m -2 K -1 ). Using experimentally calibrated correlations for bed-wall heat transfer and vertical particle dispersion, this study implements an axially discretized zonal model of a counterflow fluidized bed receiver to explore how bubbling fluidization may enable indirect cavity particle receivers. High bed-wall heat transfer coefficients support solar fluxes on angled cavity walls > 200 kW m -2 at peak aperture fluxes of 980 kW m -2 while maintaining external wall temperatures < 950 °C. Lateral particle dispersion enables hotter particles near the receiver leading edge to mix with cooler particles further from the leading edge to lower maximum external wall temperatures. Parametric studies identify how mass fluxes, particle dispersion, and solar concentrations impact indirect receiver thermal efficiency and uniformity for a CSP plant. These studies provide a basis for the design of indirect fluidized-bed cavity receivers that can maintain particle outlet temperatures for TES above 750 °C.

14 SOLAR ENERGY↗
DOE OSTI2024

Utilizing waste heat in wastewater treatment plants for water desalination: Modeling and Multi-Objective optimization of a Multi-Effect desalination system using Decision Tree Regression and Pelican optimization algorithm

This paper examines the feasibility of using waste heat from wastewater treatment plants (WWTPs) for water desalination. A model was developed to utilize waste heat from the gensets at As Samra WWTP in Jordan, using real data and TRNSYS® software to calculate available waste heat. The desalination process was then modeled with ASPEN PLUS® software, focusing on multi-effect desalination (MED). Both series and parallel configurations for the MED system were compared. The study investigated the effects of system feeding flow rate, feeding pressure, and heat input on productivity, performance ratio, and recovery ratio. The study also introduces a novel optimization technique combining machine learning and modern optimization algorithms to maximize system productivity and performance. Initially, a decision tree regression (DTR) model is developed to establish relationships between key independent variables (flow rate, feed pressure, and heat input) and dependent variables (productivity, performance ratio, and recovery ratio). The Pelican Optimization Algorithm (POA) is then used to identify the optimal values of the independent variables for maximum productivity and performance. The results show that using a series configuration yields a system productivity of 3984.2 kg/hr, a performance ratio of 3.78, and a recovery ratio of 0.991 at a feed flow rate of 4000 kg/hr, feed pressure of 3 bars, and heat input of 719 kW. Optimal productivity (4421 kg/hr), performance ratio (3.81), and recovery ratio (0.851) are achieved at a feed flow rate of 5166 kg/hr, feed pressure of 3.2 bars, and heat input of 794 kW. In conclusion, the techno-economic assessment indicates a levelized cost of water of 1.63 USD/m 3 for parallel configurations and 1.65 USD/m 3 for series configurations, with a payback period of less than two years.

42 ENGINEERING↗