SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Performance evaluation methods, including hardware-in-the-loop, metrics, and additional considerations relevant to validating microgrid controller performance in the context of microgrid co-design are presented.
Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.
Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett., 2021, 11, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN rep to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN rep , we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body Δ TB potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules—for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.
Post-irradiation examination (PIE) is being performed in support of tristructural isotropic (TRISO) coated particle fuel development and qualification for High Temperature Gas-cooled Reactors (HTGRs). AGR-1 was the first in a series of TRISO fuel irradiation experiments initiated in 2006 under the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program [Petti et al. 2010]; this work continues to be funded by the Department of Energy's Office of Nuclear Energy in support of Advanced Reactor Technologies (ART). AGR-1 fuel compacts were fabricated at Oak Ridge National Laboratory (ORNL) in 2006 and irradiated for 3 years in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL) to demonstrate and evaluate fuel performance under HTGR irradiation conditions. PIE is being performed at INL and ORNL to study how the fuel behaved during irradiation, and to test fuel performance during exposure to elevated temperatures at or above temperatures that could occur during a depressurized conduction cooldown event. This report summarizes safety testing and post-safety testing PIE recently completed at ORNL on irradiated AGR-1 Compact 4-2-2.
The shape of an rf pulse is distorted due to dispersion encountered in acceleration through traveling-wave linear accelerator structures. Simulations are made to ascertain the severity of this distortion in cavities designed to operate at various group velocities. The pulse suffers maximum degradation when propagated through accelerating cavities with a phase advance per cell in the vicinity of {pi}, where the group velocity reaches its minimum value. Several cavities are simulated to study the pulse distortion and compared with experiments performed on a high phase advance structure H60VG3, which has a phase advance of 5{pi}/6 per cell. A circuit model and a mode matching code are used to perform these simulations on accelerating structures consisting of multiple cells. Beam loading is taken into account and the implications on energy dispersion are also analyzed. Means of mitigating for this energy deviation are discussed.
Utilization of wind energy has been investigated for a long period of time by different researchers in different ways. Out of which, the Horizontal Axis Wind Turbine and the Vertical Axis Wind Turbine have now advanced design, but still there is scope to improve their efficiency. The Vertical Axis Wind Turbine (VAWT) has the advantage over Horizontal Axis Wind Turbine (HAWT) for working on omnidirectional air flow without any extra control system. A modified H-rotor Darrieus type VAWT is analysed in this paper, which is a lift based wind turbine. The effect of solidity (i.e. chord length, no. of blades) on power coefficient (C{sub P}) of H-rotor for different tip speed ratios is numerically investigated. The study is conducted using time dependent RANS equations using SST k-ω model. SIMPLE scheme is used as pressure-velocity coupling and in all cases, the second order upwind discretization scheme is chosen for getting more accurate solution. In results, different parameters are compared, which depict the performance of the modified H-rotor Darrieus type VAWT. Double layered H-rotor having inner layer blades with longer chord gives higher power coefficient than those have inner layer blades with smaller chord.
After progressively incorporating ENERGY STAR® for Homes Versions 1, 2, and 3 into its standard practices over the years, this builder, Brookside Development, was seeking to build an even more sustainable product that would further increase energy efficiency, while also addressing indoor air quality, water conservation, renewable-ready, and resiliency. These objectives align with the framework of the DOE Challenge Home program, which "builds upon the comprehensive building science requirements of ENERGY STAR for Homes Version 3, along with proven Building America innovations and best practices. Other special attribute programs are incorporated to help builders reach unparalleled levels of performance with homes designed to last hundreds of years." Consortium for Advanced Residential Buildings (CARB) partnered with Brookside Development on the design optimization and construction of the first home in a small development of seven planned new homes being built on the old Singer Estate in Derby, CT.
Creation of high-performance advanced elastocaloric materials is essential for the execution of elastocaloric refrigeration that can be an environment-friendly and high-efficiency substitute for the widely used traditional vapor-compression cooling technology. Here we have developed a bulk polycrystalline Ni-Ti-Cu-Co shape memory alloy exhibiting large elastocaloric effect, low stress hysteresis and room temperature working temperature, all of which are of great importance to and urgently demanded for high-efficiency room-temperature elastocaloric refrigeration. This newly developed (Ni 42.5 Ti 50 Cu 7.5 ) 99 Co 1 alloy shows a large room-temperature elastocaloric effect with directly measured adiabatic temperature change up to -14.4 K during unloading. The stress hysteresis of the isothermal superelastic stress-strain curve is as low as 60 MPa when the maximum tensile strain is 2.7%. Owing to the large elastocaloric effect and low stress hysteresis, a very high coefficient of performance up to 19 is achieved on the material level. This newly developed (Ni 42.5 Ti 50 Cu 7.5 ) 99 Co 1 alloy is a robust candidate for efficient elastocaloric refrigeration. Advanced in-situ synchrotron high-energy X-ray diffraction technique was employed to reveal the phase transformation sequence and to accurately determine the crystal structure of different phases, based on which the lattice compatibility between the transforming phases was evaluated and the phase transformation strain was predicted, providing in-depth fundamental understanding of the martensitic transformation in this newly developed alloy. This work could be useful for designing high-performance elastocaloric materials for solid-state cooling applications.
This document summarizes the progress of VTO battery R&D projects supported during the fiscal year 2022. Its R&D focus was on the development of high-energy batteries for PEVs. The electrochemical energy storage roadmap describes ongoing and planned efforts to develop electrochemical storage technologies for EVs. To advance battery technology, which can in turn improve market penetration of PEVs, the program investigates various battery chemistries to overcome specific technical barriers, e.g., battery cost, performance, life (both the calendar life and the cycle life), its tolerance to abusive conditions, and its recyclability/sustainability.
This document summarizes the progress of VTO battery R&D projects supported during the fiscal year 2023. Its R&D focus was on the development of high-energy batteries for PEVs. The electrochemical energy storage roadmap describes ongoing and planned efforts to develop electrochemical storage technologies for EVs. To advance battery technology, which can in turn improve market penetration of PEVs, the program investigates various battery chemistries to overcome specific technical barriers, e.g., battery cost, performance, life (both the calendar life and the cycle life), its tolerance to abusive conditions, and its recyclability/sustainability.
Research is in progress to develop flight strategy concepts for avoidance and recovery from microburst wind shears. The objectives of this study are to evaluate the performance of various strategies for recovery from wind shear encountered during the approach-to-landing, examine the associated piloting factors, and evaluate the payoff of forward-look sensing. Both batch and piloted simulations are utilized. The industry-recommended manual recovery technique is used as a baseline strategy. Two advanced strategies were selected for the piloted tests. The first strategy emulates the recovery characteristics shown by prior optimal trajectory analysis, by initially tracking the glideslope, then commanding a shallow climb. The second strategy generates a flight path angle schedule that is a function of airplane energy state and the instantaneous shear strength. All three strategies are tested with reactive sensing only and with forward-look sensing. Piloted simulation tests are in progress. Tentative results indicate that, using only reactive alerts, there appears to be little difference in performance between the various strategies. With forward-look alerts, the advanced guidance strategies appear to have advantages over the baseline strategy. Relatively short forward-look alert times, on the order of 10 or 15 seconds, produce a far greater recovery benefit than optimizing a recovery from a reactive alert.
The U.S. Department of Energy Environmental Management (DOE-EM) Office of Tank Waste Management sponsored Cementitious Barriers Partnership (CBP) is chartered with providing the technical basis for implementing cement-based waste forms and radioactive waste containment structures for long-term disposal. DOE needs in this area include the following to support progress in final treatment and disposal of legacy waste and closure of High-Level Waste (HLW) tanks in the DOE complex: - Long-term performance predictions and - Flow sheet development and flow sheet enhancements - Conceptual designs for new disposal facilities The DOE-EM Cementitious Barriers Partnership is producing software and experimental programs resulting in new methods and data needed for end-users involved with environmental cleanup and waste disposal. Both the modeling tools and the experimental data have already benefited the DOE sites in the areas of performance assessments by increasing confidence backed up with modeling support, leaching methods, and transport properties developed for actual DOE materials. In 2014, the CBP Partnership released the CBP Software Toolbox - 'Version 2.0' which provides concrete degradation models for: 1) sulfate attack, 2) carbonation, 3) chloride initiated rebar corrosion, and includes constituent leaching. These models are applicable and can be used by both DOE and the Nuclear Regulatory Commission (NRC) for service life and long-term performance evaluations and predictions of nuclear and radioactive waste containment structures across the DOE complex (Figure 1) including Future SRS salt-stone and HLW tank performance assessments and special analyses - Hanford site HLW tank closure projects and other projects in which cementitious barriers are required - The Advanced Simulation Capability for Environmental Management (ASCEM) project which requires source terms from cementitious containment structures as input to their flow simulations - Regulatory reviews of DOE performance assessments - Nuclear Regulatory Commission reviews of commercial nuclear power plant (NPP) structures which are part of the overall US Energy Security program to extend the service life of NPPs In addition, the CBP experimental programs have had a significant impact on the DOE complex by providing specific data unique to DOE sodium salt wastes at Hanford and SRS which are not readily available in the literature. Two recent experimental programs on cementitious phase characterization and on technetium (Tc) mobility have provided significant conclusions as summarized below: Recent mineralogy characterization discussed in this paper illustrates that sodium salt waste form matrices are somewhat similar to but not the same as those found in blended cement matrices which to date have been used in long-term thermodynamic modeling and contaminant sequestration as a first approximation. Utilizing the CBP generated data in long-term performance predictions provides for a more defensible technical basis in performance evaluations. - In addition, recent experimental studies related to technetium mobility indicate that conventional leaching protocols may not be conservative for direct disposal of Tc- containing waste forms in vadose zone environments. These results have the potential to influence the current Hanford supplemental waste treatment flow sheet and disposal conceptual design. (authors)
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.
The U.S. nuclear energy expansion goals are driving the demand for manufacturing routes that can rapidly produce large, complex, near net shape components. Powder metallurgy hot isostatic pressing (PM HIP) is an advanced manufacturing technique that can be economically scaled-up, while alleviating the supply chain challenges that forging and casting face in terms of cost and lead time constraints. This makes PM-HIP a viable technology to aid and accelerate large-scale part manufacturing for nuclear applications. However, large-scale qualification and deployment of this technology require a thorough understanding of the influence of powder feedstock quality, powder handling history, hot isostatic pressing (HIP) parameters, and subsequent heat treatment on microstructural evolution and elevated temperature mechanical performance. The present work focusses on 316 austenitic stainless steel (SS316) which is most commonly used in high temperature environments for nuclear applications Results from this study show that PM HIPed SS316 meets ASME tensile requirements at room temperature and at elevated temperature. However, creep performance of PM-HIPed SS316 remains inferior to its wrought counterpart, demonstrating that tensile performance alone is not a reliable metric for long duration high temperature integrity. Further, this report delineates powder derived microstructural features that govern creep damage, with key evidences pointing to “microstructural inheritance” from gas atomized powder feedstocks. Commercial SS316 powders of varying chemical compositions and recycling histories were studies, and the results showed large differences in elemental segregation, oxide surface layers and secondary phase distributions. Multi-scale characterization revealed segregation of chromium, molybdenum, manganese and silicon at the boundaries and the precipitation of manganese-, silicon-, and molybdenum-oxides. During HIP consolidation, these surface oxides transform into decorated prior particle boundaries (PPBs) and grain boundary inclusions that persist through conventional post-HIP solution annealing treatment. The retained oxides in post-HIP microstructures were found to influence grain growth, precipitation behavior, and ultimately creep cavitation and fracture. Such post-HIP heat treatments are therefore limited by a complex trade-off between grain growth, and oxide coarsening which aggravate creep damage by acting as nucleation sites for cavities. The objective of this work is to establish an integrated processing–structure–property framework for PM-HIP 316 stainless steel by investigating the influence of powder feedstock characteristics in pre- and post-HIP processing as well as to understand the significance of post-HIP heat treatment on microstructural evolution and creep properties. The results from this report emphasize the significance of powder feedstock integrity in improving creep performance of PM-HIPed SS316, by highlight the effect of rapid solidification, elemental segregation, oxide formation and powder recycling on microstructural defect inheritance following HIP consolidation. Rather than considering HIP processing, solution annealing, and mechanical performance independently, this report treats powder production, HIP consolidation, post-HIP thermal processing, and creep deformation as interconnected stages within a continuous metallurgical process. The resulting framework will provide a scientific basis for developing feedstock engineering strategies capable of improving long-term reliability of PM-HIP stainless steels and accelerating their qualification for advanced nuclear applications.
Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.
A penalized maximum-likelihood estimation is proposed to perform hyperspectral (spatio-spectral) image reconstruction for X-ray fluorescence tomography. The approach minimizes a Poisson-based negative log-likelihood of the observed photon counts, and uses a penalty term that has the effect of encouraging local continuity of model parameter estimates in both spatial and spectral dimensions simultaneously. The performance of the reconstruction method is demonstrated with experimental data acquired from a seed of arabidopsis thaliana collected at the 13-ID-E microprobe beamline at the Advanced Photon Source. The resulting element distribution estimates with the proposed approach show significantly better reconstruction quality than the conventional analytical inversion approaches, and allows for a high data compression factor which can reduce data acquisition times remarkably. In particular, this technique provides the capability to tomographically reconstruct full energy dispersive spectra without compromising reconstruction artifacts that impact the interpretation of results.
The Storage Futures Study (SFS) is a multiyear research project to explore the role and impact of energy storage in the evolving electricity sector of the United States. The SFS is designed to examine the potential impact of energy storage technology advancement on the deployment of utility-scale storage and the adoption of distributed storage, and the implications for future power system infrastructure investment and operations. This specific report synthesizes current and projected cost performance assumptions along with location availability for storage technologies through 2050 that will be used in scenario analysis for the SFS at both the bulk power and distribution system scales. For comparison and context, this report also presents a synthesis of current cost and performance characteristics of energy storage technologies for storage durations ranging from minutes to months and including mechanical, thermal, and electrochemical storage technologies for the electricity sector. This information is intended to cover a broad range of storage technologies that are currently receiving significant attention from the investment community as well as in the media. In the report, we emphasize that energy storage technologies must be described in terms of both their power (kilowatts [kW]) capacity and energy (kilowatt-hours [kWh]) capacity to assess their costs and potential use cases.
Bottom-up load modeling of buildings offers a versatile approach to simulating baseline demand and scenarios of future technology evolution and adoption at the individual building level. This capability is essential to understanding how future load shapes may change with the adoption of electric equipment and vehicles, particularly as it relates to grid planning and infrastructure investments. Traditionally, grid planning techniques have used historical load data to predict future load and infrastructure needs. However, with the anticipated rise in adoption of electrification technologies such as heat pumps and electric vehicles, historical data become less reliable predictors of the future. By employing ResStock, a high-fidelity building stock modeling tool, we can fine-tune electrification scenarios and aggregate models to represent varying geographic resolutions of the grid system, while considering the underlying features of homes. This may enable a more accurate and responsive approach to anticipate and plan for the evolving landscape of energy demands. We present a new framework that leverages building stock energy modeling to identify building models that align with the load shapes and housing attributes of buildings with AMI data. This approach applies two model layers: (1) a classification step that identifies the presence of air conditioning, electric heating, and electric water heating, and (2) an optimization routine that identifies building energy models aligning with load profile data from advanced metering infrastructure meters. This report demonstrates one approach to deploying this framework, and presents results for three test cases that use both modeled and AMI data to assess performance. For a test case using AMI data in Fort Collins, Colorado, we observed a median monthly electricity load CV-RMSE of 16.6%, and a top ten daily heating and cooling median absolute percent error of 7.7% and 8.3%, respectively. For each AMI meter, we identify a set of potential energy models so that downstream use-cases can account for uncertainty driven by variability of baseline technologies and occupant behavior, which impact the response to electrification and energy efficiency scenarios. Our results indicate that ResStock has potential as a scalable solution for modeling residential energy demand at local grid resolutions. Its performance depends on location-specific factors, underlying building characteristics, and the level of aggregation, offering a path towards more precise and adaptive distribution grid planning for the evolving energy landscape.