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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Capture of novel sp 3 hybridized Z-BN by compressing boron nitride nanotubes with small diameter

Experimental synthesis of new sp 3 hybridized carbon/boron nitride structures remains challenging despite that numerous sp 3 structures have been proposed in theory. Here, we showed that compressed multi-walled boron nitride nanotubes (MWBNNTs) and boron nitride peapods (C 60 @BNNTs) with small diameters could transform into a new sp 3 hybridized boron nitride allotrope (Z-BN). This strategy is considered from the topological transition point of view in boron nitride nanotubes upon compression. Due to the increased curvature in compressed small-diameter MWBNNTs, the uncommon 4- and 8-membered rings in Z-BN could be more favorably formed. And the irreversible tube collapse is proved to be a critical factor for the capture of the formed Z-BN, because of the competition between the resilience of tube before collapse and the stress limitation for the lattice stabilization of Z-BN upon decompression. In this case, Z-BN starts to form above 19.0 GPa, which is fully reversible below 45 GPa and finally becomes quenchable at 93.5 GPa. Consequently, this collapse-induced capture of the high-pressure phase could also be extended to other tubular materials for quenching novel sp 3 structures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A hybrid CNN-LSTM surrogate model for hyper-resolution spatiotemporal flood forecasting in Norfolk, Virginia

Study region: Norfolk, Virginia, United States Study focus: Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features. New hydrologic insights for the region: The hybrid CNN-LSTM model was trained using the physics-based hydrodynamic model simulations obtained from the Two-dimensional Unsteady FLOW (TUFLOW) model for Norfolk, Virginia, and achieved high predictive accuracy across diverse flood-prone areas. The reduced computational time from four to six hours using TUFLOW to 3.2 min per event using CNN-LSTM enables rapid flood inundation mapping and early warning applications. The model effectively captured both spatial flood extents and their temporal evolution across different flooding scenarios, providing forecasts at a 2.5-m spatial resolution and 15-min temporal resolution and a one-hour-ahead prediction horizon. While challenges remain in terms of transferability to new regions and real-time data assimilation, this approach demonstrates strong potential for supporting operational flood risk management in coastal urban environments.

Coastal urban flooding↗
DOE OSTI2022

Simulation and analysis of the 3D double shock-induced recompression of spall using a hybrid PAGOSA with FLIP+MPM

In this work, an innovative hybrid algorithm PAGOSA with FLIP + MPM is first presented by coupling PAGOSA with the particle FLIP + MPM, and applied to systematically exploring the 3D recompression of spall induced by double-shock waves in ductile materials as the few existing studies are currently limited to the pure longitudinal recompression of spall, and ignore the 3D effects. This algorithm solves the difficulties the grid-based methods encounter when applied to capturing the fracture in material. We first validate the capabilities of PAGOSA with FLIP + MPM to predict different fracture/fragmentation cases by solving two benchmark problems and comparing with the analytical solutions or the experiment results. The convergences and the infinity-norm errors are also investigated. Subsequently, the 3D effects are demonstrated by simulating the spallation in material using PAGOSA with FLIP + MPM and comparing with those in the longitudinal cases. Additionally, some factors that affect the spallation with 3D effects are also discussed. Finally, the recompressions of spall in material with/without 3D effects are systematically simulated and analyzed. The recompression of spall driven by a high explosive is also simulated to show the ability of PAGOSA with FLIP + MPM to handle this kind of problem. The conditions that can lead to a recompression of spall are explored. Also, numerical results show that PAGOSA with FLIP + MPM can accurately predict different fracture cases, that the 3D effects play an important role in fracture/fragmentation, and the recompression of spall is very sensitive to the occurrence conditions, shows great differences when considering 3D effects in real applications. Moreover, the present hybrid method can be easily extended to other grid-based techniques employed for fracture in materials.

3D effects↗
DOE OSTI2024

Coupled aero-hydro-geotech real-time hybrid simulation of offshore wind turbine monopile structures

Real-time hybrid simulation (RTHS) divides a structural system into an analytical and experimental substructure. The former is based on a well-established analytical model while the latter consists of a physical model in the laboratory, for which there is not a well-established analytical model. This paper extends real-time hybrid simulation to monopile-type Offshore Wind Turbines (OWTs) to enable the investigation of their behavior considering the response of pile foundations under operational and more severe conditions. The embedded foundation and surrounding soil of the OWT are modeled physically in a soil box in the laboratory while the remaining parts of the system and loading are modeled analytically. The program OpenFAST, developed by the National Renewable Energy Laboratory (NREL), is linked to the RTHS coordinator to determine the hydrodynamic and aerodynamic loads acting on the OWT, along with modeling the dynamics of the electric power generation equipment and associated controller for the OWT. The RTHS framework along with its initial implementation and validation are described in this paper. RTHSs of a 5 MW OWT subjected to operational and more severe conditions are performed to experimentally validate the framework. The framework offers a realistic approach to investigate the behavior of OWT structures supported on monopiles. Furthermore, this approach accounts for the coupled response of the OWT structure with its foundation, while experimentally capturing the nonlinearities of the soil-foundation interaction in real-time.

17 WIND ENERGY↗
DOE OSTI2022

Constructing O2/O3 homogeneous hybrid stabilizes Li-rich layered cathodes

With the advantages of high energy density and low manufacture cost, lithium-rich layered oxides (LLOs), typically with a layered O3-type structure, are regarded as promising cathodes for lithium-ion batteries (LIBs), but their broad usages are hindered by severe voltage decay over cycling. Although recent progress in O2-type LLOs has aroused interest for their less voltage decay, the critical barrier of unsatisfactory capacity retention has not been overcome yet. In this work, to tackle these handicaps, we design a new type of LLO (O2/O3-type LLO) with a homogeneous hybrid structure, where the O2 and O3 lattice stacking sequences are arranged alternatively. Benefitting from this novel O2/O3 hybrid structure, the designed material shows greatly improved voltage and capacity stability than that of pure O2- and O3-type LLOs. Revealed by in-situ synchrotron X-ray diffraction and operando differential electrochemical mass spectra, the O2/O3-hybrid LLO cathode shows a more reversible structural evolution, smaller volume change and suppressed oxygen loss during the electrochemical processes. Our approach has initiated a new way to reduce the capacity and voltage decay of LLOs, which endows great promise to the development of high-energy-density LIBs.

25 ENERGY STORAGE↗
DOE OSTI2023

A reduced order sulfuric acid decomposition model for a nuclear-powered hybrid sulfur cycle

A reduced order model of sulfuric acid decomposition within a bayonet chemical reactor was developed to support the U. S. Department of Energy Integrated Energy System program, and address the lack of knowledge in scaling and integration for joint chemical and nuclear processes. Sulfuric acid decomposition within a bayonet reactor was modeled to provide chemical and thermodynamic data relevant to advanced nuclear reactor-driven integrated energy systems based on desired operational scale and operational conditions. The temperature range required for high-temperature advanced nuclear reactor integrated energy systems, 750-850 °C, was shown to produce reasonable agreement (within a few percent relative error) with past models and experimental data, and yielded good efficiency results for bayonet reactor operations. The results of the reduced order model agreed with previous work from Savannah River National Labs within a maximum of 3.4% error on the decomposition of sulfur trioxide, and on previous Hybrid Sulfur flowsheets from Gorensek and Summers that showed operational temperature, pressure, and composition ranges for efficiency which made the Hybrid Sulfur cycle competitive with water electrolysis. Here, the agreement with previous high-fidelity models provided a framework for future Integrated Energy System grid evaluations with an advanced nuclear reactor and large-scale hydrogen production using a mathematical model to represent chemical operations.

08 HYDROGEN↗
DOE OSTI2026

A hybrid Penman-Monteith and machine learning model for simulating evapotranspiration and its components

Integrating physical processes with machine learning has advanced evapotranspiration (ET) simulation, yet most hybrid models fail to partition total ET into its components: soil evaporation (E) and vegetation transpiration (T). This study introduces Residual Neural Network–Penman–Monteith (RNN-PM), a novel hybrid dual-source ET model designed to overcome this limitation. The model synergizes the physically-based Penman–Monteith framework with three specialized residual neural networks trained to estimate key conductance parameters (canopy conductance, soil surface conductance, and aerodynamic conductance). Furthermore this explicit parameterization allows for the direct partitioning of total ET. Validation at National Ecological Observatory Network (NEON) flux sites using high-frequency partitioned E and T shows that RNN-PM reliably reproduces ET and the transpiration fraction (T/ET). For ET, the model achieves an average Kling–Gupta efficiency (KGE) of 0.89 and a root-mean-square error (RMSE) of 0.55 mm/day; for T/ET, the KGE is 0.87 with an RMSE of 0.06. Furthermore, RNN-PM demonstrates robust generalization, accurately simulating ET and its components well beyond the initial training dataset, even under extreme climatic conditions. This study extended the analysis by comparing the RNN-PM model with seven established dual-source ET models. The results indicate that RNN-PM outperforms both conventional machine learning models and purely physical process-based models in simulating ET components in most cases. Among the purely physical process-based dual-source models, those based on surface temperature decomposition showed improved performance as the leaf area index (LAI) decreased when evaluated against high-frequency ET component datasets. In contrast, the performance of conductance-based dual-source models declined with decreasing LAI. Although purely machine learning-based models can produce relatively accurate simulations of ET components, they often exhibit limited generalization capability, an issue that the RNN-PM model effectively overcomes. Ultimately, the RNN-PM model represents a significant advance in simulating ET components, offering a novel and scalable approach for improving the representation of land–atmosphere interactions in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Hybrid additive manufacturing of AISI 316L via asynchronous powder and hot-wire laser directed energy deposition

Hybrid Additive Manufacturing (AM) offers a way to leverage the advantages of different AM technologies, enabling the efficient production of sizeable parts without compromising material properties or geometric complexity capabilities. This study presents an asynchronous hybrid Directed Energy Deposition (DED) strategy employing laser powder DED and laser hot-wire DED. AISI 316L parts comprising multiple powder and wire segments were fabricated with optional machining on AISI 316L substrates to investigate how quality is impacted by (i) alternative process sequences (laser powder DED followed by laser hot-wire DED and vice versa), (ii) machined vs. as-printed interfacial conditions, and (iii) material deposition on top vs. alongside previously built segments. Optical microscopy, X-ray computed tomography, and Vickers hardness were used to characterize the morphology and microstructure of the parts, localized porosity and lack of fusion defects, bulk density, and mechanical properties. Interfacial machining was necessary for dimensional control but promoted lack of fusion voids, resulting in a 99.71 ± 0.01% dense part. As-printed interfaces resulted in a denser part (99.82 ± 0.02%) at the expense of dimensional accuracy. The hardness of the parts with as-printed and machined interfaces was 196 ± 0.37 HV and 192 ± 0.40 HV, respectively, compared to 156 ± 1.4 HV for the substrate. Depositing powder alongside or on top of wire sections resulted in interfaces with a hardness of 217 ± 2.2 HV, compared to 185 ± 3.4 HV for the wire-powder interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mechanical properties and microstructure of 316L stainless steel produced by hybrid manufacturing

Hybrid manufacturing is a combination of additive (deposition) and subtractive (machining) manufacturing in a single machine tool. Such a system can be used for near net shape manufacturing and component repair using either similar or dissimilar materials. Integrated into a single system, transition between additive and subtractive manufacturing can occur immediately and be leveraged to generate large components by alternating between the processes. In this investigation, we show how the interleaved capabilities can reduce overall cycle time by up to 68 %, improve average relative elongation to failure by 71 %, and reduce the average relative porosity fraction by 83 % when compared to traditional additive manufactured components. Results from this investigation builds the foundation needed for hybrid manufacturing to be applicable towards the manufacture of large complex components such as nosecones and marine propulsors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Residual strain and microstructure evolution in 316L stainless steel fabricated by hybrid additive/subtractive manufacturing

The extreme thermal gradients in additive manufacturing (AM) result in residual strains. Subsequent machining operations can relieve the AM strains but can introduce additional strains. AM and machining on the same platform, also called interleaving, during hybrid manufacturing has been shown to improve productivity. However, the impacts of these operations on the resulting residual strains are not well understood. In this investigation, neutron diffraction was used to measure residual strains in hybrid manufactured 316L stainless steel where blown-powder laser directed energy deposition (DED), and machining operations were interleaved in 1-inch intervals. We observe that the dominant texture developed during AM, resulting from different spot sizes and deposition parameters, significantly affects residual strain evolution. A dominant <011> texture along the build direction does not show any strain localization during interleaved deposition and machining whereas a dominant <001> texture shows strong tensile strain localization consistent with the pauses for machining at the end of AM step. Using coolant during machining can further alter the residual strain evolution. Our work shows that a combination of AM parameters and machining strategy can be used to influence the nature and magnitude of residual strains in components fabricated using convergent manufacturing technologies.

Hybrid manufacturing↗
DOE OSTI2026

Feedforward-feedback ammonia control at a water resource recovery facility based on a digital twin with hybrid model

Ammonia-based aeration control (ABAC) at full-scale Water Resource Recovery Facilities (WRRFs) can be challenged by diurnal loading and transport delays. This work addressed these challenges using a hybrid feedforward–feedback controller built on Activated Sludge Model 1 (ASM1), marking the first full-scale deployment to pair a mechanistic feedforward core with data-driven corrections. The objectives were to improve ammonia setpoint tracking, assess performance of the mechanistic model when enhanced with data-driven corrections, and document full-scale operation. The hybrid model incorporates two data-driven components: (1) a Mechanistic Error Forecasting Engine (MEFE), consisting of a multivariate linear regressor and a long short-term memory (LSTM) ensemble. Defying expectations, low-parameter models outperformed more complex alternatives, reducing the mechanistic error by 71%. (2) A Residual Oscillation Forecasting Engine (ROFE), based on Fast Fourier Transform, reduced the remaining error by another 35%. Two proportional–integral (PI) feedback loops further (i) trim the feedforward output and (ii) eliminate residual controller error in the final aerobic zone. In full-scale operation, the controller reduced mean-squared error (MSE) by 94% over the baseline and produced more stable dissolved oxygen (DO) setpoints. Overall, it was proven that layering multi-timescale data-driven models on a mechanistic core can yield reliable ABAC performance at WRRFs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Investigation of interfacial structures for hybrid manufacturing

Hybrid manufacturing is a combination of additive and subtractive manufacturing in a single machine. Typically, planar substrate substrates are used for deposition and do not correspond to scenarios encountered in repair applications where the substrate can often be non-planar. Hybrid manufacturing opens the possibility for repairs by leveraging the five-axis mill to prepare the substrate for deposition. However, as the substrate geometry changes, so does the associated heat transfer during deposition and subsequent microstructures. This paper focuses on understanding the changes in microstructure and material properties with changing substrate geometries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of humidity of build chamber in hybrid manufacturing systems on part performance

Here hybrid manufacturing combines additive with subtractive machining operations which often require the use of a coolant, that can increase the humidity of the build chamber. The water vapor can be adsorbed on the powder surfaces and subsequently dissociate into hydrogen and oxygen during melting and can be absorbed by part during solidification. Here, we investigated the effect of build chamber humidity during hybrid manufacturing on the part quality on two most common classes of steels i.e., austenitic (316L) and ferritic (grade 91). We report that the humidity increase has a negligible effect on part performance of both classes of steels.

316L stainless steel↗
DOE OSTI2023

Hybrid manufacturing by additive friction stir deposition, metrology, CNC machining, and microstructure analysis

Aerospace flight panels must provide high strength with low mass. For aluminum panels, it is common practice to begin with a wrought plate and remove the majority of the material to attain the desired structure, comprising a thinner plate with the desired pattern of reinforcement ribs. As an alternative, this study implements hybrid manufacturing, where aluminum is first deposited on a baseplate only at the rib locations using additive friction stir deposition (AFSD). Structured light scanning is then used to measure the printed geometry. This geometry is finally used as the stock model for computer numerical control (CNC) machining. This paper details the hybrid manufacturing process that consists of: AFSD to print the preform, structured light scanning to generate the stock model and tool path, three-axis CNC machining, and post-process measurements for part geometry and microstructure.

42 ENGINEERING↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2023

Porous potassium tantalate-reduced graphene oxide nano cube architecture for high performance hybrid supercapacitors

Energy storage has always been a major concern in the present-day situation. Advanced energy storage devices are batteries, supercapacitor and solar cells. However, advancements have been noteworthy in the field of high-performance hybrid supercapacitors. On this note, we have fabricated a hybrid supercapacitor electrode material Potassium tantalate nano cube (KT NCs) and its reduced graphene oxide composite (KT-rGO NCs) and tested its electrochemical performance. The materials showed high performances with specific capacitance of 565 F/g for KT NCs and 850 F/g for KT-rGO NCs respectively. Energy densities of both KT NCs & KT-rGO NCs are 28.24 Wh/Kg and 29.50 Wh/kg with good retention capacities. Further detailed study of both KT NCs and KT-rGO NCs are carried out with characterization techniques like XRD, FTIR, BET, Raman and HRTEM for structural analysis and electrochemical measurements to analyse various parameters pertaining to its charge storage capacity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of microphase separation on the limiting current density in hybrid organic-inorganic copolymer electrolytes

Hybrid organic-inorganic block copolymer electrolytes are of interest to enable batteries containing lithium metal anodes. The conductive block is a standard polymer electrolyte of poly(ethylene oxide) and the mechanically rigid block is an inorganic poly(acryloisobutyl polyhedral oligomeric silsesquioxane) polymer. Here, in this paper, we compare a poly(acryloisobutyl polyhedral oligomeric silsesquioxane)-b-poly(ethylene oxide)-b-poly(acryloisobutyl polyhedral oligomeric silsesquioxane) (POSS-PEO-POSS) triblock copolymer and a poly(ethylene oxide)-b- poly(acryloisobutyl polyhedral oligomeric silsesquioxane) (PEO-POSS) diblock copolymer mixed with lithium bis(trifluoromethanesulfonyl)imide salt. We have experimentally measured the limiting current density in lithium symmetric cells containing hybrid organic-inorganic electrolytes at 90 °C. The cells were polarized at a large range of applied current density. The diblock copolymer electrolyte exhibited a clear plateau in cell potential at all current densities below the limiting current density. At low applied current density, the triblock copolymer electrolyte also exhibited a clear plateau in cell potential. At currents approaching the limiting current density, the triblock copolymer electrolyte exhibited an underdamped potential profile. The cell potential did not reach a plateau at current densities above the limiting current in both systems. The diblock and triblock copolymer electrolytes were fully characterized using electrochemical methods to determine the ionic conductivity, cation current fraction, salt diffusion coefficient, and open circuit voltage as a function of salt concentration. Cell potential and salt concentration as functions of position in the cell at various current densities were calculated using Newman's concentrated solution theory. The theoretical limiting current density was calculated to be the current density at which salt is depleted at the cathode. We see quantitative agreement between experimental measurements and theoretical predictions for the limiting current density in the diblock copolymer electrolyte which has an ordered structure at all salt concentrations, while the experimental limiting current density is lower than the theoretical prediction for the triblock copolymer electrolyte, which exhibits a disordered morphology at high salt concentrations.

25 ENERGY STORAGE↗
DOE OSTI2026

Hybrid geothermal-wind integration in the US energy grid

Here, we combine US wind generation—a cheap yet intermittent source of electricity—with the latest geothermal resource estimates to understand the technoeconomic possibilities of pairing enhanced geothermal systems (EGSs) with existing wind farms to develop a hybrid energy system. Using observed generation data from 583 wind farms, generation gaps are quantified and geographically paired with the latest EGS estimates. Results demonstrate that EGS potential within a 1 km 2 footprint can supplement wind generation at 56% of onshore wind farms. Each wind farm can be supplemented by EGSs when 10% of its surface-occupying footprint is available. The cost of EGSs at wind farms is lowest in the western US and southern Texas border and highest in the central US. While further experiments are warranted, a wind + EGS hybrid system offers an opportunity to increase power output from the same land footprint while maximizing the use of existing electrical infrastructure.

EGS↗