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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Hybrid Doping Strategy with High‐Entropy Cu/Fe Surface Modification and Zr Bulk Incorporation for Ni‐Rich Cathodes

A hybrid doping strategy combining Zr 4+ bulk doping with high-entropy Cu 2+ /Fe 3+ surface doping is developed to enhance the structural and interfacial stability of Ni-rich layered oxide cathodes. Cu and Fe are selectively introduced at the particle surface via a surface-selective ion-exchange process, forming a ≈15 nm Fe-rich layer while preserving the layered framework. Compared to the pristine cathode, the hybrid sample exhibits significantly improved electrochemical performance in both half-cell and full-cell configurations. In half-cells, the hybrid retains 88.5% and 90.2% after 100 cycles at 1C under 4.6 and 4.5 V, respectively. During high-voltage full-cell cycling, the hybrid cathode maintains over 80% capacity retention, whereas the pristine counterpart retains less than 10% under identical conditions over the same cycling period. XPS, EELS, and DEMS analyses confirm improved oxygen retention, suppressed gas evolution, and stable surface chemistry, while DFT calculations indicate enhanced Me–O bonding in the selected Fe 0.75 Cu 0.25 (Mn 1/16 Co 2/16 Ni 13/16 )O 2 surface composition, which is identified through DFT-calculated mixing energy reaching a minimum at this ratio, indicating the most thermodynamically favorable configuration. In conclusion, these results demonstrate the effectiveness of this hybrid doping strategy in mitigating coupled degradation pathways in Ni-rich cathodes.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Hybrid Quantum–Classical Graph Transformers for Efficient Sentiment Analysis

Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT), a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self-attention. The QGT models variable-length sentences as token graphs, where both the embedding encoding and the self-attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and 𝑘 -nearest-neighbor graph structures and evaluate it on five benchmark sentiment-classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving 29.4 × fewer parameters while requiring 3–5 × fewer samples to reach comparable performance. These findings highlight the potential of graph-based quantum models as scalable and data-efficient architectures for natural language understanding.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2019

Additive Manufacturing and Hot-Fire Testing of Bimetallic GRCop-84 and C-18150 Channel-Cooled Combustion Chambers Using Powder Bed Fusion and Inconel 625 Hybrid Directed Energy Deposition

Additive manufacturing (AM) is an advanced fabrication technique that is demonstrating tremendous potential to reduce fabrication lead times and costs for liquid rocket engine components. The additive manufacturing technology lends itself to fabricate components with complex features such as internal coolant channels in combustion chambers that would otherwise require complex manufacturing operations. A requirement for high performance engines is to use high conductivity, high strength materials such as copper-alloys for combustion chamber liners to provide adequate wall temperatures and meet subsequent structural margins. A further requirement of this configuration is to minimize weight by defining and fabricating material in discrete locations as required. NASA and Industry partner, Virgin Orbit, have been working to advance these technologies through development of bimetallic additive manufacturing techniques under a public-private partnership through NASA’s Announcement of Collaborative Opportunity (ACO). This partnership is advancing a bimetallic hybrid additively manufactured combustion chamber that integrates Powder Bed Fusion (PBF), specifically Selective Laser Melting (SLM), and Directed Energy Deposition (DED) blown powder techniques to optimize the chamber materials and subsequent assembly. The SLM process is being developed for the combustion chamber liner to use copper-alloys GRCop-84 (Copper-Chrome-Niobium) or C-18150 (Copper-Chrome-Zirconium). The hybrid DED blown powder technology is used to apply an integrated structural jacket and manifolds using an Inconel 625 superalloy on the outer surface of the SLM copper liner. The hybrid DED technology being used on this program is a DMG Mori Seiki AM machining center which integrates the DED blown powder with an integral subtractive (traditional) machining to minimize overall setups. A series of chambers were fabricated using these techniques with GRCop-84/Inconel 625 and C-18150/Inconel and hot-fire tested at NASA Marshall Space Flight Center (MSFC) in LOX/Kerosene (RP-1). This paper describes the process development to integrate these AM technologies into an integrated bimetallic assembly, the design of the chamber, results from hot-fire testing, and further development.

Gradl, Paul R.↗
NASA NTRS2019

Additive Manufacturing and Hot-fire Testing of Bimetallic GRCop-84 and C-18150 Channel-Cooled Combustion Chambers using Powder Bed Fusion and Inconel 625 Hybrid Directed Energy Deposition

Additive manufacturing (AM) is an advanced fabrication technique that is demonstrating tremendous potential to reduce fabrication lead times and costs for liquid rocket engine components. The additive manufacturing technology lends itself to fabricate components with complex features such as internal coolant channels in combustion chambers that would otherwise require complex manufacturing operations. A requirement for high performance engines is to use high conductivity, high strength materials such as copper-alloys for combustion chamber liners to provide adequate wall temperatures and meet subsequent structural margins. A further requirement of this configuration is to minimize weight by defining and fabricating material in discrete locations as required. NASA and Industry partner, Virgin Orbit, have been working to advance these technologies through development of bimetallic additive manufacturing techniques under a public-private partnership through NASA’s Announcement of Collaborative Opportunity (ACO). This partnership is advancing a bimetallic hybrid additively manufactured combustion chamber that integrates Powder Bed Fusion (PBF), specifically Selective Laser Melting (SLM), and Directed Energy Deposition (DED) blown powder techniques to optimize the chamber materials and subsequent assembly. The SLM process is being developed for the combustion chamber liner to use copper-alloys GRCop-84 (Copper-Chrome-Niobium) or C-18150 (Copper-Chrome-Zirconium). The hybrid DED blown powder technology is used to apply an integrated structural jacket and manifolds using an Inconel 625 superalloy on the outer surface of the SLM copper liner. The hybrid DED technology being used on this program is a DMG Mori Seiki AM machining center which integrates the DED blown powder with an integral subtractive (traditional) machining to minimize overall setups. A series of chambers were fabricated using these techniques with GRCop-84/Inconel 625 and C-18150/Inconel and hot-fire tested at NASA Marshall Space Flight Center (MSFC) in LOX/Kerosene (RP-1). This paper describes the process development to integrate these AM technologies into an integrated bimetallic assembly, the design of the chamber, results from hot-fire testing, and further development.

Gradl, Paul↗
DOE OSTI2020

Dynamic Performance Enhancement for Nonlinear Stochastic Systems Using RBF Driven Nonlinear Compensation with Extended Kalman Filter

In this paper, a novel hybrid control method is proposed to enhance the control performance of the PI based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. Firstly, to enhance the tracking performance of the PI controller where the PI parameters are fixed in many actual control systems, the compensative signal is formed using the extended Kalman filter(EKF) based state estimator and driven by the radial basis function (RBF) neural network based compensator. In addition, the weights of RBF is trained to minimize the entropy criterion of tracking error as the process is subjected to non-Gaussian disturbances. Meanwhile, since the precise statistical property of noises is hard to obtain for many industrial processes, the kernel density estimation (KDE) technique is employed in this paper to estimate the entropy. The convergence of RBF network is discussed and the stability of the resulted closed-loop hybrid control system is analyzed in mean square sense. Finally, a numerical example and a practical system testing are given to illustrate the effectiveness of the proposed control method.

stochastic systems, PI Controller, Entropy, Extend↗
DOE OSTI2025

Many-body perturbation theory with hybrid density functional theory starting points accelerated by adaptively compressed exchange

We report on the use of the adaptively compressed exchange (ACE) operator to accelerate many-body perturbation theory (MBPT) calculations, including G 0 W 0 and the Bethe–Salpeter equation (BSE), for hybrid density functional theory starting points. We show that by approximating the exact exchange operator with the low-rank ACE operator, substantial computational savings can be achieved with systematically controllable errors in the quasiparticle energies computed with full-frequency G 0 W 0 and the optical absorption spectra and vertical excitation energies computed by solving the BSE within density matrix perturbation theory. Our implementation makes use of the ACE-accelerated electronic Hamiltonian to carry out both G 0 W 0 and BSE without explicitly computing empty states. We show the robustness of the approach and present the computational gains obtained on both the central processing unit and graphics processing unit nodes. In conclusion, our work will facilitate the exploration and evaluation of fine-tuned hybrid starting points aimed at enhancing the accuracy of MBPT calculations without involving computationally demanding self-consistency in Hedin’s equations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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↗
NASA NTRS1996

CI Controls for Energy and Environment

Computational intelligence (CI) is a rapidly evolving field that utilizes life imitating metaphors for guiding model building including, but not limited to neural networks, fuzzy logic, genetic algorithms, artificial life, and hybrid CI paradigms. Although the boundaries between artificial intelligence (AI) and CI are not distinct, their research communities are separate and distinct. CI researchers tend to focus on processing numerical data from sensors, while the AI community generally relies on symbolic computing to capture human knowledge. In both areas, there is a great deal of interest and activity in hybrid systems that can offset the limitations of individual methods, extend their capabilities, and create new capabilities. Examples of the benefits that can accrue from hybrid systems are contained.

Biondo, Samuel J.↗
NASA NTRS2022

Functional Hazard Assessment for the eVTOL Aircraft Supporting Urban Air Mobility (UAM) Applications: Exploratory Demonstrations

The active development community surrounding electric vertical takeoff and landing (eVTOL) aircraft has demonstrated potential to bring new technological capabilities to market, encouraging visions of widespread and diverse Urban Air Mobility (UAM) applications. New capabilities always come with safety considerations, some familiar and others less so. OEMs who intend to obtain FAA type certification for their eVTOL designs must plan for and execute sufficient safety engineering processes to demonstrate that credible hazards associated with these eVTOL designs are adequately mitigated. This work provides an orientation for eVTOL stakeholders, especially new entrants and those in hybrid roles, to the safety and regulatory context for assuring eVTOL aircraft for UAM applications. We further present selections of functional hazard assessment (FHA) performed on a reference eVTOL concept, with process and decision narration. The exercise allows exploration of several safety considerations specific to eVTOL systems and demonstrates the FHA process together with negotiation of some of its options and variations.

Hazard Analysis↗
DOE OSTI2022

Chapter 10: Process Heating for Industry

The overall objective of this chapter is to help the reader better understand the technical feasibility, barriers and opportunities for renewable thermal energy systems (RTES). Solar thermal (ST) derived heat, which can be concentrating or non-concentrating, can be considered a sub-set of RTES solutions that can provide heat for industry. This chapter will focus on Industrial Process Heat (IPH) and District Heating Systems and will also highlight the difference between stand-alone and hybrid solutions. Different thermal energy sources (traditional and new) and technologies, that can provide the end-user heat delivered through many mediums, systems and for different purposes. Among various RTES, ST technologies will be the focus of this chapter.

district heating↗
DOE OSTI2024

Positioning Accuracy in a Concurrent Robot-CNC Hybrid Manufacturing System

Abstract Additive manufacturing (AM) has gained notoriety for offering advantages over traditional manufacturing methods, such as increased design complexity and flexibility. However, it has not found widespread use beyond rapid prototyping. One hindrance to the acceptance of AM processes in industry is the time and cost of fabrication per component. While metal AM by itself can be inexpensive, extra manufacturing steps in the form of subtractive manufacturing (SM) may need to be performed to reach final part tolerances, leading to hybrid additive-subtractive manufacturing (HASM) of a part, which increases time and cost. A potential area to reduce cost is through increasing the efficiency of the HASM process by conducting additive and subtractive manufacturing simultaneously. Usually, HASM is performed in a process where AM is completed in one machine or cell and transferred to another machine or cell for SM in a sequential assembly line process. This efficiency decreases part cost, but high aspect ratio parts or parts with internal geometry that require interleaved additive deposition and machining cannot be produced. One unexplored solution to simultaneous HASM that allows for interleaved operations is to operate the deposition head and machining spindle concurrently within the same machine envelope, known as concurrent HASM (CHASM). In this type of process, both AM and SM occur simultaneously on a batch of small parts or a single large part, maintaining a high efficiency without sacrificing the full range of complex geometries that AM allows for. A potential approach to the single-machine method could be to combine a robot and mill within the same envelope. A challenge to this approach, however, is control of both systems. Most machine controllers have limited external communication or, if a robot has been integrated, only offer movement of either the robot or mill at any given time. As a result, systems must pause either the AM or SM process to switch between them rather than working simultaneously. The present work investigates the positional accuracy of such a CHASM system comprised of a robotic arm and a 3-axis mill. Open-loop tests with limited communication between machines are performed on the system to verify positional error during concurrent robot-mill movements. Under certain conditions, it is demonstrated that position error can stay within 2 mm for the duration of a single layer; however, these tests show that, generally, the open-loop positioning performance of the system is inadequate for CHASM without part-specific hand-tuning of parameters. Based on these results, a set of requirements for successful robot-CNC CHASM is proposed for future integrations.

Goodwin, Jesse↗
NASA NTRS1985

Voyager Cartography

The Jovian and Saturnian satellites are being mapped at several scales from Voyager 1 and 2 data. The maps include specially formatted color mosaics, controlled photomosaics, and airbrush maps. More than 500 Voyager images of the Jovian and Saturnian satellites were radiometrically processed in preparation for cartographic processing. Of these images, 235 were geometrically transformed to map projections for base mosaic compilations. Special techniques for producing hybrid photomosaic/airbrush maps of Callisto are under investigation. The techniques involve making controlled computer mosaics of all available images with highest resolution images superimposed on lowest resolution images. The mosaics are then improved by airbrushing: seams and artifacts are removed, and image details enhanced that had been lost by saturation in some images. A controlled mosaic of the northern hemisphere of Rhea is complete, as is all processing for a similar mosaic of the equatorial region. Current plans and status of the various series are shown in a table.

Batson, R. M.↗
DOE OSTI2021

A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems

This article proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

In-situ Photophysical Monitors and Corrective Algorithms for Photovoltaic Film Deposition and Rapid Thermal Processing in Scalable Roll-to-Roll Manufacturing

This project evaluated three photophysics-based approaches for in situ process monitoring of the scalable deposition of perovskite photovoltaic films. Based on stakeholder input and the results of laboratory trials and literature analysis, two techniques, angle resolved elastic light scattering (ARLS) and time-resolved photoluminescence (TRPL) were prototyped and demonstrated with a mixture of simulation, offline and online tests. Also, a hybrid gas flow and humidity control enclosure was constructed and demonstrated to stabilize the ambient conditions around the slot die deposition process and were demonstrated to meet the target humidity control in situ. Due in part to a delayed start and COVID-related lab restrictions and personnel changes, some of the milestones were not met. In spite of these challenges, however, both techniques were prototyped through several iterations and demonstrated significant promise for rapid in line perovskite characterization within the targeted characterization time windows and these techniques are under further development for use in DE-FOA000235:2357-1586. The TRPL system was fully mounted and demonstrated on the scaled slot die roll to roll and sheet coating systems at the Washington Clean Energy Testbeds (WCET). Motivated by significant internal and stakeholder interest, the successful prototyping achieved here, the ongoing need for in-situ characterization for the scale-up of perovskite manufacturing, and the benefits of in situ photophysical characterization for future funded process development research, we are continuing with the principle aims of the project to develop effect in situ photophysical probes for fast analysis of the optoelectronic and structural quality of sheet based and roll to roll process perovskites. TRPLS and ARLS probes proposed in the project are to be completed with external support from the Washington Research Foundation at the WCET. We are currently actively engaged in the acquisition of components and plan to complete and test the probes within three months. It is our intention that these probes will then be used in ongoing perovskite development with our research and commercialization partners including First Solar and other member of the US MAP consortium in future funded development work.

14 SOLAR ENERGY↗
DOE OSTI2025

Substrate matters: Coupled phonon modes of a spherical particle on a substrate probed with EELS

Using vibrational electron energy loss spectroscopy (vib-EELS) combined with numerical modeling, we investigate the physical mechanisms governing the phonon coupling between a spherical particle sustaining multipolar surface phonon modes and an underlying thin film. Depending upon their dielectric composition, a variety of hybrid phonon modes arise in the EEL spectrum due to the interaction between polarization charges in the particle and film. Mirror charge effects and phonon mode hybridization are the active mechanisms acting on dielectric and metallic-type films, respectively. Furthermore, processes beyond dipole-dipole interactions are required to describe the sphere-film coupling.

Localized surface phonon response↗