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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Hybrid height and slope figuring method for grazing-incidence reflective optics

Grazing-incidence reflective optics are commonly used in synchrotron radiation and free-electron laser facilities to transport and focus the emitted X-ray beams. To preserve the imaging capability at the diffraction limit, the fabrication of these optics requires precise control of both the residual height and slope errors. However, all the surface figuring methods are height based, lacking the explicit control of surface slopes. Although our preliminary work demonstrated a one-dimensional (1D) slope-based figuring model, its 2D extension is not straightforward. In this study, a novel 2D slope-based figuring method is proposed, which employs an alternating objective optimization on the slopes in the x - and y -directions directly. An analytical simulation revealed that the slope-based method achieved smaller residual slope errors than the height-based method, while the height-based method achieved smaller residual height errors than the slope-based method. Therefore, a hybrid height and slope figuring method was proposed to further enable explicit control of both the height and slopes according to the final mirror specifications. An experiment to finish an elliptical-cylindrical mirror using the hybrid method with ion beam figuring was then performed. Both the residual height and slope errors converged below the specified threshold values, which verified the feasibility and effectiveness of the proposed ideas.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hybrid Data-Driven Physics-Based Model Framework Implementation: Towards a Secure Cyber-Physical Operation of the Smart Grid

False data injection cyber-attack detection models on smart grid operation have been much explored recently, considering analytical physics-based and data-driven solutions. Recently, a hybrid data-driven physics-based model framework for monitoring the smart grid is developed. However, the framework has not been implemented in real-time environment yet. In this paper, the framework of the hybrid model is developed within a real-time simulation environment. OPAL-RT real-time simulator is used to enable Hardware-in-the-Loop testing of the framework. IEEE 9-bus system is considered as a testing grid for gaining insight. The process of building the framework and the challenges faced during development are presented. The performance of the framework is investigated under various false data injection attacks.

false data injection attack, machine learning, sta↗
DOE OSTI2023

Joint Resource Modeling and Assessment for Hybrid Distributed Solar and Wind Systems

The inherent variability and uncertainty in distributed energy resources can presents myriad challenges to the planning and operations of power systems. These risks are poised to become larger as the penetration of renewable energy sources rises in the power generation mix. Hybrid solar-wind energy systems are able to mitigate some of these risks by their complementary resource availability. Surface solar and wind fields are coupled and correlated in both space and time. Appropriately estimating the hybrid solar wind energy system requires simulating the spatio-temporal structure of these fields that can be produced for each time horizon. We introduce a novel joint spatio-temporal stochastic differential equation (SPDE) approach that captures the spatio-temporal dynamics of solar and wind fields and their joint dependency over a domain for each time step. In the case study on Colorado, we consider nonstationary three-level hierarchical spatio temporal models for both hourly solar irradiance data and wind speed data in Colorado. Dependence between the solar irradiance data and wind speed data is captured by a shared spatio-temporal random effect. Our approach performs well in terms of the prediction score criterion.

joint modeling↗
DOE OSTI2026

Fidelity Comparison of Time-Bin and Fock State Encoding in Hybrid Quantum Systems Under Channel and Transduction Effects

Future quantum networks are expected to integrate a heterogeneous combination of quantum systems, owing to the diverse advantages offered by different physical platforms in terms of scalability, coherence times, and interfacing capabilities. Within the context of this emerging quantum communication paradigm, this paper presents an analytical comparison of two photonic encoding schemes—time-bin and Fock state (single rail)—in hybrid quantum systems where flying qubits are entangled with stationary matter qubits. We evaluate their resilience against fiber channel and quantum transduction effects by calculating the fidelity of the final states relative to their ideal forms. Employing the characteristic function approach, we derive analytical fidelity expressions and investigate their dependence on parameters such as transmissivity, noise levels, fiber length, and source generation success probability. Additionally, we simulate the scenario with a dedicated QuTiP software implementation to verify the validity of the theoretical models. Our findings reveal that due to its inherent single-mode structure, the Fock state encoding consistently outperforms time-bin encoding in fidelity, as this structure significantly minimizes susceptibility to losses compared to the two-mode nature of the time-bin scheme. This analysis offers valuable insights for future hybrid quantum communication and information processing applications.

Fiorini, Francesco [Pisa U.]↗
DOE OSTI2023

Hybrid-Magnon Quantum Devices: Strategies and Approaches

Hybrid magnonics offers novel paths towards quantum information by utilizing the unique advantages of magnon excitations. We review two strategies of hybrid magnonics for advancing quantum computing, i.e., multi-magnon entanglement and nonreciprocal magnon transduction. We also present our efforts on low-damping magnetic materials, short-wavelength magnon transduction, and nanowire superconducting circuits.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modeling and Validation of Offset Cancellation for Hybrid Photonic-Electronic Transimpedance Amplifier Using All-Electronic Circuits

Hybrid photonic-electronic circuits involving crossdomain feedback signals are difficult to validate and troubleshoot. This paper describes an approach to model and validate an offset-cancellation loop for a hybrid transimpedance amplifier (TIA) using all-electronic circuits. An equivalent electrical model is developed for the tunable interferometer attenuators used for offset trimming, and it includes test features to extract the system performance. The stability analysis of the system shows 75 ◦ of phase margin and 102 dB of gain margin. Measurement results demonstrate good agreement with analysis and successful TIA offset cancellation.

Marchant, Jared↗
DOE OSTI2022

A Hybrid Data-Driven and Model-Based Anomaly Detection Scheme for DER Operation

This paper proposes a hybrid data and model-based anomaly detection scheme to secure the operation of distributed energy resources (DERs) in distribution grids. Data-driven autoencoders are set up at the edge device level and they use local DER operational data as inputs. The abnormal statuses are detected by analyzing reconstruction errors. In parallel, modelbased state estimation (SE) is set up at the central level and it uses system-wide models and measurements as data inputs. The anomalies are identified by analyzing measurement residuals. The hybrid scheme preserves the benefits of both data-driven and model-based analyses and thus improves the robustness and the accuracy of anomaly detection. Numerical tests based on the model of a real distribution feeder in Southern California highlight the proposed scheme's effectiveness and benefits.

anomaly detection↗
DOE OSTI2023

A Hybrid Optimization and Deep Learning Algorithm for Cyber-Resilient DER Control

With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update.

cyber-resilient algorithm↗
DOE OSTI2026

Hybrid Symbolic-Numerical Modeling and Parametric Stability Analysis of DC–AC Power Systems

Hybrid DC-AC power systems integrating diverse inverter-based resources (IBRs) and multi-terminal high-voltage direct current (MTDC) networks represent a promising architecture for future power grids, while introducing challenges for modeling, stability analysis, and control design. This paper develops a hybrid symbolic-numerical modeling framework and tool to characterize the parametric small-signal stability of DC-AC coupled power systems. The proposed approach constructs parametric state-space models to enable efficient representation of system dynamics under varying control parameters and network configurations, with target parameters retained as symbolic variables and the remainder treated numerically. The stability analysis framework covers eigenvalue, sensitivity, and stability boundary and region characterization. Enhanced linear matrix inequality (LMI) techniques are proposed to directly certify small-signal stability over regions of parameter space while also reducing the conservativeness and computational burden. The resulting tools and frameworks enable rapid parametric model construction across diverse grid conditions, thereby facilitating stability-informed control and operation in complex DC–AC power systems.

DC–AC power systems↗
DOE OSTI2024

Taylor-Expansion-Based Robust Power Flow in Unbalanced Distribution Systems: A Hybrid Data-Aided Method

Traditional power flow methods often adopt certain assumptions designed for passive balanced distribution systems, thus lacking practicality for unbalanced operation. moreover, their computation accuracy and efficiency are heavily subject to unknown errors and bad data in measurements or prediction data of distributed energy resources (ders). to address these issues, this paper proposes a hybrid data-aided robust power flow algorithm in unbalanced distribution systems, which combines taylor series expansion knowledge with a data-driven regression technique. the proposed method initiates a linearization power flow model to derive an explicitly analytical solution by modified taylor expansion. to mitigate the approximation loss that surges due to the der integration and bad data, we further develop a data-aided robust support vector regression approach to estimate the errors efficiently. comparative analysis in the 13-bus and 123-bus ieee unbalanced feeders shows that the proposed hybrid algorithm achieves superior computational efficiency, with guaranteed accuracy and robustness against outliers.

data-driven↗
DOE OSTI2023

Model Predictive Voltage Control of Large-Scale PV or Hybrid PV-BESS Plants

Increased penetration level of inverter-based resources (IBRs) and renewable energy in the power grid has called for more requirements from the control of IBRs. In this paper, the voltage-reactive power control of a photovoltaic (PV) power plant or hybrid PV-battery energy storage system (BESS) plant connected to a bulk power grid, whilst meeting the grid requirements, is studied. In this paper, a continuous-set model predictive control (MPC) formulation is proposed for voltage-reactive power control. For the same, an aggregated dynamic PV plant model is developed based on the recursive system equivalencing method. The formulation is then implemented in PSCAD simulation on a 125 MW PV plant or hybrid PV-BESS plant. The MPC implementation achieves 10.13% voltage improvement based on PSCAD simulation results in a balanced fault case study. Simulation results further demonstrate that MPC provides improved voltage support over the conventional proportional-integral (PI) controller post-fault occurrence.

Abu rub, Omar↗
DOE OSTI2024

Conceptual Structural Design and Analysis of a 20 T Hybrid Cos Dipole for Future Particle Colliders

Here, to reach high collision energy for future high-energy particle colliders, like the Future Circular Collider (FCC) or the Muon Collider, it is required to achieve high field strength of the bending dipoles. Currently, the practical limit for Nb$_{\text{3}}$ Sn technology is around 16 T and, in order to further increase the magnetic field, the superconducting magnet community is considering High Temperature Superconductors (HTS), in particular Bi-2212 and REBCO conductors. However, their relevant higher cost has led the community to consider a hybrid approach where HTS materials are used in the high field region of the coils with so-called insert coils, and Low Temperature Superconductors are involved in the lower field part ($< $ 16 T) with so-called outsert coils. This paper describes the conceptual mechanical design of a 20 T hybrid cos$\theta$ dipole configuration. The high stress levels that the structure is facing due to the high magnetic field are discussed. Moreover, it presents the results of the optimization analysis of the shell-based support structure based on the key-and-bladder technology that provides the azimuthal pre-stress during room temperature assembly and cooldown to cryogenic temperatures. The aim of this work is to present a feasible design that satisfies the stress requirements.

D'Addazio, Marika [Politecnico di Torino (Italy); ↗
DOE OSTI2022

A Hybrid DC Fault Primary Protection Algorithm for Multi-Terminal HVdc Systems

Protection against dc faults is one of the main technical hurdles faced when operating converter-based HVdc systems. Protection becomes even more challenging for multi-terminal dc (MTdc) systems with more than two terminals/converter stations. In this paper, a hybrid primary fault detection algorithm for MTdc systems is proposed to detect a broad range of failures. Sensor measurements, i.e., line currents and dc reactor voltages measured at local terminals, are first processed by a top-level context clustering algorithm. For each cluster, the best fault detector is selected among a detector pool according to a rule resulting from a learning algorithm. The detector pool consists of several existing detection algorithms, each performing differently across fault scenarios. The proposed hybrid primary detection algorithm: i) offers superior performance compared to an individual detector through a data-driven approach; ii) detects all major fault types including pole-to-pole (P2P), pole-to-ground (P2G), and external dc faults; iii) identifies faults with various fault locations and impedances; iv) is more robust to noisy sensor measurements compared to existing methods; v) does not require exhaustive simulation and sampling for training the model. Performance and effectiveness of the proposed algorithm are evaluated and verified based on time-domain simulations in the PSCAD/EMTDC software environment. The results confirm satisfactory operation, accuracy, and detection speed of the proposed algorithm under various fault scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Analyzing Gas Turbine-Generator Performance of the Hybrid Power System

We report kinetic energy available from the turbomachinery may boost short-term ramp rates during load shifting between generator and fuel cell that may be exploited to optimize the performance of turbine-fuel cell hybrid systems during load following. The paper starts with modeling a gas turbine, gearbox, and generator sitting on the rigid shafts as a two-mass model primarily for estimating the rotational inertia available from the turbogenerator. The gas turbine and its environment impose constraints of available space allowances, measurements from high-speed rotating parts, lack of accessibility to the shaft of measure, making replacements and/or required installation adjustments of the torque transducer difficult. It is demonstrated that in the absence of torque measurement, the data containing only a slow excursion of the rotor motion in response to a slight load change is sufficient to determine inertia and damping coefficients from the model. Furthermore, the generator’s reactive power capability was investigated because it serves as an asset in integrated hybrid systems to meet dynamic reactive power demands, reducing the burden on the fuel cell’s power electronics control in generating reactive power.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A TCN-Based Hybrid Forecasting Framework for Hours-Ahead Utility-Scale PV Forecasting

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven fluctuation forecasting (FF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the FF model to achieve more accurate forecast of intra- hour PV power drops, and iii) reconciling TF and FF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the FF model on capturing large power fluctuations caused by cloud movements. Here, the framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by 20% - 30% compared with state-of-the-art methods.

42 ENGINEERING↗
DOE OSTI2020

Hybrid Li-S pouch cell with a reinforced sulfide glass solid-state electrolyte film separator

Here, in this paper, sulfide glass electrolyte powder with a nominal composition of (Li 2 S) 60 (SiS 2 ) 28 (P 2 S 5 ) 12 is hot pressed at a 330°C and 12 MPa into separator films using non-woven fiberglass reinforcement. The reinforced separator films are 93% dense, bendable, and have an ionic conductivity of 0.7 mS/cm at room temperature. When integrated into hybrid Li-S coin cells with liquid electrolyte, these reinforced glass separators blocked the parasitic polysulfide shuttle sufficiently to enable reversible cycling without LiNO 3 co-salt. Furthermore, the scalability of the reinforced glass separator technology is demonstrated by assembling and testing a hybrid single-layer pouch cell.

25 ENERGY STORAGE↗
DOE OSTI2023

Environmental stability of additively manufactured siliconized silicon carbide for applications in hybrid energy systems

A key consideration for the successful operation of hybrid energy systems will be the environmental stability of materials used for their construction, particularly when experiencing service environments containing water vapor at high temperatures. Here, we report results from the characterization of siliconized silicon carbide (Si-SiC) prepared via binder jet additive manufacturing and reactive silicon melt infiltration after being exposed to environments representative of those in solid oxide fuel cell (SOFC) anodes, and to exhaust gases inside a microturbine operating on natural gas. In both cases, it was found that oxide scales formed on the surface and that these scales were dense, continuous, and well-bonded to the substrates, although there was evidence of transverse and longitudinal cracking most likely as a result of mismatches in the thermal expansion of the scale and the substrate. Measured values of the thickness of the oxide scale were compared to those predicted by parabolic oxidation kinetics of silicon, but the potential effects of silica volatilization induced by water vapor, and silica reduction when exposed to hydrogen are discussed. The overall results showed that the oxide scale is expected to be protective under the conditions of hybrid power generation systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hybrid Nanostructured Ni(OH) 2 /NiO for High Capacity Lithium–ion Battery Anodes

A straightforward hydrothermal process followed by a controlled calcination technique is introduced for the synthesis of a Ni(OH) 2 modified NiO nanohybrid structure. Conversion materials as Li-ion battery anodes, NiO in this case, suffer from capacity fade and structural/morphological instability during lithiation and delithiation. The novelty of this work is in utilizing this hybrid configuration to increase the specific capacity and enable reversible electrochemistry. Here, we study the lithiation/delithiation process of NiO using a suite of spectroscopy and microscopy techniques from the atomic to electrode scale. We propose a mechanism for a reversible redox couple behavior of the NiO electrode by means of a hybrid Ni(OH)2/NiO structure. The ultimate objective of this work is to guide the development of anode with rationally-designed heterogeneity to create high capacity Li-ion batteries with excellent cycling and rate performance.

25 ENERGY STORAGE↗