SEARCH · Engineering Papers

Results for “Hybrid Work”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Vibrational Fermi Resonance in Atomically Thin Black Phosphorus

Fermi resonance is a phenomenon involving the hybridization of two coincidentally quasi-degenerate states that is observed in the vibrational or electronic spectra of molecules. Despite numerous examples in molecular systems, vibrational Fermi resonances in dispersive semiconducting systems remain largely unexplored due to the rarity of occurrence. Here we report a vibrational Fermi resonance in atomically thin black phosphorus. The Fermi resonance arises via anharmonic mixing of a fundamental Raman mode and a Davydov component of an infrared mode, leading to a doublet with mixed character. The extent of Fermi coupling can be modulated by the application of external biaxial strain. The consequences of Fermi hybridization are revealed by electronic resonance effects in the thickness-dependent and excitation-wavelength-dependent Raman spectrum, which is predicted by ab initio hybrid functional simulations including excitonic interactions. This work reveals new insight into electron–phonon coupling in black phosphorus and demonstrates a novel method for modulating Fermi resonances in 2D semiconductors.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

Quantum solver for single-impurity Anderson models with particle-hole symmetry

Quantum embedding methods, such as dynamical mean-field theory (DMFT), provide a powerful framework for investigating strongly correlated materials. A central computational bottleneck in DMFT is in solving the Anderson impurity model (AIM), whose exact solution is classically intractable for large bath sizes. In this work, we benchmark a quantum-classical hybrid solver tailored for particle-hole symmetric AIMs, using the variational quantum eigensolver to prepare the ground state of the model with shallow quantum circuits. The solver uses shallow quantum ansätze and one set of variational parameters to prepare the ground state and its particle and hole excitations, enabling the construction of the impurity Green’s function through a continued-fraction expansion. We evaluate the performance of this approach across a few bath sizes and interaction strengths under noisy, shot-limited conditions. We compare three optimization routines (COBYLA, Adam, and L-BFGS-B) in terms of convergence and fidelity, assess the benefits of estimating a quantum-computed moment correction to the variational energies, and benchmark the approach by comparing the density of states computed from the impurity Green’s function against that obtained using a classical pipeline. Our results demonstrate the feasibility of Green’s function construction on near-term devices and establish practical benchmarks for quantum impurity solvers embedded within self-consistent DMFT loops.

Karabin, Mariia [ORNL]↗

DOE OSTI2021

Develop Wake Mitigation Strategy (CRADA CRD-17-00693 Final Report)

Wind turbines in a wind farm typically operate individually to maximize their own performance and do not take into account information from nearby turbines. In an autonomous wind farm, enabling cooperation to achieve farm-level objectives, turbines will need to use information from nearby turbines to optimize performance, ensure resiliency when other sensors fail, and adapt to changing local conditions. A key element of achieving an autonomous wind farm is to develop algorithms that provide necessary information to ensure reliable, robust, and efficient operation of wind turbines in a wind plant using local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. In this work consensus control is applied in a hybrid analysis to data from an existing wind farm to demonstrate the benefit of consensus control.

17 WIND ENERGY↗

DOE OSTI2021

Federated Quantum Machine Learning

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Resilience for Advanced Distributed Wind Systems: Identifying the resilience benefits of advanced controls and hybrid systems for distributed wind

Under the Department of Energy (DOE) Wind Energy Technologies Office (WETO), Idaho National Laboratory (INL) has been tasked with defining the resilience benefits of distributed wind systems for the Microgrids, Infrastructure Resilience, and Advanced Controls Launchpad (MIRACL) project. This project is a collaboration between the National Renewable Energy Laboratory (NREL), Sandia National Laboratories (SNL) and Pacific Northwest National Laboratory (PNNL). In the final year of this project, INL is collaborating with the other labs to bring together key results from our previous work on resilience, cybersecurity and risk, distributed wind hybrid systems, and valuation of distributed wind.

17 WIND ENERGY↗

DOE OSTI2025

Emergent properties of magnons coupled to microwave photons (Final Technical Report)

Studying the emergent properties of hybrid quantum material platforms holds great promise for advancing quantum technologies and transforming our ability to control quantum mechanical interactions. A key development in recent years is the recognition that magnons – the elementary quanta of spin waves – can serve as fundamental building blocks in quantum systems. Understanding the mechanisms behind the generation and control of hybrid quasiparticles based on magnons could pave the way for engineering new materials for quantum coherent processing and quantum computing. This research project focused on the precise control of light-matter interactions in magnetic hybrid systems and nanostructures, where light is carried by microwave photons and matter by magnons in engineered magnetic metamaterials. Furthermore, we investigated hybridized magnon interactions in magnetic nanostructures and devices. To this end, the project has developed new measurement techniques and systematically studied novel material systems for efficient magnon-photon coupling. A key focus was uncovering the fundamental mechanisms governing magnon-photon and magnon-phonon interactions – critical for utilizing magnons as coherent information transducers between carriers. Specifically, this research addressed: 1. Investigate the dispersion and collective properties of magnon hybrid systems in new material systems. 2. Develop magnonic hybrids with non-zero wavevectors and achieve effective control of magnon-polariton properties through engineered magnonic behaviors. 3. Determine how spin-orbit torques interact with electromagnetic fields in the strong coupling regime. This work generated new insights into the physics of magnonic hybrid systems and their emergent properties by broadening the range of material platforms and measurement techniques used to study magnon-photon and magnon-magnon coupling. The findings deepen our understanding of magnon-polaritons and lay the groundwork for novel spintronic devices with potential applications in quantum information science.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

The Ghent Hybrid model in NuWro: a new neutrino single-pion production model in the GeV regime

Neutrino-induced single-pion production constitutes an essential interaction channel in modern neutrino oscillation experiments, with its products building up a significant fraction of the observable hadronic final states. Frameworks of oscillation analyses strongly rely on Monte Carlo neutrino event generators, which provide theoretical predictions of neutrino interactions on nuclear targets. Thus, it is crucial to integrate state-of-the-art single-pion production models with Monte Carlo simulations to prepare for the upcoming systematics-dominated landscape of neutrino measurements. In this work, we present the implementation of the Ghent Hybrid model for neutrino-induced single-pion production in the NuWro Monte Carlo event generator. The interaction dynamics includes coherently-added contributions from nucleon resonances and a non-resonant background, merged into the pythia branching predictions in the deep-inelastic regime, as instrumented by NuWro. This neutrino-nucleon interaction model is fully incorporated into the nuclear framework of the generator, allowing it to account for the influence of both initial- and final-state nuclear medium effects. We compare the predictions of this integrated implementation with recent pion production data from accelerator-based neutrino experiments. The results of the novel model show improved agreement of the generator predictions with the data and point to the significance of the refined treatment of the description of pion-production processes beyond the ∆ region.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics

Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerbated as the amount and complexity of the data increases. Quantum-based vision transformer models could potentially alleviate this issue by reducing the training and operating time while maintaining the same predictive power. Although current quantum computers are not yet able to perform high-dimensional tasks, they do offer one of the most efficient solutions for the future. In this work, we construct several variations of a quantum hybrid vision transformer for a classification problem in high-energy physics (distinguishing photons and electrons in the electromagnetic calorimeter). We test them against classical vision transformer architectures. Our findings indicate that the hybrid models can achieve comparable performance to their classical analogs with a similar number of parameters.

Unlu, Eyup B. (ORCID:0000000266836463)↗

DOE OSTI2021

Coreset Clustering on Small Quantum Computers

Many quantum algorithms for machine learning require access to classical data in superposition. However, for many natural data sets and algorithms, the overhead required to load the data set in superposition can erase any potential quantum speedup over classical algorithms. Recent work by Harrow introduces a new paradigm in hybrid quantum-classical computing to address this issue, relying on coresets to minimize the data loading overhead of quantum algorithms. We investigated using this paradigm to perform k-means clustering on near-term quantum computers, by casting it as a QAOA optimization instance over a small coreset. We used numerical simulations to compare the performance of this approach to classical k-means clustering. We were able to find data sets with which coresets work well relative to random sampling and where QAOA could potentially outperform standard k-means on a coreset. However, finding data sets where both coresets and QAOA work well—which is necessary for a quantum advantage over k-means on the entire data set—appears to be challenging.

42 ENGINEERING↗

DOE OSTI2022

Optimizing the fuel efficiency of an opposed piston engine for electric power generation

This paper investigates the optimal crankshaft motion for an opposed piston (OP) engine in a novel hybrid architecture to maximize fuel efficiency. The OP engine was selected for this work due to its inherent thermodynamic benefits and the balanced nature of the engine which can achieve downsizing through reducing the number of cylinders rather than the individual cylinder volume. The typical geartrain required on an OP engine was exchanged for two electric motors, reducing friction loss and decoupling the crankshafts. Using the motors to control the crankshaft motion profiles, this architecture introduces capabilities to dynamically vary compression ratio, combustion volume, and scavenging dynamics. To leverage these opportunities, an optimization scheme was developed utilizing nonlinear optimization of a 0-D model to compute the crankshaft motion profile that maximizes the work generated by the system. This optimization was then iteratively coupled with a high fidelity model which supplies the cylinder flow boundary conditions. This iterative approach reduces the model complexity used in the optimal control problem (OCP) while capturing the gas exchange dynamics critical to the 2-stroke cycle of the OP engine. By using the rate of change of motor torque as the input to the OCP, the torque fluctuation in a single cycle can be limited to ensure tracking feasibility. The results show crankshaft velocity slows during the compression stroke and conversely accelerates during the expansion stroke, reducing the peak motor torque required for control and thus reducing the motor losses. The extended residence time at top dead center, however, leads to an increase in heat transfer, illustrating the trade-off between the work extraction efficiency and the indicated engine efficiency.

Engineering↗

DOE OSTI2026

Energy Consumption, Performance, and Cost Estimates for Medium and Heavy-Duty Vehicles Based on 2022 Assumptions

Assumptions for this work was collected and the analysis was completed in FY22. This contains information for more than 20 types of medium and heavy duty vehicles. Vehicles with various levels of hybridization, electric and fuel cell powertrains are considered in this work. More details are available in the report published by Argonne accessible from https://vms.taps.anl.gov/research-highlights/u-s-doe-vto-hfto-r-d-benefits/. TechScape, a convenient data visualization tool is also provided by Argonne for this data, accessible from [TechScape Web](https://vms.taps.anl.gov/data/techscape-web-2023/).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Bidirectional phonon emission in two-dimensional heterostructures triggered by ultrafast charge transfer

Photoinduced charge transfer in van der Waals heterostructures occurs on the 100 fs timescale despite weak interlayer coupling and momentum mismatch. However, little is understood about the microscopic mechanism behind this ultrafast process and the role of the lattice in mediating it. Here, using femtosecond electron diffraction, we directly visualize lattice dynamics in photoexcited heterostructures of WSe 2 /WS 2 monolayers. Following the selective excitation of WSe 2 , we measure the concurrent heating of both WSe 2 and WS 2 on a picosecond timescale—an observation that is not explained by phonon transport across the interface. Using first-principles calculations, we identify a fast channel involving an electronic state hybridized across the heterostructure, enabling phonon-assisted interlayer transfer of photoexcited electrons. Phonons are emitted in both layers on the femtosecond timescale via this channel, consistent with the simultaneous lattice heating observed experimentally. Taken together, this shows our work indicates strong electron–phonon coupling via layer-hybridized electronic states—a novel route to control energy transport across atomic junctions.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Demonstration of Vertical 2T-nC FeRAM Hybrid Cell and Its Scalability for High-Density 3-D Ferroelectric Capacitor Memory

In this work, we present a comprehensive experimental and modeling study on the scaling of vertical 2T-nC ferroelectric random access memory (FeRAM) hybrid cells, comprising n metal-ferroelectric–metal (MFM) capacitors, to demonstrate a high-performance and high-density 3-D capacitor memory. Our contributions include: 1) successful process integration of vertical 2T-3C FeRAM cells by stacking MFM structures on top of Si CMOS transistors; 2) experimental validation of memory cell functionality, confirming the feasibility of the vertical 2T-nC FeRAM architecture; 3) an analysis of scaling effects on parasitic capacitance in densely integrated 3-D arrays, using 3-D technology computer-aided design (TCAD) simulations; 4) exploration of aggressive stacking of write bitlines (WBLs) to enhance memory density, where ferroelectric linear capacitance ( C FE ) enables self-boosted inhibition under the V W /2 scheme, but renders the V W /3 scheme ineffective due to intolerable write disturbances; and 5) assessment of horizontal scaling, revealing significant increases in read disturbances caused by interplane capacitance between adjacent WBLs ( C Z ). This work represents an early exploration into the potential of 2T-nC FeRAM as a scalable and efficient 3-D memory solution.

42 ENGINEERING↗

DOE OSTI2025

High-energy $η^{(')}π$ photoproduction and the nature of exotic waves

The observation of hybrid mesons in photoproduction experiments can provide essential insight into the inner workings of quantum chromodynamics in the strong coupling regime. In particular, the study of final $η^{(')}π$ states is of great interest due to the presence of the lowest lying hybrid candidate with manifestly exotic quantum numbers, the $π_1$(1600). In this work, a double-vector exchange model with Reggeized $ρ$ and $ω$ trajectories is developed to describe the photoproduction of $η^{(')}π$ in the high-mass region. Results are presented for the differential cross sections and forward-backward asymmetries in the energy region of interest to the GlueX experiment. The model contains no free parameters, and reproduces the magnitude and t-dependence of existing CLAS data at $E_γ$ = 5 GeV. The model predicts a stronger asymmetry in the $η'π$ channel than in the $ηπ$ channel, consistent with what has previously been observed in pion beam experiments. This suggests that the sizeable production of exotic odd waves in $η'π$ is not necessarily related to the presence of gluon-rich environments. Confirmation of these predicted asymmetries from forthcoming GlueX data would enable further predictions of the low-energy spectrum.

Montaña, Glòria [Thomas Jefferson National Acceler↗

DOE OSTI2020

Integration of a Concentrating Solar Steam Topping Turbine to an Existing Geothermal Binary Power Plant (CRADA CRD-17-700 Final Report)

U.S. Geothermal Inc. is a publicly traded leading renewable energy company focused on the development, production, and sale of electricity from geothermal energy with operating generation facilities at Raft River, Idaho; San Emidio, Nevada; and Neal Hot Springs, Oregon. (U.S. Geothermal was acquired by Ormat Technologies, Inc. since the execution of this project). This project will evaluate the integration of a concentrating solar steam topping cycle with a geothermal bottoming binary cycle. This hybrid plant configuration will enable high efficiency conversion of the solar heat to electrical power, while simultaneously increasing the power output of the bottoming cycle. Such a design has the potential to reduce the cost of electrical power from concentrating solar power (CSP) and geothermal plants, and a viable design could be deployed at existing geothermal plants that have experienced resource productivity decline as well as in greenfield projects. This analysis will examine US Geothermal’s Raft River power plant for implementation of this concept. INL and NREL have previously collaborated in the analysis of geo-solar hybrid power plants. These two labs will again work together to model the solar field, steam-topping, and geothermal bottoming cycles in order to investigate the performance of the proposed cycle. This analysis will provide US Geothermal with technical information including equipment specifications and estimated power generation necessary to advance the concept toward implementation. A successful project outcome has two parts: One would be for US Geothermal to have a process flow diagram (heat and mass balance), and performance parameters for the major pieces of equipment. US Geothermal could then use this information evaluate the techno-economic feasibility of the hybrid power cycle, and serve as the basis for subsequent detailed equipment design, purchase, and installation of the cycle. The second would be for the developed solution to be published by DOE for use by other geothermal developers both to retrofit underperforming plants and to lower the LCOE of new developments.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Joining Techniques for Novel Metal Polymer Hybrid Heat Exchangers

In the United States, over 50% of the unrecovered energy from industrial processes is in the form of low-grade heat (< 220°C). Materials and maintenance costs of common heat exchangers are typically too high to justify their usage. Polymers, though more affordable, are usually unsuitable for HX applications due to their low thermal conductivity (~0.2 W/mK). Here, we show that metal-polymer hybrids may be attractive from both performance and cost perspectives. The use of polymers further increases the resistance to corrosion by sulfuric and carbonic acids often present in flue gases. An ongoing work explores different configurations of layered polyimide-copper macroscale hybrids for heat exchanger applications using numerical simulations. This paper explores a manufacturing pathway for producing such layered hybrid tubes that involves directly rolling and bonding tapes made of polymer and copper foil into tubes. A critical problem in the fabrication process is the bonding of metal and polymers. We explore approaches involving adhesives (epoxy, acrylic and silicone) for metal/polymer interfaces and direct welding (ultrasonic) for metal/metal interfaces that can be integrated into the manufacturing process. We report characterizations of the thermomechanical properties of these joining processes. This work paves the way for realizing cost-effective manufacturing of heat exchangers for low grade waste heat recovery.

Kuntumalla, Gowtham↗

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 OSTI2024

Simulation of Direct-Drive Hybrid Using Two Opposed Beams for Inertial Fusion Energy

Xcimer Energy is working to deliver high levels of laser light at the costs required for applications in fusion energy. The architecture of their integrated laser system is unique, in that it naturally supports a target which is illuminated by two opposed beams, and in a manner that can readily complement a reactor. This INFUSE proposal was submitted to investigate a target concept termed the Direct-Drive Hybrid or DDH, and make special use of radiation hydrodynamic simulations and expertise available to the Laboratory for Laser Energetics. The associated capabilities are unique to the field, and take great advantage of investments and advances made by the DOE and DOD over many decades. The primary goals were to stand up the DDH design in calculations for the first time, study and refine the concept, and provide the scientific basis for understanding and projecting proposals by Xcimer Energy. The main accomplishments have been summarized below.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗