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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Solid-State Transformer and Hybrid Transformer With Integrated Energy Storage in Active Distribution Grids: Technical and Economic Comparison, Dispatch, and Control

Solid-state transformer (SST) and hybrid transformer (HT) are promising alternatives to the line-frequency transformer (LFT) in smart grids. The SST features medium-frequency isolation, full controllability for voltage regulation, reactive power compensation, and the capability of battery energy storage system (BESS) integration with multiport configuration. The HT has a partially-rated converter for fractional controllability and can integrate a small BESS. Fast grid-edge voltage fluctuations from increased solar photovoltaic (PV) and electric vehicle (EV) penetration are difficult to manage for mechanical load tap changers. Hence, along with the trend towards more BESS in the grid, the controllability and the storage integration capability of the SST and HT are of strong interest. However, a review of literature shows existing SST and HT research is mostly at converter level, while system-level assessments are scarce. Assessing technical and economic impacts is critical to understanding the benefits and role of the SST and HT to guide future research, which is presented for the first time in this article. Experimental results from medium-voltage (MV) SST and MV HT prototypes are shown to confirm equipment-level feasibility, where the voltage controllability waveforms of a MV HT prototype are reported for the first time. Comparative simulations are performed on a modified IEEE 34-bus system. Here, a grid-model-less decentralized grid-edge voltage control method and a day-ahead BESS dispatch method are proposed for the SST and HT. The simulations show that the SST and HT with integrated storage can host more PV, achieve peak shaving, mitigate voltage fluctuation and reverse power flow, and support energy arbitrage for operational cost reduction, as compared to the LFT. Moreover, comprehensive analyses of net present value (NPV) and internal rate of return (IRR) are performed under different installed PV capacities, HT’s partial converter ratings, and BESS capacities. Sensitivities to future cost reductions of the PV and BESS are studied. Although the NPV and IRR are currently negative, 60% capital cost reduction or 150% revenue increase will make the SST and HT economically viable in the use case studied.

14 SOLAR ENERGY↗
DOE OSTI2019

An asymmetric hybrid cryptosystem using equal modulus and random decomposition in hybrid transform domain

In this paper, an asymmetric hybrid cryptosystem with coherent superposition, equal modulus and random decomposition in hybrid transform domain is proposed. To further strengthen the security of the cryptosystem, a hyperchaotic system is used as a pixel-swapping procedure. The hybrid transform is generated by utilizing fractional Fourier transform of various orders and Walsh transform. The hyperchaotic framework’s parameters and starting conditions alongside the fractional orders of the fractional Fourier transform extend the key-space and consequently give extra strength to the proposed cryptosystem. The designed cryptosystem has an extended key-space to avoid any brute-force attack and is non-linear in nature. The scheme is validated on gray-scale images. Computer based simulations have been performed to verify the validity and the performance of the proposed cryptosystem against different types of attacks. Results demonstrate that the proposed cryptosystem not only offers higher protection against noise attacks but is also invulnerable to special attack.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1978

A fast computation of complex convolution using a hybrid transform

The cyclic convolution of complex values was obtained by a hybrid transform that is a combination of a Winograd transform and a fast complex integer transform. This new hybrid algorithm requires fewer multiplications than any previously known algorithm.

Reed, I. S.↗
DOE OSTI2025

Geothermal well testing pressure prediction by using a hybrid transformer model system: FORGE well use case

Geothermal has huge potential to become an indispensable component in achieving the goal of sustainable energy economy, given its capability to provide consistent baseload power to the electric grid. Injection tests are crucial in geothermal energy system as they naturally help to evaluate reservoir properties, understand fluid flow and even enhance reservoir performance. In this research, we developed a hybrid model system that integrates machine learning (ML) regression, a physics-based mathematical model, and transformer deep learning. Trained and validated using FORGE injection test dataset, this system can forecast the pressure variations both upward and downward over time. The pressure prediction achieved prediction accuracy within 3-6% variance of true pressure values. The system can significantly save time and reduce costs by testing only a few cycles and then using model predictions for further analysis, instead of conducting additional real injection cycle tests. The developed model system also holds promise for designing injection test processes and maintaining well production in geothermal energy. Presented at the IMAGE ‘25 Conference led by Shell.

FORGE↗
NASA NTRS1978

A fast complex integer convolution using a hybrid transform

It is shown that the Winograd transform can be combined with a complex integer transform over the Galois field GF(q-squared) to yield a new algorithm for computing the discrete cyclic convolution of complex number points. By this means a fast method for accurately computing the cyclic convolution of a sequence of complex numbers for long convolution lengths can be obtained. This new hybrid algorithm requires fewer multiplications than previous algorithms.

Reed, I. S.↗
NASA NTRS1990

Multi-target tracking using a hybrid joint transform correlator

A technique using data association target tracking in a motion sequence via an adaptive joint transform correlator is presented. The massive data in the field of view can be reduced to a few correlation peaks. The average velocity of a target during the tracking cycle is then determined from the location of the correlation peak. A data-association algorithm is used for the analysis of these correlation signals, for which multiple targets can be tracked. A phase-mostly liquid-crystal TV is used in the hybrid joint transform correlation system, and simultaneous tracking of three targets is demonstrated.

Yu, Francis T. S.↗
DOE OSTI2018

Lab-scale Development of a Hybrid Capture System with Advanced Membrane, Solvent system, and Process Integration

The objective of the project is to achieve lab-scale demonstration of a transformational hybrid membrane/solvent system for the capture of CO 2 from flue gas. A novel process integration scheme was proposed to overcome the low partial pressure of CO 2 present in flue gas. This scheme takes advantage of the potential synergies inherent in the membrane and solvent capture systems. The proposed hybrid technology replaces the second-stage membrane with a solvent capture process. The combustion air is used as a sweeping gas in the solvent stripper. This overall configuration has the major advantages of requiring much less air and eliminating the problems associated with oxygen slip in the membrane-based systems. Even more importantly, because of the presence of the air sweep in the stripper, much lower temperatures are required for solvent regeneration, which enables heat integration to the point that no process steam is required. This project was to perform lab-scale demonstration of Liquid Ion Solutions (LIS) transformational hybrid membrane/solvent system for post-combustion CO 2 capture while demonstrating significant progress toward achievement of the overall fossil energy performance goals of 90% CO 2 capture rate with 95% CO 2 purity at a cost of electricity 30% less than baseline capture approaches. This report is written chronologically based on budget periods. Each budget period discusses the work performed and its results. At the end of each budget period the milestone charts are provided to show the progress. This project was not able to meet its stated goals. The performances of the power plant equipped with the hybrid process and Baseline Case B12B were compared. The simulation results showed that the power plant equipped with hybrid CO2 capture process has an improved thermal efficiency of 33.4% compared to the Baseline Case B12B, which is 32.5%. However, the COE for the hybrid process is $146.3/MWh which is higher than the $142.8/MWh for Case B12B. If this process were to be applied as a retrofit, it may make sense to use this configuration as it does not require any change in an existing plant configuration and steam cycle. However, for a new powerplant the capital cost is higher than the Case B12B. On the membrane development side, we successfully showed that interfacially-controlled envelope (ICE) membranes work. The inclusion of surface modified nano-particles results in much higher gas diffusivities. The Gen-1 ICE membrane developed under this project showed exceptional improved permeability for CO 2 . However, we ran into major problems which were associated with the scalability and reproducibility of the poly(phosphozene) polymers. The issues with scale-up are and batch to batch variability is also discussed. At the end of this report we have provided our recommendations on materials development for better membrane materials and alluded to the use of much simpler chemistry while keeping the concept of ICE membrane intact. It is also clear that vacuum and compression equipment is costly. If a process which can eliminate the use of vacuum and compression equipment would significantly decrease the overall cost of capture. Some thoughts regarding such system is also provided in the outlook portion of this report.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Improving the Concrete Crack Detection Process via a Hybrid Visual Transformer Algorithm

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management.

42 ENGINEERING↗
DOE OSTI2022

New Hybrid Model for Evaluating the Frequency-Dependent Leakage Inductance of a Variable Inductance Transformer (VIT)

Skin and proximity effects can cause a significant drop in the effective leakage inductance of a transformer when the operating frequency is increased. Although the magnetic image method-based double-2-D model can calculate the lowfrequency leakage inductance with sufficient accuracy, it is inherently a frequency-independent model. While Dowell’s 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a hybrid model is proposed that uses superposition to combine a modified Dowell’s model with the double-2-D model. The proposed model is investigated on a variable inductance transformer (VIT)—a partially-filled transformer whose leakage inductance can be varied by moving one of the windings mechanically. The frequency-dependent leakage inductances of the VIT evaluated using the hybrid model are in excellent agreement with the corresponding finite element method (FEM) simulated and experimentally measured values, thereby validating the proposed hybrid model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1975

Digital transmitter for data bus communications system

An improved digital transmitter for transmitting serial pulse code modulation (pcm) data at high bit rates over a transmission line is disclosed. When not transmitting, the transmitter features a high output impedance which prevents the transmitter from loading the transmission line. The pcm input is supplied to a logic control circuit which produces two discrete logic level signals which are supplied to an amplifier. The amplifier, which is transformer coupled to the output isolation circuitry, converts the discrete logic level signals to two high current level, ground isolated signals in the secondary windings of the coupling transformer. The latter signals are employed as inputs to the isolation circuitry which includes two series transistor pairs operating into a hybrid transformer functioning to isolate the transmitter circuitry from the transmission line.

Proch, G. E.↗
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 OSTI2022

Vulnerability and resilience of urban energy ecosystems to extreme climate events: A systematic review and perspectives

We reviewed the present studies on the vulnerability and resilience of the energy ecosystem (most parts of the energy ecosystem), considering extreme climate events. This study revealed that the increased interactions formed during the transformation of the energy landscape into an ecosystem could notably increase the vulnerability of the energy infrastructure. Such complex ecosystem cannot be assessed using the present state of the art models used by the energy system modelers. Therefore, this study introduces a novel analogy known as the COVID analogy to understand the propagation of disruption within and beyond the energy ecosystem and organized the present state of the art based on the COVID analogy. The analogy helps to categorize the vulnerability of the energy infrastructure into three stages. The study revealed that although there are many publications covering the vulnerability and resilience of the energy infrastructure, considering extreme climate events, the majority are focused on the direct impact of extreme climate on the energy ecosystem. In addition, most of the studies do not consider the impact of future climate variations during this assessment. The propagation of disruptions was assessed mainly for wildfires and hurricanes. Further, there is a clear research gap in considering vulnerability assessment for interconnected energy infrastructure. Here, the transformation of energy systems into a complex ecosystem notably increases the complexity, making it difficult to assess vulnerability and resilience. A shift from a centralized to decentralized modeling architecture could be beneficial when considering the complexities brought by that transformation. Hybrid models consisting of both physical and data-driven machine learning techniques could also be beneficial in this context.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Modular Controllable Transformers (MCT)

Large Power Transformers (LPTs) are at the heart of the electricity infrastructure and are critical pieces in today’s grid. Aging assets, long turn-around times, transportation delays and dependence on foreign manufacturing for these components have created conditions for points of failure across the system. Moreover, there is no available dynamic control on these passive sections of the grid. For all the automation embedded on the low voltage side of the low voltage side of the system there is absolutely no flexibility on the high voltage side. Changes in power flow patterns are brought about by altering dispatch patterns for generators which requires a coordinated effort and is often time consuming. Traditional FACTS based approaches such as UPFC or HVDC light involve massive modifications to the existing infrastructure. Recent instances of physical attacks on LPTs and the slow recovery process associated with fixing or replacing these damaged units has created an increased awareness for this issue. LPT designs are highly customized owing to specific field requirements. Long lead times coupled with transportation delays make procuring and installing LPTs a lengthy process. In an attempt to minimize the recovery time following an LPT failure, transformer manufacturers have developed mobile transformer units. These units typically consist of either a single-phase or three-phase transformer mounted on a truck and characterized by compact form factors. By reducing the assembly time and transport time these units offer a fast and temporary solution to LPT failures. Several efforts led by DOE, Edison Electric Company (EEI) and North American Electric Reliability Corporation (NERC) have led to the RecX program. RecX transformers consist of single phase transformers transported on specialized assemblies to enable fast restoration and minimal installation time. NERC and EEI have instituted transformer sharing programs like Spare Equipment Database (SED) as well as Spare Transformer Equipment Database (STEP) which encourage utilities to maintain and share a fleet of LPTs. Mobile transformers are typically limited to less than 100 MVA to enable transportation on a trailer. Also, the efficiency of mobile transformer units and RecX transformers is lower since they are temporary solutions. The ideal replacement solution would be one that could utilize a set of standardized transformers while incorporating flexibility to influence metrics like power flow, apparent impedance and voltage profiles. In view of these issues the team at Georgia Tech proposed a concept called Modular Controllable Transformers (MCTs). This project was commissioned as a part of DOE’s Transformer Resiliency and Advance Component (TRAC) program. The approach consists of splitting one large unit rated upwards of 100 MVA with smaller modular, standardized units. Each of these smaller units are augmented with a converter to add power flow, voltage and impedance control. Splitting the power level into multiple units has two important implications. By making smaller units, the transportation times can be reduced drastically. Moreover, even though these units have very specific impedance and other parameters, the augmented converters can make it emulate the unit being replaced. Thus, faster replacement times and added controls are achieved making this an extremely resilient approach. In the event of one modular units failure, the other two controllable units can operate in the most resilient fashion keeping the net system overloads to a minimum. Moreover, for the same probability of failure only a fraction of the system capacity is lost. The converter is augmented on to the tertiary winding of the transformer making it retrofittable in the field without massive modifications to the system. The material proposed in this report highlights design considerations to minimize recovery time as well as results displaying overall improvement in resiliency. The cost analysis of this approach shows significant savings compared to UPFC and HVDC lite based approaches. Extensive analysis was conducted on numerous aspects of this project over the course of this project. Simulation studies were conducted to prove the 5MVA converter design. Challenging BIL constraints and transient issues were addressed over the course of this project. Simulations on EMTP platform presented in consequent sections were used to finely tune the control for the same. A final design for a back-to-back 5MVA neutral-point-clamped (NPC) converter was completed in this project. Furthermore, switching components like high power IGBTs from IXYS Westcode Press-Pack series and ABB Stackpack series were identified for this application. These devices exhibit a blocking voltage of 6.5 kV and a nominal collector current of 900 A. Potential housing options as well as existing commercial option were identified for this converter design. A 56 MVA modular transformer unit was designed with Delta Star Inc. with consultation and inputs from all project members. The transformer cooling systems were chosen to be Oil Natural Air Forced (ONAF) to optimize ease of transport. A full Product Requirement Document (PRD) for the converter and the transformer was generated. The team also investigated key aspects of resiliency and how to quantify the same. A metric was developed to analyze resiliency and numerous simulation cases were presented. Increased (N-1) resiliency and outage resistance was showcased through simulation on the Texas Large scale power system. Dynamic control capabilities were also showcased on numerous system level simulations. An IEEE 30 bus system was used to show the ability of the MCT approach to reduce line congestion. Power flow control; a part of the MCTs abilities, showcased a 29% reduction in transformer loading on the same system. Full control over tie line flows was demonstrated on a modified IEEE 13 bus system. Dynamic LTC capabilities were shown through simulation and were compared to traditional LTCs. Furthermore, the ability of the MCTs to alter apparent transformer impedance was simulated to promote standardization of modular transformer units while retaining the ability to emulate a varied range of impedance characteristics. Thus granular control over power flow, voltages, impedances and was presented. Regulatory analysis conducted by Oak Ridge National Laboratory (ORNL) further bolstered the efficacy of the MCT approach and verified the added resiliency achieved due to this approach. As a conclusion, it was proved that the resilience improvement obtained from using the MCT approach is justified by just one failure in a 30-year period. This means that the benefits obtained from using the MCT approach far outweigh the cost of setting this approach up. As a part of the future efforts, the team is proposing building a (12.47-24) kV/400 kVA scaled down unit to control 5MVA of power flows. Emphasis would be laid on using commercial components to speed up the build of these units. Further the actual transport of the modular transformer unit would be a part of this operation and would provide insight into transportation issues. The approach shows much promise and adds key dynamic control on passive sub transmission and transmission sectors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Combined Water and CO2 Direct Air Capture System (CRADA 521)

The primary objective of this project is to demonstrate the outstanding technical and economic performance of a transformational Hybrid DAC (HDAC) technology that simultaneously captures CO2 and water from the air. Air is passed over a CO2 selective sorbent to remove ≥85% of the CO2 from the air stream. The atmospheric water extraction (AWE) section of the unit utilizes a novel isothermal pressure swing regeneration cycle with desiccant beds thermally coupled by heat pipes that provide a passive heat transfer mechanism to “cancel” the heat of water vapor adsorption. By proving system performance through this demonstration project, our initial technoeconomic analysis (TEA) will be validated, showing that HDAC technology: 1) is deployable in many more locations with limited water resources, 2) improves the financial returns, and 3) reduces risks from volatility in the price of CO2.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2025

Co-designing Spectral Transformation Oracles with Hybrid Oscillator-Qubit Quantum Processors: From Algorithms to Compilation

We co-design a family of quantum eigenvalue transformation oracles that can be efficiently implemented on hybrid discrete- or continuous-variable (qubit or qumode) hardware. To illustrate the oracle’s representation-theoretic power and near-term experimental accessibility, we encode a Gaussian imaginary time-evolution spectral filter. As a result, we define a continuous linear combination of unitaries block encoding. Due to the ancillary qumode’s infinite-dimensional nature, continuous-variable qumodes constitute a powerful compilation tool for encoding continuous spectral functions without discretization errors while minimizing resource requirements. We then focus on the ubiquitous task of preparing eigenstates in quantum spin models. For completeness, we provide an end-to-end compilation which expresses high-level oracles in terms of an experimentally realizable instruction set architecture in both 1D and 2D. Finally, we examine the leading-order effects of physical errors and highlight open research directions. Our algorithms scale linearly with the spatial extent of the target system and are applicable to both near-term and large-scale quantum processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗