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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Digital Twin of Scalable Quantum Clouds

Quantum computing has emerged as a transformative technology capable of solving complex problems beyond the limit of classical systems. The rapid development of quantum processors has led to the proliferation of cloud-based quantum computing services offered by platforms such as IBM, Google, and Amazon. These platforms introduce unique challenges in resource allocation, job scheduling, and multi-device orchestration as quantum workloads become increasingly complex. In this work, we present a digital twin of quantum cloud infrastructures: a framework designed to model and simulate the behavior of real quantum cloud systems. Developed in Python using the SimPy discrete-event simulation library, the framework replicates key aspects of quantum cloud environments, including detailed quantum device modeling, job lifecycle management, and job fidelity. It incorporates noise-aware fidelity estimation, making it the first of its kind to simulate superconducting gate-based quantum cloud systems at an administrative level with job fidelity. We present use cases as proof of concept, demonstrating that our quantum cloud simulation framework can act as a digital twin of a quantum cloud and support the modeling and implementation of practical systems.

Luo, Waylon [Kent State University]↗
DOE OSTI2023

Development of a Scalable Risk-informed Predictive Maintenance Cloud-based Strategy at Nuclear Power Plants

The fact that light-water reactor operation and maintenance costs are prohibitively expensive and contribute to the premature decommissioning of nuclear power plants is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology, as its low cost, computing and storage adaptability, and ability to host applications across numerous virtual infrastructures potentially make it a cost-effective alternative to onsite storage and diagnostics. In this paper, a technological assessment is carried out on a provisional cloud deployment architecture for a nuclear power plant predictive monitoring system. This cloud-based monitoring system would enable maintenance and diagnostic analysts and other authorized plant users to remotely monitor equipment functionality, thus enabling early fault detection and effective predictive maintenance practices. To provide data processing and storage, sensor device networking, and database management, the Microsoft Azure cloud platform is utilized as part of the proposed cloud architecture; however, this analysis could be extended to other cloud computing service providers as well. The focus of this paper is on application of cloud resources for enabling predictive maintenance, identification of technological hurdles associated with moving to a cloud-computing-based architecture, and potential benefits from moving to a centralized cloud system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Advancing Scientific Productivity through Better Scientific Software: Developer Productivity and Software Sustainability Report

The Exascale Computing Project (ECP) provides a unique opportunity to advance computational science and engineering (CSE) through an accelerated growth phase in extreme-scale computing. Central to the project is the development of next-generation applications and software technologies that can exploit emerging architectures for optimal performance and provide high-fidelity, multiphysics, multiscale capabilities. However, disruptive changes in computer architectures and the complexities of tackling new frontiers in extreme-scale modeling, simulation, and analysis present daunting challenges to the productivity of software developers and the sustainability of software artifacts. Members of the CSE community - especially at extreme scales but more broadly at all scales of computing - face an urgent need to improve developer productivity, positively impacting product quality, development time, and staffing resources, and software sustainability, reducing the cost of maintaining, sustaining, and evolving software capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Beyond DERMS: Platform Development, Flexibility, Prediction, and Value Measurements

This report describes the results from the “Beyond DERMS” project that aims to build, deploy, and demonstrate - at megawatt scale with thousands of devices - a holistic platform that supports the integrated operation and planning of future power distribution networks with bi-directional power flows, many diverse distributed energy resources (DERs), and inverter-based resources. The results demonstrate how emerging ‘internet of energy’ technologies can be leveraged to simultaneously solve the grid problems of today and prepare for the challenges of tomorrow in a way that goes beyond the capabilities of existing distributed energy resource management systems (DERMS).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Development of a Cloud-based Application to Enable a Scalable Risk-informed Predictive Maintenance Strategy at Nuclear Power Plants

Light-water reactor operations and maintenance (O&M) costs are prohibitively high, thus contributing to the premature decommissioning of nuclear power plants (NPPs). This is partly due to how the equipment is monitored. In recent years, cloud computing has emerged as a dominant technology by virtue of its low costs, computing and storage adaptability, and ability to host applications over numerous types of virtual infrastructures. Cloud computing can be a cost-effective alternative to onsite storage and diagnostics. This paper conducts a techno-economic assessment of a provisional cloud deployment architecture for a NPP predictive monitoring (PdM) system. The cloud-based monitoring system would enable maintenance and diagnostics (M&D) analysts and other authorized plant users to remotely monitor equipment functionality so as to enable PdM practices and early detection of faults. The Microsoft Azure cloud platform is included in the proposed cloud architecture to provide data processing and storage, sensor device networking, and database management; however, this analysis could be extended to other cloud computing service providers as well. For the techno-economic assessment, technical feasibility is measured in terms of network performance metrics such as response time, latency, and throughput, whereas economic feasibility is measured in terms of operational costs and capital expenditures. Finally, this report covers certain regulatory and security aspects that may concern licensees looking to implement cloud computing. The report focuses on the integration of sensor database storage, the application of cloud resources to PdM, and the identification of technological and economic hurdles associated with moving to a cloud-computing-based architecture.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Detailed Structural and Chemical Analysis of Amorphous Compounds in Superconducting Qubit Systems

With massive improvements in device coherence times and gate fidelity over the past two decades, superconducting quantum devices have emerged as a leading technology platform for next generation quantum computing. While many of these improvements have been driven through optimized device designs and geometries, the presence of defects, impurities, interfaces, and surfaces in the constituent materials continue to limit performance and present a critical barrier in achieving scalable quantum systems with long coherence times. As part of the Superconducting Quantum Materials and Systems (SQMS) center, we have applied state-of-the-art materials characterization techniques, such as scanning/transmission electron microscopy, secondary ion mass spectrometry, and atom probe tomography, to identify structural defects and chemical inhomogeneities in superconducting qubits. In this review, we discuss the findings of these efforts as they relate to the inherent surfaces, interfaces, impurities, and defects in superconducting qubits. Through this comprehensive investigation, we provide insight into possible sources of dissipation in these systems. Further, we supplement these findings with potential strategies for mitigating these undesired features in an effort to systematically and intelligently improve the coherence times of superconducting qubits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Establishing Validation Sites for Field-Level Emissions Quantification from Grain Sorghum in Southern Great Plains

Establish publicly available open-source, high-resolution datasets of greenhouse gas (GHG) emissions, meteorology, soil, and vegetation to support testing and validation of emerging biofuel production monitoring technologies. The project team deployed latest technologies to collect “Gold Standard” data for field- level fluxes of CO 2 , N 2 O, CH 4 and H 2 O in grain sorghum production systems in Texas, Oklahoma, and Kansas. The Oklahoma State University led SMARTFARM was initiated on Oct 1, 2020, due to COVID-19 pandemic delay. We installed closed-path dual laser Eddy Covariance (EC) flux measurement system along with a well-established open-path EC system for simultaneously measuring field-level fluxes of CO 2 , N 2 O, CH 4 , and H2O. The eddy flux data are combined with sub-acre soil moisture and temperature measurements, plant and soil analysis data (nutrient, composition, carbon, growth, and phenology), spatial surveys of soil EC, and pH. Grain sorghum crop was managed including planting and harvesting by producers. All teams recruited postdocs and students for the project and submitted purchase orders for field equipment. The team has developed a draft impact sheet and finalized the advisory board for the project.

09 BIOMASS FUELS↗
DOE OSTI2025

TEAMER – Harvesting Subsea Water Motion to Provide Clean Electrical Power (Abstract)

Power generation from vortex induced vibration (VIV) is an emerging field with new technologies that are just beginning to be field tested. In this TEAMER project, WITT Energy will be field testing the power production of its pendulum system in two different configurations to evaluate its performance. The amount of power produced by a VIV instrument depend on the frequency at which it vibrates. The vibrational frequency is controlled by the device shape and its interaction with the flow velocity. Computer models are used to predict vibration frequencies as a function of flow velocity but they may be inaccurate due to complicated bathymetry and bottom roughness that is not captures by the models. This project will test the WITT VIV device in the Sequim Bay channel over a range of tidal velocities. Two tests will be completed with different pipe lengths that are expected to vary the vibrational frequency and therefore the power produced. PNNL will use an acoustic doppler current profiler (ADCP) to monitor the flow conditions during the tests. These tests will help WITT Energy evaluate how closely field operations follow computer model predictions and how best to configure their device for electricity production.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

ArCS: A Magnetized LArTPC in a Test Beam

Over the past few decades, Liquid Argon Time Projection Chambers (LArTPCs) have emerged as a central technology for rare-event detection, due to their calorimetric and imaging capabilities. Adding a magnetic field to LArTPCs would enable charge identification and momentum measurements via curvature. For neutrino experiments, this is crucial for wrong-sign neutrino rejection, electron/positron and electron/photon discrimination, and improved momentum reconstruction. The ArCS (Argon detector with Charge Separation) experiment at Fermilab's Test Beam Facility will place a 47 40 90 cm LArTPC inside a 0.7 T magnet to: (i) establish charge sign discrimination for electrons and positrons, (ii) reconstruct particle momenta via curvature, and (iii) determine the minimal magnetic field needed for these measurements. This poster will present the project status, with updates on installation and simulations of expected performance.

Cicogna, Giulia [Bologna U.]↗
DOE OSTI2026

Towards a More Predictive Framework for Laser-Driven Particle Sources through Experimental Data-Informed Models

Laser-driven particle acceleration (LDPA) has emerged as a critical technology for high-energydensity physics applications since its discovery at Lawrence Livermore National Laboratory twenty years ago. However, realizing the full potential of these particle sources requires understanding the fundamental acceleration mechanisms and developing enhanced target designs for improved performance. This research addressed the need for controllable, high-performance laser-driven proton sources through two complementary approaches: experimentally investigating sheath field dynamics in multi-picosecond laser regimes and developing novel three-dimensional printed microstructured targets to achieve enhanced particle acceleration.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Identifying Decoherence Mechanisms in Superconducting Qubits through Advanced Materials Characterization

Although superconducting qubits have emerged as a leading technology platform for quantum computing through large improvements in device coherence times and gate fidelity in recent years, the presence of defects and impurities at the interfaces and surfaces in the constituent materials continue to limit performance and serve as a critical barrier in achieving scalable quantum systems. Understanding and eliminating these sources of quantum decoherence in superconducting qubit devices requires dedicated studies aimed at establishing robust structure-property relationships that will enable researchers to target and eliminate defects strategically. As part of the Superconducting Materials and Systems (SQMS) center, we have extensively employed state-of-the-art materials characterization techniques, including scanning/transmission electron microscopy, secondary ion mass spectrometry, atom probe tomography, x-ray diffraction, and x-ray photoelectron spectroscopy in conjunction with device measurements to elucidate such relationships. In this talk, I will discuss some of our recent findings, including linking atomic defects to microwave loss in surface oxides, linking impurities in the Josephson Junction to qubit parameters, and linking low temperature precipitates to device performance. By applying these insights, we have been able to strategically develop and implement mitigation strategies for reliable fabrication of high coherence superconducting qubits.

Murthy, A. [Fermilab] (ORCID:0000000176776866)↗
DOE OSTI2026

Identifying Decoherence Mechanisms in Superconducting Qubits through Advanced Materials Characterization

Although superconducting qubits have emerged as a leading technology platform for quantum computing through large improvements in device coherence times and gate fidelity in recent years, the presence of defects and impurities at the interfaces and surfaces in the constituent materials continue to limit performance and serve as a critical barrier in achieving scalable quantum systems. Understanding and eliminating these sources of quantum decoherence in superconducting qubit devices requires dedicated studies aimed at establishing robust structure-property relationships that will enable researchers to target and eliminate defects strategically. As part of the Superconducting Materials and Systems (SQMS) center, we have extensively employed state-of-the-art materials characterization techniques, including scanning/transmission electron microscopy, secondary ion mass spectrometry, atom probe tomography, x-ray diffraction, and x-ray photoelectron spectroscopy in conjunction with device measurements to elucidate such relationships. In this talk, I will discuss some of our recent findings, including linking atomic defects to microwave loss in surface oxides, linking impurities in the Josephson Junction to qubit parameters, and linking low temperature precipitates to device performance. By applying these insights, we have been able to strategically develop and implement mitigation strategies for reliable fabrication of high coherence superconducting qubits.

Murthy, A. [Fermilab] (ORCID:0000000176776866)↗
DOE OSTI2022

Review of Selected Advances in Electrical Capacitance Volume Tomography for Multiphase Flow Monitoring

Electrical Capacitance Volume Tomography (ECVT) has emerged as an attractive technology for addressing instrumentation requirements in various energy-related multiphase flow systems. ECVT can monitor multiple flow conditions and reconstruct real-time 3D images from capacitance measurements using a large set of electrode plates placed around the processes column enclosing the sensed flow system. ECVT is non-intrusive and allows the measurement of changes in mutual capacitance between all possible plate pair combinations. The objective of this paper is to provide a comprehensive review of recent advances in ECVT, enabling robust monitoring of multiphase flows, especially water-containing multiphase flows.

02 PETROLEUM↗
DOE OSTI2025

Multiple-amplifier sensing charged-coupled device: model and improvement of the node removal efficiency

The multiple-amplifier sensing charge-coupled device (MAS-CCD) has emerged as a promising technology for astronomical observation, quantum imaging, and low-energy particle detection due to its ability to reduce the readout time for the same readout noise level compared with its predecessor, the skipper-CCD, by reading out the same charge packet through multiple inline amplifiers. Previous works identified a new parameter in this sensor, called node removal inefficiency (NRI), related to inefficiencies in charge transfer and residual charge removal from the sense node of each amplifier after readout. These inefficiencies can lead to distortions in the measured signals similar to those produced by the charge transfer inefficiencies in standard CCDs. We introduce more details in the mathematical model of the NRI mechanism and provide techniques to quantify its magnitude from the measured data. It also proposes a new operation strategy that significantly reduces its effect with minimal alterations of the timing sequences or voltage settings for the other signals of the sensor. The proposed technique is demonstrated experimentally on a 16-amplifier MAS-CCD. At the same time, the experimental data demonstrate that this approach minimizes the NRI effect to levels comparable with other sources of distortion such as the charge transfer inefficiency in scientific devices.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Cooperative Load Scheduling for Multiple Aggregators Using Hierarchical ADMM: Preprint

Demand response (DR) serves an important role in improving the efficiency and stability of power systems. In recent years, with advances in communication and smart device technologies, many aggregators have emerged to facilitate end customer participation in DR programs. These aggregators, equipped with customized optimal control algorithms, are capable of providing various grid services. Among them is load scheduling during DR events, namely following a load signal provided by the utility company while minimizing overall customer discomfort. However, as the number of aggregators keeps increasing, it becomes challenging for utility companies to conduct load scheduling for multiple aggregators and generate reference signals for each of them. This paper proposes an optimization framework using hierarchical alternating direction method of multipliers (H-ADMM) to optimally generate load following signals for multiple aggregators. Under this framework, utility and multiple aggregators work in a cooperative manner, aiming at minimizing an overall system cost from different levels of the power system hierarchy, while protecting user privacy. A case study has been conducted in a system with multiple aggregators, based on control of HVAC loads. Experimental results validate the effectiveness of the proposed algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

DIGITAL APPLICATIONS USING REAL-TIME VEHICLE EXHAUST INFORMATION

Vehicle emission is a major source of air pollution that causes a significant number of deaths globally. It has a profound impact on energy and the environment as well. The existing vehicle emission monitoring system is unable to help mitigating the pollution properly and therefore, requires precise real-time pollution measurement. The purpose of this paper is to discuss novel applications using the real-time measurement of pollutants from a vehicle tailpipe where exhaust gases enter the environment. Today, it is possible to measure such emission due to the emergence of affordable digital technologies such as the Internet of Things (IoT), wireless connectivity, cloud platform, and artificial intelligence. This paper discusses how digital technologies can be used for real-time monitoring of NOx gas as a measure of vehicle emission and predictive analytics applications. A description of data collection and pre-processing methodologies, actual collected data, and an approach to identify patterns between inputs such as vehicle speed and altitude and output such as NOx emission are included. Applying a simple neural network has produced promising results and is a first step towards developing predictive applications.

Digital, Vehicle Exhaust, IoT, AI, 1.4.2, Predicti↗