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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

Chance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty

When uncontrollable resources fluctuate, Optimum Power Flow (OPF), routinely used by the electric power industry to re-dispatch hourly controllable generation (coal, gas and hydro plants) over control areas of transmission networks, can result in grid instability, and, potentially, cascading outages. This risk arises be cause OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under the OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition, which is borne by simulations of real grids, would likely resulting in automatic line tripping to protect lines from thermal stress, a risky and undesirable outcome which compromises stability. Smart grid goals include a commitment to large penetration of highly fluctuating renewables, thus calling to reconsider current practices, in particular the use of standard OPF. Our Chance Constrained (CC) OPF corrects the problem and mitigates dangerous renewable fluctuations with minimal changes in the current operational procedure. Assuming availability of a reliable wind forecast parameterizing the distribution function of the uncertain generation, our CC-OPF satisfies all the constraints with high probability while simultaneously minimizing the cost of economic re-dispatch. CC-OPF allows efficient implementation, e.g. solving a typical instance over the 2746-bus Polish network in 20s on a standard laptop.

Energy Planning, Policy, & Economy(29)↗
DOE OSTI2026

A Brief Survey of Data Streaming Technologies

Streaming data is data that is emitted at variable volumes in a continuous, incremental manner with the goal of low-latency processing often at a different physical location. Network infrastructure is used to facilitate the connection between data sources and sinks, and must be robust to handle the requirements of the workflow. The U.S. Department of Energy Office of Science (DOE SC) a federal agency supporting fundamental scientific research for energy and the Nation’s largest supporter of basic research in the physical sciences. DOE SC has the responsibility for operating $\mathbf{1 0}$ National Laboratories, and 28 scientific user facilities supporting advanced supercomputers, particle accelerators, large x-ray light sources, neutron scattering sources, and other specialized facilities for nanoscience and genomics. This paper investigates the state of streaming data workfows, and details some of the approaches to this challenging problem.

Kissel, Ezra↗
DOE OSTI2024

Establishing capabilities for quantum computing and simulations for energy applications

Quantum information science (QIS) is creating potential transformative opportunities to exploit intricate quantum mechanical phenomena in new ways for obtaining and processing information to advance many areas of science and engineering. Since the National Quantum Initiative Act was signed into law in 2018, developing QIS capability and competency is one of the most urgent tasks of DOE to make sure the US win the quantum race. The QIS contains four pillars: quantum computing, quantum simulations, quantum sensing, and quantum networking. To apply QIS in energy related applications, the key is to develop the capability of quantum computing & simulation tools. In this project, we propose to develop the capability of quantum computing and simulations at NETL to target fossil energy related problems. We will install a simulator (e. g. IBM qiskit) on the NETL supercomputer to simulate the environments of quantum computer. Based on the current available quantum algorithms for quantum chemistry, we will develop quantum computing codes to perform simulation which will focus on fossil energy sector challenges, including CO2 capture & conversion, sensing, and fuel conversion. We then will seek opportunities to run our codes on real quantum computers (such as IBM-Q, google Sycamore, etc.). Through this project, the new capability of quantum computing and simulations will be established at NETL. In addition, the NETL workforce in this area will be trained ready to conduct more complicated tasks in line with NETL missions to enhance the nation’s energy foundation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Regulatory Networks Controlling Plant Cold Acclimation or Low Temperature Regulatory Networks Controlling Cold Acclimation in Arabidopsis (2011 JGI User Meeting)

The U.S. Department of Energy Joint Genome Institute (JGI) invited scientists interested in the application of genomics to bioenergy and environmental issues, as well as all current and prospective users and collaborators, to attend the annual DOE JGI Genomics of Energy & Environment Meeting held March 22-24, 2011 in Walnut Creek, Calif. The emphasis of this meeting was on the genomics of renewable energy strategies, carbon cycling, environmental gene discovery, and engineering of fuel-producing organisms. The meeting features presentations by leading scientists advancing these topics. Mike Thomashow of Michigan State University gives a presentation on on "Low Temperature Regulatory Networks Controlling Cold Acclimation in Arabidopsis" at the 6th annual Genomics of Energy & Environment Meeting on March 23, 2011."

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Science and Technology Review: Getting to Carbon Neutral

At Lawrence Livermore National Laboratory, we focus on science and technology research to ensure our nation’s security. We also apply that expertise to solve other important national problems in energy, bioscience, and the environment. Science & Technology Review is published eight times a year to communicate, to a broad audience, the Laboratory’s scientific and technological accomplishments in fulfilling its primary missions. The publication’s goal is to help readers understand these accomplishments and appreciate their value to the individual citizen, the nation, and the world. Achieving carbon neutrality requires a well-considered combination of technologies, private involvement, and public policies. As described in the article beginning on p. 4, Lawrence Livermore National Laboratory developed the Getting to Neutral: Options for Negative Carbon Emissions in California report, a three-pillared approach to bridge the gap between California’s initial plans to reduce carbon emissions and the state’s goal to achieve net zero emissions by 2045. The cover image represents the network of strategies and stakeholders involved in achieving carbon neutrality for the environment.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks

The existence of “barren plateau landscapes” for generic discrete-variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in m modes can be generically compiled efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. Here, we further demonstrate efficient trainability of m mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with m.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Design and simulation of a SiPM-on-tile ZDC for the future EIC, and its performance with graph neural networks

We present a design for a high-granularity zero-degree calorimeter (ZDC) for the upcoming Electron-Ion Collider (EIC). The design uses SiPM-on-tile technology and features a novel staggered-layer arrangement that improves spatial resolution. To fully leverage the design’s high granularity and non-trivial geometry, we employ graph neural networks (GNNs) for energy and angle regression as well as signal classification. The GNN-boosted performance metrics meet, and in some cases, significantly surpass the requirements set in the report on science requirements and detector requirements for the EIC (Yellow Report), laying the groundwork for enhanced measurements that will facilitate a wide physics program. Our studies show that GNNs can significantly enhance the performance of high-granularity CALICE-style calorimeters by automating and optimizing the software compensation algorithms required for these systems. This improvement holds true even in the case of complicated geometries that pose challenges for image-based AI/ML methods.

Calorimeter↗
DOE OSTI2017

Revealing Fundamental Physics from the Daya Bay Neutrino Experiment Using Deep Neural Networks

Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. Here in this work, we show that meaningful physical content can be revealed by transforming the raw data into a learned high-level representation using deep neural networks, with measurements taken at the Daya Bay Neutrino Experiment as a case study. We further show how convolutional deep neural networks can provide an effective classification filter with greater than 97% accuracy across different classes of physics events, significantly better than other machine learning approaches.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

(Bi 0.2 Sb 0.8 ) 2 Te 3 based dynamic synapses with programmable spatio-temporal dynamics

Neuromorphic computing has recently emerged as a promising paradigm to overcome the von-Neumann bottleneck and enable orders of magnitude improvement in bandwidth and energy efficiency. However, existing complementary metal-oxide-semiconductor (CMOS) digital devices, the building block of our computing system, are fundamentally different from the analog synapses, the building block of the biological neural network—rendering the hardware implementation of the artificial neural networks (ANNs) not scalable in terms of area and power, with existing CMOS devices. In addition, the spatiotemporal dynamic, a crucial component for cognitive functions in the neural network, has been difficult to replicate with CMOS devices. Here, we present the first topological insulator (TI) based electrochemical synapse with programmable spatiotemporal dynamics, where long-term and short-term plasticity in the TI synapse are achieved through the charge transfer doping and ionic gating effects, respectively. We also demonstrate basic neuronal functions such as potentiation/depression and paired-pulse facilitation with high precision (>500 states per device), as well as a linear and symmetric weight update. We envision that the dynamic TI synapse, which shows promising scaling potential in terms of energy and speed, can lead to the hardware acceleration of truly neurorealistic ANNs with superior cognitive capabilities and excellent energy efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior

Electric vehicles are a sustainable substitution to conventional vehicles. This work introduces an integrated framework for urban fast charging infrastructure to address the range anxiety issue. A mesoscopic simulation tool is developed to generate trip trajectories, and simulate charging behavior based on various trip attributes. The resulting charging demand is the key input to a mixed-integer nonlinear program that seeks charging station configuration. The model minimizes the total system cost including charging station and charger installation costs, and charging, queuing, and detouring delays. The problem is solved using a decomposition technique incorporating a commercial solver for small networks, and a heuristic algorithm for large-scale networks, in addition to the Golden Section method. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Furthermore, the required infrastructure to support urban trips is explored for future market shares and technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

SU-F-18C-10: Clinical Implementation of Radiochromic Film Based CTDI Measurements

Purpose: To evaluate accuracy and reproducibility of a radiochromic film-based protocol to measure computer tomography dose index (CTDI) as a part of annual QA on CT scanners and kV CBCT systems attached to linear accelerators. Methods: Energy dependence of Gafchromic XR-QA2(R) film model was tested over imaging beam qualities (50 – 140 kVp). Film pieces were irradiated in air to known values of air kerma in air (up to 10 cGy). Change in film reflectance was determined with an in-house written code using images produced by a flatbed document scanner. Calibration curves for each beam quality were created, and film responses for same air-kerma values were compared.Sets of film strips were placed into holes of a CTDI phantom and irradiated for several clinical scanning protocols on CT-simulators and CBCT systems. Film reflectance change was converted into dose to water and used to calculate CTDIvol values. Measured CTDIvol values were compared to tabulated CTDIvol values. Results: Average variations of ±5.2% in the mean film reflectance change were observed in the energy range of 80 to 140 keV, and 11.1% between 50 and 140 keV. The averaged measured CTDI values presented a mean variation for the same machine and protocol of 2.6%. However, measured CTDI values were in average 10% lower than tabulated CTDI values for CT-simulators, and 44% higher for CBCT systems. Conclusion: We found that in relatively broad range of beam qualities used in diagnostic radiology variation of film response is within ±5% resulting in ±15% systematic error in dose estimates if a single calibration curve is used. Relatively large discrepancy between measured and tabulated CTDI values for different protocols and imaging systems used within radiotherapy department strongly support the trend towards replacing CTDI value with equilibrium dose measurement in the center of cylindrical phantom as suggested by TG-111. This work was supported by the Natural Sciences and Engineering Research Council of Canada, Contract No. 386009; Partial support by CREATE, Medical Physics Research Training Network grant of the Natural Science and Engineering Research Council, Contract No. 432290.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Reconfigurable Cascaded Thermal Neuristors for Neuromorphic Computing

While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, an alternative route is explored based on a new class of spiking oscillators called “thermal neuristors”, which operate and interact solely via thermal processes. Utilizing the insulator-to-metal transition (IMT) in vanadium dioxide, a wide variety of reconfigurable electrical dynamics mirroring biological neurons is demonstrated. Notably, inhibitory functionality is achieved just in a single oxide device, and cascaded information flow is realized exclusively through thermal interactions. To elucidate the underlying mechanisms of the neuristors, a detailed theoretical model is developed, which accurately reflects the experimental results. In conclusion, this study establishes the foundation for scalable and energy-efficient thermal neural networks, fostering progress in brain-inspired computing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Geothermal district energy systems coupled with seasonal underground thermal energy storage: a U.S. techno-economic screening by climate and geology

In the United States, cooling-dominated commercial building loads can cause geothermal heat pump-based district energy systems to accumulate a long-term subsurface thermal imbalance, motivating the incorporation of seasonal underground thermal energy storage. We developed a transferable workflow to evaluate geothermal district systems that pair ground heat exchangers with seasonal underground thermal energy storage. Using standardized hourly loads for seven commercial buildings and a uniform cost framework, we simulated ten U.S. cities with a physics-based ground heat exchanger model, subsurface storage simulations, and economic assessment to isolate the roles of climate and hydrogeology. In cooling-dominated cities, underground thermal energy storage supplied the majority of annual cooling, cutting electricity use and summer peaks substantially while achieving levelized costs comparable to or below conventional chiller-boiler plants. In cooler climates, the storage share shrunk, required borefield size and costs rose, and levelized cost of energy increased nearly linearly with declining underground thermal energy storage fraction, indicating storage fraction as the primary economic lever. Sensitivity analysis showed capital risk dominated by borefield drilling and surface heating, ventilation, and air-conditioning and piping, with underground thermal energy storage costs secondary. This workflow provides a transparent foundation for site-specific design and screening of next-generation geothermal district energy systems.

25 ENERGY STORAGE↗
DOE OSTI2017

The role of cardiac energy metabolism in cardiac hypertrophy and failure

In mammalian heart, incessant production of cellular energy is vital for maintaining continuous mechanical pumping function providing the body for oxygen and nutrients. To ensure this essential function, cardiac muscle adapt to increased energy demand or compromised energy supply by reprogramming the network of genes whose products are necessary to match the production of energy to consumption. Failure in this regulation leads to severe cardiac dysfunction and has been associated with cardiac pathogenesis including cardiac hypertrophy, failure and diabetes. Metabolic adaptations are induced by network of transcriptional pathways that are activated by a variety of factors such as hormones, nutrients, second messengers and oxygen. The metabolic phenotype of the heart is maintained by pathways controlling transcriptional regulators, which include peroxisome proliferator-activated receptors, estrogen-related receptors and nuclear respiratory factors, as well as their common coactivator protein peroxisome proliferator-activated receptor γ coactivator 1. These central regulators of gene expression are complemented with factors such as hypoxia inducible factor 1, which is activated in insufficient oxygenation of the tissue. Here, we discuss how these pathways relate to the cardiac metabolism and how they interact with pathways controlling the contractile phenotype of the heart. - Highlights: • Energy metabolic changes are associated with cardiac adaptation and pathogenesis. • Cardiac energy substrate utilisation is controlled by complex transcriptional networks. • Transcriptional pathways controlling heart metabolic and contractile phenotype overlap.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗