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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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Designed to be the world's most comprehensive, open, and collaborative energy information network, Open Energy Information (OpenEI - openei.org) supplies essential energy data to decision makers and supports a global energy transformation. The platform, sponsored by the U.S. Department of Energy (DOE) and developed by the National Renewable Energy Laboratory (NREL), is intended for globalcontribution and collaboration.
A system for communicating information on an energy distribution network is disclosed. In one embodiment, the system includes a local supervisor on a communication network, wherein the local supervisor can collect data from one or more energy generation/monitoring devices. The system also includes a command center on the communication network, wherein the command center can generate one or more commands for controlling the one or more energy generation devices. The local supervisor can periodically transmit a data signal indicative of the data to the command center via a first channel of the communication network at a first interval. The local supervisor can also periodically transmit a request for a command to the command center via a second channel of the communication network at a second interval shorter than the first interval. This channel configuration provides effective data communication without a significant increase in the use of network resources.
Peak ion energy is an important figure-of-merit in short-pulse, laser-driven ion acceleration and is dependent on an associated acceleration time. Standard metrics for these quantities depend on analytical results such as the self-similar fluid model or empirical models based on relatively small experimental and simulation datasets. In this work we attempt to use a data-informed neural network (NN) as a surrogate model for a large ensemble of PIC simulations to investigate an effective acceleration time. We explore the application of a stacked convolutional and recurrent NN architecture for improved regression by incorporating the time dependencies of the data into the training process. Of particular note is how pretraining a network on lower fidelity data, e.g. 1D analytical results, greatly improves the network's ability to learn more complex, higher fidelity data. Finally, the dependency of the acceleration time on various laser and plasma parameters is explored.
Abstract Teleporting energy to remote locations is new challenge for quantum information science and technology. Developing a method for transferring local energy in laboratory systems to remote locations will enable non‐trivial energy flows in quantum networks. From the perspective of quantum information engineering, we propose a method for distributing local energy to a large number of remote nodes using hyperbolic geometry. Hyperbolic networks are suitable for energy allocation in large quantum networks since the number of nodes grows exponentially. To realise long‐range quantum energy teleportation (QET), we propose a hybrid method of quantum state telepotation and QET. By transmitting local quantum information through quantum teleportation and performing conditional operations on that information, QET can theoretically be realized independent of geographical distance. The method we present will provide new insights into new applications of future large‐scale quantum networks and potential applications of quantum physics to information engineering.
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Provides a summary of State and Local Energy Efficiency Action Network (SEE Action) information resources available to state energy officials, organized by topic.
Kawerak will work with partners as a clearinghouse for energy information, provide training for remote and onsite technical assistance to communities on energy projects, work with communities to update their portions of the regional energy plan and contribute results to state energy planners, and host an annual regional energy summit to share information and provide training while networking with other regions to collaborate on energy issues and projects. These projects will look to leverage current and future DOE and other funding opportunities and to capture the Norton Sound Economic Development Corporation funding for shovel ready energy projects.
Energy systems models typically take the form of complex partial differential equations which make multiple forward calculations prohibitively expensive. Fast and data-efficient construction of surrogate models is of utmost importance for applications that require parameter exploration such as design optimization and Bayesian calibration. In presence of a large number of parameters, surrogate models that capture correct dependencies may be difficult to construct with traditional techniques. The issue is addressed here with the formulation of the surrogate model constructed via Physics-Informed Neural Networks (PINN) which capture the dependence with respect to the parameters to estimate, while using a limited amount of data. Since forward evaluations of the surrogate model are cheap, parameter exploration is made inexpensive, even when considering a large number of parameters.
Here we describe how new strategies for combining conventional photo- and soft- lithographic techniques with high-resolution patterning and etching strategies are needed in order to produce multi-scale fluidic platforms that address the full range of functional scales seen in complex biological and chemical systems. The smallest resolution required for an application often dictates the fabrication method used. Micromachining and micro-powder blasting yield higher throughput, but lack the resolution needed to fully address biological and chemical systems at the cellular and molecular scales. In contrast, techniques such as electron beam lithography or nanoimprinting allow nanoscale resolution, but are traditionally considered costly and slow. Other techniques such as photolithography or soft lithography have characteristics between these extremes. Combining these techniques to fabricate multi-scale or hybrid fluidics allows fundamental biological and chemical questions can be answered. In this study, a combination of photolithography and electron beam lithography are used to produce two multi-scale fluidic devices that incorporate porous membranes into complex fluidic networks to control the flow of energy, information, and materials in chemical form. In the first device, materials and energy were used to support chemical reactions. A nanoporous membrane fabricated with e-beam lithography separates two parallel, serpentine channels. Photolithography was used to write microfluidic channels around the membrane. The pores were written at 150nm and reduced in size with silicon dioxide deposition from plasma enhanced chemical vapor deposition (PECVD) and atomic layer deposition (ALD). Using this method, the molecular weight cutoff (MWCO) of the membrane can be adapted to the system of interest. In the second approach, photolithography was used to fabricate 200nm thin pores. The pores confined microbes and allowed energy replenishment from a media perfusion channel. The same device can be used for study of intercellular communication via the secretion and uptake of signal molecules. Pore size was tested with 750nm fluorescent polystyrene beads and fluorescein dye. The 200nm PDMS pores were shown to be robust enough to hold 750nm beads while under pressure, but allow fluorescein to diffuse across the barrier. Further testing showed that extended culture of bacteria within the chambers was possible. Finally, these two examples show how lithographically defined porous membranes can be adapted to two unique situations and used to tune the flow of chemical energy, materials, and information within a microfluidic network.
Despite increased reports of energy sector-focused threat actors using living-off-the-land (LOTL) techniques, it is unlikely LOTL techniques will wholly supplant malware in energy sector operational technology (OT)-focused cyber operations. Threat actors leverage LOTL techniques to access energy sector networks, abstracting process information and maintaining persistence. Although threat actors using LOTL techniques have successfully interrupted energy sector industrial control environments, designed features of OT-specific malware likely increase the cyber-physical impact of an attack and delay recovery of critical functions and services. Malicious actors will very likely continue to use LOTL techniques for stealth, while designing malware to bolster final impacts on cyber-physical systems in energy sector OT environments.
Significant expansion of wind energy development will be required to achieve the scenarios outlined in the U.S. Department of Energy's (DOE)'s Wind Vision: 20% wind energy by 2030 and 35% wind energy by 2050. Wind energy currently provides nearly 5% of the nation's electricity but has the potential to provide much more. The wind industry and the DOE's Wind Energy Technologies Office are addressing technical wind energy challenges, such as reducing turbine costs and increasing energy production and reliability. The Office recognizes that public acceptance of wind energy can be challenging, depending on the proximity of proposed wind farms to local populations. Informed decision makers and communities equipped with unbiased information about the benefits and impacts of wind energy development are better prepared to navigate the sometimes contentious development process. In 2014, DOE established six Regional Resource Centers (RRCs) across the United States to communicate unbiased, credible information about wind energy to stakeholders through regional networks. The RRCs provide ready access to this information to familiarize the public with wind energy; raise awareness about potential benefits and issues; and disseminate data on siting considerations such as turbine sound and wildlife habitat protection. This document summarizes the status and drivers for U.S. wind energy development during 2016. RRC leaders provided a report of wind energy development in their regions, which was combined with findings from National Renewable Energy Laboratory (NREL) researchers to provide an account of the state of the regions, as well as updates on developments in individual states. NREL researchers and state partners added updates for all states that are not directly supported by an RRC. Accounts for each region include updates on renewable portfolio standards, the Clean Power Plan, workforce development, manufacturing and economic development, and individual state updates for installed wind capacity, ongoing policy developments, planned projects and their status, transmission progress reports, etc. This report also highlights the efforts of the RRCs to engage stakeholders in their individual regions.
Highlights: • Energy-related human-building interaction is analyzed at the biophysical system scale. • Energy flow-networking model of environmental building performance is suggested. • Information is suggested as a holistic building performance metric. - Abstract: This article addresses the utmost upstream impacts of human–building energy interaction by proposing a network-based model, algorithms, and indicators. Hypothesizing that human behavior is a key factor for the symbiotic development of building and the geobiosphere system, the author seeks emergy (spelled with an “m”)-information integrated measures to indicate dynamic system-level performance of the interaction with building energy flow topology. To validate the hypothesis and methods, four representative building cases were tested on the building form (envelope) of (i) a building with no occupant intervention (baseline), (ii) a building controlled by responsive human behavior (bioclimatic adaptation), (iii) a building with reinforced insulation and behavior-dominated control (passive design), and (iv) a net-zero energy building (NZEB). The results demonstrate that adaptive human behavior in building operation increases the information content and complexity of energy-flow networking, improving performance and sustainability. Findings also reveal that increasing information, complexity, and power (energy availability over time) parallel the general energetic features of developing biophysical systems (greater feedback, internalization, and recycling of materials and energies). It becames clear that active behavioral response is a dominant agent of sustainable environments even on a far broader system scale.
Particle detectors record the interactions of subatomic particles and their passage through matter. The identification of these particles is necessary for in-depth physics analysis. While particles can be identified by their individual behavior as they travel through matter, the full context of the interaction in which they are produced can aid the classification task substantially. We have developed the first convolutional neural network for particle identification which uses context information. This is also the first implementation of a four-tower siamese-type architecture both for separation of independent inputs and inclusion of context information. The network classifies clusters of energy deposits from the NOvA neutrino detectors as electrons, muons, photons, pions, and protons with an overall efficiency and purity of 83.3% and 83.5%, respectively. We show that providing the network with context information improves performance by comparing our results with a network trained without context information.
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Spiking neural networks drawing inspiration from biological constraints of the brain promise an energy-efficient paradigm for artificial intelligence. However, challenges exist in identifying guiding principles to train these networks in a robust fashion. In addition, training becomes an even more difficult problem when incorporating biological constraints of excitatory and inhibitory connections. In this work, we identify several key factors, such as low initial firing rates and diverse inhibitory spiking patterns, that determine the overall ability to train in the context of spiking networks with various ratios of excitatory to inhibitory neurons. The results indicate networks with biologically-realistic excitatory:inhibitory ratios can reliably train at low activity levels and in noisy environments. Additionally, the Van Rossum distance, a measure of spike train synchrony, provides insight into the importance of inhibitory neurons to increase network robustness to noise. This work supports further biologically-informed large-scale networks and energy efficient hardware implementations.
We develop a methodology based on Bayesian inference over Probabilistic Graphical Models (PGMs) to understand and quantify risk in solar-centered grids using targeted measurements and learned system behavior. Being non-prescriptive but, rather, able to infer system behavior and, ultimately, address risk queries from data, our machine learning-type paradigm is tailored for diverse topologies and threat scenarios often associated with distributed energy generation and photovoltaic distributed energy resources (PV-DERs) in particular. We describe algorithmic processes for: (i) learning the structure of PGMs that result from attack-prone PV-DER-proliferated distribution systems, (ii) quantifying cause-effect relationships, and (iii) evaluating risk queries based on diverse evidence. The contributions are illustrated on a residential grid subject to output impairment attacks on its PV-DER infrastructure.
Supply disruptions and infrastructure failures in natural gas networks present critical challenges to energy reliability and risk-informed planning. This study evaluates two supply prioritization strategies, Maximum Delivery Prioritization (MDP) and Demand-Based Prioritization (DBP), within an arbitrary natural gas network under conditions of supply shortage. Model performance under both strategies is assessed in response to node and edge failure using demand satisfaction metrics, system-wide and localized dependency scores, and geographic information system (GIS)-based spatial analysis. Results show that DBP better preserves supply for high-demand nodes, while MDP offers broader coverage. The underlying network topology plays a critical role in shaping prioritization outcomes. Integrated GIS visualization enhances the interpretability of vulnerability assessments, revealing structurally critical components and localized vulnerabilities. The proposed framework supports scalable, data-driven decision-making for infrastructure planners and engineers, enabling improved disruption recovery and efficiency in constrained natural gas networks. These insights contribute to the development of more robust energy systems capable of withstanding stress and disruptions.
Digital networks are the foundation of the information services, and play an expanding and indispensable role in our lives, via the Internet, email, mobile phones, etc. However, these networks consume energy, both through the direct energy use of the network interfaces and equipment that comprise the network, and in the effect they have on the operating patterns of devices connected to the network. The purpose of this research was to investigate a variety of technology and policy issues related to the energy use caused by digital networks, and to further develop several energy-efficiency technologies targeted at networks.