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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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Case study examines a common PV inverter failure that can drive uncommon issues in the field - namely, damage to the AC protection equipment from DC fault currents for short periods during transformer-less inverter power electronic failures.
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In this study, a three-port resonant converter with a high-frequency isolation link for hybrid unmanned/autonomous aerial vehicle (UAV/AAV) applications is analyzed. The rectified engine-generator set output supplies the first input ports while the other is connected to a battery energy storage system. Input ports consist of half-bridge high-frequency inverters, whereas output port utilizes a semi-bridgeless active rectifier. This rectifier is formed by replacing rectifier low-side diodes with active switching devices. A phase-shift duty-cycle control is implemented for switching signals to regulate the power share and flow between ports. The proposed control system implemented achieves zero voltage switching (ZVS), reduces switching losses, and minimum current flow optimization ensures regulation in a wide range of output power. Compared to the existing bidirectional pulse width modulation (PWM) converters, the proposed system decreases the number of power conversion stages and provides centralized control without needing communications between the ports. The proposed control technique operating in closed-loop control achieves the power flow between ports under various load conditions. Finally, theoretical analysis demonstrates the proposed converter’s viability and laboratory experimental validations at 500 W full power.
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Alternating current optimal power flow (OPF) analysis is critical for efficient and reliable operation of power systems. For large systems or repetitive computations, the traditional methods such as the direct and gradient methods, or non-traditional methods, such as the genetic algorithm and simulating annealing, are time-consuming and unsuitable for real-time computing. The work in this paper proposes a novel framework to obtain the optimal solution of power flow in real-time using a combination of convolutional neural networks and a self-attention mechanism. All parameters of the power networks are rearranged in an image-like shape of a multi-channel image where each channel is a two-dimensional matrix. The proposed approach is adaptive with every input size of power systems as well as frequent variations of network topologies without intervention to the framework core. The encompassment of all power system contexts in which all parameters of internal elements, generation costs, and topology information are included, contributes to the higher accuracy of inference compared to other current machine-learning-based OPF-solving methods. Besides, the proposed framework established on ubiquitous platforms is effortlessly integrated into current infrastructures of power systems, and the great efficiency along with the computation speed may serve as a critical point for practical implications, such as enabling faster decision-making during real-time operations, predicting system contingencies, and remedial actions based on an offline pre-trained model. Furthermore, this supervised learning process is applied to the dataset of four case studies of meshed power systems: the IEEE 5-bus system (IEEE-5), the IEEE 30-bus system (IEEE-30), the IEEE 39-bus system (IEEE-39), and the IEEE 57-bus system (IEEE-57) to prove the efficacy of the proposed method.
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Here, we characterized the front-end electronics used to process high-frequency signals from low-gain avalanche diodes (LGADs) at the Fermilab Test Beam Facility. LGADs are silicon detectors employed for charged particle tracking, offering exceptional spatial and temporal resolution. The purpose of this characterization was to understand how the time resolution is influenced by the front-end electronics. To achieve this, we developed a setup capable of generating input signals with varying amplitudes. The output results demonstrated that signal processing by the front-end electronics plays a crucial role in enhancing time resolution. We showed that the time resolution achieved by the FEE board is better than 2 p s at the 1 σ level.
Sustainable energy conversion, chemical manufacturing, and separations are central to addressing the world’s energy and environmental challenges. Electrochemical platforms stand as a cornerstone in addressing these challenges because they are low exergy and can be powered on renewable electrons. In electrochemical systems, bipolar membranes (BPMs) are emerging as a unique class of ion exchange membranes poised to revolutionize various electrochemical processes via pH control of anode and cathode chambers and in situ pH adjustment. In this Spotlight Review, we provide a comprehensive review of electrochemical platforms utilizing BPMs for energy conversion (water electrolyzers for hydrogen production, fuel cells, and flow batteries), chemical manufacturing (electrolyzers that convert carbon dioxide into value-added chemicals and nitrate into ammonia), and separations. The motivation for using BPMs, as well as their performance and durability, in electrochemical platforms are disseminated. We also discuss current challenges that impede BPM electrochemical systems from competing with state-of-the-art electrochemical systems using monopolar ion-exchange membranes (e.g., anion/hydroxide exchange membranes and cation/proton exchange membranes). Here, the review also covers molecular modeling and continuum modeling efforts to understand the basic mechanisms that govern BPM performance.
A lateral Josephson junction in which the surface of a 3D topological insulator serves as the weak link should support topologically protected excitations related to Majorana fermions. The resulting 4π-periodic current-phase relationship could be detected under high-frequency excitation by the suppression of odd Shapiro steps. Here, in this study, we demonstrate such devices through the self-formation of a Pd-Te superconducting layer from a telluride topological insulator, and observe suppressed first and third Shapiro steps. Other devices, including those where the Pd-Te layer is bolstered by an additional Al layer, show no suppression of Shapiro steps, a difference supported by simulations. Though we rule out the known trivial causes of suppressed Shapiro steps in our devices, we nevertheless argue that corroborating measurements and disorder-aware theoretical descriptions of these systems are needed before confidently claiming the observation of Majorana states.
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Today’s power grid is becoming more diverse and integrated with high-level distributed energy resources and smart control technologies that is creating a new set of grid management challenges in terms of large-scale, nonlinear, and non-convex problem modeling, complex and time-consuming computation, as well as difficult uncertainty handling. This project focused on solving a challenging multi-period security-constrained generation scheduling problem, which is of great importance for maximizing the social welfare of real-time dispatch, day-ahead market, as well as weekly planning of power systems. Our developed software explored parallel optimization algorithms for complex and realistic power system models, and develop fast, efficient, and robust grid optimization solutions on the high-performance computing platform that will enable increased grid economics, flexibility, resilience, as well as energy security in the United States.