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At least 217 records · Page 12

Dynamic Learning of Correlation Potentials for a Time-Dependent Kohn-Sham System

We develop methods to learn the correlation potential for a time-dependent Kohn-Sham (TDKS) system in one spatial dimension. We start from a low-dimensional two-electron system for which we can numerically solve the time-dependent Schr¨odinger equation; this yields electron densities suitable for training models of the correlation potential. We frame the learning problem as one of optimizing a least-squares objective subject to the constraint that the dynamics obey the TDKS equation. Applying adjoints, we develop efficient methods to compute gradients and thereby learn models of the correlation potential. Our results show that it is possible to learn values of the correlation potential such that the resulting electron densities match ground truth densities. We also show how to learn correlation potential functionals with memory, demonstrating one such model that yields reasonable results for trajectories outside the training set.

97 MATHEMATICS AND COMPUTING↗

Electron dynamics in extended systems within real-time time-dependent density-functional theory

Due to a beneficial balance of computational cost and accuracy, real-time time-dependent density-functional theory has emerged as a promising first-principles framework to describe electron real-time dynamics. Here we discuss recent implementations around this approach, in particular in the context of complex, extended systems. Results include an analysis of the computational cost associated with numerical propagation and when using absorbing boundary conditions. Here we extensively explore the shortcomings for describing electron–electron scattering in real time and compare to many-body perturbation theory. Modern improvements of the description of exchange and correlation are reviewed. In this work, we specifically focus on the Qb@ll code, which we have mainly used for these types of simulations over the last years, and we conclude by pointing to further progress needed going forward.

2D materials↗

Open Source Modeling for Power Systems Dynamics

Time-domain simulations for power systems (both phasor and electromagnetic transients) are mostly dominated by commercial tools, such as PSS/E or PSCAD. However, with the increasing penetration of inverter-based resources and changes in model paradigm, new open-source tools are looking into become a possible alternative for a range of stakeholders. In this presentation we will focus on how to improve communication with industry and other stakeholders to properly showcase the value of open source simulators. Participants will identify current barriers facing the adoption of open-source time-domain tools, and will discuss how to improve the bench-marking processes across different tools, while maintaining software modularity to include new features and models as new challenges continue to happen in energy systems.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗

Adaptive Time-Division Multiplexing Driving System for Solid State Lighting with Multiple Tunable Channels

Next generation solid state lighting enables unlimited control of light and serve much broader functions beyond the basic lighting for illuminance. The emerging lighting for productivity, well-being, healthcare, and growth have revolutionized the scope of lighting. To empower this unlimited controllability, a multi-channel tunable power-electronics driver is essential but has not been satisfactorily addressed so far. Existing solutions are lossy, large, and expensive, due to introduction of additional buck power conversion stages. This paper proposes a time-division multiplexing (TDM) LED driving system which eliminates those buck stages. The proposed design can directly pair with off-the-shelf pulse width modulated (PWM) controllers and since it inherits entire feature sets, it can achieve low loss at standby and at dimmed condition. The simple analog implementation avoids usage of expensive DSPs/MCU. GaN’s high switching frequency capability can push up the multiplexing frequency so a GaN-based flyback was built and proved the concept.

Xue, Lincoln↗

Optimized Quantum Program Execution Ordering to Mitigate Errors in Simulations of Quantum Systems

Simulating the time evolution of a physical system at quantum mechanical levels of detail - known as Hamiltonian Simulation (HS) - is an important and interesting problem across physics and chemistry. For this task, algorithms that run on quantum computers are known to be exponentially faster than classical algorithms; in fact, this application motivated Feynman to propose the construction of quantum computers. Nonetheless, there are challenges in reaching this performance potential. Prior work has focused on compiling circuits (quantum programs) for HS with the goal of maximizing either accuracy or gate cancellation. Our work proposes a compilation strategy that simultaneously advances both goals. At a high level, we use classical optimizations such as graph coloring and travelling salesperson to order the execution of quantum programs. Specifically, we group together mutually commuting terms in the Hamiltonian (a matrix characterizing the quantum mechanical system) to improve the accuracy of the simulation. We then rearrange the terms within each group to maximize gate cancellation in the final quantum circuit. Furthermore, these optimizations work together to improve HS performance and result in an average 40% reduction in circuit depth. This work advances the frontier of HS which in turn can advance physical and chemical modeling in both basic and applied sciences.

97 MATHEMATICS AND COMPUTING↗

Deep Reinforcement Scheduling of Energy Storage Systems for Real-time Voltage Regulation in Unbalanced LV Networks with High PV Penetration

The ever-growing higher penetration of distributed energy resources (DERs) in low-voltage (LV) distribution systems brings both opportunities and challenges to voltage support and regulation. This paper proposes a deep reinforcement learning (DRL)-based scheduling scheme of energy storage systems (ESSs) to mitigate system voltage deviations in unbalanced LV distribution networks. The ESS-based voltage regulation problem is formulated as a multi-stage quadratic stochastic program, with the objective of minimizing the expected total daily voltage regulation cost while satisfying operational constraints. While existing voltage regulation methods are mostly focused on onetime- step control, this paper explores a day-horizon systemwide voltage regulation problem. In other words, the size of action and state spaces are extremely high-dimensional and need to be delicately handled. Furthermore, in order to overcome the difficulty of modeling uncertainties and develop a realtime solution, a learn-to-schedule feedback control framework is proposed by adapting the problem to a model-free DRL setting. The proposed algorithm is tested on a customized 6-bus system and a modified IEEE 34-bus system. Simulation results validate the effectiveness and near-optimality of voltage regulation by ESS in comparison with a deterministic quadratic program solution.

Wang, Shengyi↗

Parallel-in-Time Solution of Hyperbolic PDE Systems via Characteristic-Variable Block Preconditioning

We consider the parallel-in-time solution of both linear and nonlinear hyperbolic partial differential equation (PDE) systems in one spatial dimension. In the nonlinear setting, the discretized equations are solved with a preconditioned residual iteration based on a global linearization. The linear(ized) equation systems are approximately solved parallel-in-time using a block preconditioner applied in the characteristic variables of the underlying linear(ized) hyperbolic PDE. This change of variables is motivated by the observation that intervariable coupling between characteristic variables is weak, at least locally where spatio-temporal variations in the eigenvectors of the associated flux Jacobian are sufficiently small, while that between the original variables is not. For an ℓ-dimensional system of PDEs, applying the preconditioner consists of solving a sequence of ℓ scalar linear(ized)-advection-like problems, each associated with a different characteristic wave-speed in the underlying linear(ized) PDE. Furthermore, we approximately solve these linear advection problems using multigrid reduction-in-time (MGRIT); however, any other suitable parallel-in-time method could be used. Numerical examples are shown for the (linear) acoustics equations in heterogeneous media and for the (nonlinear) shallow water equations and Euler equations of gas dynamics with shocks and rarefactions. For many test problems, the solver converges in just a handful of iterations and with mesh-independent convergence rates.

97 MATHEMATICS AND COMPUTING↗

A Control Scheme to Mitigate the Dead-Time Effects in a Wireless Power Transfer System

In practice, a dead-time is always provided between the complementary switching instances of the inverter phase-leg devices. At higher operating frequencies, the dead-time issues in wireless power transfer (WPT) systems become critical, especially as the power level increases. In certain operating conditions, the dead-time effect in wireless power transfer system affects the switching characteristics. Consequently, the switching losses in the power semiconductor devices increase and also impact the efficiency of the overall system. In this paper, a simple control scheme is proposed to eliminate the dead-time effect (or voltage polarity reversal) in the WPT inverter. The proposed control scheme monitors the inverter output voltage, and the switching frequency is auto-tuned to eliminate the undesired switching instances in the inverter voltage. The proposed control scheme is validated using the closed-loop simulations in PLECS, and the experimental results on a 5.6 kW WPT prototype are also presented. After eliminating the voltage-polarity-reversal at the inverter output, the inverter losses were reduced by ~40%, and the overall system losses were reduced by ~17%.

Kavimandan, Utkarsh↗

Method and apparatus for radiation detection based on time-of-flight within optical fibers

A radiation detection system using time of flight (TOF) information within multiple optical fiber complexes coupled with a scintillating material at intersections of repeatedly crossing over shape. Light detectors are placed at the ends of each fiber to detect scintillation events. A timing processor is collecting light detector signal to compute TOF difference and estimate the location and strength of radioactivity. The system is scalable in one dimension, capable of being shaped or curved, and customizable in terms of special resolution and sensitivity. The system is suitable for long range and coarse radiation detection.

Lee, Seung Jae↗

Low latency optical-based mode tracking with machine learning deployed on FPGAs on a tokamak

Active feedback control in magnetic confinement fusion devices is desirable to mitigate plasma instabilities and enable robust operation. Optical high-speed cameras provide a powerful, non-invasive diagnostic and can be suitable for these applications. Here, in this study, we process high-speed camera data, at rates exceeding 100 kfps, on in situ field-programmable gate array (FPGA) hardware to track magnetohydrodynamic (MHD) mode evolution and generate control signals in real time. Our system utilizes a convolutional neural network (CNN) model, which predicts the n = 1 MHD mode amplitude and phase using camera images with better accuracy than other tested non-deep-learning-based methods. By implementing this model directly within the standard FPGA readout hardware of the high-speed camera diagnostic, our mode tracking system achieves a total trigger-to-output latency of 17.6 μs and a throughput of up to 120 kfps. This study at the High Beta Tokamak-Extended Pulse (HBT-EP) experiment demonstrates an FPGA-based high-speed camera data acquisition and processing system, enabling application in real-time machine-learning-based tokamak diagnostic and control as well as potential applications in other scientific domains.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Deep learning unresolved lensed light curves

ABSTRACT Gravitationally lensed sources may have unresolved or blended multiple images, and for time varying sources, the light curves from individual images can overlap. We use convolutional neural nets to both classify the light curves as due to unlensed, double, or quad lensed sources and fit for the time delays. Focusing on lensed supernova systems with time delays Δt ≳ 6 d, we achieve 100 per cent precision and recall in identifying the number of images and then estimating the time delays to σΔt ≈ 1 d, with a 1000× speedup relative to our previous Monte Carlo technique. This also succeeds for flux noise levels $\sim 10{{\ \rm per\ cent}}$. For Δt ∈ [2, 6] d, we obtain 94–98 per cent accuracy, depending on image configuration. We also explore using partial light curves where observations only start near maximum light, without the rise time data, and quantify the success.

79 ASTRONOMY AND ASTROPHYSICS↗

Convenient analytical formula for cluster mean diameter and diameter dispersion after nucleation burst

Here, we propose an alternative method of estimating the mean diameter and dispersion of clusters of particles, formed in a cooling gas, right after the nucleation stage. Using a moment model developed by Friedlander [S. K. Friedlander, Ann. N. Y. Acad. Sci. 404, 354 (1983)], we derive an analytic relationship for both cluster mean diameter and diameter dispersion as a function of two of the characteristic times of the system: the cooling time and the primary constituents collision time. These formulas can be used to predict diameter and dispersion variation with process parameters, such as the initial primary constituents' concentration or cooling rate. It is also possible to use them as an input to the coagulation stage, without the need to compute complex cluster generation during the nucleation burst. We compared our results with a nodal code (NGDE) and got excellent agreement.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Quantum Networks for Resilient Power Grids: Theory and Simulated Evaluation

Quantum networks are considered the future of secure communication in the coming quantum era. Yet there lack significant efforts on developing practical quantum networks for power grids. Here, in this paper, we establish a quantum network-based power grid (QNetGrid) framework and develops a real-time, reliable, flexible, programmable, and cost-effective QNetGrid software testbed containing repeater-based quantum communication, quantum routing, real software-defined networking (SDN) switches, and real-time networked microgrids (NMs) operations. It makes the following contributions: 1) a repeater and routing based quantum network simulator (QNSim) is developed, 2) repeaters with and without quantum memories are respectively simulated in QNSim, 3) different routing scenarios in QNetGrid are investigated, 4) a real-time QNetGrid software testbed is built in RTDS incorporating QNSim and real SDN switches, and 5) various test cases are designed, and experimental results produced with the QNetGrid testbed provide valuable insights for building quantum networks in power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗

National Opacity Program: Analysis of Opacity-relevant X-ray Emission Spectra (Milestone Report ID 7121)

We have produced high energy density iron plasmas at temperatures above 1 keV and electron densities exceeding 1023 cm -3 (~ 1 g/cm 3 ) using the Orion laser at the Atomic Weapons Establishment. These plasmas were created by irradiating 50 µm diameter layered targets with frequency doubled (λ = 527 nm), 1 ps laser pulses focused to a 100 µm diameter producing an irradiance of ~ 2 x 10 18 W/cm 2 . The buried layer targets consist of 160 nm iron sulfide (FeS), 60 nm potassium chloride (KCl), and 15 nm carbon. The combined layers are tamped on both sides with 3 µm of parylene-N. The x-ray emission from the plasma was measured using two time-resolved, and four time-integrated Bragg crystal spectrometers, as well as one time-integrated imaging system. One time-resolved x-ray spectrometer measured emission from L-shell transitions in highly charged iron, the other from K-shell transitions in helium-like S 14+ and hydrogen-like S 15+ . The time-integrated spectrometers are intensity-calibrated and measured emission from K-shell transitions in sulfur, potassium, chlorine, and both K-shell and L-shell transitions in iron. The density and temperature of the plasma were determined by modeling the x-ray spectra using different spectral and hydrodynamic modeling packages. A brief overview of the uncertainties associated with the measurements and models are presented. We also give an overview of our 1-D HYDRA-DCA radiation-hydrodynamics model and improvements for future work. Our results aid in assessing experimental uncertainties associated with plasma uniformity and with x-ray emission employed as diagnostics in opacity experiments at temperatures and densities not achievable elsewhere and represent a significant step in creating and diagnosing plasmas near LTE. These results are summarized as part of the completion requirements for milestone 7121

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Forming Interphase Microgrids in Distribution Systems Using Cooperative Inverters

Interphase-microgrids formed by three islanded single-phase feeders in distribution systems are proposed in this paper. Loss of utility caused by natural and human-made disasters may isolate each residential subdivision from the utility. In this case, the household loads are supplied by distributed generation (DG) units which may not be enough to meet the load demand. This can lead to deviations in voltage and frequency, and the deviations may vary for each phase. The proposed solution is to form an interphase-microgrid by seamlessly interconnecting the islanded single-phase feeders. This interphase-microgrid can effectively balance the load demands and DG generation in distribution systems after losing the utility supply. This paper presents different case studies to demonstrate the viability of forming interphase-microgrids for residential distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Smarter building start – A distributed solution

A significant focus of research and new technologies for reducing energy use in buildings is on operating the systems more efficiently during the times when the systems are active. However, there is a large potential for energy savings in determining the periods when the systems should or should not be active. This scheduling aspect of operation is often overlooked even though relatively simple solutions can unlock substantial energy savings. In this paper we describe a smart building start (SBS) algorithm that considers multiple zones in a building to determine individual schedules for room controllers as well as the central systems based on solving a simple optimization problem. Application of the SBS algorithm to multiple interconnected systems enables a staggered start-up that minimizes peak loads and also ensures comfort is within a target range with minimal system run time. The SBS algorithm extends the capability of traditional optimal start and is designed to be simple to deploy and robust. Simulation results as well as results from tests in a real building with a VAV system are presented. The presented algorithm is applicable to any type of building with a zonal or multi-zone HVAC system. To function, it needs to be able to change setpoints in rooms and monitor room temperatures, as well as, if desired, turn the central system on or off.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Time-gated fast neutron transmission radiography system and method

The present disclosure relates to a time-gated fast neutron transmission radiography system and method. The system makes use of a pulsed neutron source for producing neutrons in a plurality of directions, with at least a subplurality of the neutrons being directed at an object to be imaged. The system also includes a neutron detector system configured to time-gate the detection of neutrons emitted from the pulsed neutron source to within a time-gated window.

Schmidt, Andrea↗