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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Out-of-time-order operators and the butterfly effect

Out-of-time-order (OTO) operators have recently become popular diagnostics of quantum chaos in many-body systems. The usual way they are introduced is via a quantization of classical Lyapunov growth, which measures the divergence of classical trajectories in phase space due to the butterfly effect. However, it is not obvious how exactly they capture the sensitivity of a quantum system to its initial conditions beyond the classical limit. In this paper, we analyze sensitivity to initial conditions in the quantum regime by recasting OTO operators for many-body systems using various formulations of quantum mechanics. Notably, we utilize the Wigner phase space formulation to derive an ħ-expansion of the OTO operator for spatial degrees of freedom, and a large spin 1∕s-expansion for spin degrees of freedom. We find in each case that the leading term is the Lyapunov growth for the classical limit of the system and argue that quantum corrections become dominant at around the scrambling time, which is also when we expect the OTO operator to saturate. We also express the OTO operator in terms of propagators and see from a different point of view how it is a quantum generalization of the divergence of classical trajectories.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales

The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.

17 WIND ENERGY↗
DOE OSTI2017

Modeling of HVAC operational faults in building performance simulation

Operational faults are common in the heating, ventilating, and air conditioning (HVAC) systems of existing buildings, leading to a decrease in energy efficiency and occupant comfort. Various fault detection and diagnostic methods have been developed to identify and analyze HVAC operational faults at the component or subsystem level. However, current methods lack a holistic approach to predicting the overall impacts of faults at the building level—an approach that adequately addresses the coupling between various operational components, the synchronized effect between simultaneous faults, and the dynamic nature of fault severity. This study introduces the novel development of a fault-modeling feature in EnergyPlus which fills in the knowledge gap left by previous studies. This paper presents the design and implementation of the new feature in EnergyPlus and discusses in detail the fault-modeling challenges faced. The new fault-modeling feature enables EnergyPlus to quantify the impacts of faults on building energy use and occupant comfort, thus supporting the decision making of timely fault corrections. Including actual building operational faults in energy models also improves the accuracy of the baseline model, which is critical in the measurement and verification of retrofit or commissioning projects. As an example, EnergyPlus version 8.6 was used to investigate the impacts of a number of typical operational faults in an office building across several U.S. climate zones. Finally, the results demonstrate that the faults have significant impacts on building energy performance as well as on occupant thermal comfort. Finally, the paper introduces future development plans for EnergyPlus fault-modeling capability.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Integration of optimal operational dispatch and controller determined dynamics for microgrid survivability

We present that the reliability and resilience of the electrical power grids are essential to industry, economy and society. Microgrids that are able to island from the bulk electrical grid are one technology that may vastly improve electrical power service to customer loads. To achieve these improvements, an islanded microgrid should be able to operate through the loss of one of its generators without shedding electrical load. The loss of one generator will typically result in significant additional loads, including transient overloads, being placed on the remaining generators. There is also the possibility of additional generator tripping during these processes (i.e. cascading failures), which would likely result in the collapse of the microgrid. The novelty of our work consists in incorporating dynamic models of generator controllers into a microgrid optimal dispatch formulation with the ultimate goal to avoid operational failures and ensure the “survivability” of all-inverter microgrids to generator loss and transient overloads. The integration of generator and controller dynamics into the optimal dispatch formulation significantly increases the computational complexity. As we develop algorithms to restore speed of the optimization, our method can be readily implemented into a new operational strategy capable of an unprecedented level of reliability against generator contingencies. In addition, we quantitatively illustrate the effect of the survivability constraints on the microgrid operating costs and how the related trade-off between capital and operating costs should be taken into account at the design stage. Finally, the methods developed here also apply to the dispatch of off-grid microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗
DOE OSTI2024

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗
DOE OSTI2019

Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters

To maximize potential energy and cost savings, networked clusters of local buildings are formed for energy transactions. Both centralized and distributed decision approaches were explored in past decades as a way to enable efficient transactive operations. However, online distributed stochastic transactive operation has been overlooked in the literature. To bridge gaps in the research, a bi-level distributed stochastic model predictive control framework was proposed to study the transactive operations of building clusters where a system-level agent is employed to coordinate multiple building agents at the subsystem level. The energy transaction is optimized by a marginal price-based particle swarm optimizer at the system level. Given the energy transaction decisions, each building can independently solve a scenario-based two-stage stochastic model to optimally dispatch the electricity and ancillary services for optimal energy performance. The effectiveness of the proposed framework and coordination algorithm are demonstrated in deterministic, stochastic, and online operations and compared to centralized decisions using several sets of experiments. Additionally, the proposed approach can realize autonomous transactive operation and be extended to community-level building clusters in a plug-and-play way.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Improving operational flexibility of integrated energy system with uncertain renewable generations considering thermal inertia of buildings

Insufficient flexibility in system operation caused by traditional "heat-set" operating modes of combined heat and power (CHP) units in winter heating periods is a key issue that limits renewable energy consumption. In order to reduce the curtailment of renewable energy resources through improving the operational flexibility, a novel optimal scheduling model based on chance-constrained programming (CCP), aiming at minimizing the lowest generation cost, is proposed for a small-scale integrated energy system (IES) with CHP units, thermal power units, renewable generations and representative auxiliary equipments. In this model, due to the uncertainties of renewable generations including wind turbines and photovoltaic units, the probabilistic spinning reserves are supplied in the form of chance-constrained; from the perspective of user experience, a heating load model is built with consideration of heat comfort and inertia in buildings. To solve the model, a solution approach based on sequence operation theory (SOT) is developed, where the original CCP-based scheduling model is tackled into a solvable mixed-integer linear programming (MILP) formulation by converting a chance constraint into its deterministic equivalence class, and thereby is solved via the CPLEX solver. We report the simulation results on the modified IEEE 30-bus system demonstrate that the presented method manages to improve operational flexibility of the IES with uncertain renewable generations by comprehensively leveraging thermal inertia of buildings and different kinds of auxiliary equipments, which provides a fundamental way for promoting renewable energy consumption.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Operation-adversarial scenario generation

This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e.g., seasons), and, at the same time, “stressful” to the system operations and dispatch decisions. The measure of stress used in this paper is based on the operating cost increases due to net load changes. The proposed operation-adversarial cGAN (OA-cGAN) internalizes a DC optimal power flow model and seeks to maximize the operating cost and achieve a worst-case data generation. The training and testing stages employed in the proposed OA-cGAN use historical day-ahead net load forecast errors and has been implemented for the realistic NYISO 11-zone system. In conclusion, our numerical experiments demonstrate that the generated operation-adversarial forecast errors lead to more cost-effective and reliable dispatch decisions.

42 ENGINEERING↗
DOE OSTI2019

Design of ECH launcher for KSTAR advanced Tokamak operation

For the advanced tokamak operation in Korea Superconducting Tokamak Advanced Research (KSTAR), up to 6 MW of electron cyclotron heating (ECH) is planned. The ECH launcher should be able to operate in steady state and serve as a central/off-axis heating and current drive, q-profile control, and control of the neoclassical tearing mode (NTM) and sawtooth. The water cooled launchers have been upgraded to include continuous operation with precise, high-speed real-time control of the beams by integration with the plasma control system (PCS) for supporting the experiments, in particular for suppressing NTM. Here, the launcher was successfully operated for 78 s with about 650 kW EC power, where the temperature of the coolant, with a flow rate of 9.6 lpm, is saturated and approximately 2 °C. The launcher control system had a maximum control cycle of 5 kHz and a motor latency of about 10 ms, and the poloidal scanning speed of the beam was 20 °/s. Additionally, for the higher localization of power deposition, the curvature and size of the mirrors in the launcher were optimized. This paper presents the ECH launcher design and its control system with the operation results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Assessment of the burning-plasma operational space in ITER by using a control-oriented core-SOL-divertor model

In future tokamaks, the control of burning plasmas will require careful regulation of the plasma density and temperature. Along with the design of effective burn-control systems, understanding how the fusion power varies in the density-temperature space is vital for the operation of fusion power plants. Here in this work, the steady-state operational space of ITER is studied using a control-oriented core-plasma model coupled to a two-point model of the scrape-off-layer (SOL) and divertor regions. The two models are coupled through the exchange of input-output parameters. The deuterium and tritium recycling from the wall are output parameters of the SOL-divertor model that are used as input parameters in the core-plasma density balance. Furthermore, the separatrix temperature, which is an output parameter of the SOL-divertor model, is incorporated into the radial core-plasma temperature profiles. Therefore, the temperature-dependent power balance of the plasma core is intimately linked to the SOL-divertor model. Both the power entering the SOL from the core, as determined by the core-plasma power balance, and the separatrix density, as dictated by the core-plasma density balance, are input parameters to the SOL-divertor model. They are control knobs in the SOL-divertor model that can be regulated using the core-plasma actuators: auxiliary power and pellet injection. There are various operational limitations, such as the saturation of the aforementioned actuators, that will prevent ITER from accessing certain high-fusion plasma regimes. The achievable tritium concentration in the fueling lines and the maximum sustainable heat load on the divertor will impose further restrictions. By accounting for these limitations, the ITER operational space is computed based on the coupled core-SOL-divertor model and visualized using Plasma Operation Contour (POPCON) plots that map performance metrics, such as the fusion to auxiliary power ratio, over the density-temperature space. Comparisons are drawn between plasmas with different recycling, confinement, and SOL-divertor conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

A Framework to Support the Fusion of Operation and Planning

The management of the nation’s power system has progressed to the point where the boundary lines between operations and planning are becoming blurred. The dynamic behavior of smart grid technologies brings pressures of incorporating the predictive capability from planning to enhance operations. These needs are requiring a smoother, better integrated interplay between the functional roles of planning and operations. This paper presents a framework to support the fusion of operation and planning. A case study of predicting the intra-hour deficiency in generation capability and ramping capacity and presenting the outputs to operators was conducted. The study shows the benefits of the fusion and the effectiveness of the framework. All the function blocks used in the study were built upon GridOPTICS Software System (GOSS), a software system middleware platform that facilitates deployment of new applications for the future power grid.

Framework↗
DOE OSTI2020

Continuous-time look-ahead flexible ramp scheduling in real-time operation

This paper develops a novel continuous-time look-ahead optimization model for co-optimizing balancing energy and flexible ramp products in real-time power systems operation. In addition, a continuous-time day-ahead operation model is developed to commit and schedule an adequate subset of generating units for providing balancing energy and ramping requirements in real-time operation. The up and down flexible ramp products are defined as continuous-time decision trajectories that continuously supply the real-time up and down ramping requirements of the system. The deliverability of flexible ramp products is secured through reserving the respective power requirements in generation capacity constraints of generating units. A function space solution method is proposed to reduce the dimensionality of day-ahead and real-time models through projecting them into finite-dimensional function spaces spanned by Bernstein polynomials, where the real-time operation decisions are modeled with a higher accuracy level than day-ahead decisions. Simulations are conducted on the IEEE-RTS system using CAISO load data, and the numerical results showcase the effectiveness of the proposed model in supplying the energy and flexible ramp requirements and avoiding ramping scarcity in the system, while reducing the operation costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Mitigating spectral bias in neural operators via high-frequency scaling for physical systems

Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes, which are present in multiscale physical systems. Therefore, they tend to produce over-smoothed solutions, which is particularly problematic in modeling turbulence and for systems with intricate patterns and sharp gradients such as multi-phase flow systems. In this work, we introduce a new approach named high-frequency scaling (HFS) to mitigate spectral bias in convolutional-based neural operators. By integrating HFS with proper variants of UNet, we demonstrate a higher prediction accuracy by mitigating spectral bias in single and two-phase flow problems. Unlike Fourierbased techniques, HFS is directly applied to the latent space, thus eliminating the computational cost associated with the Fourier transform. Additionally, we investigate alternative spectral bias mitigation through a diffusion model conditioned on neural operators. While the diffusion model integrated with the standard neural operator may still suffer from significant errors, these errors are substantially reduced when the diffusion model is integrated with a HFS-enhanced neural operator.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

State-of-the-art short-term electricity market operation with solar generation: A review

With the accelerating penetration of solar energy in energy systems, market operations concerning the solar-associated intermittency are widely discussed. How to correctly model the solar generation in the market and solve the uncertainty-based operation problems call for solutions. Unlike other renewable resources, solar power can be more easily applied to the demand side through behind-the-meter installations in a distributed manner with significant future extensibility. The corresponding operational rules in the markets are largely being revised. Over the past two decades, with the development of deregulated power markets, numerous studies related to uncertainty-based market operations tend to transform the market from a deterministic and centralized framework to a stochastic and decentralized one. As the relevant literature is most recent, this paper reviews their contributions to electricity market operations with solar integration and underlines the research directions of this topic and ongoing industry practices. A thorough review of the electricity markets worldwide with solar energy is provided. A variety of proposed mathematical solutions to the problem are also addressed.

14 SOLAR ENERGY↗
DOE OSTI2019

High water recovery and improved thermodynamic efficiency for capacitive deionization using variable flowrate operation

Water recovery is a measure of the amount of treated water produced relative to the total amount of water processed through the system, and is an important performance metric for any desalination method. Conventional operating methods for desalination using capacitive deionization (CDI) have so far limited water recovery to be about 50%. To improve water recovery for CDI, we introduce a new operating scheme based on a variable (in time) flow rate wherein a low flow rate during discharge is used to produce a brine volume which is significantly less than the volume of diluent produced. We demonstrate experimentally and study systematically this novel variable flowrate operating scheme in the framework of both constant current and constant voltage charge-discharge modes. We show that the variable flowrate operation can increase water recovery for CDI to very high values of ~90% and can improve thermodynamic efficiency by about 2- to 3-fold compared to conventional constant flowrate operation. Importantly, this is achieved with minimal performance reductions in salt removal, energy consumption, and volume throughput. Our work highlights that water recovery can be readily improved for CDI at very minimal additional cost using simple flow control schemes.

42 ENGINEERING↗
DOE OSTI2021

Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective

Here we present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core units. A performance of over 100 teraFLOPs is achieved for half-precision floating point operations on Nvidia’s A100 tensor core units. The second-order recursive Fermi-operator scheme is formulated in terms of a generalized, differentiable deep neural network structure, which solves the quantum mechanical electronic structure problem. We demonstrate how this network can be accelerated by optimizing the weight and bias values to substantially reduce the number of layers required for convergence. We also show how this machine learning approach can be used to optimize the coefficients of the recursive Fermi-operator expansion to accurately represent the fractional occupation numbers of the electronic states at finite temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Operation and development status of the J-PARC ion source

A cesium-free H{sup −} ion source driven with a LaB{sub 6} filament is being operated at the Japan Proton Accelerator Research Complex (J-PARC) without any serious trouble since the restoration from the March 2011 earthquake. The H{sup −} ion current from the ion source is routinely restricted approximately 19 mA for the lifetime of the filament. In order to increase the beam power at the linac beam operation (January to February 2013), the beam current from the ion source was increased to 22 mA. At this operation, the lifetime of the filament was estimated by the reduction in the filament current. According to the steep reduction in the filament current, the break of the filament was predicted. Although the filament has broken after approximately 10 h from the steep current reduction, the beam operation was restarted approximately 8 h later by the preparation for the exchange of new filament. At the study time for the 3 GeV rapid cycling synchrotron (April 2013), the ion source was operated at approximately 30 mA for 8 days. As a part of the beam current upgrade plan for the J-PARC, the front end test stand consisting of the ion source and the radio frequency quadrupole is under preparation. The RF-driven H{sup −} ion source developed for the J-PARC 2nd stage requirements will be tested at this test stand.

43 PARTICLE ACCELERATORS↗