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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

MrHyDE v.1.0

SAND2024-01324O MrHyDE, which stands for Multi-resolution Hybridized Differential Equations, is a general-purpose C++ package for the solution of coupled multiphysics and multiscale systems on massively parallel computing systems. MrHyDE is designed to enable moving beyond forward simulation for multiscale applications which includes optimization, control, uncertainty quantification, and stochastic inversion. The framework provides interfaces to several packages within the Trilinos framework and leverages automatic differentiation to enable adjoint capabilities for large-scale, gradient-based optimization. MrHyDE provides automated multiscale capabilities through a subgrid model interface and multiscale Dirichlet-to-Neumann maps. For extreme-scale applications, MrHyDE provides in situ data-compression algorithms to reduce memory requirements while maintaining performance. MrHyDE is a general-purpose, computational framework for the solution of multiscale and multiphysics applications. It uses a combination of structure-preserving, physics-compatible discretizations, fully implicit methods, multi-resolution schemes, or fully explicit methods. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗
DOE OSTI2023

2023 FORCE Development Status Update

Technical and economic analysis of integrated energy systems (IES) using software models is a complex process requiring multiple commodity market decision analysis, optimal control, process modeling, and stochastic analysis. Many assumptions used in traditional energy analysis tools do not hold in future energy markets with significant storage and variable renewable energy sources (VRE), let alone with multiple commodity markets. Capturing these intricate elements for accurate techno-economic analysis of IES led to the development of the Framework for Optimization of ResourCes and Economics (FORCE) tool suite under the U.S. Department of Energy’s Integrated Energy Systems crosscutting technology program. With the aim of a full framework release in 2025, many improvements to the FORCE tool suite were developed in fiscal year 2023 (FY23). These improvements broadly fit into three focuses for development of FORCE: capability, accessibility, and reliability. Capability refers to the ability of FORCE to accurately model the technical and economic viability of various IES. Accessibility refers to ease-of-use for new and existing IES analysts to efficiently set up, analyze, and produce results using FORCE. Reliability refers to the consistency of the software, allowing consistency to analysis regardless of erstwhile changes to the software. In addition to many smaller changes, there are three major capability improvements in FORCE in FY23. In summary, FORCE developments in FY23 have moved us close to all the capability requirements for FORCE 1.0 to be delivered in FY25. Inclusion of Bayesian optimization, resilience metrics, and levelized cost analysis expand the capability, accessibility, and reliability of FORCE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Per-Phase and 3-Phase Optimal Coordination of Directional Overcurrent Relays Using Genetic Algorithm

Penetration of the power grid by renewable energy sources, distributed storage, and distributed generators is becoming increasingly common. Increased utilization of these distributed energy resources (DERs) has given rise to additional protection coordination concerns, particularly where they are utilized in an unbalanced manner or where loading among phases is unbalanced. Digital relays such as the SEL-751 (produced by Schweitzer Engineering Laboratories, Pullman, WA, USA) series have the capability of being set on a per-phase basis. This capability is underutilized in common practice. Additionally, in optimization algorithms for determining relay settings, the time-overcurrent characteristics (TOCs) of relays are generally not treated as variables and are assigned before running the optimization algorithm. In this paper, TOC options themselves are treated as discrete variables to be considered in the optimization algorithm. A mixed integer nonlinear programming problem (MINLP) is set up where the goal is to minimize relay operating times. A genetic algorithm (GA) approach is implemented in MATLAB where two cases are considered. In the first case, the TOC and Time dial setting (TDS) of each relay is set on a three-phase basis. In the second case, per-phase settings are considered. Relay TDSs and TOCs are both considered as simultaneous discrete control variables. Despite the stochastic nature of using per-phase settings for unbalanced systems is found to generally allow for shorter operating times. However, for relatively balanced systems, it is best to use three-phase settings if computation time is of importance.

Matthews, Ronald C.↗
DOE OSTI2023

Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design

Abstract Porous biomaterials design for bone repair is still largely limited to regular structures (e.g. rod-based lattices), due to their easy parameterization and high controllability. The capability of designing stochastic structure can redefine the boundary of our explorable structure–property space for synthesizing next-generation biomaterials. We hereby propose a convolutional neural network (CNN) approach for efficient generation and design of spinodal structure—an intriguing structure with stochastic yet interconnected, smooth, and constant pore channel conducive to bio-transport. Our CNN-based approach simultaneously possesses the tremendous flexibility of physics-based model in generating various spinodal structures (e.g. periodic, anisotropic, gradient, and arbitrarily large ones) and comparable computational efficiency to mathematical approximation model. We thus successfully design spinodal bone structures with target anisotropic elasticity via high-throughput screening, and directly generate large spinodal orthopedic implants with desired gradient porosity. This work significantly advances stochastic biomaterials development by offering an optimal solution to spinodal structure generation and design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Resilient Distributed Frequency Regulation of Renewable Generators under Communication Interruptions

Modern power systems (MPSs) face significant challenges due to the high penetration of renewable energy sources (RESs) and new types of loads such as electric vehicles (EVs). Traditional load frequency control (LFC) methods struggle with the intermittent, stochastic nature of RESs, the near-zero inertia of power-electronics-based generators, and the mobility of controllable loads and battery systems. This paper introduces a novel resilient distributed frequency regulation method to address these issues. The proposed method employs a state space model to represent the dynamic behavior of participating power sources while accounting for stochastic switching processes to model structural and parameter variations caused by disruptions such as generator connection/disconnection, communication interruptions, and physical faults. By integrating these dynamic and stochastic components, the method treats power grids as a comprehensive stochastic hybrid system. Our method enhances conventional frequency control by incorporating local stability control, neighborhood control decoupling, and coordination feedback. Theoretical analyses establish the stability, convergence, and resilience of the proposed method, and its effectiveness is validated through case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Comparative Performance of Gaussian Plume and Backward Lagrangian Stochastic Models for Near-Field Methane Emission Estimation Using a Single Controlled Release Experiment

Methane (CH 4 ) is a major component of natural gas and a potent greenhouse gas. Increasing atmospheric methane concentrations are attributed to emissive anthropogenic activities by an average of 13 ppb per yr since 2020 and are linked to a changing global climate. Mitigating CH 4 emissions from oil and gas production sites has recently become a target to reduce overall greenhouse gas emissions; however, monitoring the efficacy of mitigation strategies depends on accurate quantification of CH 4 emissions at the facility-level. Near-field quantification of methane (CH 4 ) emissions from oil and gas (O&G) facilities remains challenging due to the effects of atmospheric variability and sensor configuration on atmospheric dispersion models. This study evaluates the performance of two atmospheric dispersion models, the Gaussian plume (GP) and backward Lagrangian stochastic (bLS), by comparing calculated CH 4 emissions to controlled single-point emissions between 0.4 and 5.2 kg CH 4 h −1 . Emissions were calculated by both models using 121 individual sets of measurements comprising five-minute averaged downwind methane mixing ratios and matching meteorological data. The comparison shows that the bLS approach achieved a higher proportion of emission estimates within a factor of two (FAC2) of the known emission rates compared to the GP approach. The emissions calculated by the bLS model also had a lower multiplicative error and reduced bias relative to GP. Other error-based metrics further confirmed the bLS model performed better, as it yielded lower RMSE and MAE than GP. Statistical analysis of the emission data shows that the lateral and vertical alignment of the source and the sensor plays a critical role in emission estimations, as measurements made closer to the plume centerline and at a distance between 40 and 80 m downwind yielded the best FAC2 agreement. High wind meander degraded the ability of both approaches to generate representative emissions, particularly with the GP approach, as it violates the modeling approach’s assumption of steady-state emissions. Data suggest emissions calculated by the bLS model are comprehensively in better agreement, but the computational demands of the modeling approach and integration into fenceline systems limit real-time applicability. While these results provide insight into model performance under controlled near-field conditions, their applicability to more complex or heterogeneous oil and gas production environments (e.g., the regions Marcellus or Unita Basins) remains limited and uncertain.

gaussian plume↗
DOE OSTI2020

Dynamic Performance Enhancement for Nonlinear Stochastic Systems Using RBF Driven Nonlinear Compensation with Extended Kalman Filter

In this paper, a novel hybrid control method is proposed to enhance the control performance of the PI based control system for a class of nonlinear and non-Gaussian stochastic dynamic processes with unmeasurable states. Firstly, to enhance the tracking performance of the PI controller where the PI parameters are fixed in many actual control systems, the compensative signal is formed using the extended Kalman filter(EKF) based state estimator and driven by the radial basis function (RBF) neural network based compensator. In addition, the weights of RBF is trained to minimize the entropy criterion of tracking error as the process is subjected to non-Gaussian disturbances. Meanwhile, since the precise statistical property of noises is hard to obtain for many industrial processes, the kernel density estimation (KDE) technique is employed in this paper to estimate the entropy. The convergence of RBF network is discussed and the stability of the resulted closed-loop hybrid control system is analyzed in mean square sense. Finally, a numerical example and a practical system testing are given to illustrate the effectiveness of the proposed control method.

stochastic systems, PI Controller, Entropy, Extend↗
DOE OSTI2023

Molecular Aspects of Transport in Thin Films of Controlled Architecture (Final Technical Report)

The scientific and technological objectives of this project are motivated by problems in energy technologies that utilize differential transport in nanoscale confined volumes to achieve molecular separations. The core scientific phenomena that control observable behavior - wetting/dewetting, hydrophobicity, stochastic fluctuations in fluid flow, electrokinetics, etc. - are fundamentally different on the nanoscale, because the underlying forces that direct molecular motion are altered upon scaling to nanometer dimensions. We proposed to address this overarching problem by pursuing two objectives. Objective 1 targeted the development of single molecule-based electrokinetic, electrochemical and spectroelectrochemical probes of transport in one-dimensional (1D) nanostructures. Objective 2 focused on the interaction between wetting phenomena and nanoconfined flows under active (electrical and/or electrochemical) control. The studies were intended to develop the design rules, structural motifs, and operating principles needed to achieve singular control over molecular transport in nano-confined volumes, thereby impacting a broad spectrum of energy-relevant separations processes.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Robust Control of Wave Energy Converters Using Unstructured Uncertainty

In the design of ocean wave energy converters, proper control design is essential to the maximization of the power generation performance for the device. However, in realistic applications, this control design must be undertaken in the presence of model uncertainty. This paper considers the use of robust control theory to optimize the nominal performance for a wave energy converter in stochastic waves, subject to the constraint that the controller be stability-robust to unstructured uncertainties. We formulate the problem as a multi-objective optimal control problem, in which the primary objective is the maximization of power generation for the nominal system, and the competing objective is the norm of the uncertainty input/output channel. This optimal control problem is nonconvex, and we therefore propose an iterative algorithm that can be used to arrive at a local optimal solution. This iterative approach employs the concept of Iterative Convex Overbounding, in the context of the classical Method of Centers. Here, the methodology is demonstrated on a model of a single, buoy- type wave energy converter.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Exploring single-cell biosynthetic noise and dynamics for enhanced betaxanthin production in Escherichia coli

Cell-to-cell variability often limits the efficiency of microbial bioproduction, yet how individual cells fluctuate over time and how these fluctuations shape population-level output remain unclear. To address this issue, we tracked a heterologous betaxanthin pathway in Escherichia coli using microfluidics-assisted time-lapse microscopy, allowing simultaneous measurement of fluctuations in betaxanthin, its biosynthetic enzyme DOD and growth across generations. Here we show that over 50% of high betaxanthin producers become medium or low producers after two divisions. Betaxanthin variation primarily originates from DOD noise, with a smaller contribution from growth rate fluctuations. We further develop a stochastic model to explore various control circuits and find that pathway enzyme or metabolite-based growth selection strategies are most effective in enhancing production. We experimentally validate the model by coupling enzyme expression to nutrient availability, which enriches high producers and boosts titer by 4.4-fold. Our results highlight key sources of metabolic heterogeneity and provide a framework for designing robust microbial processes.

Bacterial systems biology↗
DOE OSTI2022

Quantum computing in power systems

Electric power systems provide the backbone of modern industrial societies. Enabling scalable grid analytics is the keystone to successfully operating large transmission and distribution systems. However, today's power systems are suffering from ever-increasing computational burdens in sustaining the expanding communities and deep integration of renewable energy resources, as well as managing huge volumes of data accordingly. These unprecedented challenges call for transformative analytics to support the resilient operations of power systems. Recently, the explosive growth of quantum computing techniques has ignited new hopes of revolutionizing power system computations. Quantum computing harnesses quantum mechanisms to solve traditionally intractable computational problems, which may lead to ultra-scalable and efficient power grid analytics. This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices. We also review the quantum networking techniques to ensure secure communication of power systems in the quantum era. Finally, we discuss challenges and future research directions. This paper will hopefully stimulate increasing attention to the development of quantum-engineered smart grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

SDSS-IV MaNGA: How Galaxy Interactions Influence Active Galactic Nuclei

We present a comparative study of active galactic nuclei (AGN) between galaxy pairs and isolated galaxies with the final data release of the MaNGA integral field spectroscopic survey. We build a sample of 391 kinematic galaxy pairs within the footprint of the survey and select AGN using the survey's spectra. We use the comoving volume densities of the AGN samples to quantify the effects that tidal interactions have on the triggering of nuclear accretion. Our hypothesis is that the pair sample contains AGN that are triggered by not only stochastic accretion but also tidally induced accretion and correlated accretion. With the level of stochastically triggered AGN fixed by the control sample, we model the strength of tidally induced accretion and correlated accretion as a function of projected separation (r p ) and compare the model expectations with the observed volume densities of dual AGN and offset AGN (single AGN in a pair). At r p ~ 10 kpc, we find that tidal interactions induce ~30% more AGN than stochastic fueling and cause ~12% of the offset AGN to become dual AGN because of correlations. The strength of both these effects decreases with increasing r p . We also find that the [O III ] luminosities of the AGN in galaxy pairs are consistent with those found in isolated galaxies, likely because stochastically fed AGN dominate even among close pairs. Our results illustrate that while we can detect tidally induced effects statistically, it is challenging to separate tidally induced AGN and stochastically triggered AGN in interacting galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Tunable Stochasticity in an Artificial Spin Network

Metamaterials present the possibility of artificially generating advanced functionalities through engineering of their internal structure. Artificial spin networks, in which a large number of nanoscale magnetic elements are coupled together, are promising metamaterial candidates that enable the control of collective magnetic behavior through tuning of the local interaction between elements. In this work, the motion of magnetic domain-walls in an artificial spin network leads to a tunable stochastic response of the metamaterial, which can be tailored through an external magnetic field and local lattice modifications. This type of tunable stochastic network produces a controllable random response exploiting intrinsic stochasticity within magnetic domain-wall motion at the nanoscale. An iconic demonstration used to illustrate the control of randomness is the Galton board. In this system, multiple balls fall into an array of pegs to generate a bell-shaped curve that can be modified via the array spacing or the tilt of the board. A nanoscale recreation of this experiment using an artificial spin network is employed to demonstrate tunable stochasticity. Furthermore, this type of tunable stochastic network opens new paths toward post-Von Neumann computing architectures such as Bayesian sensing or random neural networks, in which stochasticity is harnessed to efficiently perform complex computational tasks.

Artificial spin network↗
DOE OSTI2022

A reinforcement learning approach to long-horizon operations, health, and maintenance supervisory control of advanced energy systems

In this work, we develop a Reinforcement Learning (RL) approach to the supervisory control problem for advanced energy systems, such as novel nuclear reactors and other demand-driven, mission-critical, and component-health-sensitive energy plants. The inclusive problem landscape considered captures the stochastic confluence of plant performance, component health evolution, power demand from the grid, diverse maintenance actions, and operator-defined goals and constraints, all considered over meaningfully long-enough reasoning horizons. Key aspects of the proposed approach are a receding horizon control-inspired technique dictating time- or event-triggered supervisory policy (re-)constructions, as well as additional capability-enabling contributions such as timescale compression, to handle long reasoning horizons and uncertainty in parts of the problem, and practical yet demonstrably-effective handling of hybrid action spaces with continuous and discrete decision variables. The resulting algorithm consists of a simulation-based RL agent constructing stochastic supervisory control policies over nontrivial action spaces and for long horizons, applying the learned policy to the system for a much shorter interval, and perpetually repeating, to construct the next long-horizon policy. That next policy will only be applied, again, for a short interval, yet originally far-in-time events move progressively closer, their associated uncertainty decreases, and new events and aspects enter the reasoning horizon. The proposed methodology bridges fundamental receding horizon concepts with the unequivocally stronger and more scalable reasoning of contemporary RL. Numerical examples using Soft Actor–Critic Deep RL illustrate the operation and efficacy of the proposed technique for a power plant tasked with health-aware load following missions in a dynamic electricity market landscape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization

Here, we consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Practical Insights on Applying Simulation-Based Control Methods in Experimental Studies

Advanced nuclear reactors are crucial to the future of energy both in the United States and around the globe. In contrast to the current operating fleet, they are characterized as being deployable in remote locations and able to operate in semi-autonomous or autonomous fashion. This leap forward necessitates a new reactor control paradigm. Because advanced nuclear reactors are still under development in the United States, the creation of new control methods to achieve autonomous operations has been based on systems modeling and simulation. However, an important factor in successfully deploying these new control methods is the ability to seamlessly transition from simulation environments to real-world settings. Control methods tested in both simulation and experimental settings need to be investigated in the context of advanced reactor applications. This work developed a series of simple controllers for Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor operating in load-following scenarios. These controllers were tested in both simulation and experimental settings, and a comparative performance analysis was performed. The simulation tests leveraged the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) software developed in a previous stage of the current effort, along with the MARVEL Reactor Excursion and Leak Analysis Program (RELAP5-3D) and Monte Carlo N-Particle (MCNP) models. The experimental tests leveraged the COMMAND software, MARVEL models, and the U.S. Department of Energy Microreactor Program’s Microreactor Automated Control System (MACS). MACS was developed to serve as a control method testbed. It was customized to mirror the MARVEL microreactor, and COMMAND enabled MACS to emulate the physics of MARVEL. The load-following controller was developed using the simulation platform, with efforts to emulate real systems by introducing actuator saturation and noise. These factors were incrementally accounted for in the controller design. After finalizing the controller design, it was implemented with the experimental setup. The experimental conditions tested included an initial test under conditions similar to the final simulation test, and two additional scenarios. The first scenario introduced additional actuator saturation to account for equipment aging over time, which was unknown to the controller. The second scenario introduced sensor delay, a phenomenon anticipated with the use of remote operations or wireless communication in advanced reactors. These tests revealed several notable differences. While the controller performed well in simulation, it exhibited several limitations when transitioning to hardware. The main challenges involved maintaining the steady-state target power, as evidenced by larger error values between the true reactor power and setpoint power, as well as persistent oscillations in controlled reactor power. These issues could lead to unacceptable transient conditions in real reactor testing. Introducing actuator aging and stochastic delays in the experimental setup significantly impacted controller performance, resulting in increased overshoot and undershoot, and exacerbated error and oscillations previously mentioned. These findings underscore the importance of experimental testbeds for testing and validating control methods, as controllers developed using only theory and/or simulation may perform unexpectedly when applied to actual hardware. This research emphasizes the need for an experimental testbed for achieving such validation.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Efficient particle control in systems with large density gradients

Simulations of large density gradients present a number of challenges for direct Monte Carlo methods, since they lead to too few particles in dilute regions and prohibitively many in the dense regions. Here, we propose a particle control methodology that gives the user more control of the number of particles per cell by introducing a variable weight for each particle. The proposed scheme is based on the stochastic weighted particle method, requires only small modifications to DSMC, and exactly conserves mass, momentum, and energy. In validation tests of systems with density ratios of order 102-104, we observe 101-102 times less variance in the dilute region compared to a DSMC solution with the same number of system particles, while introducing a moderate additional computational cost.

DSMC↗
DOE OSTI2020

Risk-averse optimal control of semilinear elliptic PDEs

In this paper, we consider the optimal control of semilinear elliptic PDEs with random inputs. These problems are often nonconvex, infinite-dimensional stochastic optimization problems for which we employ risk measures to quantify the implicit uncertainty in the objective function. In contrast to previous works in uncertainty quantification and stochastic optimization, we provide a rigorous mathematical analysis demonstrating higher solution regularity (in stochastic state space), continuity and differentiability of the control-to-state map, and existence, regularity and continuity properties of the control-to-adjoint map. Our proofs make use of existing techniques from PDE-constrained optimization as well as concepts from the theory of measurable multifunctions. We illustrate our theoretical results with two numerical examples motivated by the optimal doping of semiconductor devices.

97 MATHEMATICS AND COMPUTING↗