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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data-driven Minimum Entropy Control for Stochastic Nonlinear Systems using the Cumulant-Generating Function

Here, we present a novel minimum entropy control algorithm for a class of stochastic nonlinear systems subjected to non-Gaussian noises. The entropy control can be considered as an optimization problem for the system randomness attenuation, but the mean value has to be considered separately. To overcome this disadvantage, a new representation of the system stochastic properties was given using the cumulant-generating function based on the moment-generating function, in which the mean value and the entropy was reflected by the shape of the cumulant-generating function. Based on the samples of the system output and control input, a time-variant linear model was identified, and the minimum entropy optimization was transformed to system stabilization. Then, an optimal control strategy was developed to achieve the randomness attenuation, and the boundedness of the controlled system output was analyzed. The effectiveness of the presented control algorithm was demonstrated by a numerical example. In this paper, a data-driven minimum entropy design is presented without pre-knowledge of the system model; entropy optimization is achieved by the system stabilization approach in which the stochastic distribution control and minimum entropy are unified using the same identified structure; and a potential framework is obtained since all the existing system stabilization methods can be adopted to achieve the minimum entropy objective.

42 ENGINEERING↗
DOE OSTI2016

The radiation protection principles model as a tool in the e-waste procedures

The electrical and electronic waste (e-waste) management is a global environmental problem dominated by the precautionary principle application, resulted to preliminary and ambiguous potential adverse effects, of extensive scientific uncertainty. In order to overcome the detected stochastic effects confusions in this field, we propose the inclusion of the principles of justification-optimization-limitation and of prudent avoidance. This model is already, established in radiation protection, so that toxicity as a result of the e-waste management would decrease, whilst the precious metals would be saved. We, further, resolve the classification of rejected items as reusable or as waste, so that the procedure of dismantling and recycling becomes easier, and the collecting-transporting-placement at an e-waste landfill would be safer. In conclusion, our proposing pattern in the e-waste management enforces the sustainable reducing-reusing-recycling, saves time/money and advances safety by including more sources of e-waste (military, medical etc) that were excluded previously.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles

This paper proposes a two-stage energy management system (EMS) for power grids with massive integration of electric vehicles (EVs) and renewable energy resources. The first stage economic dispatch determines the optimal operating points of charging stations and battery swapping stations (BSS) for EVs under plug-in and battery swapping modes, respectively. The proposed stochastic model predictive control (SMPC) problem in this stage is characterized through a chance-constrained optimization formulation that can effectively capture the system and the forecast uncertainties. A distributed algorithm, the alternating direction method of multipliers (ADMM), is applied to accelerate the optimization computation through parallel computing. The second stage is aimed in coordinating the EV charging mechanisms to continuously follow the first-stage solutions, i.e., the target operating points, and meeting the EV customers' charging demands captured via the Advanced Metering Infrastructure (AMI). In this work, the proposed solution offers a holistic control strategy for large-scale centralized power grids in which the aggregated individual parameters are predictable and the system dynamics do not vary sharply within a short time-interval.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A BI-LEVEL APPROACH TO FUTURE POWER SYSTEM CO-OPTIMIZATION WITH HIGH PENETRATION OF RENEWABLE ENERGY AND RESPONSIVE DEMAND

Electricity generation accounts for nearly half of the total CO2 emissions in the United States. For this reason, the development and integration of renewable resources will play an essential role in achieving the societal objective of mitigating climate change through reduction of greenhouse gas emissions. In conjunction with the environmental benefits of renewable energy, the most common renewable sources, such as wind and solar, also increase the uncertainties surrounding generation in power systems, which adds significant challenges to the system operation and planning. The uncertainties and forecasting errors surrounding renewable generation are normally addressed through the use of reserves from traditional generators. At greater levels of renewable penetration, sufficient generator reserves may not be available or economically viable. In contrast, the promise of demand-side resources in this arena lies in the spatially widespread availability, rapid response potential, and lower cost of features that already exist in the system. However, the challenge of responsive demand arises from the need to understand, manage, and incentivize a very large number of resources to participate effectively in efficient operation of the complex power system. Since demand response comes from the distribution system, microgrids as the basic building blocks of future distribution systems will be a critical environment for the study of demand response. To support integration of microgrids with flexible loads in future power systems, the operational mode of power systems will need to evolve. Therefore, it is going to be critical to have new and efficient co-optimization methods for coordination of the various power market participants and the scheduling of resources in the power systems of the future. Motivated by the rapid increase in renewable penetration, the need for effective demand response programs, and a changing system structure, this dissertation seeks to define a new strategy that supports co-optimization of various participants in power systems with emphasis on high renewable penetration and demand response. This strategy has three components; 1) an exploration of the capabilities of different types of demand response programs in a microgrid, 2) development and implementation of a bi-level framework for co-optimization of the main grid with high renewable penetration and a microgrid with demand response capabilities, and 3) expansion of the bi-level framework from a microgrid to a general distribution system to explore the advantages of the bi-level co-optimization approach over the traditional optimization approach. Conclusions of this work illustrate that the stochastic rolling horizon approach could effectively manage the operation of a microgrid with various demand response programs. In addition, the bi-level approach is a promising co-optimization framework for the transmission and distribution levels that could increase system renewable penetration and reduce operation costs. Compared to the traditional framework, the bi-level framework yields more equitable cost sharing patterns among the market participants as well as better support for the power system evolution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Optimal Experimental Design Using a Consistent Bayesian Approach

We consider the utilization of a computational model to guide the optimal acquisition of experimental data to inform the stochastic description of model input parameters. Our formulation is based on the recently developed consistent Bayesian approach for solving stochastic inverse problems, which seeks a posterior probability density that is consistent with the model and the data in the sense that the push-forward of the posterior (through the computational model) matches the observed density on the observations almost everywhere. Given a set of potential observations, our optimal experimental design (OED) seeks the observation, or set of observations, that maximizes the expected information gain from the prior probability density on the model parameters. We discuss the characterization of the space of observed densities and a computationally efficient approach for rescaling observed densities to satisfy the fundamental assumptions of the consistent Bayesian approach. Finally, numerical results are presented to compare our approach with existing OED methodologies using the classical/statistical Bayesian approach and to demonstrate our OED on a set of representative partial differential equations (PDE)-based models.

42 ENGINEERING↗
DOE OSTI2023

Tailoring resonant magnetic perturbation to optimize fast-ion confinement during ELM control in KSTAR

Abstract 3D resonant magnetic perturbation (RMP) is one promising way to control edge localized modes that can cause excessive material erosion of tokamak first walls. However, RMP can lead to undesired degradation of plasma confinement, including fast-particle losses, which can impact the performance and safety of the reactor. This work investigates the optimization of the poloidal spectrum of the 3D field to optimize fast ion confinement during edge localized mode (ELM) suppression. In the initial step, the validity of the modeling framework is tested against experimental data. Simulations successfully replicate an increase in poloidal limiter temperature with different poloidal spectra. Then, the simulation shows improvement of fast ion confinement with a reduction of core resonant response, while edge resonant magnetic fields are maintained above the threshold to sustain the ELM suppression. Reduction of the core resonant fields keeps the Kolmogorov–Arnold–Moser surface and reduces the fast particle losses due to the stochastic magnetic field lines. The results highlight the potential of edge localization of the resonant fields to enhance the performance of fusion reactors, but further investigation is needed to improve the validation of this approach.

3D field↗
NASA NTRS1978

Specific-optimal control with a dual minimal-order observer-based compensator

The steady-state optimal control of a linear time-invariant stochastic system by means of a minimal-order dual-observer-based compensator is considered in this paper. The structure of the compensator is fixed while the associated gains are to be chosen so as to minimize a quadratic penalty on the plant state. Necessary and sufficient conditions for optimality are given, and an explicit solution is displayed. Salient features pertaining to the optimal system are: a decoupling property, a projection property, and an innovation property. Finally, it is shown that this design corresponds to a singular LQG problem, which is precisely the dual of another singular LQG problem: namely Newmann's problem. A complete picture is then given showing clearly the correspondence between the two designs.

Blanvillain, P. J.↗
DOE OSTI2017

Nonlatching positive feedback enables robust bimodality by decoupling expression noise from the mean

Fundamental to biological decision-making is the ability to generate bimodal expression patterns where two alternate expression states simultaneously exist. Here in this study, we use a combination of single-cell analysis and mathematical modeling to examine the sources of bimodality in the transcriptional program controlling HIV’s fate decision between active replication and viral latency. We find that the HIV Tat protein manipulates the intrinsic toggling of HIV’s promoter, the LTR, to generate bimodal ON-OFF expression, and that transcriptional positive feedback from Tat shifts and expands the regime of LTR bimodality. This result holds for both minimal synthetic viral circuits and full-length virus. Strikingly, computational analysis indicates that the Tat circuit’s non-cooperative ‘non-latching’ feedback architecture is optimized to slow the promoter’s toggling and generate bimodality by stochastic extinction of Tat. In contrast to the standard Poisson model, theory and experiment show that non-latching positive feedback substantially dampens the inverse noise-mean relationship to maintain stochastic bimodality despite increasing mean-expression levels. Given the rapid evolution of HIV, the presence of a circuit optimized to robustly generate bimodal expression appears consistent with the hypothesis that HIV’s decision between active replication and latency provides a viral fitness advantage. More broadly, the results suggest that positive-feedback circuits may have evolved not only for signal amplification but also for robustly generating bimodality by decoupling expression fluctuations (noise) from mean expression levels.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Criticism of generally accepted fundamentals and methodologies of traffic and transportation theory

It is explained why the set of the fundamental empirical features of traffic breakdown (a transition from free flow to congested traffic) should be the empirical basis for any traffic and transportation theory that can be reliable used for control and optimization in traffic networks. It is shown that generally accepted fundamentals and methodologies of traffic and transportation theory are not consistent with the set of the fundamental empirical features of traffic breakdown at a highway bottleneck. To these fundamentals and methodologies of traffic and transportation theory belong (i) Lighthill-Whitham-Richards (LWR) theory, (ii) the General Motors (GM) model class (for example, Herman, Gazis et al. GM model, Gipps’s model, Payne’s model, Newell’s optimal velocity (OV) model, Wiedemann’s model, Bando et al. OV model, Treiber’s IDM, Krauß’s model), (iii) the understanding of highway capacity as a particular stochastic value, and (iv) principles for traffic and transportation network optimization and control (for example, Wardrop’s user equilibrium (UE) and system optimum (SO) principles). Alternatively to these generally accepted fundamentals and methodologies of traffic and transportation theory, we discuss three-phase traffic theory as the basis for traffic flow modeling as well as briefly consider the network breakdown minimization (BM) principle for the optimization of traffic and transportation networks with road bottlenecks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2004

A Comparison of Techniques for Scheduling Earth-Observing Satellites

Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks, conditions where evolutionary and related algorithms are often effective. However, there are many such algorithms and the best one to use is not clear. Here we compare multiple variants of the genetic algorithm: stochastic hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on ten realistically-sized EOS scheduling problems. Schedules are represented by a permutation (non-temperal ordering) of the observation requests. A simple deterministic scheduler assigns times and resources to each observation request in the order indicated by the permutation, discarding those that violate the constraints created by previously scheduled observations. Simulated annealing performs best. Random mutation outperform a more 'intelligent' mutator. Furthermore, the best mutator, by a small margin, was a novel approach we call temperature dependent random sampling that makes large changes in the early stages of evolution and smaller changes towards the end of search.

Globus, Al↗
NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗
DOE OSTI2023

Process Optimization

Process optimization is the discipline of adjusting a process to optimize a specified set of parameters without violating engineering constraints. This article reviews data-driven optimization methods based on genetic algorithms and stochastic models and demonstrates their use in powder-bed fusion and directed energy deposition processes. In the latter case, closed-loop feedback is used to control melt pool temperature and cooling rate in order to achieve desired microstructure.

Sprayberry, Michael↗
DOE OSTI2022

Fed-DeepONet: Stochastic Gradient-Based Federated Training of Deep Operator Networks

The Deep Operator Network (DeepONet) framework is a different class of neural network architecture that one trains to learn nonlinear operators, i.e., mappings between infinite-dimensional spaces. Traditionally, DeepONets are trained using a centralized strategy that requires transferring the training data to a centralized location. Such a strategy, however, limits our ability to secure data privacy or use high-performance distributed/parallel computing platforms. To alleviate such limitations, in this paper, we study the federated training of DeepONets for the first time. That is, we develop a framework, which we refer to as Fed-DeepONet, that allows multiple clients to train DeepONets collaboratively under the coordination of a centralized server. To achieve Fed-DeepONets, we propose an efficient stochastic gradient-based algorithm that enables the distributed optimization of the DeepONet parameters by averaging first-order estimates of the DeepONet loss gradient. Then, to accelerate the training convergence of Fed-DeepONets, we propose a moment-enhanced (i.e., adaptive) stochastic gradient-based strategy. Finally, we verify the performance of Fed-DeepONet by learning, for different configurations of the number of clients and fractions of available clients, (i) the solution operator of a gravity pendulum and (ii) the dynamic response of a parametric library of pendulums.

Moya, Christian↗
DOE OSTI2014

Optimization of the determinant of the Vandermonde matrix and related matrices

Various techniques for interpolation of data, moment matching in stochastic applications and various methods in numerical analysis can be described using Vandermonde matrices. For this reason the properties of the determinant of the Vandermonde matrix and related matrices are interesting. Here the extreme points of the Vandermonde determinant, and related determinants, on some simple surfaces such as the unit sphere are analyzed, both numerically and analytically. Some results are also visualized in various dimensions. The extreme points of the Vandermonde determinant are also related to the roots of certain orthogonal polynomials such as the Hermite polynomials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗