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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Feedback Implementation of Zermelo's Optimal Control by Sugeno Approximation

This paper proposes an approach to implement optimal control laws of nonlinear systems in real time. Our methodology does not require solving two-point boundary value problems online and may not require it off-line either. The optimal control law is learned using the original Sugeno controller (OSC) from a family of optimal trajectories. We compare the trajectories generated by the OSC and the trajectories yielded by the optimal feedback control law when applied to Zermelo's ship steering problem.

Clifton, C.↗
DOE OSTI2022

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Online Model-Free Chance-Constrained Distribution System Voltage Control Using DERs

This paper proposes an online data-driven distributed energy resource management system (DERMS) optimization method using chance-constrained formulation to address distribution system voltage regulation. This is achieved via the local sensitivity factor (LSF)-enabled reformulation of the DER control into a linear programming (LP) problem, which is easy and computationally efficient to solve. The LSF is estimated using online measurements and does not need the assumption of node load information. The latter is usually required for existing optimization-based methods but is difficult to obtain in practice. To mitigate measurement uncertainties, a scenario-based chance-constrained formulation is constructed. Compared with other control methods, the results carried out in a realistic distribution system show that the proposed method can effectively eliminate voltage violation issues.

chance-constrained optimization↗
DOE OSTI2023

Online Model-Free Chance-Constrained Distribution System Voltage Control Using DERs: Preprint

This paper proposes an online data-driven distributed energy resource management system (DERMS) optimization method using chance-constrained formulation to address distribution system voltage regulation. This is achieved via the local sensitivity factor (LSF)-enabled reformulation of the DER control into a linear programming (LP) problem, which is easy and computationally efficient to solve. The LSF is estimated using online measurements and does not need the assumption of node load information. The latter is usually required for existing optimization-based methods but is difficult to obtain in practice. To mitigate measurement uncertainties, a scenario-based chance-constrained formulation is constructed. Compared with other control methods, the results carried out in a realistic distribution system show that the proposed method can effectively eliminate voltage violation issues.

chance-constrained optimization↗
DOE OSTI2015

SU-E-J-126: An Online Replanning Method for FFF Beams Without Couch Shift

Purpose: In a situation that couch shift for patient positioning is not preferred or prohibited (e.g., MR-Linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening filter free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here we propose a new 2-step process to address both the translational effect of FFF beams and the target deformation. Methods: The replanning method consists of an offline and an online steps. The offline step is to create a series of pre-shifted plans (PSP) obtained by a so called “warm start” optimization (starting optimization from the original plan, rather from scratch) at a series of isocenter shifts with fixed distance (e.g. 2 cm, at x,y,z = 2,0,0 ; 2,2,0 ; 0,2,0; …;− 2,0,0). The PSPs all have the same number of segments with very similar shapes, since the warm-start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two-step process is completely automated, and instantaneously fast (no optimization or dose calculation needed). The previously-developed SAM algorithm is then applied for daily deformation. We tested the method on sample prostate and pancreas cases. Results: The two-step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. The whole process takes the same time as the previously reported SAM process (5–10 min). Conclusion: The new two-step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation requiring no additional time from the SAM process. This research was supported by Elekta inc. (Crawley, UK)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Hybrid Optimization and Deep Learning Algorithm for Cyber-Resilient DER Control

With the proliferation of distributed energy resources (DERs) in the distribution grid, it is a challenge to effectively control a large number of DERs resilient to the communication and security disruptions, as well as to provide the online grid services, such as voltage regulation and virtual power plant (VPP) dispatch. To this end, a hybrid feedback-based optimization algorithm along with deep learning forecasting technique is proposed to specifically address the cyber-related issues. The online decentralized feedback-based DER optimization control requires timely, accurate voltage measurement from the grid. However, in practice such information may not be received by the control center or even be corrupted. Therefore, the long short-term memory (LSTM) deep learning algorithm is employed to forecast delayed/missed/attacked messages with high accuracy. The IEEE 37-node feeder with high penetration of PV systems is used to validate the efficiency of the proposed hybrid algorithm. The results show that 1) the LSTM-forecasted lost voltage can effectively improve the performance of the DER control algorithm in the practical cyber-physical architecture; and 2) the LSTM forecasting strategy outperforms other strategies of using previous message and skipping dual parameter update.

cyber-resilient algorithm↗
DOE OSTI2018

Making Compiler-Based Tools Accessible Online

Compiler-based tools are widely used to analyze and optimize source codes for many purposes, including extracting insights about applications’ properties or restructuring code for better performance. However, using compiler-based tools is often difficult due to tedious and error-prone steps to configure and install such tools on various machines. In this paper, we describe a solution to use CGI + Bash to expose compilerbased tools to a web page so users can directly use these tools without installing them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Making Compiler-Based Tools Accessible Online

Compiler-based tools are widely used to analyze and optimize source codes for many purposes, including extracting insights about applications’ properties or restructuring code for better performance. However, using compiler-based tools is often difficult due to tedious and error-prone steps to configure and install such tools on various machines. In this paper, we describe a solution to use CGI + Bash to expose compilerbased tools to a web page so users can directly use these tools without installing them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Digital-Twin-Enabling Technologies for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable optimized scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins (DTs) are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. DTs for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. A DT’s goal is to provide additional insights by combining and interpreting various sources of information for preventative maintenance scheduling optimization or early fault detection. DTs for condition monitoring are projected to be valuable for meeting requirements under 10 CFR 50.55a, “Codes and Standards,” and 10 CFR 50.65, “Requirements for Monitoring the Effectiveness of Maintenance at Nuclear Power Plants”. However, DT technologies are still under significant development, and the process for developing a DT for condition monitoring has not been formalized. Therefore, in this work, we present an initial framework for developing a DT, discuss and review the various challenges and considerations for DT deployment, and identify the opportunities that a DT can improve. The presented framework is intended to help developers formulate a strategy when approaching DT development for condition monitoring. In conclusion, a DT use case for a reactor coolant pump is presented to demonstrate the proposed framework.

advanced sensor instrumentation↗
DOE OSTI2025

LLGOMAX: Enhancing Industry-Standard Tools for AC Optimal Unit Commitment (CRADA Final Report)

This was a collaborative effort between Lawrence Livermore National Security, LLC ("LLNS"), as manager and operator of Lawrence Livermore National Laboratory ("LLNL") and ECCO International Inc. ("Participant"). The team designed and implemented a new approach for the Security-Constrained Alternating-Current Unit Commitment (SCACUC) problem. The SCACUC problem is a mathematical optimization problem that decides which generating units should be online and how much power should be produced (and consumed) at each point of the power grid. The decisions are made so as to minimize the total cost of supplying electricity while respecting technical constraints of the power grid, both under normal and emergency conditions. The team developed a solution for SCACUC as specified in the ARPA-E Grid Optimization Competition (GOC) Challenge 3, which put forth a forward-looking version of the problem, which many features not present in today’s electricity market specifications for SCACUC.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Guidance for Developing Digital Twins for Online Condition Monitoring of Nuclear Power Plant Components

Online condition monitoring is an area of active research that may enable optimized scheduling, maintenance, and safety of nuclear power plant components, reducing unnecessary derates while simultaneously improving operational capacity. Digital twins (DTs) are one avenue to conduct online condition monitoring and are currently being explored by national laboratories and universities alike. DTs for online condition monitoring are, in essence, state concurrent models that emulate a physical process which predicts a parameter and compares it against a measured value. The promise of DT is that they may provide additional insights by combining and interpreting various sources of information and may be used for preventative maintenance scheduling optimization or early fault detection. DTs for condition monitoring are projected to be valuable for meeting requirements under 10 CFR 50.55a and 10 CFR 50.65. However, DT technologies are still under significant development and the process for developing a DT for condition monitoring has not been formalized. Therefore, in this work, we present an initial framework for developing a DT, discuss and review the various challenges and considerations for DT deployment, and identify the opportunities that a DT can improve. Here, the presented framework is intended to help developers formulate a strategy when approaching DT development for condition monitoring. A DT use case for a reactor coolant pump is presented to demonstrate the proposed framework.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Optimization Based Data Mining Approah for Forecasting Real-Time Energy Demand

The worldwide concern over environmental degradation, increasing pressure on electric utility companies to meet peak energy demand, and the requirement to avoid purchasing power from the real-time energy market are motivating the utility companies to explore new approaches for forecasting energy demand. Until now, most approaches for forecasting energy demand rely on monthly electrical consumption data. The emergence of smart meters data is changing the data space for electric utility companies, and creating opportunities for utility companies to collect and analyze energy consumption data at a much finer temporal resolution of at least 15-minutes interval. While the data granularity provided by smart meters is important, there are still other challenges in forecasting energy demand; these challenges include lack of information about appliances usage and occupants behavior. Consequently, in this paper, we develop an optimization based data mining approach for forecasting real-time energy demand using smart meters data. The objective of our approach is to develop a robust estimation of energy demand without access to these other building and behavior data. Specifically, the forecasting problem is formulated as a quadratic programming problem and solved using the so-called support vector machine (SVM) technique in an online setting. The parameters of the SVM technique are optimized using simulated annealing approach. The proposed approach is applied to hourly smart meters data for several residential customers over several days.

data mining↗
DOE OSTI2017

Measurement of the D + -meson production cross section at low transverse momentum in p p ¯ collisions at s = 1.96 TeV

We report on a measurement of the D + -meson production cross section as a function of transverse momentum (p T ) in proton-antiproton (p$\bar{p}$) collisions at 1.96 TeV center-of-mass energy, using the full data set collected by the Collider Detector at Fermilab in Tevatron Run II and corresponding to 10 fb -1 of integrated luminosity. We use D + →K - π + π + decays fully reconstructed in the central rapidity region |y|<1 with transverse momentum down to 1.5 GeV/c, a range previously unexplored in p$\bar{p}$ collisions. Inelastic p$\bar{p}$ scattering events are selected online using minimally biasing requirements followed by an optimized offline selection. The K - π + π + mass distribution is used to identify the D + signal, and the D + transverse impact-parameter distribution is used to separate prompt production, occurring directly in the hard-scattering process, from secondary production from b-hadron decays. We obtain a prompt D + signal of 2950 candidates corresponding to a total cross section σ(D + ,1.5 T <14.5 GeV/c,|y|<1)=71.9±6.8(stat)±9.3(syst) μb. While the measured cross sections are consistent with theoretical estimates in each p T bin, the shape of the observed p T spectrum is softer than the expectation from quantum chromodynamics. The results are unique in p$\bar{p}$ collisions and can improve the shape and uncertainties of future predictions.

(K-↗
DOE OSTI2020

ZAM Modeling Study to Support the Tank Closure Cesium Removal (TCCR) 1A Unit

Currently at the Savannah River Site (SRS), the Tank Closure Cesium Removal (TCCR) is an “at-tank” process designed to remove cesium from aqueous tank waste. Cesium will be removed by ion exchange using the engineered IONSIV® R9120 form of Crystalline Silicotitanate (CST) media. The current TCCR design has two columns online in a lead-lag configuration to optimize media usage and achieve the target decontamination. Once the lead column is saturated with cesium, it will be removed from service, the lag column will rotate into the lead position, and a new column with fresh ion-exchange media will be placed into the lag position. The TCCR process for cesium removal from Tank 10H is detailed in X-SOW-H- 00002. Demonstration of the system began in early calendar year 2019 with two batches of salt solution generated by dissolving saltcake in Tank 10H, followed by processing of these batches through the TCCR system. A third TCCR Tank 10H dissolved saltcake batch is scheduled for processing soon. Upon completion of the demonstration with Tank 10H dissolved saltcake, Tank 9H salt solution will be transferred to Tank 10H and subsequently processed through the TCCR unit with new CST media (referred to as R9120-B 30x60) added to new IX columns. The TCCR processing campaign of Tank 9H salt solution is referred to as TCCR-1A .

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

Objective In recent years, motor imagery-based brain–computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement. Approach Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. Main results As a result, both the MRCP and ERD features showed the specific temporal–spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively. Significance This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

Liu, Chang↗
DOE OSTI2021

POINT: Partially Observable Imitation Network for Traffic Signal Control

Smart traffic signals bring together transportation infrastructure and advance technologies to improve the mobility and efficiency of urban transportation network. Adaptive traffic signal control studies can be categorized into modeling-based approaches and learning-based approaches. In order to take advantages of these two systems, this study developed an offline-online combined Partial Observable Imitation Network for Traffic signal control (POINT). In the offline system, the traffic signal timing optimization problem was formulated as a Mixed Integer Nonlinear Programming (MINLP) given complete traffic information, i.e., second-by-second speeds and locations of all vehicles. Furthermore, the objective of MINLP is to minimize total travel delays considering individual vehicle trajectories under Connected Vehicle (CV) environment. The calculated optimal solutions under various traffic conditions were considered as the ”expert” decisions. In the online system, an imitation neural network model was developed to learn the ”expert” signal plans generated from offline system. Given partial observable traffic conditions in real time, e.g., the aggregate-level of traffic volume, the POINT model can compute the signal timing parameters in the online system. The numerical results demonstrated that the proposed method outperformed other state-of-the-art signal control method under high and unbalanced traffic demand levels in terms of reducing travel delays and queue length.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1987

Time optimal paths and acceleration lines of robotic manipulators

The concept of acceleration lines and their correlation with time-optimal paths of robotic manipulators is presented. The acceleration lines represent the directions of maximum tip acceleration from a point in the manipulator work-space, starting at a zero velocity. These lines can suggest the number and shapes of time-optimal paths for a class of manipulators. It is shown that nonsingular time-optimal paths are tangent to one of the acceleration lines near the end-points. A procedure for obtaining near-optimal paths, utilizing the acceleration lines, is developed. These paths are obtained by connecting the end-points with B splines tangent to the acceleration lines. The near-minimum paths are shown to yield better traveling times than the straight-line path between the same end-points. The near-minimum paths can be used as initial conditions in existing optimization methods to speed-up convergence and computation time. This method can be used for online robot path planning and for interactive designs of robotic-cell layouts. Examples of time-optimal paths of a two-link manipulator, obtained by other optimization procedures and their acceleration lines, are shown.

Shiller, Zvi↗