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Results for “Real Time Optimization”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2010

Optical emission and nanoparticle generation in Al plasmas using ultrashort laser pulses temporally optimized by real-time spectroscopic feedback

With an interest in pulsed laser deposition and remote spectroscopy techniques, we explore here the potential of laser pulses temporally tailored on ultrafast time scales to control the expansion and the excitation degree of various ablation products including atomic species and nanoparticulates. Taking advantage of automated pulse-shaping techniques, an adaptive procedure based on spectroscopic feedback is applied to regulate the irradiance and enhance the optical emission of monocharged aluminum ions with respect to the neutral signal. This leads to optimized pulses usually consisting in a series of femtosecond peaks distributed on a longer picosecond sequence. The ablation features induced by the optimized pulse are compared with those determined by picosecond pulses generated by imposed second-order dispersion or by double pulse sequences with adjustable picosecond separation. This allows to analyze the influence of fast- and slow-varying envelope features on the material heating and the resulting plasma excitation degree. Using various optimal pulse forms including designed asymmetric shapes, we analyze the establishment of surface pre-excitation that enables conditions of enhanced radiation coupling. Thin films elaborated by unshaped femtosecond laser pulses and by optimized, stretched, or double pulse sequences are compared, indicating that the nanoparticles generation efficiency is strongly influenced by the temporal shaping of the laser irradiation. A thermodynamic scenario involving supercritical heating is proposed to explain enhanced ionization rates and lower particulates density for optimal pulses. Numerical one-dimensional hydrodynamic simulations for the excited matter support the interpretation of the experimental results in terms of relative efficiency of various relaxation paths for excited matter above or below the thermodynamic stability limits. The calculation results underline the role of the temperature and density gradients along the ablated plasma plume which lead to the spatial distinct locations of excited species. Moreover, the nanoparticles sizes are computed based on liquid layer ejection followed by a Rayleigh and Taylor instability decomposition, in good agreement with the experimental findings.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2011

Adaptive real-time methodology for optimizing energy-efficient computing

Dynamic voltage and frequency scaling (DVFS) is an effective way to reduce energy and power consumption in microprocessor units. Current implementations of DVFS suffer from inaccurate modeling of power requirements and usage, and from inaccurate characterization of the relationships between the applicable variables. A system and method is proposed that adjusts CPU frequency and voltage based on run-time calculations of the workload processing time, as well as a calculation of performance sensitivity with respect to CPU frequency. The system and method are processor independent, and can be applied to either an entire system as a unit, or individually to each process running on a system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Adaptive real-time methodology for optimizing energy-efficient computing

Dynamic voltage and frequency scaling (DVFS) is an effective way to reduce energy and power consumption in microprocessor units. Current implementations of DVFS suffer from inaccurate modeling of power requirements and usage, and from inaccurate characterization of the relationships between the applicable variables. A system and method is proposed that adjusts CPU frequency and voltage based on run-time calculations of the workload processing time, as well as a calculation of performance sensitivity with respect to CPU frequency. The system and method are processor independent, and can be applied to either an entire system as a unit, or individually to each process running on a system.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Fused Global-Local Economic Model Predictive Control for Real-Time Eco-Optimal Control of a Heavy Duty Truck

This paper introduces a fused global-local economic model predictive control (MPC) with varying time steps for maximizing the fuel economy of a heavy-duty truck, where global MPC is performed over a long horizon using cloud-based dynamic programming (DP), and local MPC is performed on-board the vehicle using sequential quadratic programming (SQP). Because of the computational complexity of DP, an in-depth analysis of how the discretization levels, time constant, and horizon length influence the energy savings and computational complexity of online DP has been performed. The fused optimization technique has been evaluated over different route profiles, accounting for traffic, using a medium-fidelity proprietary Volvo model. Results show 4-7% fuel savings, as compared to a baseline model.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Optimization of Integrated Energy Systems

Integrated energy systems that couple nuclear power plants with additional products including hydrogen, storage, or synthetic fuels provide a more flexible energy source that can be more economical than generating electricity alone. Determining the size and shape of these systems and optimizing their operation is challenging. INL, through the IES program, has developed optimization software to help solve these challenges. Holistic Energy Resource Optimization Network (HERON) is a software tool to optimize the size and capacity of integrated energy systems using stochastic optimization. Optimization of Real-time Capacity Allocation (ORCA) is a software tool under development to perform real-time economic optimization of these systems using economic model predictive control. An overview of these tools and a discussion of their optimization methods will be presented in this talk.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

ORCA Software Quality Assurance Plan

This document summarizes the software quality assurance (SQA) planning activities conducted for the Optimization of Real-time Capacity Allocation (ORCA) plug-in. It outlines the approaches and document structures adopted to maintain high standards of software quality. It also gives examples of certain SQA documents.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2025

Xopt and Badger: a machine learning ecosystem for real-time accelerator control and optimization

Machine learning (ML)-based black-box optimization algorithms have demonstrated significant improvements in accelerator optimization speed, often by orders of magnitude. However, deploying these algorithms in real-time facility control remains challenging due to the specialized expertise and infrastructure required. To bridge this gap, we introduce the Xopt ecosystem, a versatile suite of tools designed to make advanced ML-based optimization accessible to the broader accelerator community. This ecosystem includes Xopt, a modular Python framework that facilitates the integration of ML-based optimization algorithms with arbitrary control problems, and Badger, a graphical user interface built on top of Xopt, which enables seamless deployment of ML algorithms in real-time control systems. The Xopt ecosystem has been successfully applied towards solving challenging real-time control problems at leading international accelerator facilities, including SLAC, LBNL, Argonne, Fermilab, BNL, DESY, and ESRF, demonstrating its effectiveness in real-world optimization tasks. In this presentation, we provide an overview of Xopt’s capabilities and illustrate its impact through case studies from SLAC accelerator facilities including LCLS, LCLS-II, and FACET-II.

Roussel, Ryan [SLAC]↗
DOE OSTI2021

EMOS (Energy Management Optimization System) [SWR-21-46]

EMOS software performs a real-time hierarchal optimal control for energy systems like multi-port electric vehicles charging site with distributed energy resources (DERs) and energy storage systems (ESSs). It gathers information in real-time from electric vehicles, power grid, and DERs, solves a multi-objective energy management optimization problem, and output setpoint for chargers, ESS converters, DERs converters, and grid converters. The software incorporates a novel integration of two control tasks: a) An Energy Management Optimization (EMO), which is the brain of EMOS controller that gathers information in real-time from EVs [e.g., battery size, state-of-charge (SOC), desired SOC, and charge acceptance curve], grid (e.g., electricity price, allowed feeder capacity, ramp rate limit, and reactive power), and DERs (e.g., prediction for solar generation for PV systems). It solves a control optimization problem in real-time to find optimal setpoint for ESSs power dispatch, EVs charging rate, and grid inverters. The objectives are to (1) minimize the charging cost considering grid energy, demand charges, and battery energy, (2) minimize charging time to meet fast charging criterion, (3) keep high energy level on ESSs at the end of an operating period, while satisfying constraints related to grid, EVs, and power converters. b) Real-Time Energy Management System (RT-EMS) is a rule-based algorithm that has faster response than EMO. It receives optimal setpoint from EMO and actual measurements from the system and modify the setpoint to compensate for any fast disturbance in the system, until a new optimum solution is received. Fast disturbances may include vehicle connect/disconnect, unpredicted variation in DERs profiles, variation in grid voltage, errors in PV generation prediction, and others. In addition, RT-EMS regulates voltage at point of common coupling (PCC) by managing reactive power of grid converters.

Mohamed, Ahmed↗
DOE OSTI2023

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2012

Large-scale Stochastic Optimization for Unit Commitment and Economic Dispatch

Our FY 2012 LDRD research on stochastic optimization with its application in power grid planning and operations has advanced the state-of-the-art of this area in the following aspects: (1) Deploying stochastic unit commitment on large real-world applications, (2) Stochastic optimization coupled with real-time production cost simulation (interleaved), and (3) Real-time stochastic optimization with rolling-horizon look-ahead. In conjunction, a number of computational experiments have been performed to evaluate the capability of the latest stochastic optimization software and explore opportunities for future research. In what follows, we discuss each of these contribution areas, including the results of our computational experiments and their implications on the scalability and solvability of large-scale stochastic optimization models.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2012

Large-Scale Stochastic Optimization for Unit Commitment and Economic Dispatch

Our FY 2012 LDRD research on stochastic optimization with its application in power grid planning and operations has advanced the state-of-the-art of this area in the following aspects: (1) Deploying stochastic unit commitment on large real-world applications, (2) Stochastic optimization coupled with real-time production costsimulation (interleaved), and (3) Real-time stochastic optimization with rolling-horizon look-ahead. In conjunction, a number of computational experiments have been performed to evaluate the capability of the latest stochastic optimization software and explore opportunities for future research. In what follows, we discuss each of these contribution areas, including the results of our computational experiments and their implications on the scalability and solvability of large-scale stochastic optimization models.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗