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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Multiplexed Predictive Control of a Large Commercial Turbofan Engine

Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.

Richter, hanz↗
DOE OSTI2024

Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization

Herein this article presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. hybridM leverages the upper confidence bound tree search (UCTS) for MCTS strategy, showcasing the tree architecture’s integration into Bayesian optimization. Our innovations, including dynamic online kernel selection in the surrogate modeling phase and a unique UCTS search strategy, position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the superiority of hybrid models, highlighting their potential in Bayesian optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System

Frequency control from photovoltaic (PV) power plants has great potential to address the frequency response challenge of the power system with high penetrations of renewable generation. Using model-based approaches to determine the optimal PV headroom reserve, however, requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic 1-day operation profile of the WECC system and demonstrates a savings of more than 40% PV headroom compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

frequency control↗
DOE OSTI2026

Toward real-time optimization through model reduction and model discrepancy sensitivities

Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essential to solve these optimization problems within wall-clock-time limitations. However, this is often unattainable for complex systems, such as those modeled by nonlinear partial differential equations. One strategy for mitigating this issue is to construct a reduced-order model (ROM) that enables more rapid optimization. In particular, the use of nonintrusive ROMs—those that do not require access to the full-order model at evaluation time—is popular because they facilitate the computation of optimization solutions within the wall-clock time requirements. However, the optimization solution will be unreliable if the iterates move outside the ROM training data. This article proposes the use of hyper-differential sensitivity analysis with respect to model discrepancy (HDSA-MD) as a computationally efficient tool to augment ROM-constrained optimization and improve its reliability. The proposed approach consists of two phases: (i) an offline phase where several full-order model evaluations are computed to train the ROM, and (ii) an online phase where a ROM-constrained optimization problem is solved, a limited number of full-order model evaluations are computed, and HDSA-MD is used to enhance the optimization solution. Numerical results are demonstrated for two examples, atmospheric contaminant control and wildfire ignition location estimation, in which a ROM is trained offline using inaccurate atmospheric data. In conclusion, the HDSA-MD update yields a significant improvement in the ROM-constrained optimization solution using only one full-order model evaluation online with corrected atmospheric data.

PDE-constrained optimization↗
DOE OSTI2020

Machine Learning-Based PV Reserve Determination Strategy for Frequency Control on the WECC System: Preprint

Frequency control from Photovoltaic (PV) plants has great potential to address the frequency response challenge of the power system with high renewable penetration. However, using model-based approaches to determine the optimal PV headroom reserve requires significant online computation and is intractable for an interconnection level system. This paper proposes a machine learning based strategy, that is suitable for real-time operation, to determine the optimal PV reserve for frequency control. The proposed machine learning algorithm is trained and tested on 1,987 offline simulations of a 60% renewable penetration Western Electricity Coordinating Council (WECC) system. Furthermore, the proposed reserve determination strategy is applied on a realistic one-day operation profile of the WECC system and demonstrates over 40% PV headroom saving compared to a conservative approach. It is evident that the proposed strategy can efficiently and effectively determine the optimal PV frequency control reserve for realistic interconnection systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Restoring Critical Loads In Resilient Distribution Systems using A Curriculum Learned Controller

In this paper, we propose a curriculum learned reinforcement learning (RL) controller to facilitate distribution system critical load restoration (CLR), leveraging RL's fast online response and its outstanding optimal sequential control capability. Like many grid control problems, CLR is complicated due to the large control action space and renewable uncertainty in a heavily constrained non-linear environment with strong intertemporal dependency. The nature of the problem oftentimes causes the RL policy to converge to a poor-performing local optimum if learned directly. To overcome this, we design a two-stage curriculum in which the RL agent will learn generation control and load restoration decision under different scenarios progressively. Via curriculum learning, the trained RL controller is expected to achieve a better control performance, with critical loads restored as rapidly and reliably as possible. Using the IEEE 13-bus test system, we illustrate the performance of the RL controller trained by the proposed curriculum-based method.

curriculum learning↗
NASA NTRS1991

Coordinating Council. Fourth Meeting: NACA Documents Database Project

This NASA Scientific and Technical Information Coordination Council meeting dealt with the topic 'NACA Documents Database Project'. The following presentations were made and reported on: NACA documents database project study plan, AIAA study, the Optimal NACA database, Deficiencies in online file, NACA documents: Availability and Preservation, the NARA Collection: What is in it? and What to do about it?, and NACA foreign documents and availability. Visuals are available for most presentations.

Source record↗
DOE OSTI2019

Method and apparatus for leakage monitoring for oil-immersed electrical transformers

The present invention comprises an apparatus and method for detecting a loss of oil from an oil-immersed transformer, based on fitting a transformer top-oil temperature model to online measurements in an iterative optimization process that yields fitted values for a first model parameter representing the top-oil temperature rise over ambient temperature and a second model parameter representing the oil time constant. Among the several advantages seen in the contemplated apparatus and method is the reduction in required instrumentation, whereby transformer oil leaks are indirectly detected without requiring pressure sensors or mechanical floats, although the presence of such sensors is not excluded by the teachings herein.

Feng, Xianyong↗
DOE OSTI2021

Restoring Critical Loads in Resilient Distribution Systems Using a Curriculum Learned Controller: Preprint

In this paper, we propose a curriculum learned reinforcement learning (RL) controller to facilitate distribution system critical load restoration (CLR), leveraging RL's fast online response and its outstanding optimal sequential control capability. Like many grid control problems, CLR is complicated due to the large control action space and renewable uncertainty in a heavily constrained non-linear environment with strong intertemporal dependency. The nature of the problem oftentimes causes the RL policy to converge to a poor-performing local optimum if learned directly. To overcome this, we design a two-stage curriculum in which the RL agent will learn generation control and load restoration decision under different scenarios progressively. Via curriculum learning, the trained RL controller is expected to achieve a better control performance, with critical loads restored as rapidly and reliably as possible. Using the IEEE 13-bus test system, we illustrate the performance of the RL controller trained by the proposed curriculum-based method.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Extending ORNL HPDM capabilities for design and optimization of new refrigerant blends - FY18 3rd quarterly milestone: a trial version published online for modeling and design ac units using low GWP refrigerants

Based on exhaustive literature survey, we updated the DOE/ORNL HPDM to include most up-to-date heat transfer and pressure drop correlations suitable for modelling low GWP HFO blends and built up a comprehensive refrigerant property database including all the emerged HFO refrigerants. The upgraded HPDM has been released online, containing a desktop version for free download. The new release includes five system examples of room and commercial air conditioner (AC) units, which facilitates modelling and designing AC units using low GWP refrigerants. Geometry and configurations of the units were obtained from ORNL’s low-GWP refrigerant drop-in investigations [1] [2]. The published models have been calibrated to match the measured data. The new public-domain, free-to-use tool will help the US industry design their AC units using the new refrigerants and shorten the learning curve.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

The Trip Itinerary Optimization Platform: A Framework for Personalized Travel Information

The New Concepts Incubator team at the National Renewable Energy Laboratory (NREL) developed a three-stage online platform for travel diary collection, personal travel plan optimization and travel itinerary visualization. In the first stage, users provide a travel diary for the previous day through an interactive map and calendar interface and survey for travel attitudes and behaviors. One or more days later, users are invited via email to engage in a second stage where they view a personal mobility dashboard displaying recommended travel itineraries generated from a novel framework that optimizes travel outcomes over a sequence of interrelated trips. A week or more after viewing these recommended travel itineraries on the dashboard, users are emailed again to engage in a third stage where they complete a final survey about travel attitudes and behaviors. A usability study of the platform conducted online showed that, in general, users found the system valuable for informing their travel decisions. A total of 274 individuals were recruited through Amazon Mechanical Turk, an online survey platform, to participate in a transportation study using this platform. On average, the platform distilled 65 feasible travel plans per individual into two recommended itineraries, each optimal according to one or more outcomes and dependent on the fixed times and locations from the travel diary. For 45 percent of users, the trip recommendation algorithm returned only a single, typically automobile-centric, itinerary because there were no other viable alternative transportation modes available. Platform users generally agreed that the dashboard was enjoyable and easy to use, and that it would be a helpful tool in adopting new travel behaviors. Users generally agreed most that the time, cost and user preferred recommendations 'made sense' to them, and were most willing to implement these itineraries. Platform users typically expressed low willingness to try the carbon and calories optimized itineraries. Of the platform users who viewed the dashboard, 13 percent reported changing their travel behavior, most adopting the time, calories or carbon optimized itineraries. While the algorithm incorporates a wealth of travel data obtained from online APIs pertaining to a travelers route such as historic traffic condition data, public transit time-tables, and bike path routes, open-ended responses from users expressed an interest in the integration of even more fine-grained traffic data and the ability to dynamically model the effect of changes in travel times. Users also commonly expressed concerns over the safety of walking and biking recommendations. Responses indicate that more information about the amenities available to cyclists and pedestrians (sidewalks, shade from trees, access to food) and routes that avoid areas of perceived elevated danger would reduce barriers to implementing these recommendations. More accurate representations of personal vehicle trips (based on vehicle make and model, implications of parking) and the identification of routes that optimize caloric intensity (seeking out elevation changes or longer walks to public transit) are promising avenues for future research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Indicator-directed Dynamic Power Management for Iterative Workloads on GPU-Accelerated Systems

Modern high-performance and warehouse computing centers show strong interest in minimizing system power consumption while satisfying customers’ quality of service (QoS). Dynamic voltage and frequency scaling (DVFS) is effective for achieving this goal. Nevertheless, automating the process online and making it transparent to users must address three major challenges: (1) Complexity — today’s hardware components (e.g., CPUs, GPUs, memory, network, etc.) can be configured in several or dozens of frequency/voltage states for satisfying divergent system demands. Given their combination and the emergence of heterogeneity, searching the optimal configuration in the design space online can be timing consuming. (2) QoS guarantee — user-defined objectives such as power constraint and performance target must be monitored, predicted and ensured at the best effort. (3) Adaptability — various known and unknown workloads run on systems. Workloads characteristics should be quickly determined and configurations dynamically adjusted in accord with workloads and QoS. In this work, we focus on applications exhibiting an interesting feature – iterative or periodic, which is common among conventional HPC and emerging machine learning workloads. We propose an online dynamic power-performance (ODPP) management framework to dynamically adjust GPU DVFS configurations to meet performance and power objectives and constraints, without any code annotation or intrusion. Particularly, ODPP extracts the performance and power indicators for applications from their resources utilization profiles in a short episode. It further automatically constructs an accurate model that infers from the indicators how the application's performance and power vary with GPU core and memory frequencies. Aided with the model, for both seen and unseen applications, ODPP can quickly determine the most appropriate DVFS configuration for their execution. We evaluate ODPP on an NVIDIA GPU using multiple exascale computing (ECP) and deep learning applications.

Zou, Pengfei↗
DOE OSTI2020

Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms (Final Report)

Online tuning of particle accelerators is necessary in order to achieve optimal machine performance. However, it is also a major challenge due to the large parameter space which must be searched and the fact that many of the desir- able objectives compete with one another, and so will not reach their optimal values simultaneously. In order to mitigate these issues, we have explored using dimension-reduction techniques to reduce the size of the parameter space which must be searched and multi-objective genetic algorithms to obtain the sets of tuning parameters which provide pareto-optimal values for the objectives. These methods have enabled us to obtain improved values of the vertical emittance at the Cornell Electron Storage Ring (CESR), and to do so with greater control of orbit errors. Algorithmic tuning is a multidisciplinary endeavour, requiring expertise in beam dynamics, diagnostics, control systems and computer science, and thus a key practical problem is to formulate a common language in which experts with different specialties can communicate. We developed a solution to this problem in the form of a generic accelerator software interface that allows for rapid prototyping of optimization and control algorithms. Our interface is built on the Experimental Physics and Industrial Control Systems (EPICS) and makes possible testing control code in simulation before deployment on real accelerators, as well as deployment of third-party optimization code.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

SU-F-J-173: Online Replanning for Dose Painting Based On Changing ADC Map of Pancreas Cancer

Purpose: The introduction of MR-guided radiation therapy (RT), e.g., MR-Linac, would allow dose painting to adapt spatial RT response revealed from MRI data during the RT delivery. The purpose of this study is to investigate the use of an online replanning method to adapt dose painting from the MRI Apparent Diffusion Coefficient (ADC) map acquired during the delivery of RT for pancreatic cancers. Methods: Original dose painting plans were created based on multi-parametric simulation MRI including T1, T2 and ADC, using a treatment planning system (MONACO, Elekta) equipped with an online replanning algorithm (WSO, warm start optimization). Multiple GTVs, identified based on various ADC levels were prescribed to different doses ranging from 50–70 Gy with simultaneous integrated boost in 28 fractions. The MRI acquired after RT were used to mimic weekly MRI, on which the changing GTVs, pancreatic head and other organs-at-risk (OAR) (duodenum, stomach, small bowel) were delineated. The adaptive plan was generated by applying WSO algorithm starting from the deformed original plan based on the weekly MRI using a deformable image registration (DIR) software (ADMIRE, Elekta). The online replanning method takes <10 min. including DIR, target delineation, WSO execution and final dose calculation. Standard IGRT repositioning and full-blown reoptimization plans were also generated to compare with the adaptive plans. Results: The online replanning method significantly improved the multiple target coverages and OAR sparing for pancreatic cancers. For example, for a case with two GTVs with prescriptions of 60 and 70 Gy in pancreatic head, V100-GTV70 (the volume covered by 100% of prescription dose for GTV with 70 Gy)/V100-GTV60/V100-CTV50/V45-duodenum were (95.1/22.2/69.5/85.7), (95.0/97.0/98.6/34.3), and (95.0/98.1/100.0/38.7) for the IGRT, adaptive and reoptimization plans, respectively. Conclusion: The introduced online adaptive replanning method can effectively account for interfractional changes including tumor spatial response during MR-guided RT delivery, allowing precise delivery of dose painting. This study was partially supported by Elekta Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Robust Medium-Voltage Distribution System State Estimation using Multi-Source Data

Due to the lack of sufficient online measurements for distribution system observability, pseudo-measurements from short-term load or distributed renewable energy resources (DERs) forecasting are used. However, the accuracy of them is low and thus significantly limits the performance of distribution system state estimation (DSSE). In this paper, a robust DSSE that integrates multi-source measurement data is proposed. Specifically, the historical low-voltage (LV) side smart meters are used to forecast load and DERs injections via the support vector machine (SVM) with optimally tuned parameters. By contrast, the online smart meters at LV side are utilized to derive equivalent power injections at the MV/LV transformers, yielding more accurate pseudo-measurements compared to the forecasted injections. Furthermore, to deal with bad data caused by communication loss, instrumental errors and cyber attacks, robust DSSE that relies on generalized maximum-likelihood (GM)-estimation criterion is developed. The projection statistics are developed to adjust the weights of each measurement, leading to better balance between pseudo- and real-time measurements. Numerical results conducted on modified IEEE 33-bus system with DG integration demonstrate the effectiveness and robustness of the proposed method.

distribution system state estimation↗
DOE OSTI2025

Efficient online quantum circuit learning with no upfront training

Optimization is a promising candidate for studying the utility of variational quantum algorithms (VQAs). However, evaluating cost functions using quantum hardware introduces runtime overheads that limit exploration. Surrogate-based methods can reduce calls to a quantum computer, yet existing approaches require hyperparameter pre-training and have been tested only on small problems. Here, we show that surrogate-based methods can enable successful optimization at scale, without pre-training, by using radial basis function interpolation (RBF) to construct an adaptive, hyperparameter-free surrogate. Using the surrogate as an acquisition function drives hardware queries to the vicinity of the true optima. For 16-qubit random 3-regular Max-Cut instances with the Quantum Approximate Optimization Algorithm (QAOA), our method outperforms state-of-the-art approaches, without considering their upfront training costs. Furthermore, we successfully optimize QAOA circuits for 127-qubit random Ising models on an IBM processor using 10 4 −10 5 measurements. Strong empirical performance demonstrates the promise of automated surrogate-based learning for large-scale VQA applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

model predictive control algorithm (MPC)↗
NASA NTRS2006

A robust model predictive control algorithm for uncertain nonlinear systems that guarantees resolvability

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.

feed - forward↗