Results for “system optimization” — Page 83

NASA NTRS1990

Factorization and the synthesis of optimal feedback gains for distributed parameter systems

An approach based on Volterra factorization leads to a new methodology for the analysis and synthesis of the optimal feedback gain in the finite-time linear quadratic control problem for distributed parameter systems. The approach circumvents the need for solving and analyzing Riccati equations and provides a more transparent connection between the system dynamics and the optimal gain. The general results are further extended and specialized for the case where the underlying state is characterized by autonomous differential-delay dynamics. Numerical examples are given to illustrate the second-order convergence rate that is derived for an approximation scheme for the optimal feedback gain in the differential-delay problem.

Milman, Mark H.↗

DOE OSTI2022

Generalized optimization-based synthesis of membrane systems for multicomponent gas mixture separation

Synthesizing a membrane system to separate multicomponent gas mixture is challenging due to the combinatorial number of feasible configurations and the difficulties in describing the multicomponent permeators. Here we present a mixed-integer nonlinear programming (MINLP) model for synthesizing membrane systems for multicomponent gas mixture separation. The approach employs a richly connected superstructure to represent numerous potential system configurations, and different physics-based surrogate permeator models, such as countercurrent flow or crossflow, to be used in each stage. Moreover, to describe realistic systems, pressure drop equations can be included. We also present solution methods to accelerate the solution process. Through a case study of natural gas sweetening, we demonstrate that the proposed approach is able to obtain good solutions using an off-the-shelf global optimization solver. Finally, we expand the conventional membrane system synthesis problem by introducing feed variability in our model through a case study of an integrated reactor-separation system.

42 ENGINEERING↗

DOE OSTI2022

Enhanced deep neural networks with transfer learning for distribution LMP considering load and PV uncertainties

As the flexibility of generation and demand increases in distribution systems, the residential loads are emerging as a promising means to participate in demand response and the transactive energy market. Market pricing is an instrumental mechanism for the distribution system operator to exploit the full potential of the flexible resources. The distribution locational marginal price (DLMP) can be used to guide the residential load consumption. This type of market signal helps the distribution system operator to optimize the scheduling of all resources while satisfying related network constraints through a day-ahead market. However, solving the optimization problem for large-scale systems can be computationally expensive. To address the scalability and practicability limitations of the DLMP framework, a learning-based approach is proposed in this paper to complement the day-ahead distribution market framework. Here, the proposed approach combines long short-term memory and transfer learning to develop deep neural network that can capture the spatial–temporal correlation of the input data. The model can determine the optimal DLMP for each node in a distribution system without the system parameters required to formulate the optimization problem. Testing results on IEEE 33-bus and 123-bus systems show that the proposed approach can generate a comparable DLMP against the optimization solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRSDate not supplied

Understanding Resilience Optimization Architectures With an Optimization Problem Repository

Optimizing a system’s resilience can be challenging, especially when it involves considering both the inherent resilience of a robust design and the active resilience of a health management system to a set of computationally-expensive hazard simulations. While prior work has developed specialized architectures to effectively and efficiently solve combined design and resilience optimization problems, the comparison of these architectures has been limited to a single case study. To further study resilience optimization formulations, this work develops a problem repository which includes previously-developed resilience optimization problems and additional problems presented in this work: a notional system resilience model, a pandemic response model, and a cooling tank hazard prevention model. This work then uses models in the repository at large to understand the characteristics of resilience optimization problems and study the applicability of optimization architectures and decomposition strategies. Based on the comparisons in the repository, applying an optimization architecture effectively requires understanding the alignment and coupling relationships between the design and resilience models, as well as the efficiency characteristics of the algorithms. While alignment determines the necessity of a surrogate of resilience cost in the upper-level design problem, coupling determines the overall applicability of a sequential, alternating, or bilevel structure. Additionally, the application of decomposition strategies is dependent on there being limited interactions between variable sets, which often does not hold when a resilience policy is parameterized in terms of actions to take in hazardous model states rather than specific given scenarios.

Resilience↗

NASA NTRS1983

Automated distribution system management for multichannel space power systems

A NASA sponsored study of space power distribution system technology is in progress to develop an autonomously managed power system (AMPS) for large space power platforms. The multichannel, multikilowatt, utility-type power subsystem proposed presents new survivability requirements and increased subsystem complexity. The computer controls under development for the power management system must optimize the power subsystem performance and minimize the life cycle cost of the platform. A distribution system management philosophy has been formulated which incorporates these constraints. Its implementation using a TI9900 microprocessor and FORTH as the programming language is presented. The approach offers a novel solution to the perplexing problem of determining the optimal combination of loads which should be connected to each power channel for a versatile electrical distribution concept.

Fleck, G. W.↗

DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Active Learning for Metamaterial Optimization on HPC and QC Integrated Systems

Active learning algorithms, integrating machine learning, quantum computing and optics simulation in an iterative loop, offer a promising approach to optimizing metamaterials. However, these algorithms can face difficulties in optimizing highly complex structures due to computational limitations. High-performance computing (HPC) and quantum computing (QC) integrated systems can address these issues by enabling parallel computing. In this study, we develop an active learning algorithm working on HPC-QC integrated systems. We evaluate the performance of optimization processes within active learning (i.e., training a machine learning model, problem-solving with quantum computing, and evaluating optical properties through wave-optics simulation) for highly complex metamaterial cases. Our results showcase that utilizing multiple cores on the integrated system can significantly reduce computational time, thereby enhancing the efficiency of optimization processes. Therefore, we expect that leveraging HPC-QC integrated systems helps effectively tackle large-scale optimization challenges in general.

Kim, Seongmin↗

NASA NTRS1966

Bounded phase coordinate control Final report, 15 Jun. 1964 - 14 Jun. 1965

Methods for solving bounded phase-coordinate time- optimal control problems - Pontryagin principle

CONTROL SYSTEM↗

NASA NTRS1972

Analysis and design of a capsule landing system and surface vehicle control system for Mars exporation

The problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars were investigated. Problem areas receiving attention include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis; navigation, terrain modeling and path selection; and chemical analysis of specimens. The following specific tasks were studied: vehicle model design, mathematical modeling of dynamic vehicle, experimental vehicle dynamics, obstacle negotiation, electromechanical controls, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, chromatograph model evaluation and improvement and transport parameter evaluation.

Frederick, D. K.↗

NASA NTRS1972

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

Investigation of problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars has been undertaken. Problem areas receiving attention include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis; terrain modeling and path selection; and chemical analysis of specimens. The following specific tasks have been under study: vehicle model design, mathematical modeling of a dynamic vehicle, experimental vehicle dynamics, obstacle negotiation, electromechanical controls, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer sybsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, chromatograph model evaluation and improvement.

Frederick, D. K.↗

NASA NTRS1973

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

Problems related to the design and control of a mobile planetary vehicle to implement a systematic plan for the exploration of Mars are reported. Problem areas include: vehicle configuration, control, dynamics, systems and propulsion; systems analysis, terrain modeling and path selection; and chemical analysis of specimens. These tasks are summarized: vehicle model design, mathematical model of vehicle dynamics, experimental vehicle dynamics, obstacle negotiation, electrochemical controls, remote control, collapsibility and deployment, construction of a wheel tester, wheel analysis, payload design, system design optimization, effect of design assumptions, accessory optimal design, on-board computer subsystem, laser range measurement, discrete obstacle detection, obstacle detection systems, terrain modeling, path selection system simulation and evaluation, gas chromatograph/mass spectrometer system concepts, and chromatograph model evaluation and improvement.

Frederick, D. K.↗

NASA NTRS2019

Dynamic Analysis of the STARC-ABL Propulsion System

In the pursuit of Electrified Aircraft Propulsion (EAP), much of the attention is on the development of hybrid electric concept vehicles and their propulsion systems from a steady state performance perspective. While it is steady-state performance that largely determines the efficiency of civil air transports, engine operability and transient performance define constraints for the steady state design that impact efficiency and system viability. Neglecting dynamics and control technologies can result in an over-designed, sub optimal propulsion system or a concept that is not feasible. Thus, dynamic system studies were conducted on the propulsion system of the conceptual aircraft design known as the Single-aisle Turboelectric AiRCraft with Aft Boundary Layer propulsor (STARC-ABL). This paper describes the development of a controller to verify the baseline concept's feasibility from an operability perspective. Further, studies were conducted to identify excessive stability margin in the baseline design that could be traded for potential benefits in efficiency through an engine re design. This study revealed the potential to reduce the high pressure compressor (HPC) stall margin by 3%. Finally, a study was conducted to investigate the potential benefit of adding energy storage to the STARC-ABL concept that further improves operability and enables more gains in engine efficiency and performance. The energy storage provided an additional 0.5% stall margin can be removed from the HPC.

STARC-ABL↗

NASA NTRS2019

Dynamic Analysis of the STARC-ABL Propulsion System

In the pursuit of Electrified Aircraft Propulsion (EAP), much of the attention is on the development of hybrid electric concept vehicles and their propulsion systems from a steady state performance perspective. While it is steady-state performance that largely determines the efficiency of civil air transports, engine operability and transient performance define constraints for the steady state design that impact efficiency and system viability. Neglecting dynamics and control technologies can result in an over-designed, sub optimal propulsion system or a concept that is not feasible. Thus, dynamic system studies were conducted on the propulsion system of the conceptual aircraft design known as the Single-aisle Turboelectric AiRCraft with Aft Boundary Layer propulsor (STARC-ABL). This paper describes the development of a controller to verify the baseline concept's feasibility from an operability perspective. Further, studies were conducted to identify excessive stability margin in the baseline design that could be traded for potential benefits in efficiency through an engine re design. This study revealed the potential to reduce the high pressure compressor (HPC) stall margin by 3%. Finally, a study was conducted to investigate the potential benefit of adding energy storage to the STARC-ABL concept that further improves operability and enables more gains in engine efficiency and performance. The energy storage provided an additional 0.5% stall margin can be removed from the HPC.

STARC-ABL↗

NASA NTRS2019

Dynamic Analysis of the STARC-ABL Propulsion System

In the pursuit of Electrified Aircraft Propulsion (EAP), much of the attention is on the development of hybrid electric concept vehicles and their propulsion systems from a steady state performance perspective. While it is steady-state performance that largely determines the efficiency of civil air transports, engine operability and transient performance define constraints for the steady state design that impact efficiency and system viability. Neglecting dynamics and control technologies can result in an over-designed, sub optimal propulsion system or a concept that is not feasible. Thus, dynamic system studies were conducted on the propulsion system of the conceptual aircraft design known as the Single-aisle Turboelectric AiRCraft with Aft Boundary Layer propulsor (STARC-ABL). This paper describes the development of a controller to verify the baseline concept's feasibility from an operability perspective. Further, studies were conducted to identify excessive stability margin in the baseline design that could be traded for potential benefits in efficiency through an engine re design. This study revealed the potential to reduce the high pressure compressor (HPC) stall margin by 3%. Finally, a study was conducted to investigate the potential benefit of adding energy storage to the STARC-ABL concept that further improves operability and enables more gains in engine efficiency and performance. The energy storage provided an additional 0.5% stall margin can be removed from the HPC.

dynamic systems analysis↗

NASA NTRSDate not supplied

Enabling a Voice Management System for Space Applications, Design and Software Development

Sustainable missions, beyond low Earth orbit, will require autonomous capabilities in order to achieve NASA’s Artemis program objectives. Correspondingly, the crew must have a means to efficiently interact with these autonomous systems; this can be facilitated via voice and speech communications. Voice-based controls enable the user to access autonomous systems hands-free/eyes-free, allowing the user to better focus on critical tasks. The goal of this project was to explore the knowledge and technology needed to successfully design effective voice interfaces for autonomous systems. The main objective was to understand how a crew member, through voice interaction, could most efficiently and intuitively communicate with a notional autonomous vehicle system manager. This project leveraged prior research conducted by the University of Michigan’s Bioastronautics and Life Support System (BLiSS) team as part of a NASA Moon to Mars eXploration Systems and Habitation (M2M X-Hab) 2020 Academic Innovation Challenge. The X-Hab 2020 work from the BliSS Team resulted in an intuitive graphical user interface/user experience that was built on an Internet of Things (IOT) platform. The Voice User Interface (VUI) design for the M2M X-Hab 2021 project leveraged this technology and incorporated a voice-based assistant and NASA’s Platform for Autonomous Systems (NPAS) software. This required technologies to convert voice to text, conduct semantic interpretations, and convert responses from the autonomous system to text and to speech; additionally, the background noise environment of spacecraft was assessed, and a relatable personality for the autonomous system to facilitate human-like conversations was created. This work’s success was largely due to the diverse team that included expertise in Space Systems Engineering, Human Computer Interaction, Aerospace Engineering, Computer Science, Biomedical Engineering, and Applied Physics. The differing perspectives fostered elaborate discussions, resulting in the conception of three main interactions: (1) User-System, (2) NPAS-System, and (3) Environment-System. The system developed, i.e. the VUI, had to be unique, efficient, and intuitive; thus, the team crafted a personality for the system to enable human-like conversation. User surveys sent to students and young professionals were used to help determine these personality traits by capturing perspectives and expectations of the “Artemis Generation Astronauts”. To further simulate human-like conversations, the system had to be able to quickly interpret user speech and be able to integrate with NASA’s NPAS system for quick and reliable information transfer. Results of this research include (1) a working prototype user interface, that is compatible with NASA’s NPAS system; (2) software that demonstrates the ability to interpret user requests and respond appropriately; (3) the capability to implement fully expanded conversations between user and system using intuitive communication in four request categories; and (4) software and hardware recommendations that optimize the system’s ability to operate, i.e. be heard, in a noisy environment. The technologies chosen for this project’s demonstrations included the following: Raspberry Pi, RASA, Mozilla Deep Speech, Coqui, RTX Voice and Adobe XD. This work has laid the foundation for the development of VUI’s used for autonomy, and is intended to provide guidance for future VUI development.

Tara Vega↗

DOE OSTI2024

Toward digital design at the exascale: An overview of project ICECap

High performance computing has entered the Exascale Age. Capable of performing over 1018 floating point operations per second, exascale computers, such as El Capitan, the National Nuclear Security Administration's first, have the potential to revolutionize the detailed in-depth study of highly complex science and engineering systems. However, in addition to these kind of whole machine “hero” simulations, exascale systems could also enable new paradigms in digital design by making petascale hero runs routine. Currently, untenable problems in complex system design, optimization, model exploration, and scientific discovery could all become possible. Motivated by the challenge of uncovering the next generation of robust high-yield inertial confinement fusion (ICF) designs, project ICECap (Inertial Confinement on El Capitan) attempts to integrate multiple advances in machine learning (ML), scientific workflows, high performance computing, GPU-acceleration, and numerical optimization to prototype such a future. Built on a general framework, ICECap is exploring how these technologies could broadly accelerate scientific discovery on El Capitan. In addition to our requirements, system-level design, and challenges, we describe some of the key technologies in ICECap, including ML replacements for multiphysics packages, tools for human-machine teaming, and algorithms for multifidelity design optimization under uncertainty. As a test of our prototype pre-El Capitan system, we advance the state-of-the art for ICF hohlraum design by demonstrating the optimization of a 17-parameter National Ignition Facility experiment and show that our ML-assisted workflow makes design choices that are consistent with physics intuition, but in an automated, efficient, and mathematically rigorous fashion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1962

RENDEZVOUS AND DOCKING TECHNIQUES

To implement the primary space mission of this decade – manned lunar exploration -- the operational assistance of rendezvous and docking is being considered as an alternative to possible problems in obtaining a boost vehicle capable of direct flight. This paper concentrates on the mechanization of the rendezvous and docking phase of such a lunar mission. As indicated in previous papers, rendezvous can take place in either an earth or lunar orbit (or on the lunar surface); it can involve the mating of stages, transfer of fuel, or the return of a shuttle to a “mother” ship; direct ascent or parking orbits can be used; the orbits can be circular or more general ellipses; finally, either or both of the spacecraft can participate in the rendezvous maneuvers. Since there is no intent here to discuss the pros and cons of each approach or to cover all possible mission profiles, guidance schemes and hardware configurations, one particular profile has been selected to display the significant features of most rendezvous missions. In many respects, rendezvous is less difficult than aircraft interception since the target is friendly and there is no severe time constraint. The latter feature allows considerable freedom of design especially with the great versatility of a human in the loop. This remains true even for a purely automatic mode. Perhaps the largest design problem concerns the selection of the mission to be implemented followed by the optimization or systems engineering of a mechanization from the multitude of possible schemes and techniques. A most important factor in the optimization is that of reliability and the redundancy, alternate or backup modes, etc., associated with the approach. While a primary system can be rather easily mechanized with modest equipment requirements, reliability considerations will result in additional hardware, tighter specifications and more safety factors (e. g., propellant margin). This is a very complex area and will only briefly be mentioned in the following discussion.

Rendezvous↗

NASA NTRS1980

Optimal control model predictions of system performance and attention allocation and their experimental validation in a display design study

The influence of different types of predictor displays in a longitudinal vertical takeoff and landing (VTOL) hover task is analyzed in a theoretical study. Several cases with differing amounts of predictive and rate information are compared. The optimal control model of the human operator is used to estimate human and system performance in terms of root-mean-square (rms) values and to compute optimized attention allocation. The only part of the model which is varied to predict these data is the observation matrix. Typical cases are selected for a subsequent experimental validation. The rms values as well as eye-movement data are recorded. The results agree favorably with those of the theoretical study in terms of relative differences. Better matching is achieved by revised model input data.

Johannsen, G.↗