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Results for “Robust 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 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Performance of the Spacecraft Propulsion Research Facility During Altitude Firing Tests of the Delta 3 Upper Stage

The Spacecraft Propulsion Research Facility at the NASA Lewis Research Center's Plum Brook Station was reactivated in order to conduct flight simulation ground tests of the Delta 3 cryogenic upper stage. The tests were a cooperative effort between The Boeing Company, Pratt and Whitney, and NASA. They included demonstration of tanking and detanking of liquid hydrogen, liquid oxygen and helium pressurant gas as well as 12 engine firings simulating first, second, and third burns at altitude conditions. A key to the success of these tests was the performance of the primary facility systems and their interfaces with the vehicle. These systems included the structural support of the vehicle, propellant supplies, data acquisition, facility control systems, and the altitude exhaust system. While the facility connections to the vehicle umbilical panel simulated the performance of the launch pad systems, additional purge and electrical connections were also required which were unique to ground testing of the vehicle. The altitude exhaust system permitted an approximate simulation of the boost-phase pressure profile by rapidly pumping the test chamber from 13 psia to 0.5 psia as well as maintaining altitude conditions during extended steady-state firings. The performance of the steam driven ejector exhaust system has been correlated with variations in cooling water temperature during these tests. This correlation and comparisons to limited data available from Centaur tests conducted in the facility from 1969-1971 provided insight into optimizing the operation of the exhaust system for future tests. Overall, the facility proved to be robust and flexible for vehicle space simulation engine firings and enabled all test objectives to be successfully completed within the planned schedule.

Meyer, Michael L.↗
NASA NTRS2002

Optimal Control Design using an H(sub 2) Method for the Glovebox Integrated Microgravity Isolation Technology (G-Limit)

The acceleration environment on the International Space Station (ISS) will likely exceed the requirements of many micro-gravity experiments. The Glovebox Integrated Microgravity Isolation Technology (g-LIMIT) is being built by the NASA Marshall Space Flight Center to attenuate the nominal acceleration environment and provide some isolation for microgravity science experiments. G-LIMIT uses Lorentz (voice-coil) magnetic actuators to isolate a platform for mounting science payloads from the nominal acceleration environment. The system utilizes payload acceleration, relative position, and relative orientation measurements in a feedback controller to accomplish the vibration isolation task. The controller provides current commands to six magnetic actuators, producing the required experiment isolation from the ISS acceleration environment. This paper presents the development of a candidate control law to meet the acceleration attenuation requirements for the g-LIMIT experiment platform. The controller design is developed using linear optimal control techniques for frequency-weighted H(sub 2) norms. Comparison of the performance and robustness to plant uncertainty for this control design approach is included in the discussion.

Calhoun, Philip C.↗
NASA NTRS2014

Fixing Dataset Search

Three current search engines are queried for ozone data at the GES DISC. The results range from sub-optimal to counter-intuitive. We propose a method to fix dataset search by implementing a robust relevancy ranking scheme. The relevancy ranking scheme is based on several heuristics culled from more than 20 years of helping users select datasets.

science data↗
NASA NTRS2020

Investigation of Direct Force Control For Aerocapture at Neptune

In this work, a direct force control numerical predictor-corrector guidance architecture is developed to enable Neptune aerocapture using blunt body aeroshells. A linear aerodynamics model is formulated for a Mars Science Laboratory-derived aeroshell. The application of optimal control theory shows that the∆V-minimizing angle of attack and side-slip angle control laws are bang-bang. A closed-loop numerical predictor-corrector direct force control guidance algorithm is developed and numerically simulated using the Program to Optimize Simulated Trajectories II. A series of Monte Carlo simulations are conducted to assess the guidance robustness to uncertainties in vehicle aerodynamics,atmospheric density, and entry state. For the reference set of uncertainties, the direct force control vehicle achieves 99.7% successful science orbit insertion within a 330 m/s total∆V budget for periapsis raise, apoapsis, inclination,and ascending node corrections. Improved atmospheric knowledge and delivery state accuracy are shown to improve the success to 100% and reduce the∆V to 230 m/s. Direct force control is demonstrated to be an enabling technology for blunt body aerocapture at Neptune while providing comparable performance to existing slender body vehicles studied in literature

aerocapture↗
NASA NTRSDate not supplied

Dragonfly Entry and Descent Flight Mechanics Modeling and Analysis

Dragonfly is a New Frontiers Program mission that will deliver a rotorcraft lander to Saturn’s moon, Titan [1]. The focus of this work is to analyze the trajectory from cruise stage separation until lander separation. This analysis is done by the NASA Langley (LaRC) EDL team using Program to Optimize Trajectories II (POST2)[2]. This poster provides an overview of the current design and the robustness of the overall entry sequence as assessed using a Monte Carlo uncertainty analysis. This mission is led by Johns Hopkins Applied Physics Laboratory (APL), presented here is the design and analysis as of the Dragonfly EDL Assembly Preliminary Design Review (PDR).

Modeling↗
NASA NTRSDate not supplied

Towards an Optimal Estimation Retrieval of Cirrus Cloud Optical and Microphysical Properties Using Hyperspectral Shortwave Instruments and A Fast Radiative Transfer Algorithm

Cirrus cloud retrieval products (here, cloud optical depth, effective particle size, and cloud top height) are important inputs into numerical weather and climate models. Uncertainties in such retrieval products, as a matter of course, propagate downstream, impacting model calculations. Improvements in high-quality global cirrus cloud optical and microphysical data products from satellite observations are needed to understand and reduce retrieval uncertainties. Hyperspectral instruments produce high-resolution and information-dense radiance spectra, thus offering the opportunity to reduce uncertainties in retrieval products. An optimal estimation-based cirrus cloud optical and microphysical product retrieval is under development for the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and the Earth Surface Mineral Dust Source Investigation (EMIT) instruments, and the forthcoming Climate Absolute Radiance and Refractivity Observatory Pathfinder (CLARREO-Pathfinder). The spectral coverage of all three instruments includes the ultraviolet, visible, and near-infrared. In this study a very fast radiative transfer model, the Principal Component-based Radiative Transfer Model in the solar spectral region (PCRTM-Solar), is used for forward modeling computations. This will reduce the computational burden in the forward radiance and Jacobian calculations as the cost function is minimized, enabling the entire AVIRIS, EMIT, and CLARREO-Pathfinder spectral range to be used in the optimal estimation framework. Using the entire spectrum will maximize the information content, resulting in a more robust and more accurate retrieval. As a first step, the retrieval is being designed for single layer ice clouds over open ocean water. Preliminary results will be shown.

Jeffrey Mast↗
DOE OSTI2023

BOTTLE 1 - Introduction and BOTTLE Overview

The Bio-Optimized Technologies to keep Thermoplastics out of Landfills and the Environment (BOTTLE) Consortium aims to develop robust processes to upcycle existing waste plastics and to develop new plastics that are recyclable-by-design, both in direct alignment with DOE's Strategy for Plastics Innovation. We accomplish our work in the BOTTLE Consortium through an organizational framework that includes three primary research tasks, Deconstruction, Upcycling, and Redesign, which are supported by three cross-cutting tasks, Analysis, Characterization, and Modeling, BOTTLE also has tasks focused on Industry Engagement and Diversity, Equity, and Inclusion (DEI). This presentation will review the approach and management structure of BOTTLE, the importance of analysis-guided research, and the key metrics for carbon, economic, energy, and greenhouse gas emissions. In the FY21-FY23 period, BOTTLE has drafted and enacted a comprehensive DEI plan, assembled a world-class Technical Advisory Board (TAB) to provide constructive feedback on our performance, had our first in-person all-hands meeting in summer 2022, and on-boarded and off-boarded research activities based on active project management and analysis. From an impact perspective, BOTTLE researchers have published over 40 peer-reviewed manuscripts (many in leading journals), submitted >30 patent applications, and initiated 6 funds-in industry partnerships.

BIOMASS FUELS↗
DOE OSTI2025

A Machine Learning Approach Toward Improving QA/QC of Coated Particle Fuels

Coated particle fuels, such as the tristructural isotropic (TRISO) fuel particle, have been studied and developed for decades for high-temperature gas reactor (HTGR) applications because of their efficiency and relative stability under off-normal conditions. Critical to this development is a strong understanding of the relationship between fuel fabrication, properties and performance, as is how the former can be adjusted and improved to optimize the latter. Accordingly, wide-scale implementation of coated nuclear particle fuels requires thorough and robust quality assurance/quality control (QA/QC) methods for fabrication, characterization, and deployment

Conry, Bryan [ORNL] (ORCID:0000000236089604)↗
NASA NTRS2006

Strict Constraint Feasibility in Analysis and Design of Uncertain Systems

This paper proposes a methodology for the analysis and design optimization of models subject to parametric uncertainty, where hard inequality constraints are present. Hard constraints are those that must be satisfied for all parameter realizations prescribed by the uncertainty model. Emphasis is given to uncertainty models prescribed by norm-bounded perturbations from a nominal parameter value, i.e., hyper-spheres, and by sets of independently bounded uncertain variables, i.e., hyper-rectangles. These models make it possible to consider sets of parameters having comparable as well as dissimilar levels of uncertainty. Two alternative formulations for hyper-rectangular sets are proposed, one based on a transformation of variables and another based on an infinity norm approach. The suite of tools developed enable us to determine if the satisfaction of hard constraints is feasible by identifying critical combinations of uncertain parameters. Since this practice is performed without sampling or partitioning the parameter space, the resulting assessments of robustness are analytically verifiable. Strategies that enable the comparison of the robustness of competing design alternatives, the approximation of the robust design space, and the systematic search for designs with improved robustness characteristics are also proposed. Since the problem formulation is generic and the solution methods only require standard optimization algorithms for their implementation, the tools developed are applicable to a broad range of problems in several disciplines.

Crespo, Luis G.↗
DOE OSTI2024

Graph-Based Representations and Applications to Process Simulation

Rapid and robust convergence of a process flowsheet is critical to enable large-scale simulations that address core scientific questions related to process design, optimization, and sustainability. However, due to the highly coupled and nonlinear nature of chemical processes, efficiently solving a flowsheet remains a challenge. In this work, we show that graph representations of the underlying physical phenomena in unit operations may help identify potential avenues to systematically reformulate the network of equations and enable more robust topology-based convergence of flowsheets. To this end, we developed graph abstractions of the governing equations of vapor-liquid and liquid-liquid equilibrium separation equipment. These graph abstractions consist of a mesh of interconnected variable nodes and equation nodes that are systematically generated through PhenomeNode, a new open-source library in Python developed in this study. We show that partitioning the graph into separate mass, energy, and equilibrium subgraphs can help decouple nonlinearities and guide decomposition algorithms. By employing the graph abstraction on an industrial separation process for separating glacial acetic acid from water, we implemented a new block decomposition scheme in BioSTEAM and demonstrated that this can accelerate convergence over a traditional sequential modular approach.

Distillation↗
DOE OSTI2022

Bimetallic Ir x Pb nanowire networks with enhanced electrocatalytic activity for the oxygen evolution reaction

Metallic nanowire networks (MNNs) have attracted increasing attention due to their high surface area and tunable compositions. Although enormous efforts have been devoted to preparing noble metal MNNs with different compositions, the composition optimization and the formation mechanism of Ir-based MNNs have not been thoroughly investigated. Here, this work presents a facile method for synthesizing robust Ir-based bimetallic MNNs by chemical reduction. As unveiled by our characterization, the network structure is constructed by ultrafine nanowires evolved from aggregated nanoparticles. The element analysis confirms the even distribution of Pb and Ir elements. The introduction of Pb is found to be beneficial to the stable formation of Ir x Pb MNNs in the sol–gel process. By tuning the precursor ratio of Ir and Pb, the optimized Ir x Pb catalyst delivers an enhanced oxygen evolution reaction (OER) performance in acid media (307 mV at 10 mA cm −2 ), which is superior to that of the commercial IrO 2 catalyst. Besides, due to the robust structure of the Ir x Pb MNNs, excellent OER durability is observed in the accelerated durability test (ADT) (2000 cycles).

Bimetallic↗
DOE OSTI2022

Model predictive control for robust quantum state preparation

A critical engineering challenge in quantum technology is the accurate control of quantum dynamics. Model-based methods for optimal control have been shown to be highly effective when theory and experiment closely match. Consequently, realizing high-fidelity quantum processes with model-based control requires careful device characterization. In quantum processors based on cold atoms, the Hamiltonian can be well-characterized. For superconducting qubits operating at milli-Kelvin temperatures, the Hamiltonian is not as well-characterized. Unaccounted for physics (i.e., mode discrepancy), coherent disturbances, and increased noise compromise traditional model-based control. This work introduces model predictive control (MPC) for quantum control applications. MPC is a closed-loop optimization framework that (i) inherits a natural degree of disturbance rejection by incorporating measurement feedback, (ii) utilizes finite-horizon model-based optimizations to control complex multi-input, multi-output dynamical systems under state and input constraints, and (iii) is flexible enough to develop synergistically alongside other modern control strategies. We show how MPC can be used to generate practical optimized control sequences in representative examples of quantum state preparation. Specifically, we demonstrate for a qubit, a weakly-anharmonic qubit, and a system undergoing crosstalk, that MPC can realize successful model-based control even when the model is inadequate. These examples showcase why MPC is an important addition to the quantum engineering control suite.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2010

Adaptive Flight Control Design with Optimal Control Modification on an F-18 Aircraft Model

In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to as the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly; however, a large adaptive gain can lead to high-frequency oscillations which can adversely affect the robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient robustness. A damping term (v) is added in the modification to increase damping as needed. Simulations were conducted on a damaged F-18 aircraft (McDonnell Douglas, now The Boeing Company, Chicago, Illinois) with both the standard baseline dynamic inversion controller and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model.

Burken, John J.↗
NASA NTRSDate not supplied

Design Under Uncertainty with Design-Dependent Uncertain Variables

Uncertainty quantification (UQ) can provide a more robust understanding of a system, leading to better informed decisions earlier in the design process. The additional information that UQ provides can be leveraged during a design optimization process known as design under uncertainty that, when incorporated with multidisciplinary design and optimization, can become computationally infeasible due to the large number of responses required for meaningful results. Previous work addressed reducing the computational expense in design under uncertainty by incorporating analytic derivatives throughout polynomial chaos expansion. Although this allows design under uncertainty to be feasible for more systems, some multidisciplinary systems have design-dependent uncertain variables. This paper details an implementation of design dependent uncertain variables in a manner than preserves derivatives required for efficient gradient-based optimization throughout the process. Two analytic examples of design-dependent uncertain variables are given: the first transforms a uniform uncertain variable with one design variable and the second transforms a normal uncertain variable with two design variables. The polynomial chaos expansion (PCE) results are comparable to both the Monte Carlo (MC) results and the analytic results for the two examples. A case study that maximizes the lift-to-drag ratio with a design-dependence between the wing leading edge sweep angle and uncertain parameter percentage of laminar flow is compared to a MC and alternative optimization formulations. This paper demonstrates that design-dependent uncertain variables are valid and hold throughout PCE.

robust design↗
DOE OSTI2023

A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What’s more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate nonlinearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm-cairo with 27 qubits and ibmq-lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the STVQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.

Li, Jinyang↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗