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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A survey of nonlinear robust optimization

Robust optimization (RO) has attracted great attention from the optimization community over the past decade. RO is dedicated to solving optimization problems subject to uncertainty: design constraints must be satisfied for all the values of the uncertain parameters within a given uncertainty set. Uncertainty sets may be modeled as deterministic sets (boxes, polyhedra, ellipsoids), in which case the RO problem may be reformulated via worst-case analysis, or as families of distributions. The challenge of RO is to reformulate or approximate robust constraints so that the uncertain optimization problem is transformed into a tractable deterministic optimization problem. Most reformulation methods assume linearity of the robust constraints or uncertainty sets of favorable shape, which represents only a fraction of real-world applications. This survey addresses nonlinear RO and includes problem formulations and applications, solution approaches, and available software with code samples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Uniform redundancy allocation maximizes the robustness of flow networks against cascading failures

We study the robustness of flow networks against cascading failures under a partial load redistribution model. In particular, we consider a flow network of N lines with initial loads L 1 ,...,L N and free spaces (i.e., redundant space) S 1 ,...,S N that are independent and identically distributed with joint distribution P LS (x,y) = P[L ≤ x, S ≤ y]. The capacity C i is the maximum load allowed on line i and is given by C i = L i + S i . Furthermore, when a line fails due to overloading, it is removed from the system and (1 – ε) fraction of the load it was carrying is redistributed equally among all remaining lines in the system. The rest (i.e., ε fraction) of the load is assumed to be lost or absorbed, e.g., due to advanced circuitry disconnecting overloaded power lines or an interconnected network or material absorbing a fraction of the flow from overloaded lines. We analyze the robustness of this flow network against random attacks that remove a p fraction of the lines. Our contributions include (i) deriving the final fraction of alive lines n ∞ (p,ε) for all p,εϵ(0,1) and confirming the results via extensive simulations; (ii) showing that partial redistribution might lead to (depending on the parameter 0 < ε ≤ 1) the order of transition at the critical attack size p* changing from first to second-order; and (iii) proving analytically that flow networks achieve maximum robustness [quantified by the area ∫$^{1}_{0}$ n ∞ (p,ε)dp] when all lines have the same free space regardless of their initial load. The optimality of equal free-space allocation is also confirmed on real-world data from the UK National Power Grid.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Robust Optimization for the Day-Ahead Scheduling of Cascaded Hydroelectric Systems

Uncertain electricity prices resulting from the re-structuring of electricity market have brought new opportu-nities and challenges for hydroelectric producers. This paper presents a data-driven robust optimization approach for the day-ahead scheduling of cascaded hydroelectric systems (CHS) with electricity price uncertainty. In this paper, a minimum volume enclosing ellipsoid (MVEE) is adopted to construct an ellipsoidal uncertainty set that fully identifies and exploits the historical characteristics data. Here, a second-order conic optimization formulation of the robust counterpart is derived for efficient computation. A real-world case study is conducted to demonstrate the capability of the proposed optimization approach compared with the traditional robust optimization approach.

13 HYDRO ENERGY↗
DOE OSTI2024

Robust Disturbance Rejection Rotor Current Control of Doubly-Fed Induction Generators

This article proposes a robust transient disturbance rejection current controller (TDRCC) to improve the transient performance of doubly-fed induction generators (DFIGs) in wind turbines during grid disturbances, such as short circuits. Here, the proposed robust TDRCC replaces the proportional-integral-derivative (PID) current controllers conventionally used in the vector control scheme of the DFIG rotor side converter (RSC). The TDRCC estimates the transient disturbances caused by grid faults or other external disturbances and provides compensation to the DFIG rotor voltage in the current control loops of the RSC to improve the robustness of the controller to disturbance. A sliding-mode current controller (SMCC) is also designed to highlight the transient performance improvement of the DFIG using the TDRCC over the state-of-the-art control methods during grid short circuit faults. Simulation studies are conducted in PSCAD/EMTDC for a 3.6-MW DFIG wind turbine with the proposed TDRCC, the conventional PI controller, and the SMCC, respectively during the most severe balanced three-phase grid short circuit fault specified by the U.S. grid code as well as an unbalance single-phase grid short circuit fault. Hardware experiments are conducted on a 200-W DFIG wind turbine emulator with the three different controllers for the same three-phase short circuit fault. Simulation and hardware experiment results show that the TDRCC reduces the peak rotor current significantly when compared with the PI and SMCC controlled DFIG system and, therefore, would help prolong the lifespan of the DFIG's power electronics and reduce maintenance costs.

42 ENGINEERING↗
DOE OSTI2021

Distributionally Robust Partially Observable Markov Decision Process with Moment-Based Ambiguity

In this paper, we consider a distributionally robust partially observable Markov decision process (DR-POMDP), where the distribution of the transition-observation probabilities is unknown at the beginning of each decision period, but their realizations can be inferred using side information at the end of each period after an action being taken. We build an ambiguity set of the joint distribution using bounded moments via conic constraints and seek an optimal policy to maximize the worst-case (minimum) reward for any distribution in the set. We show that the value function of DR-POMDP is piecewise linear convex with respect to the belief state and propose a heuristic search value iteration method for obtaining lower and upper bounds of the value function. We conduct numerical studies and demonstrate the computational performance of our approach via testing instances of a dynamic epidemic control problem. Our results show that DR-POMDP can produce more robust policies under misspecified distributions of transition-observation probabilities as compared to POMDP but has less costly solutions than robust POMDP. The DR-POMDP policies are also insensitive to varying parameter in the ambiguity set and to noise added to the true transition-observation probability values obtained at the end of each decision period.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Robust Containment Queries over Collections of Trimmed NURBS Surfaces via Generalized Winding Numbers

Here, we propose a containment query that is robust to the watertightness of regions bound by trimmed NURBS surfaces, as this property is difficult to guarantee for in-the-wild CAD models. Containment is determined through the generalized winding number (GWN), a mathematical construction that is indifferent to the arrangement of surfaces in the shape. Applying contemporary techniques for the 3D GWN to trimmed NURBS surfaces requires some form of geometric discretization, introducing computational inefficiency to the algorithm and even risking containment misclassifications near the surface. In contrast, our proposed method leverages properties of the 3D solid angle to solve the relevant surface integral using a boundary formulation with rapidly converging adaptive quadrature. Batches of queries are further accelerated by memoizing (i.e., caching and reusing) quadrature node positions and tangents as they are evaluated. We demonstrate that our GWN method is robust to complex trimming geometry in a CAD model, and is accurate up to arbitrary precision at arbitrary distances from the surface. The derived containment query is therefore robust to model non-watertightness while respecting all curved features of the input shape.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Understanding and Harnessing the Robustness of Undomesticated Yarrowia lipolytica Strains for Biosynthesis of Designer Bioesters (Final Report)

This project seeks to elucidate and harness the exceptional robustness of novel and undomesticated Y. lipolytica isolates, which were identified from a genetic diversity screening for compatibility with bioenergy development. Bioenergy-relevant isolates were further developed as microbial platforms for efficient conversion of undetoxified biomass hydrolysates into designer bio-esters continuously recovered by solvent extraction. The project has three major goals. Goal 1. Elucidate and enhance the endogenous metabolism of Y. lipolytica for superior growth, sugar utilization, and lipid accumulation in undetoxified biomass hydrolysates under hypoxic conditions. Goal 2. Understand and enhance the underlying mechanism of exceptional tolerance of Y. lipolytica to organic solvents. Goal 3. Elucidate and rewire endogenous metabolism of the most robust Y. lipolytica strains for effective conversion of accumulating lipids to designer bio-esters. Significant progress has been made toward completing all research goals. We elucidated and optimized the robustness of Y. lipolytica by utilizing mixed C5 and C6 sugars in switchgrass hydrolysates (SGH) for lipid production (Aim 1). We conducted extensive omics analysis to investigate how genetic diversity among Yarrowia strains, derived from natural isolates or developed through adaptive laboratory evolution, influences lipid production when utilizing SGH. In Aim 2, novel mechanisms and underlying genetics were discovered that enabled Yarrowia strains to thrive in cultures containing high ionic liquid (IL) concentrations. Amongst other novel findings, it was found that sterols strengthened cell membranes to confer IL toxicity resistance, specifically via increased ergosterol content upon exposure to IL. In Aim 3, mechanistic studies elucidated how Y. lipolytica utilized intracellular lipids and alkanes/alkenes, leading to our discovery of novel enzymes and pathways for making short-chain esters. Most notably, thermostable chloramphenicol transferases were repurposed to function as alcohol acetyltransferases in Y. lipolytica, as well as the Gram-negative and Gram-positive bacteria Escherichia coli and thermophile Clostridium thermocellum, respectively.

09 BIOMASS FUELS↗
DOE OSTI2015

Species interactions differ in their genetic robustness

Conflict and cooperation between bacterial species drive the composition and function of microbial communities. Stability of these emergent properties will be influenced by the degree to which species' interactions are robust to genetic perturbations. We use genome-scale metabolic modeling to computationally analyze the impact of genetic changes when Escherichia coli and Salmonella enterica compete, or cooperate. We systematically knocked out in silico each reaction in the metabolic network of E. coli to construct all 2583 mutant stoichiometric models. Then, using a recently developed multi-scale computational framework, we simulated the growth of each mutant E. coli in the presence of S. enterica . The type of interaction between species was set by modulating the initial metabolites present in the environment. We found that the community was most robust to genetic perturbations when the organisms were cooperating. Species ratios were more stable in the cooperative community, and community biomass had equal variance in the two contexts. Additionally, the number of mutations that have a substantial effect is lower when the species cooperate than when they are competing. In contrast, when mutations were added to the S. enterica network the system was more robust when the bacteria were competing. These results highlight the utility of connecting metabolic mechanisms and studies of ecological stability. Cooperation and conflict alter the connection between genetic changes and properties that emerge at higher levels of biological organization.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

A model to assess the Mars Telecommunications Network relay robustness

The relatively long mission durations and compatible radio protocols of current and projected Mars orbiters have enabled the gradual development of a heterogeneous constellation providing proximity communication services for surface assets. The current and forecasted capability of this evolving network has reached the point that designers of future surface missions consider complete dependence on it. Such designers, along with those architecting network requirements, have a need to understand the robustness of projected communication service. A model has been created to identify the robustness of the Mars Network as a function of surface location and time. Due to the decade-plus time horizon considered, the network will evolve, with emerging productive nodes and nodes that cease or fail to contribute. The model is a flexible framework to holistically process node information into measures of capability robustness that can be visualized for maximum understanding. Outputs from JPL's Telecom Orbit Analysis Simulation Tool (TOAST) provide global telecom performance parameters for current and projected orbiters. Probabilistic estimates of orbiter fuel life are derived from orbit keeping burn rates, forecasted maneuver tasking, and anomaly resolution budgets. Orbiter reliability is estimated probabilistically. A flexible scheduling framework accommodates the projected mission queue as well as potential alterations.

Mars Telecommunications Orbiter (MTO)↗
NASA NTRS2021

Non-Causal Controller Approximation Methodology Augmented with Model Reference Control – Robust Design

This paper describes the development of a new control design methodology based on a non-causal controller structure and its approximation. The non-causal controller design is based on a loop shaping approach. Partial fraction decomposition is used, along with derivative approximations, to derive a proper transfer function design for a causal control structure. The premise for this non-causal control methodology is that the phase delay as a function of frequency ideally remains within approximately -90o, which tends to make the system robustly stable to uncertainties in the plant dynamics. To have a predictable control system response in lieu of the tolerance of this methodology to relatively large uncertainty in the plant dynamics, the approach is augmented with model reference control. The addition of model reference control can potentially further enhance the robustness of the design. A control system design example is presented in this paper, along with simulation results, to demonstrate the high degree of robustness of this non-causal methodology and its augmentation.

Control Systems↗
NASA NTRSDate not supplied

Robust Trajectory Optimization and GN&C Performance Analysis for NRHO Rendezvous

This paper evaluates several candidate Near-Rectilinear Halo Orbits (NRHO) rendezvous trajectory designs using linear covariance (LinCov) analysis and determines the optimal locations for NRHO rendezvous translational maneuver locations. The performance of several candidate relative trajectory designs are determined as a function of relative navigation accuracy (angles only), inertial optical navigation (OpNav), range observability maneuvers, maneuver execution errors, relative maneuver targeting, and environment uncertainties. Further, the optimal locations of rendezvous maneuvers are determined for each of the candidate reference trajectories. The long-term goal of this research is to utilize LinCov and a genetic optimization algorithm (GA) to determine a complete end-to-end optimal NRHO trajectory design that is robust to navigation errors, maneuver execution errors, and environment uncertainties. This paper represents a first step toward this goal. Three candidate rendezvous trajectories with varying numbers of range-observability maneuvers are evaluated for their robustness to uncertainties, errors, and total trajectory correction delta-v performance. Some key elements of this analysis include relative navigation performance in an NRHO, relative trajectory dispersion performance, and total 3-sigma delta-v performance. This development provides the foundation to then determine an optimal and robust end-to-end NRHO rendezvous trajectory, including the determination of the optimal locations of range observability maneuvers, if needed.

Linear Covariance Analysis↗
NASA NTRSDate not supplied

Copernicus-LinCov (COPCOV) Software Integration in Support of Robust Trajectory Optimization

Robust trajectory optimization is the process of optimizing a trajectory while accounting for system uncertainty due to a variety of potential error sources. This work highlights the development and features of a novel tool known as CopCov to support robust trajectory optimization efforts. CopCov acts as an interface between Copernicus, a generalized trajectory design and optimization tool, and LinCov, a linear covariance analysis tool. By having a direct interface between these two software packages, Copernicus can receive covariance information from LinCov through a direct feedback loop, thus enabling optimization of a trajectory that is robust to trajectory dispersions and navigation errors. This paper details the architecture of CopCov and its flexibility to operate under varying configurations, including with both tools running locally or alternatively with the tools communicating via a remote connection. Additionally, the CopCov tool is demonstrated on a simple Hohmann transfer reference trajectory with varying numbers of Trajectory Correction Maneuvers (TCMs) and varying problem formulations. This example scenario is used to highlight how the inclusion of the CopCov interface affects burn placement of both major burns and minor burns (i.e., TCMs) in the optimized solution. Results are compared against analytical solutions and against a Genetic Algorithm (GA) optimizer for independent verification and validation.

Copernicus↗
DOE OSTI2023

Robust Distribution State Estimation for Reliable Locational Marginal Pricing under Cyber-Attacks

Here this paper examines the impact of false data injection (FDI) cyber-attacks on distribution system state estimation (DSSE) and the resulting distribution locational marginal price (DLMP) in power markets. Two robust high-breakdown regression estimators, namely S- and MM- estimators, are implemented to provide resistance against FDI attacks targeting measurements and grid topology, creating leverage points. The introduced estimators are compared to the weighted least squares (WLS) with a bad data detection and rejection module (BDD) and the robust Huber M-estimator. The proposed estimators are shown to be effective and compare favorably to both existing Huber M- and the WLS with BDD in the presence of topology FDI attacks. Both the S- and MM-estimators provide good performance in the case of clean and corrupted measurements. Their performance is comparable in this case to the Huber M- and the WLS, followed by a BDD module. The simulation considered a modified distribution IEEE 13 and 34-bus systems where the impact of FDI attack scenarios is shown on the state and the DLMP pricing in the presence of distributed Generation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A robust interface finite element formulation for modeling brittle material failure problems

Abstract Failure of many brittle materials and structures can be modeled using interface‐oriented finite elements combined with intrinsic cohesive zone models. The discontinuous Galerkin (DG) finite element method provides an innovative framework for modeling brittle crack propagation with zero‐thickness interface elements, which can accommodate extrinsic cohesive laws to avoid the artificial compliance required in intrinsic cohesive models. However, robust formulations and implementations of DG methods are critical in alleviating the well‐known convergence issues for both crack nucleation and propagation with reduced instability. This paper presents a robust interface element formulation by modifying the incomplete interior penalty Galerkin (IIPG) method, which successfully avoids the initial element interface penetration across elements that occurs prior to crack nucleation, and thereby greatly reduces the instability issue as cracks open. We further verified and validated our implementation by using a bar tension test and a beam fracturing benchmark. The robustness of our proposed interface element method was demonstrated by a micromechanics fiber/matrix debonding problem with 64 fibers embedded in a bulk matrix.

Liu, Ruijie↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2021

Robust implementation of generative modeling with parametrized quantum circuits

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze, a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver’s stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires one to perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. In this work, we leverage on Rigetti’s Quantum Cloud Services (QCS™) to overcome this implementation barrier, and we study the on-hardware performance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansätze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes dataset. In this experimental benchmarking, the gradient-free optimization algorithms show an outstanding performance compared to the gradient-based solver. In particular, one of them had better performance when handling the unavoidable noisy objective function to be minimized under experimental conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Quantum theory as the most robust description of reproducible experiments

It is shown that the basic equations of quantum theory can be obtained from a straightforward application of logical inference to experiments for which there is uncertainty about individual events and for which the frequencies of the observed events are robust with respect to small changes in the conditions under which the experiments are carried out. - Highlights: • It is shown that logical inference, that is, inductive reasoning, provides a rational explanation for the success of quantum theory. • The Schrödinger equation is obtained through logical inference applied to robust experiments. • The singlet and triplet states follow from logical inference applied to the Einstein-Podolsky-Rosen-Bohm experiment. • Robustness also leads to the quantum theoretical description of the Stern-Gerlach experiment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A robust offering strategy for wind producers considering uncertainties of demand response and wind power

This paper proposes a risk-constrained decision-making approach for a wind power producer participating in the day-ahead market. In the developed model, a flexible demand response trading scheme between the wind power producer and different customers is employed. Through the proposed demand response mechanism, the wind power producer is able to trade demand response resource internally with different customers, and then trade energy externally with the market to increase the expected profit and the wind energy utilization. The uncertainties in the wind power and demand response are modeled by using the information gap decision theory approach from risk averse (robust) and risk-seeking (opportunistic) perspectives. The objective of the robust model is to maximize the robust level while satisfying the desired profit, whereas the opportunistic model aims to evaluate the possibility of achieving windfall profits with favorable uncertainties. The overall offering strategy problem is modeled as a bi-objective mixed integer nonlinear programming, which is linearized by proper techniques and solved efficiently by using the normal boundary intersection technique. In this work, simulation results show that utilizing demand response resource to mitigate wind power deviations can increase a wind power producer's profit and reduce potential risks. In addition, the results demonstrate that the proposed bi-objective optimization approach enables the wind power producer to select appropriate offering decisions with respect to uncertainties.

17 WIND ENERGY↗