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At least 127 records · Page 7

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DOE OSTI2026

Designing a Robust MEA-Based Post-Combustion Carbon Capture Process with Capture Rate Guarantees

This work presents an application of the nonlinear two-stage robust optimization solver PyROS to the model-based design and operation of a monoethanolamine scrubbing process for CO2 capture under epistemic uncertainty. Through this application, risk-averse process designs are successfully obtained for CO2 capture targets ranging from 90% to over 99%. In particular, the risk-averse solutions for CO2 capture targets of up to 98% are shown to be only marginally more expensive than their nominally optimal counterparts. Thus, the results demonstrate the utility of recently developed nonlinear robust optimization approaches for the solution of large-scale chemical process models under uncertainty.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2018

Coordination of Wind Farm and Pumped-Storage Hydro for a Self-Healing Power Grid

The increasing penetration of wind energy poses great challenges to the operation of power systems in normal and emergency states. However, energy storage technologies can help accommodate wind power uncertainty and variability due to their flexible characteristics. This paper focuses on the restoration phase, and provides a novel coordination strategy of wind and pumped-storage hydro (PSH) units for a faster and reliable self-healing process. The wind-PSH assisted power system restoration is formulated as a two-stage adaptive robust optimization problem. The first-stage problem determines the start-up sequence of generators and the energization times of transmission paths; and the second-stage problem decides load pickup sequences, wind power dispatch levels, and PSH units' operating modes. The column-and-constraint generation decomposition algorithm is applied to solve the two-stage adaptive robust optimization problem, which has a mixed-integer optimization in the inner-level problem. The developed coordination strategy is tested on the modified IEEE 39-bus system. Numerical results demonstrate that the coordinated wind and PSH units can increase the total energy served, enhance wind power dispatchability, and reduce wind power curtailment.

17 WIND ENERGY↗

DOE OSTI2022

Distributionally Robust Decision Making Leveraging Conditional Distributions

Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data. However, many practical problems call for decision-making with some auxiliary information, and DRO in the context of conditional distributions is not straightforward. We propose a conditional kernel distributionally robust optimization (CKDRO) method that enables robust decision making under conditional distributions through kernel DRO and the conditional mean operator in the reproducing kernel Hilbert space (RKHS). In particular, we consider problems where there is a correlation between the unknown variable y and an auxiliary observable variable x. Given past data of the two variables and a queried auxiliary variable, CKDRO represents the conditional distribution P(y|x) as the conditional mean operator in the RKHS space and quantifies the ambiguity set in the RKHS as well, which depends on the size of the dataset as well as the query point. To justify the use of RKHS, we demonstrate that the ambiguity set defined in RKHS can be viewed as a ball under a metric that is similar to the Wasserstein metric. The DRO is then dualized and solved via a finite dimensional convex program. The proposed CKDRO approach is applied to a generation scheduling problem and shows that the result of CKDRO is superior to common benchmarks in terms of quality and robustness.

Chen, Yuxiao↗

DOE OSTI2024

Optimal and robust reconstruction of BAO, redshift-space distortions and the Alcock-Paczynski effect

The goal of this project is to optimally reconstruct cosmological information that has been lost from large-scale clustering of galaxies due to cosmic structure growth. The outcome of our research will be used to improve dark energy and other cosmological constraints from the ongoing extended Baryon Oscillation Spectroscopic Survey (eBOSS) and the upcoming Dark Energy Spectroscopic Instrument (DESI). The large-scale galaxy clustering data contain two important features: the Baryon Acoustic Oscillations (BAO) and the overall shape from small to large scales.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

NUHOMS{sup R} EOS (Extended Optimized Storage): Robust, Advanced, and Dual Purpose Dry Storage System - 16472

AREVA TN has completed the design, analysis, and US NRC license submittal for the NUHOMS{sup R} Extended Optimized Storage (EOS) system. NUHOMS{sup R} EOS offers a large capacity and high heat load dry storage and transportation solution for UNF. EOS is not a new system, but an evolution of the already-proven safe and low-risk horizontal NUHOMS{sup R} storage system, which is one of the most widely used UNF dry storage systems in the United States. The EOS system features a 37PTH dry shielded canister (DSC) for 37 PWR UNF assemblies and an 89BTH DSC for the 89 BWR UNF assemblies. The EOS DSCs will be stored inside a horizontal storage module (HSM) that is a robust, fortress-like concrete storage overpack. Designed with the lessons learned from the Fukushima event in Japan in mind, EOS has robust margins for beyond design basis (BDB) events and offers the lowest risk and highest safety margin of any dry storage system design for recovery from BDB events. EOS also offers innovative aging management features with an aging management program (AMP) to assure safe and low dose long-term storage at the utility sites. EOS incorporates ports for AMP inspections and offers the option of using duplex stainless steel (DSS) for the DSC shell in order to negate corrosion issues. EOS is stored horizontally and above ground, making it easy and quick to access for visual inspection, monitoring, and mitigating actions during AMP. NUHOMS{sup R} EOS is optimized for high capacity, high burnup, high enrichments, and very low cooling times for UNF assemblies. It enables the highest heat load per canister (50 kW) and low dose rates. Even with high heat loads, EOS has significant margins to design basis limits. As the future of used fuel disposition will require the need to move fuel off-site to interim or consolidated storage, EOS is the ideal solution as it enables on-site loading and unloading operations that mitigate risk when transferring fuel on-site or transporting fuel off-site. The EOS design can be safely located in a traditional outdoor setting with fortress-like concrete reinforced modules that offer superior resistance to attack or can be safely operated inside of a building enclosure for those utilities desiring a more discreet siting for its used fuel storage systems. EOS has an innovative basket design that is made of high strength low alloy (HSLA) material and is easy to fabricate due to its non-welded design. Its innovative inlet and outlet air vent design, which incorporates dose reduction hardware, allows for optimal ambient airflow for highly efficient heat transfer with minimal radiation dose. The NUHOMS{sup R} EOS storage modules are interlocked in a side-by-side configuration offering superior and robust seismic performance. The license application for NUHOMS{sup R} EOS is currently under US NRC review and this system will be available for deployment in 2017. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Designing Operationally Flexible Diafiltration Membrane Systems for Critical Mineral Separations

This talk presents the design of operationally flexible diafiltration membrane systems for Li/Co recovery, and applies robust optimization methodologies using these flexible systems to generate optimal designs immune to membrane manufacturing variability and uncertain process inlet conditions. The results highlight the use of robust optimization to find design strategies and insights that may reduce technical risks arising from model uncertainties.

Yao, Jason↗

DOE OSTI2024

Treatment and Mineral Recovery from Combustion Residual Leachate and Produced Water

Able, Chad↗

DOE OSTI2017

Testing the robustness of optimal access vessel fleet selection for operation and maintenance of offshore wind farms

Optimising the operation and maintenance (O&M) and logistics strategy of offshore wind farms implies the decision problem of selecting the vessel fleet for O&M. Different strategic decision support tools can be applied to this problem, but much uncertainty remains regarding both input data and modelling assumptions. Our paper aims to investigate and ultimately reduce this uncertainty by comparing four simulation tools, one mathematical optimisation tool and one analytic spreadsheet-based tool applied to select the O&M access vessel fleet that minimizes the total O&M cost of a reference wind farm. The comparison shows that the tools generally agree on the optimal vessel fleet, but only partially agree on the relative ranking of the different vessel fleets in terms of total O&M cost. The robustness of the vessel fleet selection to various input data assumptions was tested, and the ranking was found to be particularly sensitive to the vessels' limiting significant wave height for turbine access. Also the parameter with the greatest discrepancy between the tools, implies that accurate quantification and modelling of this parameter is crucial. The ranking is moderately sensitive to turbine failure rates and vessel day rates but less sensitive to electricity price and vessel transit speed.

17 WIND ENERGY↗

NASA NTRS1996

Nickel-Cadmium Battery Operation Management Optimization Using Robust Design

In recent years following several spacecraft battery anomalies, it was determined that managing the operational factors of NASA flight NiCd rechargeable battery was very important in order to maintain space flight battery nominal performance. The optimization of existing flight battery operational performance was viewed as something new for a Taguchi Methods application.

NiCd rechargeable battery Taguchi Methods applicat↗

DOE OSTI2025

Robust A-Optimal Experimental Design for Sensor Placement in Bayesian Linear Inverse Problems

Optimal design of experiments for Bayesian inverse problems has recently gained wide popularity and attracted much attention, especially in the computational science and Bayesian inversion communities. An optimal design maximizes a predefined utility function that is formulated in terms of the elements of an inverse problem, an example being optimal sensor placement for parameter identification. The state-of-the-art algorithmic approaches following this simple formulation generally overlook misspecification of the elements of the inverse problem, such as the prior or the measurement uncertainties. This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian linear inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem. Specifically, we consider a worst-case scenario approach for the uncertain or misspecified parameters, formulate robust objectives, and propose an algorithmic approach for optimizing such objectives. Furthermore, both relaxation and stochastic solution approaches are discussed with detailed analysis and insight into the interpretation of the problem and the proposed algorithmic approach. Extensive numerical experiments to validate and analyze the proposed approach are carried out for sensor placement in a parameter identification problem.

Bayesian inverse problems↗

DOE OSTI2019

On Computation of Koopman Operator from Sparse Data

In this paper, we propose a novel approach to compute the Koopman operator from sparse time series data. In recent years there has a considerable interest in operator theoretic methods for data-driven analysis of dynamical systems and existing techniques for the approximation of the Koopman operator require rich enough data-sets. However, in many applications, the data set may not be rich enough to approximate the operators to acceptable limits. In this paper, using ideas from robust optimization, we propose an algorithm to compute the Koopman operator from sparse data. In particular, we enrich the sparse data set with artificial data points and use robust optimization techniques to obtain the transfer operator. We illustrate the efficiency of our proposed approach in three different dynamical systems, namely, a linear system, a nonlinear system and a dynamical system governed by a partial differential equation.

Sinha, Subhrajit↗

DOE OSTI2024

Robust and optimal alignment of high-dimensional data using maximum likelihood estimation through a random sample consensus framework

Abstract Correcting spatial orientations of groups of high-dimensional data sets such that they are all in a consistent coordinate system is often a time-consuming and error-prone process. Automation of this process can be accomplished by using Generalized Procrustes Analysis to estimate the relative orientations among a population of high-dimensional data sets. A least squares Procrustes solution is applied through a maximum likelihood estimation and random sample consensus framework for robustness. The likelihood model is comprised of a mixture distribution where inliers are modeled using t -distribution and outliers from a uniform distribution. Applications will focus on a synthetic data set that emulates triaxial acceleration data and also real shock data from a population of triaxial accelerometers. Outliers represent either non-rigid body responses, environmental noise, and/or sensor and data acquisition issues. The intended application for the methodology is to robustly automate the rotation of populations of experimentally collected triaxial accelerometer data sets to a single global coordinate system.

LOSAC↗

NASA NTRSDate not supplied

Generalized Linear Targeting For Cislunar Flight

An important element of Artemis and NASA’s campaign to explore the Moon is the autonomous onboard two-level targeter (TLT) used during all cislunar flight phases. The function of the TLT is to autonomously recompute the burn targets for the upcoming burn (or multiple burns) in response to navigation and vehicle dispersion providing a solution that meets all of the trajectory constraints. Although the TLT has been utilized previously as a ground-based planning tool, and flown onboard during the Artemis I mission, it’s complexity and iterative nature make is difficult to incorporate into and support rapid analyses such as robust optimal trajectory design applications where speed is essential. In this paper, a set of generalized linear targeting algorithms that mimics many of the properties of the TLT is derived. The generalized algorithms can handle single or multiple impulsive maneuvers, with multiple constraints at multiple fixed or variable times. A linear targeting algorithm for finite burn maneuvers is also derived. The generalized linear targeting algorithms are exceptionally fast and easy to implement in Monte Carlo analysis, linear covariance (LinCov) analysis, and robust optimal trajectory design. Several cislunar flight examples are provided.

Linear Covariance Analysis↗

DOE OSTI2023

Robust stellarator optimization via flat mirror magnetic fields

Stellarator magnetic configurations need to be optimized in order to meet all the required properties of a fusion reactor. In this work, it is shown that a flat-mirror quasi-isodynamic (QI) configuration (i.e. a QI configuration with sufficiently small radial variation of the mirror term) can achieve small radial transport of energy and good confinement of bulk and fast ions even if it is not very close to perfect omnigeneity, and for a wide range of plasma scenarios, including low $β$ and small radial electric field. This opens the door to constructing better stellarator reactors. On the one hand, they would be easier to design, as they would be robust against error fields. On the other hand, they would be easier to operate since, both during startup and steady-state operation, they would require less auxiliary power, and the heat loads on plasma-facing components caused by fast ion losses would be reduced to acceptable levels.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRSDate not supplied

CLAIRE: Enabling Heterogeneous Communication Network Optimization for Robust and Resilient Operations

In this paper, we present the capabilities of the CLAIRE System which provides resilient communications for NASA in presence of interference and congestion for a heterogeneous multi-vendor network. CLAIRE increases mission science data return to improve resource efficiencies and ensures resilience in the unpredictable space environment for NASA missions and communication networks. CLAIRE provides technology / waveform agnostic cognitive control plane that is instantiated at the Application Layer (APP) so that it can ride on NASA’s HDTN bundle protocol or any other protocol stack that is used by the network. The cognitive control plane is instantiated using Heartbeats (HTBTs). CLAIRE is assisted by Wideband UHF-Ka Band RF Sensing that leverages advances in the Direct Digital Transceiver (DDTRX) technology. The Wideband RF Sensing is driven by statistical signal processing and machine learning algorithms. Interference is mitigated using Dynamic Spectrum Access (DSA). Finally, CLAIRE addresses congestion using spectrum aware packet forwarding algorithm. CLAIRE provides an extensible protocol that allows passing of RF spectrum situational awareness, cross-layer sensing, delay tolerant networking and dynamic spectrum access information that can help with network optimization. Cross-Layer Sensing (CLS) and CLAIRE Decision Engine (CDE) enable spectrum and delay aware packet forwarding and Dynamic Spectrum Access during cases of severe interference.

cognitive communications↗

DOE OSTI2016

SU-F-T-215: An Investigation Of Multi-Scanner CT Hounsfield Unit Calibration for Pencil Beam Scanning Proton Therapy Using 3D Gamma Analysis

Purpose: We compare and investigate the dosimetric impacts on pencil beam scanning (PBS) proton treatment plans generated with CT calibration curves from four different CT scanners and one averaged ‘global’ CT calibration curve. Methods: The four CT scanners are located at three different hospital locations within the same health system. CT density calibration curves were collected from these scanners using the same CT calibration phantom and acquisition parameters. Mass density to HU value tables were then commissioned in a commercial treatment planning system. Five disease sites were chosen for dosimetric comparisons at brain, lung, head and neck, adrenal, and prostate. Three types of PBS plans were generated at each treatment site using SFUD, IMPT, and robustness optimized IMPT techniques. 3D dose differences were investigated using 3D Gamma analysis. Results: The CT calibration curves for all four scanners display very similar shapes. Large HU differences were observed at both the high HU and low HU regions of the curves. Large dose differences were generally observed at the distal edges of the beams and they are beam angle dependent. Out of the five treatment sites, lung plans exhibits the most overall range uncertainties and prostate plans have the greatest dose discrepancy. There are no significant differences between the SFUD, IMPT, and the RO-IMPT methods. 3D gamma analysis with 3%, 3 mm criteria showed all plans with greater than 95% passing rate. Two of the scanners with close HU values have negligible dose difference except for lung. Conclusion: Our study shows that there are more than 5% dosimetric differences between different CT calibration curves. PBS treatment plans generated with SFUD, IMPT, and the robustness optimized IMPT has similar sensitivity to the CT density uncertainty. More patient data and tighter gamma criteria based on structure location and size will be used for further investigation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-191: 4D Dose Reconstruction of Intensity Modulated Proton Therapy (IMPT) Based On Breathing Probability Density Function (PDF) From 4D Cone Beam Projection Images: A Study for Lung Treatment

Purpose: With energy repainting in lung IMPT, the dose delivered is approximate to the convolution of dose in each phase with corresponding breathing PDF. This study is to compute breathing PDF weighted 4D dose in lung IMPT treatment and compare to its initial robust plan. Methods: Six lung patients were evaluated in this study. Amsterdam shroud image were generated from pre-treatment 4D cone-beam projections. Diaphragm motion curve was extract from the shroud image and the breathing PDF was generated. Each patient was planned to 60 Gy (12GyX5). In initial plans, ITV density on average CT was overridden with its maximum value for planning, using two IMPT beams with robust optimization (5mm uncertainty in patient position and 3.5% range uncertainty). The plan was applied to all 4D CT phases. The dose in each phase was deformed to a reference phase. 4D dose is reconstructed by summing all these doses based on corresponding weighting from the PDF. Plan parameters, including maximum dose (Dmax), ITV V100, homogeneity index (HI=D2/D98), R50 (50%IDL/ITV), and the lung-GTV’s V12.5 and V5 were compared between the reconstructed 4D dose to initial plans. Results: The Dmax is significantly less dose in the reconstructed 4D dose, 68.12±3.5Gy, vs. 70.1±4.3Gy in the initial plans (p=0.015). No significant difference is found for the ITV V100, HI, and R50, 92.2%±15.4% vs. 96.3%±2.5% (p=0.565), 1.033±0.016 vs. 1.038±0.017 (p=0.548), 19.2±12.1 vs. 18.1±11.6 (p=0.265), for the 4D dose and initial plans, respectively. The lung-GTV V12.5 and V5 are significantly high in the 4D dose, 13.9%±4.8% vs. 13.0%±4.6% (p=0.021) and 17.6%±5.4% vs. 16.9%±5.2% (p=0.011), respectively. Conclusion: 4D dose reconstruction based on phase PDF can be used to evaluate the dose received by the patient. A robust optimization based on the phase PDF may even further improve patient care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-FG-CAMPUS-TeP3-01: A Model of Baseline Shift to Improve Robustness of Proton Therapy Treatments of Moving Tumors

Purpose: The shift in mean position of a moving tumor also known as “baseline shift”, has been modeled, in order to automatically generate uncertainty scenarios for the assessment and robust optimization of proton therapy treatments in lung cancer. Methods: An average CT scan and a Mid-Position CT scan (MidPCT) of the patient at the planning time are first generated from a 4D-CT data. The mean position of the tumor along the breathing cycle is represented by the GTV contour in the MidPCT. Several studies reported both systematic and random variations of the mean tumor position from fraction to fraction. Our model can simulate this baseline shift by generating a local deformation field that moves the tumor on all phases of the 4D-CT, without creating any non-physical artifact. The deformation field is comprised of normal and tangential components with respect to the lung wall in order to allow the tumor to slip within the lung instead of deforming the lung surface. The deformation field is eventually smoothed in order to enforce its continuity. Two 4D-CT series acquired at 1 week of interval were used to validate the model. Results: Based on the first 4D-CT set, the model was able to generate a third 4D-CT that reproduced the 5.8 mm baseline-shift measured in the second 4D-CT. Water equivalent thickness (WET) of the voxels have been computed for the 3 average CTs. The root mean square deviation of the WET in the GTV is 0.34 mm between week 1 and week 2, and 0.08 mm between the simulated data and week 2. Conclusion: Our model can be used to automatically generate uncertainty scenarios for robustness analysis of a proton therapy plan. The generated scenarios can also feed a TPS equipped with a robust optimizer. Kevin Souris, Ana Barragan, and Dario Di Perri are financially supported by Televie Grants from F.R.S.-FNRS.

60 APPLIED LIFE SCIENCES↗