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Results for “robust design”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗
DOE OSTI2026

Robust Design Under Uncertainty in Quantum Error Mitigation

Error mitigation techniques are crucial to achieving near-term quantum advantage. Classical postprocessing of quantum computation outcomes is a popular approach for error mitigation, which includes methods, such as zero noise extrapolation, virtual distillation, and learning-based error mitigation. However, these techniques have limitations due to the propagation of uncertainty resulting from the finite shot number of a quantum measurement. In this work, we introduce general and unbiased methods for quantifying the uncertainty and error of error-mitigated observables based on the strategic sampling of error mitigation outcomes. We then extend our approach to demonstrate the optimization of performance and robustness of error mitigation under uncertainty. To illustrate our methods, we apply them to zero noise extrapolation and Clifford date regression in the ground state of the XY model simulated using depolarizing and International Business Machines Corporation (IBM) Toronto noise models, respectively. In particular, we optimize the choice of noise levels and the allocation of shots for zero noise extrapolation and the distribution of the training circuits for Clifford data regression. While our methods are readily applicable to any postprocessing-based error mitigation approach, in practice they must not be prohibitively expensive—even though they perform optimizations of the error mitigation hyperparameters requiring sampling of a statistical distribution of error mitigation outcomes. By leveraging surrogate-based optimization, we show that our methods can efficiently perform optimal design for a zero noise extrapolation implementation. We then further demonstrate the transferability of learned zero noise extrapolation hyperparameters to other similar circuits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Robust design of semi-automated clustering models for 4D-STEM datasets

Materials discovery and design require characterizing material structures at the nanometer and sub-nanometer scale. Four-Dimensional Scanning Transmission Electron Microscopy (4D-STEM) resolves the crystal structure of materials, but many 4D-STEM data analysis pipelines are not suited for the identification of anomalous and unexpected structures. This work introduces improvements to the iterative Non-Negative Matrix Factorization (NMF) method by implementing consensus clustering for ensemble learning. We evaluate the performance of models during parameter tuning and find that consensus clustering improves performance in all cases and is able to recover specific grains missed by the best performing model in the ensemble. The methods introduced in this work can be applied broadly to materials characterization datasets to aid in the design of new materials.

Bruefach, Alexandra (ORCID:0000000209323477)↗
DOE OSTI2024

Identifiability and characterization of transmon qutrits through Bayesian experimental design

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Robust Parameter Design on Dual Stochastic Response Models With Constrained Bayesian Optimization

In engineering system design, minimizing the variations of the quality measurements while guaranteeing their overall quality up to certain levels, namely the robust parameter design (RPD), is crucial. Recent works have dealt with the design of a system whose response-control variables relationship is a deterministic function with a complex shape and function evaluation is expensive. In this work, we propose a Bayesian optimization method for the RPD of stochastic functions. Dual stochastic response models are carefully designed for stochastic functions. The heterogeneous variance of the sample mean is addressed by the predictive mean of the log variance surrogate model in a two-step approach. We establish an acquisition function that favors exploration across the feasible and optimality-improvable regions to effectively and efficiently solve the stochastic constrained optimization problem. Further, the performance of our proposed method is demonstrated by the extensive numerical and case studies. Note to Practitioners-Many manufacturing processes involve undesirable variations, which create variations in the final products. For example, many emerging manufacturing processes, such as nanomanufacturing, involve complex physical and chemical dynamics and transformation, creating variations in the manufacturing output. In such processes, it is crucial to design the manufacturing processes or products so that they have minimum variations in their quality. Meanwhile, it is also important to maintain the overall quality of the designed processes or products. Furthermore, acquiring data from many advanced manufacturing processes is often very costly, especially in the designing stage. In this work, we propose a data-driven method that automatically finds the best setting of manufacturing processes or products with the minimum variations of quality and a given constraint on the average quality satisfied. Our proposed method is used before conducting every experiment; It analyzes the historical data from previous experiments and provides a setting to be used in the next experiment. Our proposed method efficiently utilizes the historical data, and thus finds the best robust setting by conducting only a small number of experiments.

42 ENGINEERING↗
DOE OSTI2026

A robust alloy design (RAD) strategy for next-generation (IV) nuclear fission reactors

Next-generation nuclear reactors demand structural materials capable of withstanding extreme conditions, including high temperatures, intense neutron flux, and corrosive environments. Multi-Principal Element Alloys (MPEAs) have emerged as promising candidates due to their exceptional radiation tolerance, thermal stability, and compositional flexibility. This study introduces a versatile and customizable Robust Alloy Design (RAD) strategy for systematically designing MPEAs for GEN-IV reactor fuel cladding. The RAD framework integrates nuclear-relevant selection criteria, empirical parameter assessments, and high-throughput CALPHAD simulations to efficiently narrow compositional space and identify stable alloys. A unified RAD score developed for the first time, combines key performance metrics, including fuel-clad chemical interaction (FCCI), neutron absorption cross-section (NAC), valence electron configuration (VEC), and melting point factor (MPF), into a flexible ranking system adaptable to reactor-specific priorities. Among 724 candidates, V555(5Al–5Cr–5Fe–85V) emerged as the top alloy, validated experimentally with a homogeneous single-phase BCC microstructure and superior mechanical properties (nano-indentation: 3.389 ± 0.258 GPa; Vickers hardness: 240 ± 6.7 HV), significantly outperforming Zircaloy-4 and V-4Cr-4Ti. Importantly, the RAD strategy is not limited to nuclear applications; its customizable weighting system enables scalability to other extreme environments. This adaptability positions RAD strategy as a versatile tool for advanced materials design across multiple industries.

Alloy design↗
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 CO<sub>2</sub> capture under epistemic uncertainty. Through this application, risk-averse process designs are successfully obtained for CO<sub>2</sub> capture targets ranging from 90% to over 99%. In particular, the risk-averse solutions for CO<sub>2</sub> 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 OSTI2023

Designing noise-robust quantum networks coexisting in the classical fiber infrastructure

The scalability of quantum networking will benefit from quantum and classical communications coexisting in shared fibers, the main challenge being spontaneous Raman scattering noise. We investigate the coexistence of multi-channel O-band quantum and C-band classical communications. We characterize multiple narrowband entangled photon pair channels across 1282 nm-1318 nm co-propagating over 48 km of installed standard fiber with record C-band power (>18 dBm) and demonstrate that some quantum-classical wavelength combinations significantly outperform others. We analyze the Raman noise spectrum, optimal wavelength engineering, multi-photon pair emission in entangled photon-classical coexistence, and evaluate the implications for future quantum applications.

Optics↗
DOE OSTI2021

Design Status Update of the Electron-Ion Collider

The design of the electron-ion collider EIC to be constructed at Brookhaven National Laboratory has been continuously evolving towards a realistic and robust design that meets all the requirements set forth by the nuclear physics community in the White Paper. Over the past year activities have been focused on maturing the design, and on developing alternatives to mitigate risk. These include improvements of the interaction region design as well as modifications of the hadron ring vacuum system to accommodate the high average and peak beam currents. Beam dynamics studies have been performed to determine and optimize the dynamic aperture in the two collider rings and the beam-beam performance. We will present the EIC design with a focus on recent developments.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Design of robust and versatile hydrocarbon-based single-ion-conducting polymer electrolytes

Hydrocarbon-based polymers offer several advantages, including lower environmental impacts, cost effectiveness, and the ability to finely tune properties. Here, we have developed trifluoromethanesulfonimide (TFSI)-functionalized poly(norbornene) (PNB) polymers utilizing a specifically designed oxa-Michael addition of a vinyl TFSI anion to an alcohol. Our results reveal that PNB-TFSI derivatives exhibit superior thermal stability and mechanical robustness compared with Nafion. The optimized PNB-TFSI-H-48 polymer (IEC 1.86 mmol/g) exhibits equivalent performance to Nafion as an anode ionomer in a proton exchange membrane fuel cell. Exchanging the counter ion to Li + enables PNB-TFSI to be used for Li-ion battery applications. Propylene carbonate plasticized PNB-TFSI derivatives achieve an Li-ion conductivity of over 10 −5 S/cm at 30°C. This Li polymer electrolyte exhibits excellent electrochemical stability (5 V vs. Li + /Li) and good cycling in a Li symmetric cell. These results highlight the potential and rational design of PNB-TFSI polymers for next-generation energy storage and conversion technologies.

08 HYDROGEN↗
DOE OSTI2021

BETO 2021 Peer Review - Rational Design of Robust Reactor Feeding Systems for Heterogeneous Cellulosic and Agricultural Wastes Based on Biomass Quality Characteristics

Consistent and reliable preprocessing, conveyance, and reactor in-feed systems, particularly for low-cost waste feedstocks, remains a major technical challenge for the emerging Bioeconomy. By identifying critical biomass attributes and connecting them to flow and conversion behavior, science-driven system designs can address these often-overlooked solids handling challenges. The Wonderful Company (TWC) is the world's largest almond and pistachio grower, generating 250,000 dry tons/year of waste material including hulls, shells, and wood (>5 million tons/year industry wide in the U.S.). This project seeks to turn this environmental and economic liability into a sustainable and profitable resource, targeting conversion via gasification to syngas for electricity and bio-char, by addressing related material handling and feeding challenges. Based on optimized preprocessing strategies, bulk material flow, and thermal conversion properties, an overall system design will be developed. The methodology will be tested with FCIC's benchmark loblolly pine residues, to demonstrate the robustness of the overall approach and provide insight and guidance for future systems. The project will culminate in extended field trials to demonstrate an improved continuous feeding system with a commercial biomass-to-electricity gasifier vendor, and an economic analysis demonstrating a reduction in electricity production costs by maximizing on-stream time while minimizing preprocessing and CapEx costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

New Design Yields Robust Large-Area Framing Camera

Framing cameras provide a discrete series of images over a short period of time in a manner that mimics a high-speed movie camera, but with individual shutter times that must be extremely short in order to capture freeze-frame images of rapidly evolving phenomena such as high-explosive shock-driven ejecta, dynamic compression of metals, and high-velocity fluid flow. The NNSS has designed and fielded a new, large-area, gated framing camera called Kraken. The camera design emphasized manufacturability and flexibility by improving imager yield and creating a camera architecture to shorten design cycles. Design strategies and field data are presented.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Integrated Multiscale Model for Design of Robust 3D Solid-state Lithium Batteries

In FY23, we successfully established the multiscale modeling framework for probing the effects of materials microstructure on cell performance of 3D solid-state batteries. The framework covers physicochemical processes co-evolving at the atomistic and microstructure scales. Our simulations revealed the mechanism of initial interfacial degradation, formation of secondary phases, and the structure-property relationship for ion transport and mechanical stability at the interface. In addition, we also established the microstructure-performance relationships by performing sensitivity tests of various microstructure features and extracting their impact on cell performance during charge-discharge cycles. We have successfully applied our multiscale, multiphysics modeling capability to common electrode and electrolyte materials that are of interests to VTO and the experimental teams within the US-Germany collaboration. The insights we obtained from these simulations provide valuable design principles to optimize materials properties for advanced 3D solid-state batteries.

25 ENERGY STORAGE↗