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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Symmetric Rydberg controlled- Z gates with adiabatic pulses

We analyze neutral atom Rydberg C Z gates based on adiabatic pulses applied symmetrically to both atoms. Analysis with smooth pulse shapes and Cs atom parameters predicts the gates can create Bell states with fidelity F > 0.999 using adiabatic rapid passage (ARP) pulses. With globally optimized adiabatic pulse shapes, in a two-photon excitation process, we generate Bell states with fidelity F = 0.997. The analysis fully accounts for spontaneous emission from intermediate and Rydberg states, including the Rydberg lifetime in a room temperature environment, but does not include errors arising from laser noise. In conclusion, the gate protocols do not require individual addressing and are shown to be robust against Doppler shifts due to atomic motion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

A Measurement-Based Adaptive Voltage Regulation Method Considering Topology Changes

This paper proposes an online adaptive data-driven distributed energy resource (DER) dispatch optimization method for voltage control considering topology changes. By using a local sensitivity factor (LSF)-enabled voltage control, traditional DER control can be reformulated into a linear programming (LP) problem, leading to faster computation speeds. Power injection alteration and topology changes are two common operational changes in the distribution network that can affect the LSF and voltage control performance. To address this issue, a robust estimation method is developed to adjust the sensitivity matrix at each time step for the time-varying power injection changes. When topology changes occur, only the allocated predominant LSF submatrices are updated based on measurement data, allowing for a fast adaptation to the system reconfiguration. Results obtained from a real distribution feeder in Southern California demonstrate its robustness as compared to traditional volt-var control and constant LSF matrix dispatch control methods.

DERs↗
DOE OSTI2020

Scalable Open-Air Deposition of Compact ETL TiOx on Perovskite for Fullerene-Free Solar Cells

We report on a scalable, low-temperature TiOx electron transport layer (ETL) produced directly on top of perovskite in an inverted architecture device by an open-air chemical vapor deposition (OA-CVD) process. The OA-CVD pyrolysis of a titanium ethoxide precursor forms a fully inorganic, conformal, and compact TiOx film. The resultant film is mechanically robust and exhibits a fracture toughness 30-fold higher than commonly used fullerene-based ETL films. The TiOx is an effective barrier layer against environmental degradation—unencapsulated devices with the TiOx ETL retain 60% of their initial efficiency after 1000 hours in ambient aging conditions—compared to devices with a fullerene ETL which were inoperable after 144 hours. Inverted architecture devices with an optimized TiOx ETL thickness of 70nm exhibited high open-circuit voltages of 1.08V. We comment on the photocatalytic properties of TiOx and the influence of an amorphous film on maximum power point stability.

Chen, Justin↗
DOE OSTI2022

Column separation of tetravalent cerium fission products from trivalent rare earth radio-isotopes

Rapid and efficient isolation of individual rare earth element (REE) radioisotopes from complex mixtures is necessary to support the fields of nuclear forensics, medical isotope production and nuclear physics measurements. The separations must be robust and generate sufficiently high purity samples for subsequent radiological analysis. Current methodologies utilize a laborious two step Ce(IV)-iodate precipitation followed by Ce(IV)-nitrate di-(2-ethylhexyl)phosphoric acid/n-heptane extraction. This work reports an alternative method to isolate Ce fission product isotopes equilibrated with milligrams of cold carrier, from the remaining REE’s using a Ce(IV)/LN-resin (Eichrom Technologies) based separation. Optimization of Ce(IV) loading, REE(III) elution volume and subsequent Ce(III) stripping step utilized 166m Ho tracer. Mixed fission product experiments provided a further test of this LN-resin method, with analysis of purified 141/144 Ce, 147 Nd and 91 Y. Overall, while decontamination factors were lower for the LN-resin method, (vs. the combined precipitation/extraction) the simplicity of the technique and the satisfactory fission product analysis results point to the efficacy of this method.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Accelerating error correction in tomographic reconstruction

Abstract Spurred by recent advances in detector technology and X-ray optics, upgrades to scanning-probe-based tomographic imaging have led to an exponential growth in the amount and complexity of experimental data and have created a clear opportunity for tomographic imaging to approach single-atom sensitivity. The improved spatial resolution, however, is highly susceptible to systematic and random experimental errors, such as center of rotation drifts, which may lead to imaging artifacts and prevent reliable data extraction. Here, we present a model-based approach that simultaneously optimizes the reconstructed specimen and sinogram alignment as a single optimization problem for tomographic reconstruction with center of rotation error correction. Our algorithm utilizes an adaptive regularizer that is dynamically adjusted at each alternating iteration step. Furthermore, we describe its implementation in a software package targeting high-throughput workflows for execution on distributed-memory clusters. We demonstrate the performance of our solver on large-scale synthetic problems and show that it is robust to a wide range of noise and experimental drifts with near-ideal throughput.

Ali, Sajid (ORCID:0000000321864636)↗
DOE OSTI2022

Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods

Deep-learning models have been increasingly exploited in astrophysical studies, but these data-driven algorithms are prone to producing biased outputs that are detrimental for subsequent analyses. In this work, we investigate two main forms of biases: class-dependent residuals, and mode collapse. We do this in a case study, in which we estimate photometric redshift as a classification problem using convolutional neural networks (CNNs) trained with galaxy images and associated spectroscopic redshifts. We focus on point estimates and propose a set of consecutive steps for resolving the two biases based on CNN models, involving representation learning with multichannel outputs, balancing the training data, and leveraging soft labels. The residuals can be viewed as a function of spectroscopic redshift or photometric redshift, and the biases with respect to these two definitions are incompatible and should be treated individually. We suggest that a prerequisite for resolving biases in photometric space is resolving biases in spectroscopic space. Experiments show that our methods can better control biases than benchmark methods, and they are robust in various implementing and training conditions with high-quality data. Our methods hold promises for future cosmological surveys that require a good constraint of biases, and they may be applied to regression problems and other studies that make use of data-driven models. Nonetheless, the bias-variance tradeoff and the requirement of sufficient statistics suggest that we need better methods and optimized data usage strategies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Neural operator transformers capture bifurcating drift-wave turbulence in fusion plasma simulations

Self-consistent modeling of turbulence-driven transport is critical for optimizing confinement in magnetically confined fusion plasmas, such as tokamaks and stellarators. In particular, capturing the long-term co-evolution of turbulence, flow, and background plasma profiles remains computationally challenging. Direct numerical simulation of these multiscale, highly nonlinear processes is often demanding and impractical for real-time control or design optimization. To address this bottleneck, we investigate transformer-based neural operator partial differential equation surrogates for emulating the dynamics of drift-wave turbulence bifurcation mediated by zonal flows, using the modified Hasegawa–Wakatani (MHW) model as a prototypical system. We find that the finetuned neural operator model has excellent performance in capturing the multi-spatiotemporal-scales of MHW turbulence bifurcation and is robust to testing on rare and out-of-distribution dynamics. Specifically, we demonstrate that a single unified model accurately predicts both quasi-steady-state turbulence and a wide range of dynamical transition processes, such as nonlinear saturation, spontaneous suppression of turbulence, and the emergence of macroscopic zonal flows, over time horizons vastly exceeding the local turbulence correlation time. This computationally efficient approach establishes a strong foundation for fast, AI-based modeling of complex, multiscale phenomena in magnetized fusion plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Polarization transmission in the Hadron Storage Ring of the Electron-Ion Collider

The successful operation of the future Electron-Ion Collider is contingent on maintaining high hadron beam polarization up to 275 GeV. The Hadron Storage Ring lattice, however, features a symmetry-breaking interaction region that excites strong, nonsystematic spin resonances, posing a significant threat to polarization preservation. This paper systematically investigates two complementary strategies to ensure high polarization transmission. The first method involves optimizing the vertical betatron phase advance between Siberian snakes to orchestrate a cancellation of depolarizing kicks across the ring. We demonstrate through simulations that both dynamic and fixed-optics solutions based on this principle can successfully preserve polarization through the strongest resonances. The second, more powerful approach involves optimizing the snake rotation axes to suppress resonance driving terms at their source. We revisit established symmetric configurations, such as the Lee-Courant schemes, and introduce a novel, highly symmetric “Doubly Lee-Courant” (DLC) scheme, which enforces a local 𝜋 spin phase advance across every consecutive pair of snakes. Our analysis reveals a clear performance hierarchy, with the DLC configuration providing an exceptionally robust and energy-insensitive baseline for polarization preservation. We conclude that a hybrid strategy, using a DLC snake scheme as a symmetric foundation and betatron phase tuning for fine corrections, offers the most effective path forward for the EIC and future high-energy polarized-beam facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Adaptive Reinforcement Learning Control for Power Distribution in Multi-Output Resonant Converters

This paper presents an adaptive reinforcement learning (ARL)-based control framework for efficient power distribution in a multi-output resonant converter for UAV applications. The proposed system is based on a high-frequency isolated resonant architecture, where a single energy source supplies multiple propulsion loads through independently controlled output rectifiers, addressing the need for coordinated multi-motor power management. The ARL framework dynamically allocates output power by learning optimal phase-shift control actions under varying load demands and operating conditions. The agent autonomously determines control parameters that maximize conversion efficiency while ensuring accurate power sharing among multiple outputs. In addition, the proposed approach enables adaptive operation without requiring detailed system modeling or manual tuning. Experimental results demonstrate stable and efficient performance over a wide range of operating conditions, confirming the effectiveness and robustness of the learning-based control strategy for multi-output resonant converter system.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
DOE OSTI2023

Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control

Load frequency control (LFC) is a key factor to maintain the stable frequency in multi-area power systems. As the modern power systems evolve from centralized to decentralized paradigm, LFC needs to consider the decentralized scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing the convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.

Kwon, Kyung-bin↗
DOE OSTI2021

Comparison of temperature adaptive calibration methods for laser induced fluorescence based fuel-in-oil instrument

Fuel dilution of engine oil (or fuel-in-oil, FiO) is an important issue as multiple and late-cycle fuel injection, integral to many combustion efficiency and emissions improvements (e.g., downsized boosted gasoline engines and catalyst thermal management) increases FiO rate. In addition to causing general wear and corrosion in engine due to decreased oil viscosity and pH buffering, FiO is also believed to cause destructive low speed pre-ignition (or super knock) in boosted SI engines. To understand the effects of engine operating conditions on the FiO rate, an optical diagnostic capable of measuring transient FiO on minute timescales was recently developed and demonstrated on a modified GM Ecotech engine system (Neupane et al., Applied Spectroscopy 2021). The measurement is based on adding a dye to the fuel and monitoring for its presence in oil via laser-induced fluorescence (LIF). The measured LIF signal is related to FiO concentration via pre-determined calibration factors using a multivariate classical least square (CLS) method.Since fluorescence quantum yield is a function of temperature, measured LIF intensity not only depends on FiO concentration but also oil temperature. To expand the applicability of the FiO diagnostic to transient oil-temperature conditions (e.g., cold start in practical engines), this study develops a method to account for oil-temperature variations. The effect of oil temperature (20°C - 95°C) on the LIF spectra of eight FiO samples ranging from ~0.8-15% was investigated. LIF intensities of the FiO samples decreased linearly with increasing temperature; the reductions being more significant at dye peaks. We develop a new calibration model (T-adaptive CLS) incorporating the temperature (T) effects on LIF intensity that enables simultaneous calculation of FiO and oil temperature. The improved FiO diagnostic with T-adaptive calibration is more robust, and applicable to varying oil-temperature conditions. For example, when strategies such as multiple/late fuel injections are applied to overcome cold-start instability due to use of low vaporization bio-based fuels such as ethanol, FiO rate is expected to be very high; the improved T-adaptive FiO diagnostic is hence relevant for engine- and fuel-system calibration and optimization. The diagnostic could also provide validation data for flow-field and spray interaction CFD models, further broadening the diagnostic’s utility for advancing engine technology and efficiency.

Neupane, Sneha↗
DOE OSTI2023

Communication-Constrained Robust Control and Learning of Grid-Connected

The electric grid of things (EGoT) promises great potential for innovative grid services by tapping into vast load flexibility. However, the unique characteristics of EGoT, being a part of the cyber-physical electric power system, present both opportunities and challenges, especially concerning supply-demand balancing, stability, and communication constraints. Traditionally, centralized control was employed to ensure balance and stability in power systems. However, with the massive influx of EGoT devices, new strategies are needed to efficiently coordinate and control these distributed devices for optimal grid operations. While some studies have explored efficiency and economic models, there remains a gap in ensuring reliability under everyday operations and resilience during extreme conditions. Addressing this gap, this project develops the technology for an Energy Service Interface (ESI) that includes novel pricing, control, learning, and distributed optimization algorithms, which will enable utilities to recruit EGoT assets for crucial grid services such as load flexibility, voltage regulation, and situation-awareness. The key novelty of the proposed technology is the careful distribution of learning and control functions across utility and EGoT asset owners such that provably efficient and resilient grid operations are attained while respecting communication and information-exchange constraints. Specifically, the project team develops machine-learning enhanced load modeling methods to allow EGoT asset owners to learn their load capability and flexibility, and develops pricing-based and decentralized learning-based control so that asset owners can coordinate to meet system-wide demand-supply balance and reliability goals. For extreme situations involving high-impact, low-probability catastrophic events (termed the “black-sky” events), the team also develops (1) a “Feeder-Operating Center-on-a-Laptop” (FOCAL) software that can assist utility personnel in leveraging EGoT assets to accelerate the service recovery of damaged feeders, and (2) distributed optimization algorithms that can coordinate the operation points of EGoT devices under severe communication constraints. The proposed technology has been extensively tested and evaluated through simulations and on a testbed. In summary, as we transition into a more interconnected and digital power grid era, our project’s findings and developments offer a pivotal step toward guaranteeing both efficiency and resilience in the face of both everyday operations and rare “black-sky” events.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Advanced All-Fluorinated Electrolytes for Extended Cycle Life and Stability of Li||SPAN Batteries

Achieving long-term stability and consistent capacity in lithium (Li) metal batteries with sulfurized polyacrylonitrile (SPAN) cathodes requires precisely engineered electrolytes to optimize interphase formation and redox reversibility. Here, this study presents 1,1-difluoro-2-(2-methoxyethoxy)ethane (DFE)-based localized high-concentration electrolytes (LHCEs), incorporating fluorinated components such as salt, solvating solvent, and diluent for improved electrode stability. Molecular dynamics simulations and surface analyses reveal that the DFE-LHCE with 1,2-bis(1,1,2,2-tetrafluoroethoxy)ethane (BTFEE) diluent produces uniform and robust interphase layers on both cathode and anode, enriched with inorganic species like LiF and Li 2 O. These properties lead to prolonged redox reversibility of the SPAN cathode, suppressed side reactions, and extended cycle life for Li||SPAN cells. Remarkably, DFE-BTFEE-LHCE enables Li||SPAN coin cells with an areal capacity of ∼7 mAh cm -2 for SPAN to retain 81.3% capacity after 200 cycles and pouch cells of 0.12 Ah with 8 mAh cm -2 of SPAN and lean electrolyte to maintain 96.4% capacity over 80 cycles. These findings pave the way for advancing Li||SPAN battery technologies.

25 ENERGY STORAGE↗
DOE OSTI2020

Inverse-Designed Photonics for Semiconductor Foundries

Silicon photonics is becoming a leading technology in photonics, displacing traditional fiber optic transceivers and enabling new applications. Further improving the density and performance of silicon photonics, however, has been challenging due to the large size and limited performance of traditional semianalytically designed components. Automated optimization of photonic devices using inverse design is a promising path forward but has, until now, faced difficulties in producing designs that can be fabricated reliably at scale. In this paper we experimentally demonstrate four inverse-designed devices made successfully in a commercial silicon photonics foundry: a spatial mode multiplexer, wavelength demultiplexer, 50–50 directional coupler, and 3-way power splitter. These devices are efficient, robust to fabrication variability, and compact, with footprints only a few micrometers across. They pave the way forward for the widespread practical use of inverse design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Scalable open-air deposition of compact ETL TiO x on perovskite for fullerene-free solar cells

We report a scalable, low-temperature TiO x electron transport layer (ETL) produced directly on top of perovskite in an inverted architecture device by an open-air chemical vapor deposition (OA-CVD) process. The OA-CVD pyrolysis of a titanium ethoxide precursor forms a fully inorganic, conformal, and compact TiO x film. The resultant film is mechanically robust and exhibits a fracture toughness 30-fold higher than commonly used fullerene-based ETL films. The TiO x is an effective barrier layer against environmental degradation—unencapsulated devices with the TiO x ETL retain 60% of their initial efficiency after 1000 hours in ambient aging conditions—compared to devices with a fullerene ETL which were inoperable after 144 hours. Inverted architecture devices with an optimized TiO x ETL thickness of 70 nm exhibited high open-circuit voltages of 1.08 V. We comment on the photocatalytic properties of TiO x and the influence of an amorphous film on maximum power point stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Traffic Signal Control With Adaptive Online-Learning Scheme Using Multiple-Model Neural Networks

This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Streamlining the Coupling of BISON and Dakota Through the NEAMS Workbench

Metallic nuclear fuels for use in advanced reactors are an active area of research and development. Robust, accurate metallic fuel performance models are necessary for the design, analysis, and licensing of such reactors. However, metallic fuel performance models require additional development; they are not as mature as uranium dioxide fuel performance models. To support further metallic fuel development, Oak Ridge National Laboratory and the University of Florida have streamlined the coupling of the BISON fuel performance code with Design Analysis Kit for Optimization and Terascale Applications (Dakota) statistical analysis tool through the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Workbench. This work included performing three different sensitivity analyses on metallic nuclear fuel models in BISON. The analyses examined were a general model of the IFR-1 experiment, the X430 experiment T654 pin, and the X430 experiment T651 pin. The results suggest that BISON and Dakota can be integrated through NEAMS Workbench to perform sensitivity and uncertainty analyses and visualize the results.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗