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Results for “fast 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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Neutron-Friendly Li-Ion Battery Coin Cell for In Situ 3D Visualization of Li Plating

Advanced battery characterization using in situ/operando neutron imaging is critical for uncovering degradation modes such as lithium (Li) plating in Li-ion batteries (LIBs). However, conventional LIBs hinder operando neutron radiography (NR) and in situ neutron micro-computed tomography (N-μCT) for visualizing Li plating near the graphite-separator interface due to strong attenuation from hydrogen-rich components like PP–PE–PP separators, electrolyte, and Fe-based spacers. In this work, we designed and tested a neutron-friendly battery (NFB) optimized for in situ Li detection during extreme fast charging (XFC). Guided by neutron attenuation cross-sections and material transmission, the NFB enables clear visualization at the graphite–separator interface, which is typically opaque in standard LIBs. Electrochemical tests show the NFB exhibits voltage/current responses like standard cells for up to 50 XFC cycles. However, its lower reversibility and capacity are likely due to Cu-coated Al spacer degradation from delamination or corrosion. We propose titanium spacers as a more stable alternative, albeit requiring custom machining. Using this optimized cell, we achieved simultaneous neutron tomography of multiple cells, capturing in situ 3D images of dead Li accumulation, particularly near graphite edges. These heterogeneous deposits and disconnected Li clusters suggest localized current density hotspots during XFC.

25 ENERGY STORAGE↗
DOE OSTI2024

Fast and accurate algorithm for calculating long-baseline neutrino oscillation probabilities with matter effects

Neutrino oscillation experiments will be entering the precision era in the next decade with the advent of high statistics experiments like DUNE, HK, and JUNO. Correctly estimating the confidence intervals from data for the oscillation parameters requires very large Monte Carlo datasets involving calculating the oscillation probabilities in matter many, many times. In this paper, we leverage past work to present a new, fast, precise technique for calculating neutrino oscillation probabilities in matter optimized for long-baseline neutrino oscillations in the Earth’s crust including both accelerator and reactor experiments. For ease of use by theorists and experimentalists, we provide fast ++ and codes . Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Learning protocols for the fast and efficient control of active matter

Exact analytic calculation shows that optimal control protocols for passive molecular systems often involve rapid variations and discontinuities. However, similar analytic baselines are not generally available for active-matter systems, because it is more difficult to treat active systems exactly. Here we use machine learning to derive efficient control protocols for active-matter systems, and find that they are characterized by sharp features similar to those seen in passive systems. We show that it is possible to learn protocols that effect fast and efficient state-to-state transformations in simulation models of active particles by encoding the protocol in the form of a neural network. We use evolutionary methods to identify protocols that take active particles from one steady state to another, as quickly as possible or with as little energy expended as possible. Our results show that protocols identified by a flexible neural-network ansatz, which allows the optimization of multiple control parameters and the emergence of sharp features, are more efficient than protocols derived recently by constrained analytical methods. Our learning scheme is straightforward to use in experiment, suggesting a way of designing protocols for the efficient manipulation of active matter in the laboratory.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries

Abstract Advances in the field of goal-directed molecular optimization offer the promise of finding feasible candidates for even the most challenging molecular design applications. One example of a fundamental design challenge is the search for novel stable radical scaffolds for an aqueous redox flow battery that simultaneously satisfy redox requirements at the anode and cathode, as relatively few stable organic radicals are known to exist. To meet this challenge, we develop a new open-source molecular optimization framework based on AlphaZero coupled with a fast, machine-learning-derived surrogate objective trained with nearly 100,000 quantum chemistry simulations. The objective function comprises two graph neural networks: one that predicts adiabatic oxidation and reduction potentials and a second that predicts electron density and local three-dimensional environment, previously shown to be correlated with radical persistence and stability. With no hard-coded knowledge of organic chemistry, the reinforcement learning agent finds molecule candidates that satisfy a precise combination of redox, stability and synthesizability requirements defined at the quantum chemistry level, many of which have reasonable predicted retrosynthetic pathways. The optimized molecules show that alternative stable radical scaffolds may offer a unique profile of stability and redox potentials to enable low-cost symmetric aqueous redox flow batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Deep reinforcement learning for optimal control of induction welding process

Optimizing induction welding (IW) process parameters for the application of joining thermoplastic composites is challenging as it requires achieving complex spatiotemporal thermal characteristics along the weld-line to obtain desired weld quality. We formulate an optimal control problem which captures these requirements and seeks to optimize the IW coil speed using a fast-acting dynamic IW process model. We develop a novel Deep Reinforcement Learning (DRL) framework to solve this computationally challenging control problem and demonstrate via simulation study that the learned DRL feedback control policy results in better spatiotemporal thermal characteristics as compared to the current state-of-the-art.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Piecewise Linear Optimization for Public EV Charging Depots in Different Utility Environments

With the proliferation of electric vehicles (EVs), EV supply equipment technology is being pushed to increasingly higher power levels, such as extreme fast charging (XFC) at 300kW or more. As more public XFC depots are connected to electric distribution systems, utility operators will mitigate their impact with pricing schemes and programs such as demand response, demand charges, and time-varying prices. In this paper we describe an XFC management system that combines an on-site battery energy storage system and an optimization application that minimizes the cost to operate the depot in light of the utility pricing programs. We have designed the optimizer specially to use fast and simple linear programming so that it can be deployed to low-cost computing platforms. We also describe how several utility pricing schemes are integrated into a single piecewise model, reducing the engineering effort to implement the optimizer in different utility environments.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Testing fast reactor fuels in a thermal reactor: Comparison of transmutation metallic fuel alloys behavior by scanning electron microscopy

To optimize nuclear waste repository performance, the destruction of minor actinide elements, particularly Np and Am, in a neutron fast spectrum reactor is possible by incorporating these elements into nuclear fuel. Evaluating the performance of minor actinide containing fuel is of paramount importance to enabling this technology. However, such a task is challenging without an available domestic fast spectrum test reactor. A comparison of fuel performance tested in an available domestic thermal reactor at the Idaho National Laboratory, the Advanced Test Reactor, and in a fast spectrum reactor in France (Phénix) is presented here in this study. This study evaluates the capability of using a cadmium shrouded test position to mimic the power profile along the fuel radius present in fast spectrum reactors so that thermally driven phenomenon (e.g., constituent redistribution) can be evaluated in a thermal reactor and determined to be prototypical of a fast reactor. Thus, optical microscopy and scanning electron microscopy has been performed on irradiated 35U-29Pu-4Am-2Np-30Zr fuel samples (where the number preceding the element is the weight percent concentration) from the two mentioned reactors that present similar irradiation temperatures and power conditions. The results indicate that fuel performance phenomena are reproducible in the two irradiation conditions. The redistribution of Zr occurred in the same manner for the two samples. Similar partitioning of U-Pu-Zr phases was observed, and the behavior of Am was similar in the analyzed specimens. Finally, the overall microstructure evolution seems not to be affected by minor actinides addition compared to expected behavior of conventional U-19Pu-10Zr ternary metal fuels for both specimens. Slight differences in fuel cladding chemical interaction were, however, observed. This difference is likely driven by difference in cladding composition rather than irradiation conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy

Abstract Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe 2 film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Pilot Testing of a Modular Oxygen Production System Using Oxygen Binding Adsorbents (Final Report)

In this project, RTI and Air Liquide have focused on the development of innovative oxygen separation materials and technologies based on reversibly binding of oxygen to enable smaller and cheaper air separations. In alignment with that object the team has had multiple achievements to advance the technology including: Synthesized and characterized novel M-CoSalen (RTIO2Sorb-1) material which showed a dynamic oxygen adsorption capacity in excess of 1wt%. Developed synthesis routes for RTIO2Sorb-1 with commercially relevant techniques and produced batches of 0.25kg and over 4 kg of total synthesis. Synthesized and characterized extrudate forms of the RTIO2Sorb-1 with relevant crush strength and maintained dynamic oxygen adsorption capacity in near 1wt%. Developed techniques for forming structured beds in fiber shapes with conventional materials. Developed a model for O 2 sorption processes to analyze various bed configurations and cycle parameters. Studied classic O 2 solid sorbents reported in the literature to learn oxygen binding mechanisms. Design a 10 kg/day O 2 VPSA pilot system with 2 or 4-bed operation. Completed a techno-economic analysis based on the experimental results and modeling. The overall objective of the project was to design, fabricate, and test a modular O 2 production system and perform a techno-economic analysis (TEA) after testing to determine the cost-benefit of the advanced modular air separation system. The goals of this technology development project were to achieve a bed-size factor (BSF) of less than 600 lb-adsorbent/TPD O 2 (ton/day O 2 ) (as compared with the state-of-the-art BSF of 850), and O 2 -purity greater than 95% at a cost that is projected to be equivalent or lower than the current state of the art (SOTA), commercially available large-scale cryogenic air separation systems. To achieve these goals and objective, the project team executed on (1) oxygen binding adsorbent optimization and scale up, (2) adsorbent material formation process studies to form the adsorbent material into structured beds for rapid pressure swing adsorption (PSA) cycles with low pressure drop, fast mass transfer, and low attrition, (3) cycle development studies to optimize the PSA process, and (4) develop simulation tools for rapid cycle modeling and numerical evaluation/optimization.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Using Artificial Neural Networks to Predict Physical Properties of Membrane Polymers

Membrane polymers are a promising technology for use in many challenging gas separation applications. Here, the techniques of computer-aided molecular design can be used to search through the massive molecular space of heteropolymers and develop a set of likely candidate repeat units matching specific physical property targets. However, reasonably accurate property prediction algorithms are needed, but these algorithms must be very fast in order to be combined with an optimization framework. Artificial neural networks (ANNs), a branch of machine learning, are applied in this work to predict the physical properties of polymers. All of the physical properties investigated were found to be predicted by ANNs with R 2 scores exceeding 0.82.

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

Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. In this work, we develop Machine Learning (ML) models that can use in-situ thermographic data to predict the micropore of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold ($\tau$) and the maximum radiance ( T max ). These features are computed, stored for each voxel in the built material, and then used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for Random Forests. Feature importance analysis based on the ML models shows that T max is more important to the voxel state than $\tau$ . The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it. Our study significantly extends the capability of using in-situ thermographic data to predict porosity in LPBF materials. Finally, since ML models are fast, they may play integral roles in the optimization and control of such AM technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A simple convertible electrolyzer in membraneless and membrane-based modes for understanding water splitting mechanism

State-of-the-art membrane-based electrolyzers such as proton exchange membrane electrolyzer cells, are costly, susceptible to degradation, and time-consuming for electrode evaluation and triple-phase boundary electrochemical reaction studies. Here, a simple convertible electrolyzer in membraneless and membrane-based modes is proposed. For the first time, this enables comprehensive investigations of water splitting with pure water, acidic and alkaline electrolytes in one cell. With the simple electrolyzer and high-speed visualization system, the influences of flow rate, electrolytes, concentration, and Nafion membrane on the oxygen evolution reactions (OERs), hydrogen evolution reactions (HERs) and electrolyzer performance are comprehensively investigated. Visualization results reveal that water splitting only occurred at the edge between the electrode and the Nafion membrane in pure water. However, they occurred on the whole electrode surface in alkaline and acidic electrolytes, indicating easily tunable reaction sites with the convertible electrolyzer. This demonstrates the feasibility of using the simple convertible electrolyzer for understanding the water splitting mechanism. The relation between electrolyte thickness and resistances with different electrolytes is also quantified. This research provides an insight for optimizing electrochemical devices while delivering an inexpensive and fast way for electrode evaluations and electrochemical reaction studies.

25 ENERGY STORAGE↗
DOE OSTI2024

Spectroscopic performance of Low-Gain Avalanche Diodes for different types of radiation

LGADs (Low-Gain Avalanche Diodes or Detectors) are a type of silicon Avalanche Photo-Diodes originally developed for the fast detection of minimum ionizing particles in high-energy physics experiments. Thanks to their fast timing performance, the LGAD paradigm enables detectors to accurately measure minimum ionizing particles with a timing resolution of a few tens of picoseconds. Such a performance is due to a thin substrate and the presence of a moderate signal gain. This internal gain of a few tens is enough to compensate for the reduced charge deposition in the thinner substrate and the noise of fast read-out systems. While LGADs are optimized for the detection of minimum ionizing particles for high-energy particle detectors, it is critical to study their performance for the detection of different types of particle, such as X-rays, gamma-rays, or alphas. In this paper, we evaluate the gain of three types of LGADs: two devices with different geometries and doping profiles fabricated by Brookhaven National Laboratory, and one fabricated by Hamamatsu Photonics with a different process. Since the gain in LGADs depends on the bias voltage applied to the sensor, pulse-height spectra have been acquired for bias voltages spanning from the depletion voltage up to the breakdown voltage. Finally, the signal-to-noise ratio of the generated signals and the shape of their spectra allow us to probe the underlying physics of the multiplication process.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Electric vehicle fast charging infrastructure planning in urban networks considering daily travel and charging behavior

Electric vehicles are a sustainable substitution to conventional vehicles. This work introduces an integrated framework for urban fast charging infrastructure to address the range anxiety issue. A mesoscopic simulation tool is developed to generate trip trajectories, and simulate charging behavior based on various trip attributes. The resulting charging demand is the key input to a mixed-integer nonlinear program that seeks charging station configuration. The model minimizes the total system cost including charging station and charger installation costs, and charging, queuing, and detouring delays. The problem is solved using a decomposition technique incorporating a commercial solver for small networks, and a heuristic algorithm for large-scale networks, in addition to the Golden Section method. The solution quality and significant superiority in the computational efficiency of the decomposition approach are confirmed in comparison with the implicit enumeration approach. Furthermore, the required infrastructure to support urban trips is explored for future market shares and technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Pose Classification Using Three-Dimensional Atomic Structure-Based Neural Networks Applied to Ion Channel–Ligand Docking

The identification of promising lead compounds showing pharmacological activities toward a biological target is essential in early stage drug discovery. With the recent increase in available small-molecule databases, virtual high-throughput screening using physics-based molecular docking has emerged as an essential tool in assisting fast and cost-efficient lead discovery and optimization. However, the best scored docking poses are often suboptimal, resulting in incorrect screening and chemical property calculation. We address the pose classification problem by leveraging data-driven machine learning approaches to identify correct docking poses from AutoDock Vina and Glide screens. To enable effective classification of docking poses, we present two convolutional neural network approaches: a three-dimensional convolutional neural network (3D-CNN) and an attention-based point cloud network (PCN) trained on the PDBbind refined set. We demonstrate the effectiveness of our proposed classifiers on multiple evaluation data sets including the standard PDBbind CASF-2016 benchmark data set and various compound libraries with structurally different protein targets including an ion channel data set extracted from Protein Data Bank (PDB) and an in-house KCa3.1 inhibitor data set. Our experiments show that excluding false positive docking poses using the proposed classifiers improves virtual high-throughput screening to identify novel molecules against each target protein compared to the initial screen based on the docking scores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Absolute contrast estimation for soft X-ray photon fluctuation spectroscopy using a variational droplet model

Abstract X-ray photon fluctuation spectroscopy using a two-pulse mode at the Linac Coherent Light Source has great potential for the study of quantum fluctuations in materials as it allows for exploration of low-energy physics. However, the complexity of the data analysis and interpretation still prevent recovering real-time results during an experiment, and can even complicate post-analysis processes. This is particularly true for high-spatial resolution applications using CCDs with small pixels, which can decrease the photon mapping accuracy resulting from the large electron cloud generation at the detector. Droplet algorithms endeavor to restore accurate photon maps, but the results can be altered by their hyper-parameters. We present numerical modeling tools through extensive simulations that mimic previous x-ray photon fluctuation spectroscopy experiments. By modification of a fast droplet algorithm, our results demonstrate how to optimize the precise parameters that lift the intrinsic counting degeneracy impeding accuracy in extracting the speckle contrast. These results allow for an absolute determination of the summed contrast from multi-pulse x-ray speckle diffraction, the modus operandi by which the correlation time for spontaneous fluctuations can be measured.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Learning to simulate high energy particle collisions from unlabeled data

In many scientific fields which rely on statistical inference, simulations are often used to map from theoretical models to experimental data, allowing scientists to test model predictions against experimental results. Experimental data is often reconstructed from indirect measurements causing the aggregate transformation from theoretical models to experimental data to be poorly-described analytically. Instead, numerical simulations are used at great computational cost. We introduce Optimal-Transport-based Unfolding and Simulation (OTUS), a fast simulator based on unsupervised machine-learning that is capable of predicting experimental data from theoretical models. Without the aid of current simulation information, OTUS trains a probabilistic autoencoder to transform directly between theoretical models and experimental data. Identifying the probabilistic autoencoder’s latent space with the space of theoretical models causes the decoder network to become a fast, predictive simulator with the potential to replace current, computationally-costly simulators. Here, we provide proof-of-principle results on two particle physics examples, Z-boson and top-quark decays, but stress that OTUS can be widely applied to other fields.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Isotopically nonstationary metabolic flux analysis of plants: recent progress and future opportunities

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST–MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast–growing algae, and produced a synergistic multi–organ flux map that is a first in MFA for any biological system. Finally, these insights could not be elucidated through other approaches and show the potential of INST–MFA to correct an oversimplified understanding of plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗