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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

In-situ Synthesis of PuCl 3 and Corresponding Raman and Density Functional Theory Vibrational Modes

The experimental and calculated Raman spectrum of PuCl 3 has been reported for the first time. PuCl 3 is a primary species found in plutonium metal refinement, specifically in pyrochemical salt processes including multicycle direct oxide reduction, metal chlorination, and electrorefining. As such, Raman signatures of PuCl 3 could serve as potential forensic indicators of material process history. A novel technique for synthesizing PuCl 3 from the in situ chlorination of plutonium metal with HCl was developed to establish these signatures. Cerium metal surrogates were utilized to ensure optimization of the plutonium experiments and to minimize personnel exposure, and all experiments were carried out in a Raman reaction chamber designed for air-tight, high vacuum environments. In situ Raman spectroscopy was employed in conjunction with density functional theory (DFT) to investigate the vibrational modes of PuCl 3 . Associated mixed oxy and hydroxyl phases are also reported. The combined Raman and DFT results have eliminated inconsistencies in Raman mode assignments for the MX 3 family of metal chlorides having P6 3 /m symmetry, and IR modes derived from the DFT calculations are additionally presented. The data observed in this study are of potential interest to nuclear forensic analyses, nuclear sample aging, nuclear energy, plutonium processing, and stockpile stewardship.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Input-response space-filling designs

Traditional space-filling designs are a convenient way to explore throughout an input space of flexible dimension and have design points close to any region where future predictions might be of interest. In some applications, there may be a model connecting the input factors to the response(s), which provides an opportunity to consider the spacing not only in the input space but also in the response space. In this paper, we present an approach for leveraging current understanding of the relationship between inputs and responses to generate designs that allow the experimenter to flexibly balance the spacing in these two regions to find an appropriate design for the experimental goals. Applications where good spacing of the observed response values include calibration problems where the goal is to demonstrate the adequacy of the model across the range of the responses, sensitivity studies where the outputs from a submodel may be used as inputs for subsequent models, and inverse problems where the outputs of a process will be used in the inverse prediction for the unknown inputs. Here, we use the multi-objective optimization method of Pareto fronts to generate multiple non-dominated designs with different emphases on the input and response space-filling criteria from which the experimenter can choose. The methods are illustrated through several examples and a chemical engineering case study.

42 ENGINEERING↗
DOE OSTI2023

Quantum Annealing with Inequality Constraints: The Set Cover Problem

Abstract Quantum annealing is a promising method for solving hard optimization problems by transforming them into quadratic unconstrained binary optimization (QUBO) problems. However, when constraints are involved, particularly multiple inequality constraints, incorporating them into the objective function poses challenges. In this paper, the authors present two novel approaches for solving problems with multiple inequality constraints on a quantum annealer and apply them to the set cover problem (SCP). The first approach uses the augmented Lagrangian method to represent the constraints, while the second approach employs a higher‐order binary optimization (HUBO) formulation. The experiments show that both approaches outperform the standard approach for solving the SCP on the D‐Wave Advantage quantum annealer. The HUBO formulation performs slightly better than the augmented Lagrangian method in solving the SCP, but its scalability in terms of embeddability in the quantum chip is worse. The results demonstrate that the proposed augmented Lagrangian and HUBO methods can successfully implement a large number of inequality constraints, making them applicable to a broad range of constrained problems beyond the SCP.

Djidjev, Hristo N.↗
DOE OSTI2025

A bilevel multistage stochastic self-scheduling model with indivisibilities for trading in the continuous intraday electricity market

In this paper, we study the profit maximization problem of a virtual power plant trading in the continuous intraday electricity market. Our virtual power plant model is compatible with renewable, and thermal assets, covering a range of virtual power plants currently participating in energy markets. We model the trading problem as a bilevel multistage stochastic program. The upper level of the problem accounts for the profit maximization of the virtual power plant with explicit modeling of the technical constraints of the operational status of the thermal power plant including minimum start-up and shut-down times, ramp-up and ramp-down rates, and minimum generation level. The upper level also decides which continuous and indivisible (fill-or-kill) orders are submitted to the market. The lower-level problem accounts for the clearing of the continuous intraday market, i.e., matching of buy and sell orders. Because of the presence of fill-or-kill orders, the lower-level problem is mixed-integer, which prevents its direct conversion to a single-level problem using duality. In order to solve this challenging problem, we develop a convex-hull extended formulation for the lower-level problem, apply duality theory to obtain a single-level stochastic equivalent formulation, and employ McCormick envelopes to turn the problem into a multistage stochastic mixed-integer linear problem, which we solve using the stochastic dual dynamic integer programming algorithm. We conduct numerical experiments and analyze the optimal trading behavior of a virtual power plant trading in an ideal continuous market without arbitrage.

Bilevel multistage stochastic programming problem↗
DOE OSTI2022

Enabling machine learning-ready HPC ensembles with Merlin

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. With complexities such as multi-component workflows, heterogeneous machine architectures, parallel file systems, and batch scheduling, care must be taken to facilitate this analysis in a high performance computing (HPC) environment. Here, we present Merlin, a workflow framework to enable large ML-friendly ensembles of scientific HPC simulations. By augmenting traditional HPC with distributed compute technologies, Merlin aims to lower the barrier for scientific subject matter experts to incorporate ML into their analysis. As a producer–consumer workflow model, Merlin enables multi-machine, cross-batch job, dynamically allocated yet persistent workflows capable of utilizing surge-compute resources. Key features of Merlin are a flexible HPC-centric interface, low per-task overhead, multi-tiered fault recovery, and a hierarchical sampling algorithm that allows for $\mathscr{O}$(N) task execution and $\mathscr{O}$(N ln N) task queuing to ensembles of millions of tasks. In addition to Merlin’s design, we test the algorithm’s performance in an HPC center and demonstrate the ability to enqueue 40 million simulations in 100 s, with a 30 millisecond per-task overhead that is independent of ensemble size. Finally, we describe some example applications that Merlin has enabled on leadership-class HPC resources, such as the ML-augmented optimization of nuclear fusion experiments and the calibration of infectious disease models to study the progression of and possible mitigation strategies for COVID-19.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Atomic cooperation in enhancing magnetism: (Fe, Cu)-doped CeCo 5

Developing permanent magnet alloys with decreased critical elements (e.g., Nd, Dy, and Co) requires identifying compositions and structures with uniaxial magneto-crystalline anisotropy energy (MAE), large magnetization, and a high ferromagnetic transition temperature (Curie temperature - T C ). One approach to minimizing the critical elements in potential permanent magnet alloys is to use overly produced Ce, which is less critical. Furthermore, reducing Co content in RCo 5 (R = Rare Earth) alloys is necessary since Co is also a critical element. An obvious choice for decreasing Co content is a substitution with non-critical Fe. However, the Fe is not stable in the lattice due to the reduced number of d -electrons. Concomitant substitution of Cu stabilizes Fe substitution. Employing first-principles electronic structure theory, we identify the weakly localized nature of cobalt in CeCo 5 , which causes high uniaxial magnetic anisotropy of ~10 MJ/m 3 . In contrast, substituted Cu delocalizes the Co’s 3 d -states, resulting in lower anisotropy. Calculations show that 10% Cu can stabilize 20% Fe subsituted for Co, which significantly enhances magnetic moment in the Ce (Co, Fe, Cu) 5 . We report this prediction is in good agreement with a single-crystal experiment in which the optimal composition was identified to be 15% of Fe and 12% Cu. The competitive non-equivalent Co sites preferred by Cu and Fe, a unique electronic structure including exchange and crystal field splitting, and rigid band shift variation are borne by 3 d states of Co, Fe, and Cu around the Fermi level, all play an essential role in tuning the magnetic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine learning for automated experimentation in scanning transmission electron microscopy

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Recovery of Scandium from Bauxite Residue Using Sulfation Roasting-Assisted Water Leaching Process

Here, this study presents a method for recovering scandium from Indonesian bauxite residue (red mud) through a sulfation roasting-assisted water leaching process. Ammonium sulfate, (NH 4 ) 2 SO 4 , was chosen as the sulfate source for its lower melting point, less corrosivity, decreased toxicity, and the potential for ammonia recovery. A modified mixing system was introduced to improve the distribution of (NH 4 ) 2 SO 4 on red mud particles by combining them in water. A series of experiments were conducted to optimize the sulfation roasting process by examining the effects of the (NH 4 ) 2 SO 4 /red mud ratio, temperature, and roasting time. Subsequently, water leaching was optimized while considering the effect of leaching time and liquid-to-solid (L/S) ratio. The leaching of scandium from the roasted red mud showed rapid kinetics, even when using only water. Under the roasting conditions of 1/1 (g/g) (NH 4 ) 2 SO 4 /red mud ratio, 700°C, 60 min roasting time, and the leaching conditions of 65°C, 60 min, and 10 mL/g (L/S), over 90% of scandium was recovered, along with 64.4% Al leaching and below 8% Fe leaching. These results were further corroborated through comprehensive analyses such as x-ray diffraction (XRD) and scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). This study offers insights into an alternative, more environmentally friendly method for Sc recovery from red mud, promoting the sustainable use of hazardous waste like red mud.

ammonium sulfate↗
DOE OSTI2024

Parallel interior-point solver for block-structured nonlinear programs on SIMD/GPU architectures

Here, we investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decompose the interior-point algorithm into two successive operations: the evaluation of the derivatives and the solution of the associated Karush-Kuhn-Tucker (KKT) linear system. Our method accelerates both operations using two levels of parallelism. First, we distribute the computations on multiple processes using coarse parallelism. Second, each process uses SIMD/GPU accelerators locally to accelerate the operations using fine-grained parallelism. The KKT system is reduced by eliminating the inequalities and the state variables from the corresponding equations. We demonstrate our method's capability on the supercomputer Polaris, a testbed for the future exascale Aurora system. Each node is equipped with four GPUs, a setup amenable to our two-level approach. Our experiments on the stochastic optimal power flow problem show that the reduction method is 50x faster than the sparse linear solver HSL MA57 running in serial on the CPU, and 6x faster than Pardiso running in parallel on CPU on the same number of processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab

The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.

Jeske, Torri↗
DOE OSTI2022

AI for Experimental Controls at Jefferson Lab

We report the AI for Experimental Controls project is developing an AI system to control and calibrate detector systems located at Jefferson Laboratory. Currently, calibrations are performed offline and require significant time and attention from experts. This work would reduce the amount of data and the amount of time spent calibrating in an offline setting. The first use case involves the Central Drift Chamber (CDC) located inside the GlueX spectrometer in Hall D. We use a combination of environmental and experimental data, such as atmospheric pressure, gas temperature, and the flux of incident particles as inputs to a sequential Neural Network (NN) to recommend a high voltage setting and the corresponding calibration constants in order to maintain consistent gain and optimal resolution throughout the experiment. Utilizing AI in this manner represents an initial shift from offline calibration towards near real time calibrations performed at Jefferson Laboratory.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Learning to Predict Arbitrary Quantum Processes

We present an efficient machine-learning (ML) algorithm for predicting any unknown quantum process ℰ over 𝑛 qubits. For a wide range of distributions 𝒟 on arbitrary 𝑛-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process ℰ, with a small average error over input states drawn from 𝒟. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to 10 6 and system size up to 50 qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.

quantum computation↗
DOE OSTI2025

Massive compression for high data rate macromolecular crystallography (HDRMX): impact on diffraction data and subsequent structural analysis

New higher-count-rate, integrating, large-area X-ray detectors with framing rates as high as 17400 images per second are beginning to be available. These will soon be used for specialized macromolecular crystallography experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some information may be lost. Can we minimize this loss with acceptable impact on structural information? To explore this question, we have considered several approaches: summing short sequences of images, binning to create the effect of larger pixels, use of JPEG-2000 lossy wavelet-based compression, and use of Hcompress, which is a Haar-wavelet-based lossy compression borrowed from astronomy. We also explore the effect of the combination of summing, binning, and Hcompress or JPEG-2000. In each of these last two methods one can specify approximately how much one wants the result to be compressed from the starting file size. These provide particularly effective lossy compressions that retain essential information for structure solution from Bragg reflections.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Designing a parallel Feel-the-Way clustering algorithm on HPC systems

This paper introduces a new parallel clustering algorithm, named Feel-the-Way clustering algorithm, that provides better or equivalent convergence rate than the traditional clustering methods by optimizing the synchronization and communication costs. Our algorithm design centers on how to optimize three factors simultaneously: reduced synchronizations, improved convergence rate, and retained same or comparable optimization cost. To compare the optimization cost, we use the Sum of Square Error (SSE) cost as the metric, which is the sum of the square distance between each data point and its assigned clusters. Compared with the traditional MPI k-means algorithm, the new Feel-the-Way algorithm requires less communications among participating processes. As for the convergence rate, the new algorithm requires fewer number of iterations to converge. As for the optimization cost, it obtains the SSE costs that are close to the k-means algorithm. In the paper, we first design the full-step Feel-the-Way k-means clustering algorithm that can significantly reduce the number of iterations that are required by the original k-means clustering method. Next, we improve the performance of the full-step algorithm by adopting an optimized sampling-based approach, named reassignment-history-aware sampling. Our experimental results show that the optimized sampling-based Feel-the-Way method is significantly faster than the widely used k-means clustering method, and can provide comparable optimization costs. More extensive experiments with several synthetic datasets and real-world datasets (e.g., MNIST, CIFAR-10, ENRON, and PLACES-2) show that the new parallel algorithm can outperform the open source MPI k-means library by up to 110% on a high-performance computing system using 4,096 CPU cores. In addition, the new algorithm can take up to 51% fewer iterations to converge than the k-means clustering algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

CCSI Toolset 3.18 Release

CCSI Toolset 3.18 Release Highlights FOQUS was updated to allow installation for users using MacOS on Apple silicon. FOQUS Cloud support was added for user plugins. The Optimality-Based Design of Experiments tutorials were updated to reflect the latest changes in the user interface flow. The plot discrete sliders were fixed for CDF and 3D plots within Uncertainty Quantification, which were not working due to a matplotlib depreciation. The installation was updated to set the default location for the PSUADE executable if found in the environment. Updates were made to allow compatibility with NumPy 1.25. Additional documentation changes were made to fix typographical errors and fix a broken link to optional software.

AS↗
DOE OSTI2021

Gemma V&V/UQ/Credibility Activities: FY2020 Progress

This report describes the credibility activities undertaken in support of Gemma code development in FY20, which include Verification & Validation (V&V), Uncertainty Quantification (UQ), and Credibility process application. The main goal of these activities is to establish capabilities and process frameworks that can be more broadly applied to new and more advanced problems as the Gemma code development effort matures. This will provide Gemma developers and analysts with the tools needed to generate credibility evidence in support of Gemma predictions for future use cases. The FY20 Gemma V&V/UQ/Credibility activities described in this report include experimental uncertainty analysis, the development and use of methods for optimal design of computer experiments, and the development of a framework for validation. These initial activities supported the development of broader credibility planning for Gemma that continued into FY21.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior

Our goal is to develop, apply and validate a holistic thermal, hydrologic, mechanical, and chemical (THMC) workflow that includes evaluation of induced seismic slip in EGS reservoirs. We will integrate experimental and modeling approaches to reduce parameter uncertainty and better predict/mitigate seismic hazard at EGS sites. Our novel approach couples 3D physics-based earthquake simulations with THMC models (THMc+E). This capability will enable improve engineering decisions at Utah-FORGE and move EGS operations toward repeatable, robust, economically viable, and socially accepted development. For example, our THMC+E models will predict circulation scenarios and related seismic hazard for a suite of flow rates and under uncertainty, thus enabling evaluation of optimal circulation strategy. Laboratory experiments will be performed to constrain key model parameters and Bayesian techniques will provide a probabilistic evaluation of parameters used in models. THMC+E simulations will enable exploration various circumstances that may hinder EGS success and develop mitigation strategies.

58 GEOSCIENCES↗
DOE OSTI2023

In-situ Synthesis of PuCl 3 and Corresponding Raman and Density Functional Theory Vibrational Modes

The experimental and calculated Raman spectrum of PuCl 3 has been reported for the first time. PuCl 3 is a primary species found in plutonium metal refinement, specifically in pyrochemical salt processes including multicycle direct oxide reduction, metal chlorination, and electrorefining. As such, Raman signatures of PuCl 3 could serve as potential forensic indicators of material process history. A novel technique for synthesizing PuCl 3 from the in-situ chlorination of plutonium metal with HCl was developed to establish these signatures. Cerium metal surrogates were utilized to ensure optimization of the plutonium experiments and to minimize personnel exposure, and all experiments were carried out in a Raman reaction chamber designed for air-tight, high vacuum environments. In-situ Raman spectroscopy was employed in conjunction with Density Functional Theory (DFT) to investigate the vibrational modes of PuCl 3 . Associated mixed oxy and hydroxyl phases are also reported. The combined Raman and DFT results have eliminated inconsistencies in Raman mode assignments for the MX 3 family of metal chlorides having P6 3 /m symmetry, and IR modes derived from the DFT calculations are additionally presented. The data observed in this study is of potential interest to nuclear forensic analyses, nuclear sample aging, nuclear energy, plutonium processing, and stockpile stewardship.

36 MATERIALS SCIENCE↗