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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Benchmarking ADMM in Nonconvex NLPs

Here, we study connections between the alternating direction method of multipliers (ADMM), the classical method of multipliers (MM), and progressive hedging (PH). The connections are used to derive benchmark metrics and strategies to monitor and accelerate convergence and to help explain why ADMM and PH are capable of solving complex nonconvex NLPs. Specifically, we observe that ADMM is an inexact version of MM and approaches its performance when multiple coordination steps are performed. In addition, we use the observation that PH is a specialization of ADMM and borrow Lyapunov function and primal-dual feasibility metrics used in ADMM to explain why PH is capable of solving nonconvex NLPs. This analysis also highlights that specialized PH schemes can be derived to tackle a wider range of stochastic programs and even other problem classes. Our exposition is tutorial in nature and seeks to to motivate algorithmic improvements and new decomposition strategies.

42 ENGINEERING↗
DOE OSTI2023

Dynamic modeling and simulation of pressure swing adsorption processes using toPSAil

Pressure swing adsorption (PSA) has attracted significant recent interest for chemical process intensification due to its potential for high energy efficiency and amenability to small, modular designs. However, the lack of simulation tools that are readily available, transparent, and trusted, has been identified as a serious impediment to widespread adoption of PSA, as well as to further research on PSA modeling, numerical solution, optimization, and control. This paper presents a complete framework for dynamic modeling and simulation of PSA processes and its implementation in an open-source simulator called toPSAil. Further, the presentation is tutorial and includes many modeling and implementation details often overlooked in existing literature. Novel methods are presented for handling flow reversals and implementing various pressure–flow relationships, along with controlled boundary conditions. Finally, the code contains several innovations designed to improve efficiency and reduce the extensive trial-and-error tuning often required to produce a working PSA cycle.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Exploring MDSplus data-acquisition software and custom devices

MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an open-source electronic hardware platform, and it consists of a microcontroller and two sensors. Finally, we read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗
DOE OSTI2015

The TriBeam system: Femtosecond laser ablation in situ SEM

Femtosecond laser ablation offers the unique ability to remove material at rates that are orders of magnitude faster than existing ion beam technologies with little or no associated damage. By combining ultrafast lasers with state-of-the-art electron microscopy equipment, we have developed a TriBeam system capable of targeted, in-situ tomography providing chemical, structural, and topographical information in three dimensions of near mm{sup 3} sized volumes. The origins, development, physics, current uses, and future potential for the TriBeam system are described in this tutorial review. - Graphical abstract: Display Omitted - Highlights: • An emerging tool, the TriBeam, for in situ femtosecond (fs) laser ablation is presented. • Fs laser ablation aided tomography at the mm{sup 3}-scale is demonstrated. • Fs laser induced deposition of Pt is demonstrated at sub-diffraction limit resolution. • Fs laser surface structuring is reviewed as well as micromachining applications.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Microstructure evolution and mechanical properties of magnesium alloys containing long period stacking ordered phase

Magnesium alloys containing rare earth (RE) in conjunction with other alloying elements, such as Zn, have drawn increasing interest due to their high strength, high ductility and high creep-resistance with the existence of long period stacking ordered (LPSO) phase. Although the LPSO phase has been studied over a decade, the question remains how it impacts the mechanical properties of Mg-RE-X alloys, where X represents a metallic element with its atomic radius smaller than Mg. This paper provides a tutorial review of the current status on the role of LPSO phase in manipulating the mechanical properties in Mg-RE-X based alloys, where some outstanding issues are also highlighted and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Thermal conductivity modeling of hybrid organic-inorganic crystals and superlattices

Hybrid organic-inorganic materials have attracted intensive research interests due to the superb physical properties that take advantage of both organic and inorganic components and their promising applications in functional devices such as light emitting diodes, solar cells, and flexible thermoelectrics. Understanding thermal transport in hybrid materials is of importance for both the reliability and the performance of the hybrid material-based systems. The organic-inorganic hybrid materials can be classified as nanocomposites, superlattices and crystals depending on the strength of organic-inorganic bonding and the feature size of organic and inorganic components. Previous research on thermal transport mainly focus on hybrid nanocomposites where phonon-interface scattering dominates the thermal transport process. However, in hybrid superlattices and crystals, organic and inorganic components are blended at the atomic scale by chemical bonds without a clear interface, and the phonon transport physics is not well understood. This article reviews the recent progress in ab-initio modeling on phonon transport in hybrid crystals and superlattices. The predicted thermal conductivity of II–VI based organic-inorganic semiconductors, organometal perovskites and organic-intercalated TiS2 superlattices are reviewed and compared with experimental data when available. This review provides guidance for modeling thermal conductivity of materials with complex atomic structures from the first principles, which could be a good tutorial for both graduate students and experienced researchers who are interested in thermal energy transport in emerging hybrid materials.

Chemistry↗
DOE OSTI2022

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey

Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. In conclusion, this primer is accompanied by an interactive online tutorial.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Sub-Sampled Imaging for STEM: Maximising Image Speed, Resolution and Precision Through Reconstruction Parameter Refinement

Sub-sampling during image acquisition in scanning transmission electron microscopy (STEM) has been shown to provide a means to increase the overall speed of acquisition while at the same time providing an efficient means to control the dose, dose rate and dose overlap delivered to the sample. In this paper, we discuss specifically the parameters used to reconstruct sub-sampled images and highlight their effect on inpainting using the beta-process factor analysis (BPFA) methodology. The selection of the main control parameters can have a significant effect on the resolution, precision and sensitivity of the final inpainted images, and here we demonstrate a method by which these parameters can be optimised for any image in STEM. As part of this paper, we also provide a link to open source code and a tutorial on its use, whereby these parameters can be tested for any datasets. When coupled with the hardware necessary to rapidly sub-sample images in STEM, this approach can have significant implications for imaging beam sensitive materials and dynamic processes.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Practical gyrokinetics

We report thousands of gyrokinetic papers have been published since the introduction of the gyrokinetic change of variables. The intent here is not to review the field, but rather the goal is to present a tutorial providing insight into why gyrokinetics is an appropriate description of turbulent transport. The focus is on turbulent transport in axisymmetric tokamaks, but many of the ideas and techniques are applicable to stellarators and other magnetic fusion devices with nested surfaces of constant pressure. Besides the origins of gyrokinetics and recent insights, gyrokinetic orderings and gyrokinetic variables are summarized. Then a compact, but careful, derivation of the simplest electrostatic gyrokinetic equation for tokamaks is presented, along with a brief mention of gyrokinetics for stellarators. The advantages of assuming scale separation between the finer spatial scales and faster time variation of the turbulence and the global behaviour and slow temporal evolution of the background are stressed. Moreover, the procedure for removing the adiabatic or Maxwell–Boltzmann response is emphasized. Scale separation allows the near Maxwellian behaviour of the background and the rapid variation of the turbulence to be described by separate, local gyrokinetic equations. The turbulent fluctuations are found by solving the nonlinear gyrokinetic equation for the non-adiabatic portion of the fluctuating distribution function. Also, generalizations of the gyrokinetic equation so that it retains electromagnetic and flow modifications are considered in detail along with some symmetry properties. In addition, ambipolarity, particle transport and heat transport are addressed, along with a discussion of the complications associated with describing momentum transport and profile evolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

What's New in GSAS-II

The General Structure and Analysis Software II (GSAS-II) package is an all-new crystallographic analysis package written to replace and extend the capabilities of the universal and widely used GSAS and EXPGUI packages. GSAS-II was described in a 2013 article, but considerable work has been completed since then. This paper describes the advances, which include: rigid body fitting and structure solution modules; improved treatment for parametric refinements and equation of state fitting; and small-angle scattering data reduction and analysis. GSAS-II offers versatile and extensible modules for import and export of data and results. Capabilities are provided for users to select any version of the code. Code documentation has reached 150 pages and 17 web-tutorials are offered. © 2014 International Centre for Diffraction Data.

Toby, Brian H.↗
DOE OSTI2022

Automated Crystal Orientation Mapping in py4DSTEM using Sparse Correlation Matching

Crystalline materials used in technological applications are often complex assemblies composed of multiple phases and differently oriented grains. Robust identification of the phases and orientation relationships from these samples is crucial, but the information extracted from the diffraction condition probed by an electron beam is often incomplete. We have developed an automated crystal orientation mapping (ACOM) procedure which uses a converged electron probe to collect diffraction patterns from multiple locations across a complex sample. We provide an algorithm to determine the orientation of each diffraction pattern based on a fast sparse correlation method. We demonstrate the speed and accuracy of our method by indexing diffraction patterns generated using both kinematical and dynamical simulations. We have also measured orientation maps from an experimental dataset consisting of a complex polycrystalline twisted helical AuAgPd nanowire. From these maps we identify twin planes between adjacent grains, which may be responsible for the twisted helical structure. All of our methods are made freely available as open source code, including tutorials which can be easily adapted to perform ACOM measurements on diffraction pattern datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Microscopy & Microanalysis 2019 in Portland, Oregon

The Microscopy Society of America (MSA), the Microanalysis Society (MAS), and the International Field Emission Society (IFES) cordially invite you to Microscopy & Microanalysis 2019 in Portland, Oregon. We are excited and honored to feature two Nobel Prize laureates as our plenary lecturers. Our first plenary speaker will be Dr. Joachim Frank, Professor of Biochemistry and Molecular Biophysics and of Biological Sciences at Columbia University, New York. He shared with Jacques Dubochet and Richard Henderson the 2017 Nobel Prize in Chemistry for “Developing cryo-electron microscopy for the high resolution structure determination of biomolecules in solution.” Our second plenary speaker will be Dr. Richard Henderson of the Medical Research Council Laboratory of Molecular Biology (MRC LMB), Cambridge, UK. He will present a talk titled “Single particle cryoEM: potential for further improvement.” As usual, M&M 2019 will present a great variety of symposia topics in analytical, life, and physical sciences. This year, we have a special symposium: “Cryo-EM – from Physics to Cell Biology: Honoring the Remarkable Legacy of Ken Downing.” It will showcase the late Dr. Downing’s work in single-particle cryo-EM, cryo-electron tomography, and the microscopy methods he used for his seminal discoveries. Preceding the M&M 2019 technical program, we will have two pre-meeting congresses. Education and training are important components of the M&M technical program each year, and 2019 is no exception. Six Sunday short courses on material science, biology, and data analysis will be presented. Additional opportunities are available in tutorials in biology and physical sciences during the meeting week. Finally, our popular microscopy outreach workshop “Project MICRO” will be in the MegaBooth all week. For a complete description of the technical program and educational opportunities, please go to http://www.microscopy.org/MandM/2019. The Call for Papers and other aspects of the program and the meeting are provided under “Scientific Program.” The Executive Program Committee welcomes you to Portland for an excellent and compelling Microscopy & Microanalysis 2019. Catch up with old friends and make new ones! Look for opportunities to attend society business meetings and socials during the week. On behalf of MSA, MAS, IFES, in cooperation with over a hundred symposia organizers and countless volunteers, we look forward to seeing you in Portland this August!

Microscopy, microanalysis↗
DOE OSTI2016

Best Practices for the Synthesis, Activation, and Characterization of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are structurally diverse materials comprised of inorganic and organic components. As the rapidly expanding field of MOF research has demonstrated, these materials are being explored for a wide variety of potential applications. In this tutorial review, we give an overview of the current best practices associated with the synthesis, activation, and characterization of MOFs. Methods described include supercritical CO 2 activation, single crystal X-ray diffraction (XRD), powder X-ray diffraction (PXRD), nitrogen adsorption/desorption isotherms, surface area calculations, aqueous stability tests, scanning electron microscopy (SEM), inductively coupled plasma optical emission spectroscopy (ICP-OES), nuclear magnetic resonance spectroscopy (NMR), and diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). A variety of different MOFs are presented to aid in the discussion of relevant techniques. In addition, some sections are accompanied by instructional videos to give further insight into the techniques, including tips, tricks, and suggestions only those at the bench could describe.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Application and Limitations of Nanocasting in Metal–Organic Frameworks

Nanocasting can be a useful strategy to transfer the catalytic metal clusters in metal-organic frameworks (MOFs) to an all-inorganic support such as silica. The incorporation of silica in the MOF pores as a secondary support has the potential to extend the application of the highly tunable metal-based active sites in MOFs to high temperature catalysis. Here, we demonstrate the applicability of the nanocasting method to a range of MOFs that incorporate catalytically attractive hexazirconium, hexacerium, or pentanickel oxide-based clusters (UiO-66, (Ce)UiO-66, (Ce)UiO-67, (Ce)MOF-808, DUT-9, and In- and Ni-post-metalated NU-1000). We describe, in tutorial form, the challenges associated with nanocasting of MOFs that are related to their small pore size and to considerations of chemical and mechanical stability, and we provide approaches to overcome some of these challenges. Finally, some of these nanocast materials feature the site-isolated clusters in a porous, thermally stable silica matrix, suitable for catalysis at high temperatures; in others, structural rearrangement of clusters or partial cluster aggregation occurs, but extensive aggregation can be mitigated by the silica skeleton introduced during nanocasting.

36 MATERIALS SCIENCE↗
DOE OSTI2019

The HANDE-QMC Project: Open-Source Stochastic Quantum Chemistry from the Ground State Up

Building on the success of Quantum Monte Carlo techniques such as diffusion Monte Carlo, alternative stochastic approaches to solve electronic structure problems have emerged over the past decade. The full configuration interaction quantum Monte Carlo (FCIQMC) method allows one to systematically approach the exact solution of such problems, for cases where very high accuracy is desired. The introduction of FCIQMC has subsequently led to the development of coupled cluster Monte Carlo (CCMC) and density matrix quantum Monte Carlo (DMQMC), allowing stochastic sampling of the coupled cluster wave function and the exact thermal density matrix, respectively. In this Article, we describe the HANDE-QMC code, an open-source implementation of FCIQMC, CCMC and DMQMC, including initiator and semistochastic adaptations. We describe our code and demonstrate its use on three example systems; a molecule (nitric oxide), a model solid (the uniform electron gas), and a real solid (diamond). An illustrative tutorial is also included.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

The Degree of Rate Control: A Powerful Tool for Catalysis Research

The “degree of rate control” (DRC) is a mathematical approach for analysing multi-step reaction mechanisms that has proven very useful in catalysis research. It identifies the “ratecontrolling transition states and intermediates” (i.e., those whose DRCs are large in magnitude). Even in mechanisms with over 30 intermediates and transition states, these are generally just a few distinct chemical species whose energies, if they could be independently changed, would achieve a faster net reaction rate to the product of interest. For example, when there is a single “rate-determining step”, the DRC for its transition state (TS) is 1, which means (by definition) that if this TS’s energy could be decreased by kBT (where kB is Boltzmann’s constant and T is temperature), the net rate would increase by a factor of e. Since the (relative) energies of these key adsorbed intermediates and transition states can be adjusted by modifying the catalyst or solvent, or even a reactant’s molecular structure, the DRC values provide important ideas for catalyst improvement. The species with large DRCs are also the ones whose energetics must be most accurately measured or calculated to achieve an accurate kinetic model for any reaction mechanism. A tutorial on DRC analysis, the calculation of DRCs, and examples of the applications of DRCs in catalysis research is presented here. Applications of DRC analysis include: clarifying reaction kinetics, improving the accuracy of computational models, improving reaction conditions, improving choice of oxidant in selective oxidation, incorporation in algorithms which calculate net reaction rates of multistep mechanisms without solving the differential equations involved, and high-throughput computational screening of catalyst materials. Since DRC values can be determined experimentally, we conclude a full microkinetic model is not required to take advantage of DRC analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗