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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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Herein we address the increasing complexity of scientific workflows in the context of high-performance computing (HPC) and their associated need for robust, adaptable, and flexible computational support systems. We explore five key trends as well as future challenges and opportunities for scientific workflows and HPC technologies.
The neptunyl (Np(V)O 2 + /Np(VI)O 2 2+ ) cation is the dominant form of 237 Np in acidic aqueous solutions and the stability of the Np(V) and Np(VI) species is driven by the specific chemical constituents present in the system. Hydrogen bonding with the oxo group may impact the stability of these species, but there is limited understanding of how these intermolecular interactions influence the behavior of both solution and solid-state species. In the current study, we systematically evaluate the interactions between the neptunyl tetrachloride species and hydrogen donors in coordination complexes and in the related aqueous solutions. Both Np(V) compounds (N 2 C 4 H 12 ) 2 [Np(V)O 2 Cl 4 ]Cl (Np(V)pipz) and (NOC 4 H 10 ) 3 [Np(V)O 2 Cl 4 ] (Np(V)morph) exhibit directional hydrogen bonding to the neptunyl oxo group while Np(VI) compounds (NC 5 H 6 ) 2 [Np(VI)O 2 Cl 4 ] (Np(VI)pyr) and (NOC 4 H 10 ) 4 [Np(VI)O 2 Cl 4 ]·2Cl (Np(VI)morph) assemble via halogen interactions. Here, the Raman spectra of the solid-state phases indicate the activation of vibrational bands when there is asymmetry of the neptunyl bond, while these spectral features are not observed within the related solution phase spectra. Density functional theory calculations of the Np(V)pipz system suggest that activation of the ν 3 asymmetric stretch and other combination modes lead to additional complexity within the solid-state spectra. Electrochemical analyses of complexes in the solution phases are consistent with the results of the crystallization experiments as the voltammetric potentials of Np(V)/Np(VI) complexes in the presence of protonated heterocycles differ from the potentials of pure Np(V) and may correlate with the hydrogen bonding interactions.
A general two-dimensional Euler zonal method has been developed for computing flows about complex airfoil geometries such as multielement and iced airfoils. The method utilizes a composite structured and unstructured grid generated using conformal mapping and Delaunay triangulation, respectively. The finite-volume Euler method is then modified to couple solutions in the zones with structured and unstructured grids. Solutions about an iced airfoil and a multielement airfoil are given as examples of applications of the scheme.
The applicability of a multigrid technique to block-structured, body-fitted meshes is examined focusing on three different strategies. In the first strategy data are exchanged between blocks in each stage of a five-stage Runge-Kutta time-stepping scheme which keeps a possible time lag between blocks to a minimum, but requires a large amount of I/O operations and storage. The second strategy is based on performing a complete Runge-Kutta cycle within a block before switching to the next. In the third strategy both a complete Runge-Kutta cycle and the residual evaluation for the restriction operator are done within a block, allowing a minimum of I/O and storage. The inviscid flow around a wing-body/engine-pylon configuration was computed on a mesh consisting of 11 computational blocks. It was found that both the first and the second strategies delivered converged results, but the third failed due to larger time lag between blocks.
Predicting the performance of various infrastructure design options in complex federated infrastructures with computing sites distributed over a wide area network that support a plethora of users and workflows, such as the Worldwide LHC Computing Grid (WLCG), is not trivial. Due to the complexity and size of these infrastructures, it is not feasible to deploy experimental test-beds at large scales merely for the purpose of comparing and evaluating alternate designs. An alternative is to study the behaviours of these systems using simulation. This approach has been used successfully in the past to identify efficient and practical infrastructure designs for High Energy Physics (HEP). A prominent example is the Monarc simulation framework, which was used to study the initial structure of the WLCG. New simulation capabilities are needed to simulate large-scale heterogeneous computing systems with complex networks, data access and caching patterns. A modern tool to simulate HEP workloads that execute on distributed computing infrastructures based on the SimGrid and WRENCH simulation frameworks is outlined. Studies of its accuracy and scalability are presented using HEP as a case-study. Hypothetical adjustments to prevailing computing architectures in HEP are studied providing insights into the dynamics of a part of the WLCG and candidates for improvements.
Foldy is a cloud-based application that allows non-computational biologists to easily utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. With many deployment options, it can be employed by individuals, labs, universities, and companies in the cloud without requiring hardware resources, but it can also be configured to utilize locally available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 6000 amino acids with AlphaFold, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. In our manuscript, we detail Foldy’s interface design, deployment strategies, and optimization for various user scenarios. We demonstrate its application through case studies including rational enzyme design and analyzing proteins with domains of unknown function. Furthermore, we compare Foldy’s interface and management capabilities with other open and closed source tools in the field, illustrating its practicality in managing complex data and computation tasks. Our manuscript underlines the benefits of Foldy as a day-to-day tool for life science researchers, and shows how Foldy can make modern tools more accessible and efficient.
Here, the design of new ligands and investigation of UO 2 2+ complexations are an essential aspect of reducing the cost of extracting uranium from seawater, improving the sorption efficiency for uranium and the selectivity for uranium over competing ions (such as the transition metal cations). The binding strengths of salicylaldoxime–UO 2 2+ complexes were quantified for the first time and compared with the binding strengths of salicylic acid–UO 2 2+ and representative amidoxime–UO 2 2+ complexes. We found that the binding strengths of salicylaldoxime–UO 2 2+ complexes are ~2–4 log β 2 units greater in magnitude than their corresponding salicylic acid–UO 2 2+ and representative amidoxime–UO 2 2+ complexes; moreover, the selectivity of salicylaldoxime towards the UO 2 2+ cation over competing Cu 2+ and Fe 3+ cations is far greater than those reported for salicylic acid and glutarimidedioxime in the literature. The higher UO 2 2+ selectivity can likely be attributed to the different coordination modes observed for salicylaldoxime–UO 2 2+ and salicylaldoxime–transition metal complexes. Density functional theory calculations indicate that salicylaldoxime can coordinate with UO 2 2+ as a dianion species formed by η 2 coordination of the aldoximate and monodentate binding of the phenolate group. In contrast, salicylaldoxime coordinates with transition metal cations as a monoanion species via a chelate formed between phenolate and the oxime N; the complexes are stabilized via hydrogen bonding interactions between the oxime OH group and phenolate. By coupling the experimentally determined thermodynamic constants and the results of theoretical computations, we are able to derive a number of ligand design principles to further improve the UO 2 2+ cation affinity, and thus further increase the selectivity of salicylaldoxime derivatives.
Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. Here, this paper carefully surveys the current state of the art in MOSO algorithms, techniques, and solvers, as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a parallel multiobjective simulation optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems.
Increasingly frequent severe weather events in recent years threaten the security of power systems and result in major power outages throughout the world. The development of reasonable power system restoration (PSR) solutions is therefore urgently needed to speed up the recovery of the power supply while at the same time steering clear of vulnerable and risky equipment. This paper aims to develop a case-sensitive PSR model for power transmission systems that can adjust restoration solutions according to the evaluated availability of outage equipment in specific blackout scenarios and weather conditions. A novel PSR model that integrates the startup of generating units, formulation of the restoration network, renewable energy sources, and availability assessment of devices is proposed. A reformulated model is also proposed to relieve the computational burden of complex PSR problems. The availability of outage equipment is comprehensively assessed based on historical operating records, fault diagnosis results, and weather conditions. The assessed availability results are sensitive to the characteristics of real blackout cases and will support system operators generate case-sensitive PSR solutions while mitigating the vulnerable equipment. The feasibility and effectiveness of the proposed PSR model and its reformulations are verified through case studies.
In web-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult. We require methods which provide an effective balance between the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexity and human cognition. Here we build on J{\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency. We demonstrate the efficacy of this approach in a web ingest experiment over the 2012 Billion Triple dataset from the Semantic Web Challenge.
Methods and algorithms are developed to enable the accurate analysis of problems that exhibit interacting physical processes with uncertainties. These uncertainties can pertain either to each of the physical processes or to the manner in which they depend on each others. These problems are cast within a polynomial chaos framework and their solution then involves either solving a large system of algebraic equations or a high dimensional numerical quadrature. In both cases, the curse of dimensionality is manifested. Procedures are developed for the efficient evaluation of the resulting linear equations that advantage of the block sparse structure of these equations, resulting in a block recursive Schur complement construction. In addition, embedded quadratures are constructed that permit the evaluation of very high-dimensional integrals using low-dimensional quadratures adapted to particular quantities of interest. The low-dimensional integration is carried out in a transformed measure space in which the quantity of interest is low-dimensional. Finally, a procedure is also developed to discover a low-dimensional manifold, embedded in the initial high-dimensional one, in which scalar quantities of interest exist. This approach permits the functional expression of the reduced space in terms of the original space, thus permitting cross-scale sensitivity analysis.
An efficient computer program called GRID2D/3D has been developed to generate single and composite grid systems within geometrically complex two- and three-dimensional (2D and 3D) spatial domains that can deform with time. GRID2D/3D generates single grid systems by using algebraic grid generation methods based on transfinite interpolation. The distribution of grid points within the spatial domain is controlled by stretching functions and grid lines can intersect boundaries of the spatial domain orthogonally. GRID2D/3D generates composite grid systems by patching together two or more single grid systems. The patching can be discontinuous or continuous. For 2D spatial domains the boundary curves are constructed by using either cubic or tension spline interpolation. For 3D spatial domains the boundary surfaces are constructed by using a new technique, developed in this study, referred to as 3D bidirectional Hermite interpolation.
An efficient computer program, called GRID2D/3D was developed to generate single and composite grid systems within geometrically complex two- and three-dimensional (2- and 3-D) spatial domains that can deform with time. GRID2D/3D generates single grid systems by using algebraic grid generation methods based on transfinite interpolation in which the distribution of grid points within the spatial domain is controlled by stretching functions. All single grid systems generated by GRID2D/3D can have grid lines that are continuous and differentiable everywhere up to the second-order. Also, grid lines can intersect boundaries of the spatial domain orthogonally. GRID2D/3D generates composite grid systems by patching together two or more single grid systems. The patching can be discontinuous or continuous. For continuous composite grid systems, the grid lines are continuous and differentiable everywhere up to the second-order except at interfaces where different single grid systems meet. At interfaces where different single grid systems meet, the grid lines are only differentiable up to the first-order. For 2-D spatial domains, the boundary curves are described by using either cubic or tension spline interpolation. For 3-D spatial domains, the boundary surfaces are described by using either linear Coon's interpolation, bi-hyperbolic spline interpolation, or a new technique referred to as 3-D bi-directional Hermite interpolation. Since grid systems generated by algebraic methods can have grid lines that overlap one another, GRID2D/3D contains a graphics package for evaluating the grid systems generated. With the graphics package, the user can generate grid systems in an interactive manner with the grid generation part of GRID2D/3D. GRID2D/3D is written in FORTRAN 77 and can be run on any IBM PC, XT, or AT compatible computer. In order to use GRID2D/3D on workstations or mainframe computers, some minor modifications must be made in the graphics part of the program; no modifications are needed in the grid generation part of the program. This technical memorandum describes the theory and method used in GRID2D/3D.
This SAND report summarizes work supported by an Engineering Sciences Research Foundation (ESRF) Lab Directed Research and Development (LDRD) project entitled “Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials.” This SAND report is written in two parts with Part 1 discusses recrystallization of our explosive exemplar and Part 2 summarizing our work with recrystallization of resveratrol. We have studied resveratrol recrystallization with a multiscale approach combining experiments, modeling and simulation. At the single crystal scale, microscopy experiments illuminate crystal time-dependent growth rates using advanced image analysis. Bench scale experiments were carried out to look at growth of multiple particles in a small reactor creating thousands of particles and analyzing the results with microscopy and μCT. For the modeling we combine kinetic Monte Carlo (kMC) models with subscale information from density functional theory (DFT) or molecular dynamics. This work is discussed in Part 1 and can also be found in a paper from the project discussing a coarse-grained kMC model specifically developed for resveratrol. For well-mixed systems, we have population balance equations (PBE) linked with species mass conservation forming a set of ordinary differential equations that can be solved quickly. For more complicated geometries, such as the vat crystallization used throughout the complex, a coupled computational fluid dynamic (CFD)/PBE method was developed to account for gradients in temperature and concentration and differences in crystallization rates throughout the domain. These simulations are more complex and require high performance computing. We present results for two cases: 5% seed fast cool with parameters fit to the well-mixed case and 5% seed slow cool using the same parameters. We show reasonable agreement with experiments though are particles are significantly larger than the experiments.
Stability of materials under extreme conditions is an important issue for safety of nuclear reactors. Presently, silicon carbide (SiC) is being studied as a cladding material candidate for fuel rods in boiling-water and pressurized water-cooled reactors (BWRs and PWRs) that would substitute or modify traditional zircaloy materials. The rate of corrosion of the SiC ceramics in hot vapor environment (up to 2200 degrees C) simulating emergency conditions of light water reactor (LWR) depends on many environmental factors such as pressure, temperature, viscosity, and surface quality. Using the paralinear oxidation theory developed for ceramics in the combustion reactor environment, we estimated the corrosion rate of SiC ceramics under the conditions representing a significant power excursion in a LWR. It was established that a significant time – at least 100 h – is required for a typical SiC braiding to significantly degrade even in the most aggressive vapor environment (with temperatures up to 2200 °C) which is possible in a LWR at emergency condition. This provides evidence in favor of using the SiC coatings/braidings for additional protection of nuclear reactor rods against off-normal material degradation during power excursions or LOCA incidents. Additionally, we discuss possibilities of using other silica based ceramics in order to find materials with even higher corrosion resistance than SiC. In particular, we found that zircon (ZrSiO4) is also a very promising material for nuclear applications. Thermodynamic and first-principles atomic-scale calculations provide evidence of zircon thermodynamic stability in aggressive environments at least up to 1535 degrees C.
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Here, we propose novel finite-dimensional spaces of well-behaved Rn → Rn transformations. The latter are obtained by (fast and highly-accurate) integration of continuous piecewise-affine velocity fields. The proposed method is simple yet highly expressive, effortlessly handles optional constraints (e.g., volume preservation and/or boundary conditions), and supports convenient modeling choices such as smoothing priors and coarse-to-fine analysis. Importantly, the proposed approach, partly due to its rapid likelihood evaluations and partly due to its other properties, facilitates tractable inference over rich transformation spaces, including using Markov-Chain Monte-Carlo methods. Its applications include, but are not limited to: monotonic regression (more generally, optimization over monotonic functions); modeling cumulative distribution functions or histograms; time-warping; image warping; image registration; real-time diffeomorphic image editing; data augmentation for image classifiers. Our GPU-based code is publicly available.