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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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Digital filter synthesis computer program expresses any continuous function of a complex variable in approximate form as a computational algorithm or difference equation. Once the difference equation has been developed, digital filtering can be performed by the program on any input data list.
First-principles electronic structure simulations are an invaluable tool for understanding chemical bonding and reactions. While machine-learning models such as interatomic potentials significantly accelerate the exploration of potential energy surfaces, electronic structure information is generally lost. Particularly in the field of heterogeneous catalysis, simulated electron band structures provide fundamental insights into catalytic reactivity. This ab initio knowledge is preserved in semiempirical methods such as density functional tight binding (DFTB), which extend the accessible computational length and time scales beyond first-principles approaches. In this paper here we present Shell-Optimized Atomic Confinement (SOAC) DFTB electronic-part-only parametrizations for bulk and surface band structures of all d-block transition metals that enable efficient predictions of electronic descriptors for large structures or high-throughput studies on complex systems outside the computational reach of density functional theory.
Abstract The intrinsic tenase (Xase) complex, formed by factors (f) VIIIa and fIXa, forms on activated platelet surfaces and catalyzes the activation of factor X to Xa, stimulating thrombin production in the blood coagulation cascade. The structural organization of the membrane-bound Xase complex remains largely unknown, hindering our understanding of the structural underpinnings that guide Xase complex assembly. Here, we aimed to characterize the Xase complex bound to a lipid nanodisc with biolayer interferometry (BLI), Michaelis–Menten kinetics, and small-angle X-ray scattering (SAXS). Using immobilized lipid nanodiscs, we measured binding rates and nanomolar affinities for fVIIIa, fIXa, and the Xase complex. Enzyme kinetic measurements demonstrated the assembly of an active enzyme complex in the presence of lipid nanodiscs. An ab initio molecular envelope of the nanodisc-bound Xase complex allowed us to computationally model fVIIIa and fIXa docked onto a flexible lipid membrane and identify protein–protein interactions. Our results highlight multiple points of contact between fVIIIa and fIXa, including a novel interaction with fIXa at the fVIIIa A1–A3 domain interface. Lastly, we identified hemophilia A/B-related mutations with varying severities at the fVIIIa/fIXa interface that may regulate Xase complex assembly. Together, our results support the use of SAXS as an emergent tool to investigate the membrane-bound Xase complex and illustrate how mutations at the fVIIIa/fIXa dimer interface may disrupt or stabilize the activated enzyme complex.
Spectroscopy with soft X-rays was used to develop new materials and novel designs for solar cells and artificial photosynthesis. In order to go beyond the widely-used trial-and-error approach of gradually improving a particular design, we started from the most general layout of a solar cell (or a photo-electrochemical device) and asked which classes of materials are promising for best performance. For example, the most general design of a solar cell consists of a light absorber, an electron donor, and an electron acceptor. These are characterized by four energy levels, which were measured by a combination of spectroscopic X-ray techniques. Tuning synchrotron radiation to the absorption edges of specific elements provided element- and bond-selectivity. The spectroscopic results were complemented by state-of-the-art calculations of the electronic states. These helped explaining the observed energy levels and the orbitals associated with them. The calculations were extended to a large class of materials (for example thousands of porphyrin dye complexes) in order to survey trends in the energy level structure. A few highlights serve as examples: 1) Organic molecules combining absorber, donor, and acceptor with atomic precision. 2) Exploration of highly p-doped diamond films as inert, transparent electron donors. 3) Surface-sensitive characterization of nanorod arrays used as photoanodes in water splitting. 4) Computational design of molecular complexes for efficient solar cells using two photons.
Using unstructured mesh CFD methods for Propulsion Airframe Aeroacoustics (PAA) analysis has the distinct advantage of precise and fast computational mesh generation for complex propulsion and airframe integration arrangements that include engine inlet, exhaust nozzles, pylon, wing, flaps, and flap deployment mechanical parts. However, accurate solution values of shear layer velocity, temperature and turbulence are extremely important for evaluating the usually small noise differentials of potential applications to commercial transport aircraft propulsion integration. This paper describes a set of calibration computations for an isolated separate flow bypass ratio five engine nozzle model and the same nozzle system with a pylon. These configurations have measured data along with prior CFD solutions and noise predictions using a proven structured mesh method, which can be used for comparison to the unstructured mesh solutions obtained in this investigation. This numerical investigation utilized the TetrUSS system that includes a Navier-Stokes solver, the associated unstructured mesh generation tools, post-processing utilities, plus some recently added enhancements to the system. New features necessary for this study include the addition of two equation turbulence models to the USM3D code, an h-refinement utility to enhance mesh density in the shear mixing region, and a flow adaptive mesh redistribution method. In addition, a computational procedure was developed to optimize both solution accuracy and mesh economy. Noise predictions were completed using an unstructured mesh version of the JeT3D code.
Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools and a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. In conclusion, MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments.
Over the years, polynuclear cyclic or torus complexes have attracted increasing interest due to their unique metal topologies and properties. However, the isolation of polynuclear cyclic organometallic complexes is extremely challenging due to their inherent reactivity, which stems from the labile and reactive metal‐carbon bonds. In this study, the pyrazine ligand undergoes a radical‐radical cross‐coupling reaction leading to the formation of a decanuclear [(Cp*)20Dy10(L1)10] ⋅ 12(C7H8) (1; where L1 = anion of 2‐prop‐2‐enyl‐2H‐pyrazine; Cp* = pentamethylcyclopentadienyl) complex, where all DyIII metal centres are bridged by the anionic L1 ligand. Amongst the family of polynuclear Ln organometallic complexes bearing CpR2Lnx units (CpR = substituted cyclopentadienyl), 1 features the highest nuclearity obtained to date. In‐depth computational studies were conducted to elucidate the proposed reaction mechanism and formation of L1, while probing of the magnetic properties of 1, revealed slow magnetic relaxation upon application of a static dc field.
Unusual cleavage of P-C and C-H bonds of the P 2 N 2 ligand in heteroleptic [Ni(P 2 N 2 )(diphosphine)] 2+ complexes results in the formation of an iminium formyl nickelate featuring a C,P,P-tridentate coordination mode.
CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.
Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. As the hardware and software have evolved, so have the theoretical and computational chemistry methods and algorithms. Parallel computers clearly changed the common computing paradigm in the late 1970s and 80s, and the field has again seen a paradigm shift with the advent of graphical processing units. This review explores the challenges and some of the solutions in transforming software from the terascale to the petascale and now to the upcoming exascale computers. While discussing the field in general, NWChem and its redesign, NWChemEx, will be highlighted as one of the early codesign projects to take advantage of massively parallel computers and emerging software standards to enable large scientific challenges to be tackled.
We investigate electron tunneling between quantum dots and molecules to propose a quantum sensor. This sensor consists of double quantum dots (DQD) with energy levels specifically tailored to mirror those of the target analyte. By analyzing the spectral distribution of electron localizations in the DQD system, we can delineate the analyte’s spectrum and deduce its composition by comparing it with a reference sample. To understand electron tunneling dynamics within the DQD/analyte complex, we performed three-dimensional computational modeling applying the effective potential approach to the InAs/GaAs heterostructure. In this modeling, we mimicked the analyte spectrum by utilizing a quantum well characterized by a quasi-discrete spectrum. Our calculations reveal the inherent potential of utilizing this method as a highly sensitive and selective sensor.
Reliability modeling for fault tolerant avionic computing systems was developed. The modeling of large systems involving issues of state size and complexity, fault coverage, and practical computation was discussed. A novel technique which provides the tool for studying the reliability of systems with nonconstant failure rates is presented. The fault latency which may provide a method of obtaining vital latent fault data is measured.
The computation of airflow over complex configurations requires a complete description of the geometry. This can be obtained from CAD data, from blueprints, or from actual models. In any case, the time required is currently estimated at 4 to 6 months. It is proposed to shorten this time by a factor of 10 to 100 through the use of automated software, a fast, highly parallel computer and a three-dimensional laser digitizer. This device can provide (x,y,z) coordinates of surface points at rates exceeding 14,500/sec. Thus, it is possible to digitize an entire model in a few minutes. The accuracy of measurement on a flat white surface is better than 0.005 inches. Higher accuracy is available at higher cost. This work discusses the challenges which remain to be addressed. In particular, the surface point data need to be converted into a surface description, the surface description needs to be made into a surface grid, and the surface grid used to make a volume grid for the flow solver. Algorithms are kept in place or in mind for all of these problems. Integration of the more mature flow solution and visualization algorithms then allows generation of solution graphics directly from a wind tunnel model.
We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.
The term "Grid" refers to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. The vision for NASN's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks that will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: The scientist / design engineer whose primary interest is problem solving (e.g., determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user if the tool designer: The computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. This paper describes the current state of IPG (the operational testbed), the set of capabilities being put into place for the operational prototype IPG, as well as some of the longer term R&D tasks.
The Office of Soil and Groundwater Remediation within the U.S. Department of Energy's Office of Environmental Management (EM) is supporting development of the Advanced Simulation Capability for Environmental Management (ASCEM). ASCEM is an open source and modular computing framework that incorporates new advances and tools for predicting contaminant fate and transport in natural and engineered systems. ASCEM integrates modeling tools into a common work-flow that facilitates integrated approaches to modeling and site characterization, and enables robust and standardized assessments of performance and risk for EM cleanup and closure activities. The ASCEM project includes collaboration with end users engaged in performance assessments and subsurface simulation. Two working groups are engaged in performance assessment activities at the Hanford Site and Savannah River Site (SRS). The Hanford Site working group is focused on evaluating an alternative conceptual model that includes fine-scale heterogeneities. The baseline conceptual model for the performance assessment includes the large-scale stratigraphy at the site. The working group is applying the ASCEM tool-set to develop and evaluate alternative conceptual models and evaluate the effect of heterogeneities on long-term transport under rainfall-driven conditions. Because the heterogeneous models require long compute times due to increased model complexity, the simulations are implemented on a high-performance computing platform to reduce simulation times. The ASCEM Akuna work-flow enables efficient organization, simulation execution, and processing and visualization of results. Current performance assessments for waste tank closure at the SRS are limited to two-dimensional axi-symmetric representations. The SRS working group is collaborating with the site contractor to address technical review issues by resolving fine-scale tank features and simulating three-dimensional flow with Richards equation for selected closure scenarios using the ASCEM simulator, Amanzi. The Akuna tool-set is being applied to support model setup, sensitivity analysis, uncertainty quantification, and visualization. Both of the ASCEM working groups are addressing issues that cannot readily be evaluated with the existing tools. In these efforts, ASCEM is being used to provide technical underpinnings for ongoing performance assessments. (authors)
Herein, we present the systematic, comparative computational study of the d − d transitions in a series of first row transition metal hexaaqua complexes, [M(H{sub 2}O){sub 6}]{sup n+} (M{sup 2+/3+} = V {sup 2+/3+}, Cr{sup 2+/3+}, Mn{sup 2+/3+}, Fe{sup 2+/3+}, Co{sup 2+/3+}, Ni{sup 2+}) by the means of Time-dependent Density Functional Theory (TD-DFT) and Ligand Field Density Functional Theory (LF-DFT). Influence of various exchange-correlation (XC) approximations have been studied, and results have been compared to the experimental transition energies, as well as, to the previous high-level ab initio calculations. TD-DFT gives satisfactory results in the cases of d{sup 2}, d{sup 4}, and low-spin d{sup 6} complexes, but fails in the cases when transitions depend only on the ligand field splitting, and for states with strong character of double excitation. LF-DFT, as a non-empirical approach to the ligand field theory, takes into account in a balanced way both dynamic and non-dynamic correlation effects and hence accurately describes the multiplets of transition metal complexes, even in difficult cases such as sextet-quartet splitting in d{sup 5} complexes. Use of the XC functionals designed for the accurate description of the spin-state splitting, e.g., OPBE, OPBE0, or SSB-D, is found to be crucial for proper prediction of the spin-forbidden excitations by LF-DFT. It is shown that LF-DFT is a valuable alternative to both TD-DFT and ab initio methods.
The use of computational simulations in the prediction of complex aerodynamic flows is becoming increasingly prevalent in the design process within the aerospace industry. Continuing advancements in both computing technology and algorithmic development are ultimately leading to attempts at simulating ever-larger, more complex problems. However, by increasing the reliance on computational simulations in the design cycle, we must also increase the accuracy of these simulations in order to maintain or improve the reliability arid safety of the resulting aircraft. At the same time, large-scale computational simulations must be made more affordable so that their potential benefits can be fully realized within the design cycle. Thus, a continuing need exists for increasing the accuracy and efficiency of computational algorithms such that computational fluid dynamics can become a viable tool in the design of more reliable, safer aircraft. The objective of this research was the development of an error estimation and grid adaptive strategy for reducing simulation errors in integral outputs (functionals) such as lift or drag from from multi-dimensional Euler and Navier-Stokes simulations. In this final report, we summarize our work during this grant.