Degree of rate control approach to computational catalyst screening
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Slides are reproduced that describe the importance of having high performance number crunching and graphics capability. They also indicate the types of research and development underway at Ames Research Center to ensure that, in the near term, Ames is a smart buyer and user, and in the long-term that Ames knows the best possible solutions for number crunching and graphics needs. The drivers for this research are real computational physics applications of interest to Ames and NASA. They are concerned with how to map the applications, and how to maximize the physics learned from the results of the calculations. The computer graphics activities are aimed at getting maximum information from the three-dimensional calculations by using the real time manipulation of three-dimensional data on the Silicon Graphics workstation. Work is underway on new algorithms that will permit the display of experimental results that are sparse and random, the same way that the dense and regular computed results are displayed.
Part 1 of this paper presents a numerical model for non-reactive physical mass transfer across a wetted wall column (WWC). In Part 2, we improved the existing computational fluid dynamics (CFD) model to simulate chemical absorption occurring in a WWC as a bench-scale study of solvent-based carbon dioxide (CO 2 ) capture. In this study, to generate data for WWC model validation, CO 2 mass transfer across a monoethanolamine (MEA) solvent was first measured on a WWC experimental apparatus. The numerical model developed in this work can account for both chemical absorption and desorption of CO 2 in MEA. In addition, the overall mass transfer coefficient predicted using traditional/empirical correlations is conducted and compared with CFD prediction results for both steady and wavy falling films. A Bayesian statistical calibration algorithm is adopted to calibrate the reaction rate constants in chemical absorption/desorption of CO 2 across a falling film of MEA. The posterior distributions of the two transport properties, i.e., Henry's constant and gas diffusivity in the non-reacting nitrous oxide (N 2 O)/MEA system obtained from Part 1 of this study, serves as priors for the calibration of CO 2 reaction rate constants after using the N 2 O/CO 2 analogy method. Finally, the calibrated model can be used to predict the CO 2 mass transfer in a WWC for a wider range of operating conditions.
The Sigma/PDP 11 Interface is a general-purpose system for interconnecting a Xerox Sigma series computer and a DEC PDP 11 series computer. Although the unit is designed to operate in a real-time environment, it can be used in nonreal-time modes as well. Several features, designed to facilitate using the system in varied applications are included in the unit; for example, the features can be used to isolate software or hardware faults. This paper contains descriptions of the hardware and software operations of the interface and examples of real-time applications in the research environment at Ames Research Center.
The overall cost of photovoltaic (PV) systems has shown a downward trend during the last decade; however, PV inverter failures account for the highest cost of operation and maintenance. To address this, reliability tools with powerful computation and better accuracy are required for the lifetime prediction and degradation evaluation of PV inverters. This paper proposes an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques-including cluster merging, adaptive model updates, and steady-state identification-to make reliability assessments for PV inverters under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. Then, a demo PV inverter system simulator is implemented, and the speedup of the total simulations of the switching model reaches 123.03 times.
Hyporheic exchange is a critical mechanism shaping hydrological and biogeochemical processes along a river corridor. Recent studies on quantifying the hyporheic exchange were mostly limited to local scales due to field inaccessibility, computational demand, and complexity of geomorphology and subsurface geology. Surface flow conditions and subsurface physical properties are well known factors on modulating the hyporheic exchange, but quantitative understanding of their impacts on the strength and direction of hyporheic exchanges at reach scales is absent. In this study, a high resolution computational fluid dynamics (CFD) model that couples surface and subsurface flow and transport is employed to simulate hyporheic exchanges in a 7-km long reach along the main-stem of the Columbia River. Assuming that the hyporheic exchange does not affect surface water flow conditions due to its negligible magnitude compared to the volume and velocity of river water, we developed a one-way coupled surface and subsurface water flow model using the commercial CFD software STAR-CCM+. The model integrates the Reynolds-averaged Navier-Stokes (RANS) equation solver with a realizable k-e two-layer turbulence model, a two-layer all y^+ wall treatment, and the volume of fluid (VOF) method, and is used to simulate hyporheic exchanges by tracking the free water-air interface as well as flow in the river and the subsurface porous media. The model is validated against measurements from acoustic Doppler current profiler (ADCP) in the stream water and hyporheic fluxes derived from a set of temperature profilers installed across the riverbed. The validated model is then employed to systematically investigate how hyporheic exchanges are influenced by surface water fluid dynamics strongly regulated by upstream dam operations, as well as subsurface structures (e.g. thickness of riverbed and subsurface formation layers) and hydrogeological properties (e.g. permeability). The results suggest that the thickness of riverbed alluvium layer is the dominant factor for reach-scale hyporheic exchanges, followed by the alluvium permeability, the depth of the underlying impermeable layer, and the assumption of hydrostatic pressure.
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We propose a new approach for multiverse analysis based on computational complexity, which leads to a new family of “computational” measure factors. By defining a cosmology as a space–time containing a vacuum with specified properties (for example small cosmological constant) together with rules for how time evolution will produce the vacuum, we can associate global time in a multiverse with clock time on a supercomputer which simulates it. We argue for a principle of “limited computational complexity” governing early universe dynamics as simulated by this supercomputer, which translates to a global measure for regulating the infinities of eternal inflation. The rules for time evolution can be thought of as a search algorithm, whose details should be constrained by a stronger principle of “minimal computational complexity”. Unlike previously studied global measures, ours avoids standard equilibrium considerations and the well-known problems of Boltzmann Brains and the youngness paradox. We also give various definitions of the computational complexity of a cosmology, and argue that there are only a few natural complexity classes.
We present an iterative algorithm for computing an invariant subspace associated with the algebraically smallest eigenvalues of a large sparse or structured Hermitian matrix A. We are interested in the case in which the dimension of the invariant subspace is large (e.g., over several hundreds or thousands) even though it may still be small relative to the dimension of A. These problems arise from, for example, density functional theory (DFT) based electronic structure calculations for complex materials. The key feature of our algorithm is that it performs fewer Rayleigh–Ritz calculations compared to existing algorithms such as the locally optimal block preconditioned conjugate gradient or the Davidson algorithm. It is a block algorithm, and hence can take advantage of efficient BLAS3 operations and be implemented with multiple levels of concurrency. We discuss a number of practical issues that must be addressed in order to implement the algorithm efficiently on a high performance computer.
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The high computational capability of graphics processing units (GPUs) is enabling and driving the scientific discovery process at large-scale. The world’s second fastest supercomputer for open science, Titan, has more than 18,000 GPUs that computational scientists use to perform scientific simu- lations and data analysis. Understanding of GPU reliability characteristics, however, is still in its nascent stage since GPUs have only recently been deployed at large-scale. This paper presents a detailed study of GPU errors and their impact on system operations and applications, describing experiences with the 18,688 GPUs on the Titan supercom- puter as well as lessons learned in the process of efficient operation of GPUs at scale. These experiences are helpful to HPC sites which already have large-scale GPU clusters or plan to deploy GPUs in the future.
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We present that friction stir welding (FSW) is a popular technique to join dissimilar materials in numerous applications. The solid state nature of the process enables joining materials with strikingly different physical properties. For welds in lap configuration, an enhancement to this technology is made by introducing a short, hard insert, referred to as a cutting-scribe, at the bottom of the tool pin. The cutting-scribe induces deformation in the bottom plate which leads to the formation of mechanical interlocks or hook like structures at the interface of two materials. A thermo-mechanical computational model employing a coupled Eulerian-Lagrangian approach is developed to quantitatively capture the morphology of these interlocks during the FSW process. Simulations using this model are validated by experimental observations. In conclusion, the identified interface morphology coupled with the predicted temperature field from this process–structure model can be used to estimate the post-weld microstructure and joint strength.
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D-200 computer family, system error analysis, reliability derivation, and power converter design for reconfigurable guidance and control computer - Vol. 7
Neuromorphic computing has several characteristics that make it an extremely compelling computing paradigm for post Moore computation. Some of these characteristics include intrinsic parallelism, inherent scalability, collocated processing and memory, and event-driven computation. While these characteristics impart energy efficiency to neuromorphic systems, they do come with their own set of challenges. One of the biggest challenges in neuromorphic computing is to establish the theoretical underpinnings of the computational complexity of neuromorphic algorithms. In this paper, we take the first steps towards defining the space and time complexity of neuromorphic algorithms. Specifically, we describe a model of neuromorphic computation and state the assumptions that govern the computational complexity of neuromorphic algorithms. Next, we present a theoretical framework to define the computational complexity of a neuromorphic algorithm. We explicitly define what space and time complexities mean in the context of neuromorphic algorithms based on our model of neuromorphic computation. Finally, we leverage our approach and define the computational complexities of six neuromorphic algorithms: constant function, successor function, predecessor function, projection function, neuromorphic sorting algorithm and neighborhood subgraph extraction algorithm.