Publications

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Extreme-scale parallel systems will require alternative methods for applications to maintain current levels of uninterrupted execution. Redundant computation is one approach to consider, if the benefits of increased resiliency outweigh the cost of consuming additional resources. We describe a transparent redundancy approach for MPI applications and detail two different implementations that provide the ability to tolerate a range of failure scenarios, including loss of application processes and connectivity.We compare these two approaches and show performance results from micro-benchmarks that bound worst-case message passing performance degradation.We propose several enhancements that could lower the overhead of providing resiliency through redundancy.

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Understanding the effects of gravity and wind loads on concentrating solar power (CSP) collectors is critical for performance calculations and developing more accurate alignment procedures and techniques. This paper presents a rigorous finite-element model of a parabolic trough collector that is used to determine the impact of gravity loads on bending and displacements of the mirror facets and support structure. The geometry of the LUZ LS-2 parabolic trough collector was modeled using SolidWorks, and gravity-induced loading and displacements were simulated in SolidWorks Simulation. The model of the trough collector was evaluated in two positions: the 90{sup o} position (mirrors facing upward) and the 0{sup o} position (mirrors facing horizontally). The slope errors of the mirror facet reflective surfaces were found by evaluating simulated angular displacements of node-connected segments along the mirror surface. The ideal (undeformed) shape of the mirror was compared to the shape of the deformed mirror after gravity loading. Also, slope errors were obtained by comparing the deformed shapes between the 90{sup o} and 0{sup o} positions. The slope errors resulting from comparison between the deformed vs. undeformed shape were as high as {approx}2 mrad, depending on the location of the mirror facet on the collector. The slope errors resulting from a change in orientation of the trough from the 90{sup o} position to the 0{sup o} position with gravity loading were as high as {approx}3 mrad, depending on the location of the facet.

This paper introduces a new analytical 'stretch' function that accurately predicts the flux distribution from on-axis point-focus collectors. Different dish sizes and slope errors can be assessed using this analytical function with a ratio of the focal length to collector diameter fixed at 0.6 to yield the maximum concentration ratio. Results are compared to data, and the stretch function is shown to provide more accurate flux distributions than other analytical methods employing cone optics.

A rigorous computational fluid dynamics (CFD) approach to calculating temperature distributions, radiative and convective losses, and flow fields in a cavity receiver irradiated by a heliostat field is typically limited to the receiver domain alone for computational reasons. A CFD simulation cannot realistically yield a precise solution that includes the details within the vast domain of an entire heliostat field in addition to the detailed processes and features within a cavity receiver. Instead, the incoming field irradiance can be represented as a boundary condition on the receiver domain. This paper describes a program, the Solar Patch Calculator, written in Microsoft Excel VBA to characterize multiple beams emanating from a 'solar patch' located at the aperture of a cavity receiver, in order to represent the incoming irradiance from any field of heliostats as a boundary condition on the receiver domain. This program accounts for cosine losses; receiver location; heliostat reflectivity, areas and locations; field location; time of day and day of year. This paper also describes the implementation of the boundary conditions calculated by this program into a Discrete Ordinates radiation model using Ansys{reg_sign} FLUENT (www.fluent.com), and compares the results to experimental data and to results generated by the code DELSOL.

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Prediction is defined in the American Heritage Dictionary as follows: 'To state, tell about, or make known in advance, especially on the basis of special knowledge.' What special knowledge do we demand of modeling and simulation to assert that we have a predictive capability for high consequence applications? The 'special knowledge' question can be answered in two dimensions: the process and rigor by which modeling and simulation is executed and assessment results for the specific application. Here we focus on the process and rigor dimension and address predictive capability in terms of six attributes: (1) geometric and representational fidelity, (2) physics and material model fidelity, (3) code verification, (4) solution verification, (5) validation, and (6) uncertainty quantification. This presentation will demonstrate through mini-tutorials, simple examples, and numerous case studies how each attribute creates opportunities for errors, biases, or uncertainties to enter into simulation results. The demonstrations will motivate a set of practices that minimize the risk in using modeling and simulation for high-consequence applications while defining important research directions. It is recognized that there are cultural, technical, infrastructure, and resource barriers that prevent analysts from performing all analyses at the highest levels of rigor. Consequently, the audience for this talk is (1) analysts, so they can know what is expected of them, (2) decision makers, so they can know what to expect from modeling and simulation, and (3) the R&D community, so they can address the technical and infrastructure issues that prevent analysts from executing analyses in a practical, timely, and quality manner.