The objective of this project was to develop and apply enabling tools and methods towards advanced combustion diagnostics and control of fired-equipment in large-scale petrochemical manufacturing. There are a number of technology gaps and opportunities for combustion optimization, including technologies involving advanced in-situ measurements, modeling, and thermal imaging. These technologies intersect most of manufacturing and energy systems within the chemical industry. This project leveraged the success of a previous DOE funded project led by Dow, where we co-developed an in-situ tunable diode laser (TDL) analyzer platform (with Analytical Specialties Inc, now owned by Yokogawa Electric Corp.). The TDL platform has been tested and proven in a number of combustion processes within Dow and outside of Dow. The primary focus of this project was on combustion diagnostics and control applied towards furnaces, fired heaters and boilers. Special emphasis was placed on the development and application of in-situ measurements for O2, CO and methane since these combustion gases are key variables in optimizing and controlling combustion processes safely. Current best practice in the industry relies on measurements that suffer from serious performance gaps such as limited sampling volume (point measurements), poor precision and accuracy, and poor reliability. Phase I of the project …

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This work studies the performance and scalability characteristics of"hybrid'" parallel programming and execution as applied to raycasting volume rendering -- a staple visualization algorithm -- on a large, multi-core platform. Historically, the Message Passing Interface (MPI) has become the de-facto standard for parallel programming and execution on modern parallel systems. As the computing industry trends towards multi-core processors, with four- and six-core chips common today and 128-core chips coming soon, we wish to better understand how algorithmic and parallel programming choices impact performance and scalability on large, distributed-memory multi-core systems. Our findings indicate that the hybrid-parallel implementation, at levels of concurrency ranging from 1,728 to 216,000, performs better, uses a smaller absolute memory footprint, and consumes less communication bandwidth than the traditional, MPI-only implementation.

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