Compression and Conditional Emulation of Climate Model Output

Numerical climate model simulations run at high spatial and temporal resolutions generate massive quantities of data. As our computing capabilities continue to increase, storing all of the data is not sustainable, and thus it is important to develop methods for representing the full datasets by smaller compressed versions. We propose a statistical compression and decompression algorithm based on storing a set of summary statistics as well as a statistical model describing the conditional distribution of the full dataset given the summary statistics. The statistical model can be used to generate realizations representing the full dataset, along with characterizations of the uncertainties in the generated data. Thus, the methods are capable of both compression and conditional emulation of the climate models. Considerable attention is paid to accurately modeling the original dataset--one year of daily mean temperature data--particularly with regard to the inherent spatial nonstationarity in global fields, and to determining the statistics to be stored, so that the variation in the original data can be closely captured, while allowing for fast decompression and conditional emulation on modest computers

Significantly Improving Lossy Compression for Scientific Data Sets Based on Multidimensional Prediction and Error-Controlled Quantization

Today's HPC applications are producing extremely large amounts of data, such that data storage and analysis are becoming more challenging for scientific research. In this work, we design a new error-controlled lossy compression algorithm for large-scale scientific data. Our key contribution is significantly improving the prediction hitting rate (or prediction accuracy) for each data point based on its nearby data values along multiple dimensions. We derive a series of multilayer prediction formulas and their unified formula in the context of data compression. One serious challenge is that the data prediction has to be performed based on the preceding decompressed values during the compression in order to guarantee the error bounds, which may degrade the prediction accuracy in turn. We explore the best layer for the prediction by considering the impact of compression errors on the prediction accuracy. Moreover, we propose an adaptive error-controlled quantization encoder, which can further improve the prediction hitting rate considerably. The data size can be reduced significantly after performing the variable-length encoding because of the uneven distribution produced by our quantization encoder. We evaluate the new compressor on production scientific data sets and compare it with many other state-of-the-art compressors: GZIP, FPZIP, ZFP, SZ-1.1, and ISABELA. Experiments show that our compressor is the best in class, especially with regard to compression factors (or bit-rates) and compression errors (including RMSE, NRMSE, and PSNR). Our solution is better than the second-best solution by more than a 2x increase in the compression factor and 3.8x reduction in the normalized root mean squared error on average, with reasonable error bounds and user-desired bit-rates.Comment: Accepted by IPDPS'17, 11 pages, 10 figures, double colum

Numerical modelling of hailstone impact on the leading edge of a wind turbine blade

The scale of modern blades means that tip speeds in excess of 100ms-1 are now common in utility scale turbines. Coupling this with a hailstone terminal velocity ranging from 9ms-1 to 40ms-1, the relative impact velocity becomes highly significant. There is little published data on the performance of blade materials under these impact conditions and as such this work aims to understand the impact phenomena more clearly and consequently characterize the impact performance of the constitutive blade materials. To better understand hailstone impact, the LS-DYNA explicit dynamics code was employed to simulate hailstone impact on the blade leading edge. A Smooth Particle Hydrodynamics approach (SPH) was chosen to represent the hailstone geometry. It was found that the forces and stresses created during hail impact are significant and in some cases damaging, therefore posing both short and long term risks to the material integrity. It was also found that coating systems such as the gel coat provide essential – and in extreme conditions, sacrificial – protection to the composite substrate

Screening of energy efficient technologies for industrial buildings' retrofit

This chapter discusses screening of energy efficient technologies for industrial buildings' retrofit

A review of contemporary techniques for measuring ergonomic wear comfort of protective and sport clothing

Protective and sport clothing is governed by protection requirements, performance, and comfort of the user. The comfort and impact performance of protective and sport clothing are typically subjectively measured, and this is a multifactorial and dynamic process. The aim of this review paper is to review the contemporary methodologies and approaches for measuring ergonomic wear comfort, including objective and subjective techniques. Special emphasis is given to the discussion of different methods, such as objective techniques, subjective techniques, and a combination of techniques, as well as a new biomechanical approach called modeling of skin. Literature indicates that there are four main techniques to measure wear comfort: subjective evaluation, objective measurements, a combination of subjective and objective techniques, and computer modeling of human–textile interaction. In objective measurement methods, the repeatability of results is excellent, and quantified results are obtained, but in some cases, such quantified results are quite different from the real perception of human comfort. Studies indicate that subjective analysis of comfort is less reliable than objective analysis because human subjects vary among themselves. Therefore, it can be concluded that a combination of objective and subjective measuring techniques could be the valid approach to model the comfort of textile materials

Carbon capture from natural gas combined cycle power plants: Solvent performance comparison at an industrial scale

Natural gas is an important source of energy. This article addresses the problem of integrating an existing natural gas combined cycle (NGCC) power plant with a carbon capture process using various solvents. The power plant and capture process have mutual interactions in terms of the flue gas flow rate and composition vs. the extracted steam required for solvent regeneration. Therefore, evaluating solvent performance at a single (nominal) operating point is not indicative and solvent performance should be considered subject to the overall process operability and over a wide range of operating conditions. In the present research, a novel optimization framework was developed in which design and operation of the capture process are optimized simultaneously and their interactions with the upstream power plant are fully captured. The developed framework was applied for solvent comparison which demonstrated that GCCmax, a newly developed solvent, features superior performances compared to the monoethanolamine baseline solvent