Refrigeration and chiller optimization is an important and well studied topic in mechanical engineering, mostly taking advantage of physical models, designed on top of over-simpli ed assumptions, over the equipments. Conventional optimization techniques using physical models make decisions of online parameter tuning, based on very limited information of hardware speci cations and external conditions, e.g., outdoor weather. In recent years, new generation of sensors is becoming essential part of new chiller plants, for the rst time allowing the system administrators to continuously monitor the running status of all equipments in a timely and accurate way. e explosive growth of data owing to databases, driven by the increasing analytical power by machine learning and data mining, unveils new possibilities of data-driven approaches for real-time chiller plant optimization.is paper presents our research and industrial experience on the adoption of data models and optimizations on chiller plant and discusses the lessons learnt from our practice on real world plants. Instead of employing complex machine learning models, we emphasize the incorporation of appropriate domain knowledge into data analysis tools, which turns out to be the key performance improver over state-of-the-art deep learning techniques by a signi cant margin. Our empirical *
An experimental study of damage tolerance under quasi-static indentation (QSI) was performed for sandwich composite panels consisting of 16-ply carbon-epoxy facesheets bonded to an aluminum honeycomb core. To determine how indentation damage and compression strength after indentation depend on the facesheet layup, three facesheet stacking sequences were used, varying the maximum ply angle change and placement of the outermost 0 ply. Similarly, to determine the effect of core parameters on damage and strength following indentation, three cores with varying density and thickness were studied. Specimens were indented in QSI to the barely visible indentation damage threshold by spherical indenters of 25.4 or 76.2 mm diameters. Damaged specimens were tested to failure in compression to determine the post-indentation compressive strength and resulting failure mode. Compression-after-indentation (CAI) strength is compared to the undamaged strength obtained from edgewise-compression tests of specimens with the same geometry type. Three distinct failure modes were observed in the CAI experiments: compressive fiber failure, delamination buckling and global instability. Post-indentation compressive strength was independent of indenter size and there was no clear propensity for a particular failure mode dependent on a given specimen geometry. Specimens with a high core density and facesheets with a primary ply angle change of 90 were found to be the most damage resistant. Specimens with facesheets having the outer 0 plies closest to the center of the laminate were found to be the most damage tolerant.
In this paper we develop a methodology to quantify the value to consumers of the non-price characteristics of airline networks. Our research demonstrates that analyses that ignore the quality effects associated with expanded airline networks generate incorrect findings and thus should not form the basis for policy decisions regarding airline transactions. Appropriately incorporating quality effects into quality-adjusted fares reverses the conclusion that hub airports yield lower consumer welfare due to generally higher fares than other airports. From the perspective of consumer welfare in this industry, to evaluate potential airline mergers, alliances, slot swaps or other transactions, one should not focus solely on the effect of concentration on nominal fares, rather, one should account for the welfare-enhancing effects of larger airline networks.
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