An efficient surrogate model for emulation and physics extraction of large eddy simulations

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations and statistical modeling. In this paper, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as {simplifying assumptions} for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations.Comment: Submitted to JASA A&C

Projected Increase of the East Asian Summer Monsoon (Meiyu) in Taiwan by Climate Models With Variable Performance

The active phase of the East Asian summer monsoon (EASM) in Taiwan during May and June, known as Meiyu, produces substantial precipitation for water uses in all sectors of society. Following a companion study that analysed the historical increase in the Meiyu precipitation, the present study conducted model evaluation and diagnosis based on the EASM lifecycle over Taiwan. Higher and lower skill groups were identified from 17 Couple Model Intercomparison Project Phase 5 (CMIP5) models, with five models in each group. Despite the difference in model performance, both groups projected a substantial increase in the Meiyu precipitation over Taiwan. In the higher skill group, weak circulation changes and reduced low‐level convergence point to a synoptically unfavourable condition for precipitation. In the lower skill group, intensified low‐level southwesterly winds associated with a deepened upper level trough enhance moisture pooling. Thus, the projected increase in Meiyu precipitation will likely occur through the combined effects of (1) the extension of a strengthened North Pacific anticyclone enhancing southwesterlies; and (2) more systematically, the Clausius–Clapeyron relationship that increases precipitation intensity in a warmer climate. The overall increase in the Meiyu precipitation projected by climate models of variable performance supports the observed tendency toward more intense rainfall in Taiwan and puts its early June 2017 extreme precipitation events into perspective

Tequila Regulates Insulin-Like Signaling and Extends Life Span in Drosophila melanogaster

The aging process is a universal phenomenon shared by all living organisms. The identification of longevity genes is important in that the study of these genes is likely to yield significant insights into human senescence. In this study, we have identified Tequila as a novel candidate gene involved in the regulation of longevity in Drosophila melanogaster. We have found that a hypomorphic mutation of Tequila (Teq(f01792)), as well as cell-specific downregulation of Tequila in insulin-producing neurons of the fly, significantly extends life span. Tequila deficiency-induced life-span extension is likely to be associated with reduced insulin-like signaling, because Tequila mutant flies display several common phenotypes of insulin dysregulation, including reduced circulating Drosophila insulin-like peptide 2 (Dilp2), reduced Akt phosphorylation, reduced body size, and altered glucose homeostasis. These observations suggest that Tequila may confer life-span extension by acting as a modulator of Drosophila insulin-like signaling