Nostradamus: Weathering Worth

Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.Comment: 12 pages, 13 figure

CFD validation of moment balancing method on drag-dominant tidal turbines (DDTTs)

Current performance analysis processes for drag-dominant tidal turbines are unsuitable as disk actuator theory lacks support for varying swept blockage area, bypass flow downstream interaction, and parasitic rotor drag, whereas blade element momentum theory is computably effective for three-blade lift-dominated aerofoil. This study proposes a novel technique to calculate the optimal turbine tip speed ratio (TSR) with a cost-effective and user-friendly moment balancing algorithm. A reliable dynamic TSR matrix was developed with varying rotational speeds and fluid velocities, unlike previous works simulated at a fixed fluid velocity. Thrust and idle moments are introduced as functions of inlet fluid velocity and rotational speed, respectively. The quadratic relationships are verified through regression analysis, and net moment equations are established. Rotational speed was a reliable predictor for Pinwheel’s idle moment, while inlet velocity was a reliable predictor for thrust moment for both models. The optimal (Cp, TSR) values for Pinwheel and Savonius turbines were (0.223, 2.37) and (0.63, 0.29), respectively, within an acceptable error range for experimental validation. This study aims to improve prevailing industry practices by enhancing an engineer’s understanding of optimal blade design by adjusting the rotor speed to suit the inlet flow case compared to ‘trial and error’ with cost-intensive simulations.Published versionThe authors would like to thank Nanyang Technological University for providing the computing facilities needed to carry out this study, as well as the Interdisciplinary Graduate School scholarship for funding this project

Simulation validation of moment balancing method for drag-dominant tidal turbines

Drag-dominated turbines play a key role in the application of urban windfarm and multi-flow direction tidal arrays because of their low cut-in speed and omnidirectional characteristics. A performance analysis study of Pinwheel and Savonius tidal turbines has been carried out using Computational Fluid Dynamics (CFD) software to define the optimal power coefficient (Cp) and Tip-Speed-Ratios (TSR). The classic Disk Actuator model assumes a fixed virtual disc with or without porous holes perpendicular to the inflow direction. This is unsuitable for drag-dominant turbine because of the rotating virtual disc of the rotor plate of a vertical-axis turbine, the unaccounted bypass flow interaction on the downstream flow boundary for a horizontal-axis turbine, and parasitic force acting on the rotor/support walls for both. Therefore, a more applicable model is required for the tidal turbine realm. The focus of this study is to propose a novel method to find the optimal TSR of a drag-dominant turbine with a cost-effective and user-friendly Moment Balancing algorithm. The CFD models were inspired and scaled from experimental findings in the literature review. Both models were made comparable using a parametric study to equalize the blockage area at 12%. After careful analysis of different solver settings, steady k-epsilon model was selected, and grid independence tests were conducted. V-shaped TSR matrix was developed with varying turbine rotational speeds and fluid inlet velocity, unlike previous works simulated at a fixed velocity. For Pinwheel and Savonius, the TSR range for simulations is 0.64-5.0 and 0.3-1.0 respectively. Thrust Moment (Acting) is calculated when the turbine is stationary, but the fluid motion exerts load and rotates it. Idle Moment (Resisting) is calculated when the turbine is rotating at a given speed and the water is stationary hence, a load is exerted on the turbine. Linear regression analysis was performed and coefficients for thrust and idle moment were calculated, thus, formulating an equation for the net moment of Pinwheel and Savonius. It is found that the power coefficient is maximum or zero when idle and thrust moment offset each other at the neutral point. The optimal TSR are found for Pinwheel at 2.37 and Savonius at 0.63 with 15.6% and 11.1% error rate respectively for experimental validation. Based on the findings, thrust and idle moment have a positive and negative quadratic relationship respectively with the inlet velocity. A hill-shaped curve is observed between power coefficient and TSR. The optimal TSR for Pinwheel is higher than Savonius, thereby a higher rotational and lower inlet speed should be adjusted accordingly and vice versa. The proposed algorithm is expected to improve and simplify an engineer’s understanding of the turbine’s optimal TSR by adjusting the rotor speed to suit the inlet flow case. The computational cost is greatly reduced through replacing net moment simulations by combining thrust and idle moment simulations. Upon commercial launch of the algorithm, the tidal energy development will become robust and more affordable.Nanyang Technological UniversityPublished versionThe authors would like to offer their appreciation and thanks to Collaborative Initiative, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore for the support in computing facility in model optimization and IGP scholarship

An ML-Powered Human Behavior Management System

International audienceOur work aims to develop novel technologies for building an efficient data infrastructure as a backbone for a human behavior management system. Our infrastructure aims at facilitating behavior modeling, discovery, and exploitation, leading to two major outcomes: a behavior data management back-end and a high-level behavior specification API that supports mining, indexing and search, and AI-powered algorithms that provide the ability to extract insights on human behavior and to leverage data to advance human capital. We discuss the role of ML in populating and maintaining the back-end, and in exploiting it for human interest