Electrical Interoperability Evaluating of Wireless Electric Vehicle Charging Systems Based on Impedance Space

In the commercialization process of wireless electric vehicle charging (WEVC), it is essential to ensure the interoperability between diverse WEVC systems due to the wide application of various coil configurations and compensation topologies. This paper proposes a novel electrical interoperability evaluation method based on impedance indices and corresponding feasible space in the complex plane. Firstly, the electromagnetic description of the coil system is introduced to reveal the energy flow process of WEVC system. Further, two key impedance indices and their feasible space are derived and verified. Interoperability evaluation results show that the reference devices in Chinese WEVC standard GB/T 38775.6 and GB/T 38775.7 are able to achieve the requirements of power capability. Moreover, it is necessary to reduce the duty cycle of rectifier when the battery voltage rises so as to narrow down the variation of load resistance and avoid dangerous working conditions. The proposed method can effectively evaluate the electrical interoperability of WEVC systems from different manufacturers under different power or distance levels before conducting experiments.

Contrastive Losses and Solution Caching for Predict-and-Optimize

Many decision-making processes involve solving a combinatorial optimization problem with uncertain input that can be estimated from historic data. Recently, problems in this class have been successfully addressed via end-to-end learning approaches, which rely on solving one optimization problem for each training instance at every epoch. In this context, we provide two distinct contributions. First, we use a Noise Contrastive approach to motivate a family of surrogate loss functions, based on viewing non-optimal solutions as negative examples. Second, we address a major bottleneck of all predict-and-optimize approaches, i.e. the need to frequently recompute optimal solutions at training time. This is done via a solver-agnostic solution caching scheme, and by replacing optimization calls with a lookup in the solution cache. The method is formally based on an inner approximation of the feasible space and, combined with a cache lookup strategy, provides a controllable trade-off between training time and accuracy of the loss approximation. We empirically show that even a very slow growth rate is enough to match the quality of state-of-the-art methods, at a fraction of the computational cost.

Tools and methodologies for box-wing aircraft conceptual aerodynamic design and aeromechanic analysis

A way to face the challenge of moving towards a new greener aviation is to exploit disruptive aircraft architectures; one of the most promising concept is the PrandtlPlane, a box-wing aircraft based on the Prandtl's studies on multiplane lifting systems. A box-wing designed accordingly the Prandtl “best wing system” minimizes the induced drag for given lift and span, and thus it has the potential to reduce fuel consumption and noxious emissions. For disruptive aerodynamic concepts, physic-based aerodynamic design is needed from the very early stages of the design process, because of the lack of available statistical data; this paper describes two different in-house developed aerodynamic design tools for the PrandtlPlane conceptual aerodynamic design: AEROSTATE, for the design of the box-wing lifting system in cruise condition, and THeLMA, aiming to define the layout of control surfaces and high lift devices. These two tools have been extensively used to explore the feasible space for the aerodynamic design of the box-wing architecture, aiming to define preliminary correlations between performance and design variables, and guidelines to properly initialize the design process. As a result, relevant correlations have been identified between the rear-front wing loading ratio and the performance in cruise condition, and for the rear-front flap deflections and the aeromechanic characteristics in low speed condition.

Development and performance analysis of a PCM based cooling fan for hot climate of Bangladesh

In hot climatic regions, some kind of cooling system is necessary to avoid warmth and humidity. Many of the available cooling systems are not economic and sustainable. In this study, sustainable and feasible space/room cooling systems have been experimentally analyzed. A solar operated cooling system with two options have been designed and their performances are compared. Phase Change Material (PCM) is proposed to store thermal energy instead of a costly battery. A 1200-watt compressor and fin-type condenser are used to construct the vapor compression system. When the incoming air is passed through the cooling coil, it gets cool. For this cooling coil, 50 feet copper tube is used. The front side copper tube diameter of the fan is 3/8 inch and the backside tube diameter is 1/2 inch. It took about 35 minutes and 5 minutes to minimize the room temperature at the desired level in the case of the stand fan and duct fan, respectively. Furthermore, the stand fan and duct fan systems reduced 3 ℃ and 6 ℃ of the outside temperature, respectively.

Global optimization via inverse distance weighting and radial basis functions

Global optimization problems whose objective function is expensive to evaluate can be solved effectively by recursively fitting a surrogate function to function samples and minimizing an acquisition function to generate new samples. The acquisition step trades off between seeking for a new optimization vector where the surrogate is minimum (exploitation of the surrogate) and looking for regions of the feasible space that have not yet been visited and that may potentially contain better values of the objective function (exploration of the feasible space). This paper proposes a new global optimization algorithm that uses inverse distance weighting (IDW) and radial basis functions (RBF) to construct the acquisition function. Rather arbitrary constraints that are simple to evaluate can be easily taken into account. Compared to Bayesian optimization, the proposed algorithm, that we call GLIS (GLobal minimum using Inverse distance weighting and Surrogate radial basis functions), is competitive and computationally lighter, as we show in a set of benchmark global optimization and hyperparameter tuning problems. MATLAB and Python implementations of GLIS are available at http://cse.lab.imtlucca.it/~bemporad/glis.