Unsteady shock wave dynamics in accelerating and decelerating flight

Increasingly agile manoeuvre is an advantage in the flight of aircraft, missiles and aerial vehicles, but the principles of accelerating aerodynamics in the transonic regime are only now being fully investigated. This study contributes to the understanding of shock and separation effects on drag during axial acceleration, using a simple geometric configuration. Unsteady shock wave behavior was numerically investigated for an axisymmetric cone-cylinder using a commercial solver and the Moving Reference Frame acceleration technique. This acceleration technique was validated using unsteady numerical and experimental methods. The cone-cylinder was accelerated from Mach number 0.6 to Mach number 1.2 at 100g constant and deceleration was from Mach number 1.2 until Mach number 0.6 at –100g constant. Three cone angles were tested for the cone-cylinder with uniform cylinder diameter. Acceleration through the transonic Mach regime was characterised by a delayed and gradual shock wave development when compared to steady state, demonstrating a clear flow history effect. Deceleration through the transonic Mach regime was characterised by shock wave propagation from the base to the nose. New flow structures appeared during deceleration that do not have counterparts in the steady state, including shock interactions and propagating expansion-compression features. Gross changes in the unsteady drag coefficient curves for each cone-angle are explained with reference to unsteady shock wave behaviour for accelerating and decelerating motion.

Numerical Study on Control of Sunroof Buffeting

Sunroof has become one of the essential features of a luxury car, and it provides natural air circulation and good illumination into the car. But the primary problem associated with it is the buffeting noise which causes discomfort to the passengers. Though adequate studies were carried out on sunroof buffeting, efficient control techniques are needed to be developed from fundamental mechanism. To reduce the buffeting noise, flow modifications at the entrance of the sunroof is considered in this study. The internal portion of the car with sunroof is simplified into a shear driven open cavity, and two-dimensional numerical simulations are carried out using commercial solver, ANSYS Fluent. Reynolds averaged Navier-Stokes equation is used with the realizable k-? turbulence model. The unsteady numerical result obtained in this study is validated with the available experimental results for the dominant frequency. The prediction is good agreement with experiment. Flow modification technique is proposed to control the sunroof buffeting by implementing geometric modifications. A hump has been placed near the leading edge of the cavity which resulted in significant reduction of pressure oscillations. Parametric studies have been performed by varying the height of hump and the distance of hump from the leading edge. There is no prominent difference when the height of the hump is varied. As the distance of the hump from the leading edge is reduced, the sound pressure level decreases.

Accelerate the optimization of large-scale manufacturing planning using game theory

This paper studies a real-world manufacturing problem, which is modeled as a bi-objective integer programming problem. The variables and constraints involved are usually numerous and dramatically vary according to the manufacturing data. It is very challenging to directly solve such large-scale problems using heuristic algorithms or commercial solvers. Considering that the decision space of such problems is usually sparse and has a block-like structure, we propose to use decomposition methods to accelerate the optimization process. However, the existing decomposition methods require that the problem has strict block structures, which is not suitable for our problem. To deal with problems with such block-like structures, we propose a game theory based decomposition algorithm. This new method can overcome the large-scale issue and guarantee convergence to some extent, as it can narrow down the search space and accelerate the convergence. Extensive experimental results on real-world industrial manufacturing planning problems show that our method is more effective than the world fastest commercial solver Gurobi. The results also indicate that our method is less sensitive to the problem scale comparing with Gurobi.

Intraroute Resource Replenishment with Mobile Depots

In numerous practical vehicle-routing applications, larger vehicles are employed as mobile depots to support a fleet of smaller vehicles that perform certain tasks. The mobile depots offer the possibility of keeping the task vehicles operational by supplying them en route with certain resources. For example, in two-echelon distribution systems, small task vehicles are used to navigate narrow streets and to deliver/collect goods or to collect waste, and larger vehicles serve as mobile depots to replenish the goods to be delivered or to receive collected goods or waste at the outskirts of the urban area. Accessibility constraints may also be imposed by regulations on emissions, which make some areas only accessible for environmentally friendly vehicles such as, for example, battery-powered electric vehicles. Especially if the respective refueling infrastructure is sparse, mobile refueling stations seem to be an interesting alternative. In this paper, we introduce the vehicle-routing problem with time windows and mobile depots (VRPTWMD) to capture the routing decisions of the described applications in a generalized fashion. The VRPTWMD is characterized by fleets of task vehicles (TVs) and support vehicles (SVs). The SVs may serve as mobile depots to restore either the load or the fuel capacity of the TVs that are used to fulfill the customer requests. We present a mixed-integer program for the VRPTWMD with which small instances can be solved using a commercial solver. Moreover, we develop a high-quality hybrid heuristic composed of an adaptive large neighborhood search and a path relinking approach to provide solutions on larger problem instances. We use a newly generated set of large VRPTWMD instances to analyze the effect of different problem characteristics on the structure of the identified solutions. In addition, our approach shows very convincing performance on benchmark instances for the related two-echelon multiple-trip VRP with satellite synchronization, which can be viewed as a special case of the VRPTWMD. Our heuristic is able to significantly improve a large part of the previous best-known solutions while spending notably less computation time than the comparison algorithm from the literature.

Airplane Seating Assignment Problem

SARS-CoV-2, the virus that causes COVID-19, began infecting humans in late 2019 and has since spread to over 57 million people and caused over 1.75 million deaths, as of December 27, 2020. In response to reduced demand and travel restrictions as a result of COVID-19, airlines experienced a 94% reduction in passenger capacity worldwide in April and an estimated 60% reduction in passengers transported for all of 2020. SARS-CoV-2 has been shown to spread on airplanes by infected passengers, so minimizing the risk of secondary infections aboard aircraft may save lives. We present the airplane seating assignment problem (ASAP) to minimize transmission risks on airplanes, and we provide two models to solve ASAP. We show that both models can be effectively solved using a standard commercial solver and that seating assignments provided by these models have lower aggregate risk than the strategy of blocking the middle seats, given the same number of passengers. The available risk models for aircraft are based on influenza data, and hence risk models based on SARS-CoV-2 should be developed to maximize the benefits of our research.

The electric vehicle routing problem with partial recharge and vehicle recycling

In this paper, a new variant of the electric vehicle (EV) routing problem, which considers heterogeneous EVs, partial recharge, and vehicle recycling, is investigated based on logistic companies' practical operation. A mixed integer linear programming (MILP) model is proposed to formulate the problem. For small-scale scenarios, commercial solver, e.g., CPLEX, is leveraged. For large-scale instances faced by practical applications, a hybrid metaheuristic is designed through integrating a modified Greedy Algorithm with the Variable Neighborhood Search (VNS). The proposed algorithm was tested by real-world instances from JD, an e-commerce enterprise in China. Computational results indicate that partial recharge and vehicle recycling can save costs effectively. It also shows that the number of charging stations is an important factor for the application of EVs.

Shooting-Bouncing-Rays Technique to Model Mine Tunnels: Theory and Accuracy Validation

We present a shooting-bouncing rays technique for electromagnetic modeling of wireless propagation in long tunnels focusing on the accuracy of ray-tracing computation. The examples demonstrate excellent agreement with the traditionally more accurate but less efficient alternative ray-tracing approach using path corrections based on image theory and with a commercial solver.

An Efficient Semianalytical Modal Analysis of Rectangular Waveguides Containing Metamaterials with Graded Inhomogeneity

Rectangular waveguides containing inhomogeneous metamaterials with graded refractive-index profiles have potential applications in bending waveguides and radiation-enhanced antennas, and accurate eigenvalue solutions are prerequisite. Commonly used commercial electromagnetic solvers such as HFSS, COMSOL, and CST could not efficiently calculate the eigenvalues of waveguides containing graded refractive-index dielectrics. In this paper, an accurate and efficient semianalytical method based on the modal expansion has been proposed to solve these waveguides. The proposed method has been employed to calculate the eigenvalues, including the cutoff wavenumbers and dispersion relations, for metamaterials with various graded refractive-index profiles. Calculated results are then validated by comparison, using commercial solver HFSS, which indicates the superiority of the proposed method in accuracy and efficiency. Below-cutoff backward wave propagation is observed in waveguides filled with graded refractive-index metamaterials, which provides a new approach for waveguide miniaturization.

Weighted proximity search

Proximity search is an iterative method to solve complex mathematical programming problems. At each iteration, the objective function of the problem at hand is replaced by the Hamming distance function to a given solution, and a cutoff constraint is added to impose that any new obtained solution improves the objective function value. A mixed integer programming solver is used to find a feasible solution to this modified problem, yielding an improved solution to the original problem. This paper introduces the concept of weighted Hamming distance that allows to design a new method called weighted proximity search. In this new distance function, low weights are associated with the variables whose value in the current solution is promising to change in order to find an improved solution, while high weights are assigned to variables that are expected to remain unchanged. The weights help to distinguish between alternative solutions in the neighborhood of the current solution, and provide guidance to the solver when trying to locate an improved solution. Several strategies to determine weights are presented, including both static and dynamic strategies. The proposed weighted proximity search is compared with the classic proximity search on instances from three optimization problems: the p-median problem, the set covering problem, and the stochastic lot-sizing problem. The obtained results show that a suitable choice of weights allows the weighted proximity search to obtain better solutions, for 75$$\%$$ % of the cases, than the ones obtained by using proximity search and for 96$$\%$$ % of the cases the solutions are better than the ones obtained by running a commercial solver with a time limit.

Development and Validation of an Advanced Actuator Line Model for Wind Turbines

As wind turbine technology proceeds towards the development of more advanced and complex machines, modelling tools with fidelity higher than the ubiquitous Blade Element Momentum (BEM) method are needed. Among them, the Actuator Line Method (ALM) stands out in terms of accuracy and computational cost. Moving from this background, an advanced ALM method has been developed within the commercial solver CONVERGE®. As elements of novelty, this tool features a Lagrangian method for sampling the local inflow velocity and a piece-wise smearing function for the force projection process. Various sub-models for both Horizontal Axis Wind Turbines (HAWTs) (e.g. the Shen tip loss correction) and Vertical Axis Wind Turbines (VAWTs) (e.g. the MIT dynamic stall model) has also been included. Aim of the research is to address the new challenges posed by modern machines. HAWTs are in fact getting larger and larger, shifting the research focus on the interaction of increasingly deformable blades with the atmosphere at the micro- and mesoscale level. VAWTs on the other hand, whose popularity has arisen in the last years, thanks to their advantages in non-conventional applications, e.g. floating offshore installations, are extremely complex machines to study, due to their inherently unsteady aerodynamics. The approach has been validated on selected test cases, i.e. the DTU 10MW turbine and a real 2-blade H-rotor, for which both high-fidelity CFD and experimental data are available.