1,588 research outputs found

    Multi objective optimization in charge management of micro grid based multistory carpark

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    Distributed power supply with the use of renewable energy sources and intelligent energy flow management has undoubtedly become one of the pressing trends in modern power engineering, which also inspired researchers from other fields to contribute to the topic. There are several kinds of micro grid platforms, each facing its own challenges and thus making the problem purely multi objective. In this paper, an evolutionary driven algorithm is applied and evaluated on a real platform represented by a private multistory carpark equipped with photovoltaic solar panels and several battery packs. The algorithm works as a core of an adaptive charge management system based on predicted conditions represented by estimated electric load and production in the future hours. The outcome of the paper is a comparison of the optimized and unoptimized charge management on three different battery setups proving that optimization may often outperform a battery setup with larger capacity in several criteria.Web of Science117art. no. 179

    Optimal design and control of an automated bike parking system

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    Large cities and metropolitan areas are facing parking space problems due to the increasing number of commuters who choose bicycles as the main mode of transportation. The bicycle parking should provide enough space for the bicycle, as well as corridors and isles to reach the space. Inspired by the automated storage and retrieving systems and by the cities’ cycling encouraging plans and their problem of space and location for the bicycle parking, an automated bicycle parking system is introduced in this work. First, the growth of cycling and the subsequent issues that the cities and cyclists confronting are investigated. Then the traditional and existing solutions and their deficiencies are explored. The automated parking system is studied as a solution which meets and improves the deficiencies of the existing solutions, it takes minimum space and encourages the use of bicycle by providing a more secure parking experience. To exhibit the superiority of the automated system, this thesis follows the design, model, and manufacture of such a system. however, to even improve the system further, a study in the optimization of some parts of the system to reduce energy consumption has been commenced. The design, manufacturing, and installation of the system’s exterior are not included in this work

    On the design of smart parking networks in the smart cities: an optimal sensor placement model

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    Smart parking is a typical IoT application that can benefit from advances in sensor, actuator and RFID technologies to provide many services to its users and parking owners of a smart city. This paper considers a smart parking infrastructure where sensors are laid down on the parking spots to detect car presence and RFID readers are embedded into parking gates to identify cars and help in the billing of the smart parking. Both types of devices are endowed with wired and wireless communication capabilities for reporting to a gateway where the situation recognition is performed. The sensor devices are tasked to play one of the three roles: (1) slave sensor nodes located on the parking spot to detect car presence/absence; (2) master nodes located at one of the edges of a parking lot to detect presence and collect the sensor readings from the slave nodes; and (3) repeater sensor nodes, also called ''anchor'' nodes, located strategically at specific locations in the parking lot to increase the coverage and connectivity of the wireless sensor network. While slave and master nodes are placed based on geographic constraints, the optimal placement of the relay/anchor sensor nodes in smart parking is an important parameter upon which the cost and e ciency of the parking system depends. We formulate the optimal placement of sensors in smart parking as an integer linear programming multi-objective problem optimizing the sensor network engineering e ciency in terms of coverage and lifetime maximization, as well as its economic gain in terms of the number of sensors deployed for a specific coverage and lifetime. We propose an exact solution to the node placement problem using single-step and two-step solutions implemented in the Mosel language based on the Xpress-MPsuite of libraries. Experimental results reveal the relative e ciency of the single-step compared to the two-step model on di erent performance parameters. These results are consolidated by simulation results, which reveal that our solution outperforms a random placement in terms of both energy consumption, delay and throughput achieved by a smart parking network

    Multi-Objective structural optimization of repairs of blisk blades

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    Modern manufacturing technologies offer multiple options to extend the service life of expensive jet engine components through repairs. In this context, the repair processes of blade-integrated disks (blisks) are of particular interest, as the complex design makes replacement of this part very costly. However, currently, repairs of blisks are mainly done manually and repair design decisions still rely on the expertise of maintenance technicians. From a scientific perspective, these subjective, experience-based decisions are a major drawback, as today’s computational methods allow for systematic analysis and evaluation of design alternatives. The present doctoral thesis contributes to the decision-making process related to the repair of blisk blades by blending and patching by providing an engineering optimization framework and simulation routines for structural assessment of different repair designs. First, an object-oriented optimization framework is developed that is ideally suited to address engineering optimization problems such as blisk repair optimization. The design of the software architecture is chosen to achieve a high degree of flexibility and modularity. In particular, the framework provides a unified interface for global and local derivative-free optimization algorithms and custom engineering optimization problems. Thereby, optimization of single- as well as multi-objective problems is supported. The broad applicability of the framework in engineering optimization is demonstrated using examples from wind energy research. Furthermore, the optimization framework forms a suitable environment for structural multi-objective optimization of blend and patch repairs. The second part of this thesis is devoted to the application of the optimization framework to blend repairs of a compressor blisk. The geometry of the removed blade part and the resulting blend is parameterized by three geometric design variables. The two objectives of the optimization correspond to two modal criteria, because especially the vibration behavior of blades is affected by this kind of geometric modification. To check if frequency requirements are harmed by the repair the first objective reflects the deviation of the natural frequencies of the repaired blade to the natural frequencies of the nominal blade. The second objective considers resonance conditions by evaluating the proximity of natural frequencies to excitation frequencies. Pareto optimal repair designs are found by solving the derived optimization problem using appropriate structural mechanics models of a blade sector and employing the developed optimization framework. By analyzing the optimal blend shapes for two different damage patterns, it is shown that the characteristics of Pareto frontiers, like the occurrence of discontinuities, are damage-specific. Therefore, it is concluded that design decisions on blend repairs have to be made on a case-by-case basis. The third part of this thesis is concerned with the multi-objective optimization of patch repairs. While blend repairs change the blade geometry, patch repairs restore the original blade contour. In terms of structural integrity, the most significant modification due to patching is hence associated with the welding process to join patch and blade. The remaining residual stresses, affect the strength of the repaired blade, are therefore the most critical aspect of patch repairs. Utilizing the engineering optimization framework and the parametric simulation model, a multi-objective optimization problem is solved considering the length of the weld and the fatigue strength of the repaired blade. In addition to fatigue strength properties, the weld length is selected as an optimization goal, since the manufacturing effort of the high-tech repair is of practical importance. Pareto optimal repair designs are presented for a damage pattern at the leading edge. The optimization results are further complemented by subsequent thermal and mechanical simulations of the welding and heat treatment process. Different patch geometries are classified from the Pareto optimal solutions. Depending on the preferences in terms of weld length and the High-Cycle Fatigue strength of different load cases, short or long patches are to be used. In addition, the results show that some potential patch designs are not optimal in any case, and therefore can be completely excluded. Finally, the benefits of the unified interface of the engineering optimization framework are emphasized. Different optimization settings of a patch repair optimization are presented and compared utilizing the hypervolume metric. Concluding remarks on the potential of computational methods for improved repair design and an outlook on future maintenance of blisks complete this work.DFG/SFB 871/119 193 472./E

    The Merits of Sharing a Ride

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    The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads

    Using genetic algorithm optimization as a multi-gravity assist trajectory design tool

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    This master thesis presents the development of a genetic algorithm for optimizing interplanetary trajectories using multi-planetary gravity assists while considering time as an additional objective in the fitness evaluation. The objective of the research is to address the challenges of designing efficient trajectories that minimize both delta-v and travel duration. The aim of this thesis is to develop an all-encompassing deep space mission trajectory design tool where a trade-off between the total delta-v used and the arrival time can be made, in the interest of the overall mission profile. The results obtained highlight the effectiveness of genetic algorithms in finding optimal multi-planetary gravity assist trajectories and contribute to the advancement of trajectory optimization techniques for future space missions

    High-density parking for autonomous vehicles.

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    In a common parking lot, much of the space is devoted to lanes. Lanes must not be blocked for one simple reason: a blocked car might need to leave before the car that blocks it. However, the advent of autonomous vehicles gives us an opportunity to overcome this constraint, and to achieve a higher storage capacity of cars. Taking advantage of self-parking and intelligent communication systems of autonomous vehicles, we propose puzzle-based parking, a high-density design for a parking lot. We introduce a novel method of vehicle parking, which leads to maximum parking density. We then propose a heuristic method to solve larger problems, and mathematically prove that the method produces near-optimal results. To improve layout designs reducing vehicular movements, we propose a use of a meta-heuristic algorithm integrated with a deep reinforcement learning method. Finally, to take advantage of these puzzle-based designs in large-scale, we propose a modular layout design. This design process consists of two steps: i) design of a high-density modular lot, which we call sub-lot, and ii) integration of these sub-lots into a large parking lot. We have conducted a set of experiments to determine which sub-lot size provide the best performance in terms of density and retrieval time

    Multi-level evolutionary algorithms resource allocation utilizing model-based systems engineering

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    This research presents an innovative approach to solve the resource allocation problems using Multi-level Evolutionary Algorithms. Evolutionary Algorithms are used to solve resource allocation problems in different domains and their results are then incorporated into a higher level system solution using another Evolutionary Algorithm to solve base camp planning problems currently faced by the U.S. Department of Defense. Two models are introduced to solve two domain specific models: a logistics model and a power model. The logistic model evaluates routes for logistics vehicles on a daily basis with a goal of reducing fuel usage by delivery trucks. The evaluation includes distance traveled and other constraints such as available resource levels and priority of refilling. The Power model incorporates an open source electrical distribution simulator to evaluate the placement of structures and generators on a map to reduce fuel usage. These models are used as the fitness function for two separate Evolutionary Algorithms to find solutions that reduce fuel consumption within the individual domains. A multi-level Evolutionary Algorithm is then presented, where the two Evolutionary Algorithms share information with a higher level Evolutionary Algorithm that combines the results to account for problem complexity from the interfacing of these systems. The results of using these methods on 5 different base camp sizes show that the techniques provide a considerable reduction of fuel consumption. While the Evolutionary Algorithms show significant improvement over the current methods, the multi-level Evolutionary Algorithm shows better performance than using individual Evolutionary Algorithms, with the results showing a 19.25 % decrease in fuel consumption using the multi-level Evolutionary Algorithm --Abstract, page iii

    Evolutionary testing of autonomous software agents

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    A system built in terms of autonomous software agents may require even greater correctness assurance than one that is merely reacting to the immediate control of its users. Agents make substantial decisions for themselves, so thorough testing is an important consideration. However, autonomy also makes testing harder; by their nature, autonomous agents may react in different ways to the same inputs over time, because, for instance they have changeable goals and knowledge. For this reason, we argue that testing of autonomous agents requires a procedure that caters for a wide range of test case contexts, and that can search for the most demanding of these test cases, even when they are not apparent to the agents’ developers. In this paper, we address this problem, introducing and evaluating an approach to testing autonomous agents that uses evolutionary optimisation to generate demanding test cases. We propose a methodology to derive objective (fitness) functions that drive evolutionary algorithms, and evaluate the overall approach with two simulated autonomous agents. The obtained results show that our approach is effective in finding good test cases automatically
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