Recherche
Filtrage par:
College
Engineering and Applied Science
Supprimer la restriction College: Engineering and Applied Science
Nombre de résultats à afficher par page
Résultats de recherche
-
- Type:
- Article
- Description/Abstract:
- The methods and outcome of a senior undergraduate project related to the control of a turbulent cylinder wake flow using plasma actuators are summarized in this article. The study integrates computational fluid dynamics (CFD) with experimentation and combines fluid mechanics with flow control research, crossing the boundaries between engineering disciplines.Comput. Appl. Eng. Educ.
- Creator/Author:
- Cohen, Kelly; McLaughlin, Thomas; Seaver, Christopher A., and Aradag, Selin
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/08/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2009-02
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- Comparison of approximate approaches to solving the Travelling Salesman Problem and its application to UAV swarming. International Journal of Unmanned Systems Engineering. 3(1): 1-16. The Travelling Salesman Problem (TSP) is a widely researched Non-deterministic Polynomial-time hard optimization problem with a range of important applications in a wide spectrum of disciplines including aerospace engineering. In this paper, a comparison of different approaches to solve the TSP and also its application towards swarming of UAVs is considered. The objective of the TSP is to determine the optimal route associated with the shortest tour connecting all targets just once. Genetic Algorithms (GA) are one of the most widely applied techniques for solving this class of optimization problems. Two other techniques, 2-opt and Particle Swarm Optimization, are used and the results are compared with those obtained using GA. The comparison is made for different numbers of targets, using salient figures of merit such as computational time required and the cost function which is the minimum solution (distance) obtained. Results show that the 2-opt approach with the closest neighbour as initial starting point for the search yields superior performance. In the Multiple Travelling Salesman Problem, we propose a cluster-first approach which allocates each specific UAV to a subset of targets. The 200 targets are divided into four clusters corresponding to the four UAVs and then TSP algorithms like 2-opt and GA are employed to solve each cluster. This approach drastically reduces the computational time and also gives much better results than the conventional technique of directly applying GA over the 200 targets.
- Creator/Author:
- Cohen, Kelly; Boone, Nathan, and Sathyan, Anoop
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2015-01
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- Not Available
- Creator/Author:
- Cohen, Kelly and Ernest, Nick
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2015-12
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- This study introduces the technique of Genetic Fuzzy Trees (GFTs) through novel application to an air combat control problem of an autonomous squadron of Unmanned Combat Aerial Vehicles (UCAVs) equipped with next-generation defensive systems. GFTs are a natural evolution to Genetic Fuzzy Systems, in which multiple cascading fuzzy systems are optimized by genetic methods. In this problem a team of UCAV's must traverse through a battle space and counter enemy threats, utilize imperfect systems, cope with uncertainty, and successfully destroy critical targets. Enemy threats take the form of Air Interceptors (AIs), Surface to Air Missile (SAM) sites, and Electronic WARfare (EWAR) stations. Simultaneous training and tuning a multitude of Fuzzy Inference Systems (FISs), with varying degrees of connectivity, is performed through the use of an optimized Genetic Algorithm (GA). The GFT presented in this study, the Learning Enhanced Tactical Handling Algorithm (LETHA), is able to create controllers with the presence of deep learning, resilience to uncertainties, and adaptability to changing scenarios. These resulting deterministic fuzzy controllers are easily understandable by operators, are of very high performance and efficiency, and are consistently capable of completing new and different missions not trained for.
- Creator/Author:
- Schumacher, Corey; Cohen, Kelly; Ernest, Nicholas; Casbeer, David, and Kivelevitch, Elad
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2015-05
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- This work presents a methodology for real-time estimation of wildland fire growth, utilizing a fire growth model based on a set of partial differential equations for prediction, and harnessing concepts of space-time Kalman filtering and Proper Orthogonal Decomposition techniques towards low dimensional estimation of potentially large spatio-temporal states. The estimation framework is discussed in its criticality towards potential applications such as forest fire surveillance with unmanned systems equipped with onboard sensor suites. The effectiveness of the estimation process is evaluated numerically over fire growth data simulated using a well-established fire growth model described by coupled partial differential equations. The methodology is shown to be fairly accurate in estimating spatio-temporal process states through noise-ridden measurements for real-time deployability.
- Creator/Author:
- Sharma, Balaji R.; Cohen, Kelly, and Kumar, Manish
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2013-10
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- Fire is a natural component of many ecosystems but wildland fires often do pose serious threats to public safety, properties and natural resources. Forest fire acts as a dominant factor in reshaping of terrain and change of the ecosystem of a particular area. The total damage due to wildland fire shows an increasing trend over the past decade. Forest Fire Decision Support Systems (FFDSS) have been developed for the last thirty years all over the world that supplies valuable information on forest fire detection, fire behavior and other aspects of forest fires but lacks in developing intelligent fire suppression strategies. In this paper, an effort has been made to generate intelligent fire suppression strategies with efficient resource allocation using the Genetic Algorithm based optimization tool in a heterogeneous and uncertain scenario. The goal of this research is to perform intelligent resource allocation along with the generation of optimal firelines that minimizes the total burned area due to wildland fire. The solutions generated at each generations of the Genetic Algorithm (GA) are used to build the firelines in a heterogeneous terrain where advanced forest fire propagation model is used to evaluate the fitness values of each generated solutions. The optimal firelines thus obtained through the Simulation-Optimization technique minimizes the total damage due to wildland fire and eliminates the chance of any fire escape i.e., firefront reaching the fireline positions before they are built. Such techniques integrated with the existing FFDSS hold promise in effectively controlling forest fires.
- Creator/Author:
- Manish, Kumar; Cohen, Kelly, and HomChaudhuri, Baisravan
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2010-07
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- A general methodology has been developed for the design of a robust control law for a family of lightly damped second order problems. In this research effort, the passivity approach has been extended to systems having non-collocated input/output pairs by introducing an observer that incorporates the nominal dynamical model of the plant. The developed passive observer-based control law emulates numerous dynamic vibration absorbers which are tuned to a targeted frequency using classical methods and the tuning ratios are time-invariant. However, the uniqueness of this approach is that the damping parameters of the emulated absorbers are continuously varied by means of a fuzzy logic control algorithm to provide near minimum-time suppression of vibration. The developed approach is applied to both several benchmarks in the field of structural dynamics as well as experiments using piezo-ceramic sensors and actuators. Results show that this methodology provides stability and performance robustness on the one hand as well as requiring relatively low amount of actuation authority for desired nominal plant closeloop behavior.
- Creator/Author:
- Weller, Tanchum; Cohen, Kelly, and Ben-Asher, Joseph
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2001-11
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- Unmanned Air Vehicle (UAV) teams are anticipated to provide surveillance support through algorithms, software, and automation. It is desirable to have algorithms that compute effective and efficient routes for multiple UAVs across a variety of missions. These algorithms must be realizable, practical, and account for uncertainties. In surveillance missions, UAVs act as mobile wireless communication nodes in a larger, underlying network consisting of targets where information is to be collected and base stations where information is to be delivered. The role of UAVs in these networks has primarily been to maintain or improve connectivity while undervaluing routing efficiency. Moreover, many current routing strategies for UAVs ignore communication constraints even though neglecting communication can lead to suboptimal tour designs. Generating algorithms for autonomous vehicles that work effectively despite these communication restrictions is key for the future of UAV surveillance missions. A solution is offered here based on a variation of the traditional vehicle routing problem and a simple communication model. In this work, the new routing formulation is defined, analyzed, and a heuristic approach is motivated and described. Simulation results show that the heuristic algorithm gives near-optimal results in real-time, allowing it to be used for large problem sizes and extended to dynamic scenarios.
- Creator/Author:
- Cohen, Kelly; Sabo, Chelsea, and Kingston, Derek
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2014-01
- License:
- All rights reserved
-
- Type:
- Article
- Description/Abstract:
- The problem of assigning a group of Unmanned Aerial Vehicles (UAVs) to perform spatially distributed tasks often requires that the tasks will be performed as quickly as possible. This problem can be defined as the Min–Max Multiple Depots Vehicle Routing Problem (MMMDVRP), which is a benchmark combinatorial optimization problem. In this problem, UAVs are assigned to service tasks so that each task is serviced once and the goal is to minimize the longest tour performed by any UAV in its motion from its initial location (depot) to the tasks and back to the depot. This problem arises in many time-critical applications, e.g. mobile targets assigned to UAVs in a military context, wildfire fighting, and disaster relief efforts in civilian applications. In this work, we formulate the problem using Mixed Integer Linear Programming (MILP) and Binary Programming and show the scalability limitation of these formulations. To improve scalability, we propose a hierarchical market-based solution (MBS). Simulation results demonstrate the ability of the MBS to solve large scale problems and obtain better costs compared with other known heuristic solution.
- Creator/Author:
- Sharma, Balaji R.; Cohen, Kelly; Ernest, Nicholas; Kumar, Manish, and Kivelevitch, Elad
- Submitter:
- Kelly Cohen
- Date Uploaded:
- 02/03/2017
- Date Modified:
- 04/05/2017
- Date Created:
- 2014-01
- License:
- All rights reserved
-
- Type:
- Image
- Description/Abstract:
- The purpose of this project is to consider and evaluate the economic attractiveness of a process producing 250,000 lb/h of acetic acid by the selective carbonylation of methanol via carbon monoxide with the use of an iridium tri-iodide catalyst. Initial cost estimates of this process will be completed prior to the completed rigorous design to ensure the process is economically viable and meets product specifications. A full cost analysis will then be made on the initial design to further verify the process will meet product specifications and is economically attractive to the business. A recommendation to continue investigating will be made based on the analysis of the investment cost and annual operating costs of this reaction/separation system. An economic analysis was performed on the selected design. The total capital cost for this project is $145 million, with a yearly utility and incremental cost of $62 million and $90 million, respectively. The required ten-year return on investment was found to be 15%, when the Acetic Acid is sold at a price of $437 /ton. When the Acetic Acid is sold at $745/ton (achieves 15% 1-year ROI), an ROI of 547% is achieved for a 10-year period. In order to achieve a five-year ROI of 15%, the selling price of Acetic Acid is only increased to roughly $455/ton.
- Creator/Author:
- Kleiman, Christopher; Buchanan, Sean; Grunwald, Stephan, and Metzger, Devin
- Submitter:
- Stephen W. Thiel
- Date Uploaded:
- 05/18/2015
- Date Modified:
- 04/29/2016
- License:
- All rights reserved