Heuristic Algorithms Applied to Multidisciplinary Design Optimization of Unconventional Airship Configuration

Ceruti, Alessandro; Voloshin, Vitaly; Marzocca, Pier

doi:10.2514/1.c032439

Cited by 35 publications

(13 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This force represents a natural static lift, corresponding roughly to 1Kg for each m 3 of helium involved in the careen. We suppose here that this force is applied in the center of buoyancy B different from the center of inertia G. u air B .V.g  (7) where V is the volume of the careen, air is the density of the air, and g the gravity.…”

Section: I3 Weight and Buoyancymentioning

confidence: 99%

“…Usually, ellipsoidal shapes are used for airships [3][4][5]. However, and in order to optimize their performances, different original shapes are tested in the last years [6][7][8][9]. One main difference between traditional ellipsoidal shapes and unconventional ones is the fact that in the first case the theory is well established for over a century [10][11], and in second case everything remains to be done in the fields of modeling and control.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Actuators Design of an Unconventional Airship

Khamlia

Bennaceur

Azouz

et al. 2015

Volume 4A: Dynamics, Vibration, and Control

View full text Add to dashboard Cite

This paper presents a dynamic modeling of a quadrotor flying wing airship. This airship is a scale model of airships dedicated for freight transport. It is equipped with steerable thrusters that made over-actuated. A control strategy based on the linearization of the local equation and the use of backstepping control is applied to this model. We made a focus on the relations between the control vectors obtained from the control strategy and the response of the airship actuators. Numerical results show the benefits of the proposed algorithm.

show abstract

Section: I3 Weight and Buoyancymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Actuators Design of an Unconventional Airship

Khamlia

Bennaceur

Azouz

et al. 2015

Volume 4A: Dynamics, Vibration, and Control

View full text Add to dashboard Cite

show abstract

“…Multidisciplinary optimisation methods (20,21) can be applied for trajectory optimisation when there are multiple competitive/contradictory requirements (e.g. performing an assigned mission while avoiding restricted airspace/threats).…”

Section: Introductionmentioning

confidence: 99%

New flight trajectory optimisation method using genetic algorithms

Dancila

Botez

2021

Aeronaut. j.

View full text Add to dashboard Cite

This paper presents a new flight trajectory optimisation method, based on genetic algorithms, where the selected optimisation criterion is the minimisation of the total cost. The candidate flight trajectories evaluated in the optimisation process are defined as flight plans with two components: a lateral flight plan (the set of geographic points that define the flight trajectory track segments) and a vertical flight plan (the set of data that define the altitude and speed profiles, as well as the points where the altitude and/or speed changes occur). The lateral components of the candidate flight plans are constructed by selecting a set of adjacent nodes from a routing grid. The routing grid nodes are generated based on the orthodromic route between the flight trajectory’s initial and final points, a selected maximum lateral deviation from the orthodromic route and a selected grid node step size along and across the orthodromic route. Two strategies are investigated to handle invalid flight plans (relative to the aircraft’s flight envelope) and to compute their flight performance parameters. A first strategy is to assign a large penalty total cost to invalid flight profiles. The second strategy is to adjust the invalid flight plan parameters (altitude and/or speed) to the nearest limit of the flight envelope, with priority being given to maintaining the planned altitude. The tests performed in this study show that the second strategy is computationally expensive (requiring more than twice the execution time relative to the first strategy) and yields less optimal solutions. The performance of the optimal profiles identified by the proposed optimisation method, using the two strategies regarding invalid flight profile performance evaluation, were compared with the performance data of a reference flight profile, using identical input data: initial aircraft weight, initial and final aircraft geographic positions, altitudes and speed, cost index, and atmospheric data. The initial and final aircraft geographic positions, and the reference flight profile data, were retrieved from the FlightAware web site. This data corresponds to a real flight performed with the aircraft model used in this study. Tests were performed for six Cost Index values. Given the randomness of the genetic algorithms, the convergence to a global optimal solution is not guaranteed (the solution may be non-optimal or a local optima). For a better evaluation of the performance of the proposed method, ten test runs were performed for each Cost Index value. The total cost reduction for the optimal flight plans obtained using the proposed method, relative to the reference flight plan, was between 0.822% and 3.042% for the cases when the invalid flight profiles were corrected, and between 1.598% and 3.97% for the cases where the invalid profiles were assigned a penalty total cost.

show abstract

“…By increasing the performance in a single one of these aspects, typically other characteristics degrade to unacceptable levels. In this scenario, optimization can help in finding the trade‐off point assuring a balanced configuration (Ceruti, Voloshin, & Marzocca, ). The Fitness function definition in these cases is something extremely challenging, because several needs must be kept in consideration, aiming to assure the highest satisfaction to the end user.…”

Section: Introductionmentioning

confidence: 99%

Engineering optimization based on dynamic technique for order preference by similarity to ideal solution fitness: Application to unmanned aerial vehicle wing airfoil geometry definition

Ceruti

Fiorini

Boggi³

et al. 2018

Multi Criteria Decision Anal

Self Cite

View full text Add to dashboard Cite

Modern engineering requires finding an optimal trade‐off point among conflicting requirements that must be satisfied to reach the end user's satisfaction. One of the most challenging problems in multidisciplinary optimization is to find a fitness function that can best translate what the designer really aims to obtain. In this paper, a particle swarm optimization (PSO) algorithm is coupled to a fitness function based on the definition of a technique for order preference by similarity to ideal solution (TOPSIS) in order to obtain a new algorithm called PSOTOP. The fitness of a candidate solution is found by comparing its attributes to those of an “ideal best” and “ideal worst” solution, which is dynamically updated at each iteration of the algorithm. The advantage of this solution is that the fitness allows combining attributes of different magnitude and measurement units in an effective way; this approach can be applied to whatsoever optimization problem in engineering, economics, medicine, and statistics. This paper presents a case study dealing with the optimal design of an airfoil to be adopted on the wing of an unmanned air vehicle to support civil protection operations in order to show how this strategy can impact the design of a complex product. The main limitation of this approach relates to the fact that the user must possess a good knowledge of the specific problem to be solved in order to set proper ranges for design parameters and attributes' weight in the fitness evaluation.

show abstract

Heuristic Algorithms Applied to Multidisciplinary Design Optimization of Unconventional Airship Configuration

Cited by 35 publications

References 16 publications

Modeling and Actuators Design of an Unconventional Airship

Modeling and Actuators Design of an Unconventional Airship

New flight trajectory optimisation method using genetic algorithms

Engineering optimization based on dynamic technique for order preference by similarity to ideal solution fitness: Application to unmanned aerial vehicle wing airfoil geometry definition

Contact Info

Product

Resources

About