New Modular Product-Platform-Planning Approach to Design Macroscale Reconfigurable Unmanned Aerial Vehicles

Chowdhury, Souma; Maldonado, Víctor; Tong, Weiyang; Messac, Achille

doi:10.2514/1.c033262

Cited by 20 publications

(6 citation statements)

References 26 publications

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“…A minimum number of modules is extracted from the configurations, such as wings and stabilizers, and, in the product platform planning phase, are modeled and analyzed with low-order methods, considering performance and failure criteria. An in-depth explanation of the modular product platform planning method is presented in [10]. The result is the initial aerodynamic sizing of a minimum number of feasible variants and the starting point for high-order analysis.…”

Section: Multidisciplinary Approachmentioning

confidence: 99%

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Exploring the Performance Boundaries of a Small Reconfigurable Multi-Mission UAV through Multidisciplinary Analysis

Santos,

Rogers,

De Rezende

et al. 2023

Aerospace

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The performance of a small reconfigurable unmanned aerial vehicle (UAV) is evaluated, combining a multidisciplinary approach in the computational analysis of additive manufactured structures, fluid dynamics, and experiments. Reconfigurable UAVs promise cost savings and efficiency, without sacrificing performance, while demonstrating versatility to fulfill different mission profiles. The use of computational fluid dynamics (CFD) in UAV design produces higher accuracy aerodynamic data, which is particularly important for complex aircraft concepts such as blended wing bodies. To address challenges relating to anisotropic materials, the Tsai–Wu failure criterion is applied to the structural analysis, using CFD solutions as load inputs. Aerodynamic performance results show the low-speed variant attains an endurance of 1 h, 48 min, whereas its high-speed counterpart is 29 min at a 66.7% higher cruise speed. Each variant serves different aspects of small UAS deployment, with low speed envisioned for high-endurance surveying, and high speed for long-range or time-critical missions such as delivery. The experimental and simulation results suggest room for design iteration, in wing area and geometry adjustments. Structural simulations demonstrated the need for airframe improvements to the low-speed configuration. This paper highlights the potential of reconfigurable UAVs to be useful across multiple industries, advocating for further research and design improvements.

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Section: Multidisciplinary Approachmentioning

confidence: 99%

“…This offers a great advantage over systems designed for a single specific mission because different setups can provide enhanced performance at different mission profiles. Moreover, the use of a reconfigurable UAV has been seen to provide a 26% cost reduction with respect to off-the-shelf products [10].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Performance Boundaries of a Small Reconfigurable Multi-Mission UAV through Multidisciplinary Analysis

Santos,

Rogers,

De Rezende

et al. 2023

Aerospace

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“…Unlike the conventional PSO algorithm, MDPSO provides: (i) an explicit diversity preservation capability that mitigates the possibility of premature stagnation of particles, and (ii) an ability to deal with both discrete and continuous design variables. MDPSO has been used to solve a wide variety of highly non-convex (often multimodal) mixed-integer nonlinear programming problems in wind farm design [50] and design of unmanned aerial vehicles [51]. Further description of the MDPSO algorithm can be found in the following paper [44].…”

Section: Optimization Algorithm: Particle Swarm Optimizationmentioning

confidence: 99%

Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring

Ghassemi

Lulekar

Chowdhury

2019

AIAA Aviation 2019 Forum

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This paper presents an advancement to an approach for model-independent surrogate-based optimization with adaptive batch sampling, known as Adaptive Model Refinement (AMR). While the original AMR method provides unique decisions with regards to "when" to sample and "how many" samples to add (to preserve the credibility of the optimization search process), it did not provide specific direction towards "where" to sample in the design variable space. This paper thus introduces the capability to identify optimum location to add new samples. The location of the infill points is decided by integrating a Gaussian Process-based criteria ("q-EI"), adopted from Bayesian optimization. The consideration of a penalization term to mitigate interaction among samples (in a batch) is crucial to effective integration of the q-EI criteria into AMR. The new AMR method, called AMR with Penalized Batch Bayesian Sampling (AMR-PBS) is tested on benchmark functions, demonstrating better performance compared to Bayesian EGO. In addition, it is successfully applied to design surface riblets for bio-inspired passive flow control (where highfidelity samples are given by costly RANS CFD simulations), leading to a 10% drag reduction over the corresponding baseline (i.e., riblet-free aerodynamic surface).

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“…However, in their research, the commonality matrix does not readily represent the platform plan for modular products. In order to fix this, Chowdhury et al 10 modified the commonality matrix definition and the commonality constraint. Chowdhury et al used the CP 3 method to design and optimize a family of unmanned aerial vehicles to satisfy different endurances.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a modular platform is the main trend nowadays, the definitions which are quoted from the existing literature are as follows; Meyer and Lehnerd 2 in 1997, Ulrich 9 in 1995 and Chowdhury et al 10 in 2016 have most relevantly reflected the context.…”

Section: Introductionmentioning

confidence: 99%

Product-family shared-component selection based on the consistency constraint function

Hou

Shan

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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This article presents an engineering method based on the collaborative optimization consistency constraint function for selecting shared components in modular platforms; the method allows the operator to minimize the differences in design variables across various models simultaneously. A case study using the body-in-white information on three vehicles serves as an example. Static bending conditions and static torsion conditions are comprehensively considered with the mass and the maximum displacement as the optimization objectives in Isight and Hyperworks (both of which are commercially available software applications commonly used by automotive engineers.) The results suggest that this method can be easily and effectively utilized in the conceptual phase of car design.

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New Modular Product-Platform-Planning Approach to Design Macroscale Reconfigurable Unmanned Aerial Vehicles

Cited by 20 publications

References 26 publications

Exploring the Performance Boundaries of a Small Reconfigurable Multi-Mission UAV through Multidisciplinary Analysis

Exploring the Performance Boundaries of a Small Reconfigurable Multi-Mission UAV through Multidisciplinary Analysis

Adaptive Model Refinement with Batch Bayesian Sampling for Optimization of Bio-inspired Flow Tailoring

Product-family shared-component selection based on the consistency constraint function

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