MDOPT - A Multidisciplinary Design Optimization System Using Higher Order Analysis Codes

The optimal design of multidisciplinary systems under uncertainty involves three types of iterative analyses- multidisciplinary analyses, reliability analyses, and optimization. If these processes are carried out in a nested manner in reliability based multidisciplinary design optimization (RBMDO), the computational expense can be enormous. Computationally efficient simultaneous analysis and design (SAND) or All-AtOnce methods have been developed earlier for deterministic MDO. Single loop RBDO methods have also been developed to integrate reliability analysis and optimization in a single loop. This paper develops a novel formulation of RBMDO problems with both system and component level reliability constraints. The formulation allows for solution with a single design optimization loop by using the KKT optimality conditions of the first-order reliability method (FORM) as constraints in the design optimization along with disciplinary consistency constraints. Alternative solution processes which remove the FORM MPP’s from the decision space and many constraints from the problem are developed. The alternative solution methods are more efficient and more robust than using nonlinear programming to solve the single loop formulation. A numerical example is solved with the two proposed solution algorithms. I. Introduction In the design of multidisciplinary systems it is often necessary to make decisions under uncertainty. This requires that multidisciplinary systems modeling be integrated with uncertainty analysis and optimization. A common example of such an engineering design application would be the design of an aircraft wing under uncertain aerodynamic loading conditions, with multiple structural components with uncertain capacities, such that the entire structural system achieves a prescribed level of reliability. The problem has coupled physics, in that the pressure distribution is dependent upon the deflection, and the deflection is dependent on the pressure distribution. Such designs have been performed using multidisciplinary optimization (MDO). Other examples where MDO may be applied include electronics packaging, in which the disciplines of heat transfer and circuit analysis are coupled, the design of new types of aircraft and integrating them into an existing fleet of aircraft, and even the management of interconnected homeland infrastructures. While MDO has matured with the development of different types of algorithms for various types of problems and design organizations, there has been little work to incorporate reliability analysis into MDO. However, system and component reliabilities are often a major concern in the design of multidisciplinary systems. MDO can be computationally expensive due to the need to achieve consistency across multiple disciplinary analysis codes; each disciplinary analysis code may be computationally expensive to run. Further, the optimal design of multidisciplinary systems under uncertainty involves three types of iterative procedures- multidisciplinary analyses, reliability analyses, and optimization. The integration of these three procedures is referred to in this paper as reliability based multidisciplinary design optimization (RBMDO). If these processes are carried out simultaneously, the computational expense can be enormous. Computational savings can often be achieved with simultaneous analysis and design (SAND) or All-At-Once methods, which allow for the solution of the multidisciplinary analyses and the optimization in one single loop. Single loop RBDO methods allow for the integration of reliability analysis and optimization in a single loop. Applying SAND and single-loop RBDO together may allow for large computational savings when applied to problems in RBMDO. This paper develops a novel formulation of RBMDO problems with both system and component level reliability constraints based on the SAND method for MDO and by use of the KKT conditions of the first order reliability method (FORM). The resulting formulation may be solved as a standard nonlinear programming problem. While the single loop formulation presented in this paper is a solution of the RBMDO problem, robustness and efficiency are improved through two improved algorithms which can remove the MPP’s from the decision space, thereby significantly reducing the optimizer’s workload.

This paper presents a framework for multi-objective and multidisciplinary design optimization using highfidelity analysis tools. In this framework the aerodynamic performance is evaluated based on a Navier‐Stokes equation solver, and the structure dynamics is computed using commercially available finite element software. We employ a genetic algorithm as a robust design optimization tool to facilitate the multi-objective optimization. We also use the response surface approach to tackle the difficulties associated with the organizational complexity and computational burden inherent in the multidisciplinary optimization. The coupling between the fluid solver and structural solver is realized through a thin-plate spline interpolation algorithm. The proposed approach is then used to perform aerostructural optimization of a three-dimensional transonic compressor blade. Our numerical results show that this method can improve the existing design and reduce the required computational time by orders of magnitude.

†‡ A Multidisciplinary Design Optimization (MDOPT) framework was developed for air vehicle design and analysis. The developed MDOPT system contains a collection of technology modules for performing optimization studies that can be configured and controlled by a Graphical User Interface (GUI). A CORBA based command and control layer called the Inter-domain Communication Facility (ICF) was developed for this framework to allow the user to interactively create ad hoc workflows between remotely distributed engineering processes running interactively or within batch queuing systems such as PBS. An overview of the design of the ICF and related software technologies are presented. The interaction of the ICF process flows with the MySQL-based Database Management Facility (DMF) and the practical design considerations related to the DMF are also discussed. Finally, a sample optimization workflow that allows human interaction is shown to demonstrate the flexibility of the system.

This paper presents a Multidisciplinary Design Optimization (MDO) framework that is intended to be employed in the early design stages of Unmanned Aerial Vehicles (UAVs) when the primary focus is o ...

The exploration of polar regions has become increasingly critical due to rapid climate change and the strategic significance of Arctic resources. Autonomous Underwater Vehicles (AUVs) serve as the primary modality for data acquisition in these harsh, ice-covered environments. However, the operational reliability of AUVs is severely compromised by multi-source uncertainties, including complex under-ice hydrodynamics, acoustic communication failures, variable sea ice draft, and stochastic sensor errors. Traditional deterministic optimization methods often fail to account for these variabilities, leading to designs that are optimal in theory but fragile in practice. This paper proposes a comprehensive Multidisciplinary Design Optimization (MDO) framework integrated with Monte Carlo simulations to enhance the robustness of AUVs operating under polar ice. We define a stochastic coupling mechanism that links hydrodynamic, structural, energy, and navigation subsystems. By embedding Monte Carlo sampling within the optimization loop, we evaluate the probability of mission success and enforce reliability constraints against environmental and systemic uncertainties. The results demonstrate that the proposed robust optimization approach significantly reduces the variance in mission performance metrics compared to deterministic baselines. We provide a detailed analysis of the trade-offs between optimality and reliability, offering a theoretical foundation for the next generation of polar-class AUVs.

Due to technological evolution and the development of several environmental aerospace projects, the aeronautical community has been implementing advanced design strategies, aiming to find next-generation configurations that allow mitigating the undesirable impact of aircraft on the environment. Unconventional solutions have attracted the attention of designers, and several aircraft concepts have been proposed in order to achieve ambitious goals. Given the lack of significant design experience with unconventional aircraft, Multidisciplinary Design Optimization (MDO) frameworks enable to understand the impact of various technologies, obtaining reductions in energy use per passenger-kilometer beyond that provided by the configuration itself. In this thesis, multifidelity and multidisciplinary optimization methodologies have been applied towards designing a next-generation commercial airliner, which combines a Box-Wing configuration with Boundary Layer Ingestion (BLI) engines. This project presents four fundamental objectives: (i) To understand the current design issues, methods, and evolving trends of unconventional configurations. This is performed through a compilation of information in the form of a stateof-the-art literature review. (ii) To develop a low-fidelity MDO method to determine the main geometric, aerodynamic, stability, propulsion and performance characteristics of the aircraft, based on its design requirements and constraints. (iii) To carry out Computational Fluid Dynamics (CFD) simulations and wind-tunnel experiments on a scale-model of the aircraft, in order to perform a back-to-back analysis of non-boundary layer ingesting and boundary layer ingesting versions of the aircraft. (iv) To implement a high-fidelity Aerodynamic Shape Optimization method for the Box-Wing concept based on Reynolds-Averaged Navier-Stokes (RANS) equations.

Multidisciplinary design optimization of long-range slender guided rockets considering aeroelasticity and subsidiary loads

The main goal of the paper is to develop automatic control design methods for flexible aircraft. The motivation for the research is that such automatic control generation enables the inclusion of the control design algorithms into the multidisciplinary design optimization (MDO) of aircraft design. In such an extended MDO framework, called co-design, the sizing, structural dynamics, aerodynamics and the controllers of the aircraft are optimized in one single step. This way control technologies can be included early-on in the preliminary design stage of aircraft design. Since the control design is model based, first a control oriented aeroservoelastic (ASE) model needs to be developed. The modeling is done via the bottom-up modelling approach. The model generation also needs to be automatic due to parameter changes resulting from the MDO process. The research focuses on flexible aircraft, therefore, the control algorithms include baseline, manoeuvre load alleviation, gust load alleviation and flutter suppression controllers. All of these algorithms needs to be developed in such way that they can automatically executed in the MDO process. The overall MDO framework is implemented in the Remote Component Environment (RCE) and the aircraft investigated is the T-Flex demonstrator of the FLIPASED project. The paper presents the main concepts of the modeling and control synthesis, analysis for the above mentioned four controllers and the most important aspects of integrating such automatic control design methods into the RCE environment.

DOI: 10.2514/1.19969 Multidisciplinary designoptimization isanimportant approachfor the conceptualdesignof space planesbecause these planes are characterized by disciplines that interact with one another. A multidisciplinary design optimization problemof asingle-stage-to-orbit space plane isformulated andsolved inthis study.Themodeling andoptimization ofrigidbodycharacteristics,suchastrimandstability,arefocusedonbecauseasingle-stage-to-orbitspaceplanehas a tendency for considerable shift of both the aerodynamic center and the center of gravity. Moreover, the design of the air-breathing engines are integrated with the airframe and its effect on the rigid body characteristics are also modeled in the framework of multidisciplinary design optimization. Using the all-at-once-based multidisciplinary design optimization approach, which incorporates sparse nonlinear programming and metamodeling, the design of the vehicle and its flight trajectory are successfully optimized. Finally, the characteristics of the optimal solution are investigated, especially the relationships among the airframe-engine integration, rigid body characteristics, and payload transportation capability.

: This report documents the specific dynamics and mobility factors within a U.S. Army ground vehicle Design of Experiments (DOE) approach to designing an Occupant Centric Platform (OCP) using multi-disciplinary design optimization (MDO). Simplified dynamic and mobility factors that reflect pertinent vehicle design performance in these areas and that can be used in an MDO for an OCP with an underbody blast survivability focus are described. The calculation of each factor is based on portable scripts that fit well into the MDO framework.

The objective of this research is to examine how to design low-airframe-noise transport aircraft using Multidisciplinary Design Optimization (MDO). This involves optimizing aircraft to minimize maximum-take-off-weight, while constraining noise at the approach condition. A design methodology which incorporates noise as a design constraint into an MDO formulation is presented. An MDO framework was designed by integrating aircraft conceptual design tools previously developed at Virginia Tech with the Aircraft Noise Prediction Program (ANOPP). Design studies are presented for cantilever wing and Strut-Braced Wing (SBW) transport aircraft with 300 passengers and a 7,700 nm range. The results show that reducing airframe noise by reducing approach speed alone, will not provide significant noise reduction without a large performance and weight penalty. Therefore, more dramatic changes to the aircraft design are needed to achieve a significant airframe noise reduction. Another study showed that the trailing-edge (TE) flap can be eliminated, as well as all the noise associated with that device, without incurring a significant weight and performance penalty. If noise due to the leading-edge (LE) slats and landing gear are reduced, which is currently being pursued, the elimination of the flap will be very significant as the clean wing noise will be the next ‘noise barrier’. Lastly, an airframe noise analysis showed that a SBW aircraft, with short fuselage-mounted landing gear, could have a similar or potentially a lower airframe noise level than a cantilever wing aircraft.

A web services-based multidisciplinary design optimization framework for complex engineering systems with uncertainties

In this paper, we develop an easy-to-implement approximate method to take uncertainties into account during a multidisciplinary optimization. Multidisciplinary robust design usually involves setting up a full uncertainty propagation within the system, requiring major modifications in every discipline and on the shared variables. Uncertainty propagation is an expensive process, but robust solutions can be obtained more easily when the disciplines affected by uncertainties have a significant effect on the objectives of the problem. A heuristic method based on local uncertainty processing (LOUP) is presented here, allowing approximate solving of specific robust optimization problems with minor changes in the initial multidisciplinary system. Uncertainty is processed within the disciplines that it impacts directly, without propagation to the other disciplines. A criterion to verify a posteriori the applicability of the method to a given multidisciplinary system is provided. The LOUP method is applied to an aircraft preliminary design industrial test case, in which it allowed to obtain robust designs whose performance is more stable than the one of deterministic solutions, relatively to uncertain parameter variations.

A quasi-three-dimensional aerodynamic solver is developed for the aerodynamic analysis of wings in a transonic regime that is able to capture the effect of BLS in hybrid laminar flow control (HLFC) application or transition to turbulent flow for natural laminar flow (NLF). The tool provides accurate results, but without the high computational cost of high-fidelity tools. The solver combines the use of an Euler flow solver characterized by an integral boundary-layer method and linear stability analysis using a approximation for transition prediction. In particular, a conical transformation is adopted, including the determination of the shock-wave position. The solver is implemented in a multidisciplinary design optimization (MDO) framework, including wing weight estimation and aircraft performance analysis. The framework consists of different modules: aerodynamics, structure, suction system analysis, and performance evaluation. Using a genetic algorithm and considering HLFC technology, wing MDO has been performed to find the optimum wing planform and airfoil shape. A backward-swept wing (BSW) aircraft, developed inside the Cluster of Excellence–Sustainable and Energy Efficient Aviation (A) is studied. Novel technologies such as active flow control, limited maximum load factors due to load alleviation, and novel materials allow a fuel weight reduction of 6%.

Many optimization and coordination methods for multidisciplinary design optimization (MDO) have been proposed in the last three decades. Suitable MDO benchmark problems for testing and comparing these methods are few however. This article presents a new MDO benchmark problem based on the design optimization of an ADXL150 type lateral capacitive micro-accelerometer. The behavioral models describe structural and dynamic effects, as well as electrostatic and amplification circuit contributions. Models for important performance indicators such as sensitivity, range, noise, and footprint area are presented. Geometric and functional constraints are included in these models to enforce proper functioning of the device. The developed models are analytical, and therefore highly suitable for benchmark and educational purposes. Four different problem decompositions are suggested for four design cases, each of which can be used for testing MDO coordination algorithms. As a reference, results for an all-in-one implementation, and a number of augmented Lagrangian coordination algorithms are given.