Aerostructural Optimization of a Transonic Compressor Rotor

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.

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.

Model-Based Systems Engineering (MBSE) supports the system-level design of complex products effectively. Currently, system design and optimization for complex products are two distinct processes that must be executed using different software or platforms, involving intricate data conversion processes. Applying multidisciplinary optimization to validate system optimization often necessitates remodeling the optimization objects, which is time-consuming, labor-intensive, and highly error-prone. A critical activity in systems engineering is identifying the optimal design solution for the entire system. Multidisciplinary Design Optimization (MDO) and reliability analysis are essential methods for achieving this. This paper proposes a SysML-based multidisciplinary reliability design optimization modeling method. First, by analyzing the definitions and mathematical models of multidisciplinary reliability design optimization, the SysML extension mechanism is employed to represent the optimization model based on SysML. Next, model transformation techniques are used to convert the SysML optimization model generated in the first stage into an XML description model readable by optimization solvers. Finally, the proposed method’s effectiveness is validated through an engineering case study of an in-vehicle environmental control integration system. The results demonstrate that this method fully utilizes SysML to express MDO problems, enhancing the efficiency of design optimization for complex systems. Engineers and system designers working on complex, multidisciplinary projects can particularly benefit from these advancements, as they simplify the integration of design and optimization processes, facilitating more reliable and efficient product development.

§A Multidisciplinary Design Optimization (MDOPT) framework has been developed for air vehicle design and analysis. The developed MDOPT system contains a collection of technology modules for performing optimization studies by means of a Graphical User Interface (GUI), and combining robust numerical optimization schemes with higher order computational analysis. A variety of multidisciplinary objective and constraint functions are available, including aerodynamic, weight, mission performance, and stability and control characteristics. This paper is intended to provide an overview of the MDOPT development and test case results for a generic fighter configuration, funded under an Air Force Contract, F33615-982-3014, with Boeing cost match funds. The period of development was established with contract activity beginning in September 1998 and ending April 2002. Continued MDOPT development and design applications are being pursued within Boeing using internal funding.

Multidisciplinary design optimization has great potential to support the turbomachinery development process by improving designs at reduced time and cost. As part of the industrial compressor design process, we seek for a rotor blade geometry that minimizes stresses without impairing the aerodynamic performance. However, the presence of structural mechanics, aerodynamics, and their interdisciplinary coupling poses challenges concerning computational effort and organizational integration. In order to reduce both computation times and the required exchange between disciplinary design teams, we propose an inter- instead of multidisciplinary design optimization approach tailored to the studied optimization problem. This involves a distinction between main and side discipline. The main discipline, structural mechanics, is computed by accurate high-fidelity finite element models. The side discipline, aerodynamics, is represented by efficient low-fidelity models, using Kriging and proper-orthogonal decomposition to approximate constraints and the gas load field as coupling variable. The proposed approach is shown to yield a valid blade design with reasonable computational effort for training the aerodynamic low-fidelity models and significantly reduced optimization times compared to a high-fidelity multidisciplinary design optimization. Especially for expensive side disciplines like aerodynamics, the multi-fidelity interdisciplinary design optimization has the potential to consider the effects of all involved disciplines at little additional cost and organizational complexity, while keeping the focus on the main discipline.

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

A blowout preventer (BOP) is a large valve that encases an oil well on the surface. The valve may be closed while drilling if overpressure from a reservoir causes formation fluids such as oil and natural gas to back up within the wellbore and endanger the rig. A pipe ram is one of the critical components in the BOP system, which is designed to seal around a drill pipe, restricting the flow in the annulus between the outside of the drill pipe and the wellbore. The pipe ram design family, which contains hundreds of pipe ram designs, intends to cover different configurations to suit a variety of dill pipe sizes. To assure their structural integrity under service loads, these pipe rams need to be designed to meet the stress requirements per the American Petroleum Institute (API) Specification 16A and ASME (American Society of Mechanical Engineers) Boiler and Pressure Vessel Code Section VIII, Division 2. With a conventional manual design workflow, it would take a significant amount of time and effort to complete all the designs for the product family with code compliance. Therefore, a scalable solution is highly desirable for delivery of customer-needed ram blocks with much shorter lead time. MDO (Multi-Disciplinary Optimization) involves multi-code integration, CAE (Computer-Aided Engineering) workflow automation, design space exploration, and optimization. MDO-enabled, automated design optimization is becoming increasingly popular in both scientific and engineering communities. In this paper, a methodology for integration of CAD (Computer Aided Design), FEA (Finite Element Analysis), cost, and optimization packages to enable FEA automation and design optimization is presented. A BOP pipe ram is adopted as the use case. The ram block geometry was parameterized before being imported into an FEA package. The FEA workflow was automated such that once a set of geometric parameters are given, the preprocessing, solving, and postprocessing steps can be automatically completed. As part of the FEA postprocessing, stress linearization analysis per the API and ASME BPVC codes has also been automated, which had never been done in the past. A manufacturing cost analysis package can also be used to consume the parameterized geometry for automatic manufacturability assessment and cost predictions. The stress analysis and cost analysis workflows are conducted separately but also orchestrated by an MDO package. Reports that contain the analysis results are sequentially generated for various design permutations. The MDO-enabled automated design and cost analysis approach could substantially enhance efficiency and consistency in performing FEA and cost studies and producing analysis reports. It is also the backbone for automated design optimization, which could significantly improve the product performance and reliability and, meanwhile, minimize the development and product cost.

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 ...

In order to design a vehicle body with high strength and high stiffness, a multi-disciplinary design process should include careful consideration of multi-disciplinary design constraints to properly account for vehicle static stiffness (bending/torsional), durability, Noise/Vibration/Harshness (NVH), crash worthiness, light weight vehicle structure during the early stage of vehicle design process. With this approach, fast development of new vehicle body structures can be achieved with minimal number of iterations to match the conflicting design goals from each discipline. In the current research, a multi-disciplinary design optimization (MDO) based on a meta model is developed and refined to apply for the design of body structure. In an effort to apply the MDO for vehicle body structure, 4 phase procedures were established in the current research. In Phase I, a base model is created. In Phase II, an effect analysis is carried out. In Phase III, a meta model is created. Finally in Phase IV, using the optimization algorithm, the meta model created in Phase III is eventually refined through the process of optimization. In this research, static stiffness (bending / torsional), dynamic stiffness (1st torsion mode) were used for constrained conditions and the mass minimization was the object function for optimization.

Battery thermal management system (BTMS) is a complex and highly integrated system, which is used to control the battery thermal conditions in electric vehicles (EVs). The BTMS consists of many subsystems that belong to different disciplines, which poses challenges to BTMS optimization using conventional methods. This paper develops a general variable fidelity-based multidisciplinary design optimization (MDO) architecture and optimizes the BTMS by considering different systems/disciplines from the systemic perspective. Four subsystems and/or subdisciplines are modeled, including the battery thermodynamics, fluid dynamics, structure, and lifetime model. To perform the variable fidelity-based MDO of the BTMS, two computational fluid dynamics (CFD) models with different levels of fidelity are developed. A low fidelity surrogate model and a tuned low fidelity model are also developed using an automatic surrogate model selection method, the concurrent surrogate model selection (COSMOS). An adaptive model switching (AMS) method is utilized to realize the adaptive switch between variable-fidelity models. The objectives are to maximize the battery lifetime and to minimize the battery volume, the fan's power, and the temperature difference among different cells. The results show that the variable-fidelity MDO can balance the characteristics of the low fidelity mathematical models and the computationally expensive simulations, and find the optimal solutions efficiently and accurately.

Research on multidisciplinary fatigue optimization design method in structural design of high speed train

†‡ 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.

Hybrid-Game Strategies for multi-objective design optimization in engineering

Microelectromechanical systems (MEMS) are a highly multidisciplinary field and this has large implications on their applications and design. Designers are often faced with the task of balancing the modelling, simulation and optimisation that each discipline brings in order to bring about a complete whole system. In order to aid designers, strategies for navigating this multidisciplinary environment are essential, particularly when it comes to automating design synthesis and optimisation. This paper outlines a new multi-objective and multidisciplinary strategy for the application of engineering design problems. It employs a population-based evolutionary approach that looks to overcome the limitations of past work by using a non-hierarchical architecture that allows for interaction across all disciplines during optimisation. Two case studies are presented, the first focusing on a common speed reducer design problem found throughout the literature used to validate the methodology and a more complex example of design optimisation, that of a MEMS bandpass filter. Results show good agreement in terms of performance with past multi-objective multidisciplinary design optimisation methods with respect to the first speed reducer case study, and improved performance for the design of the MEMS bandpass filter case study.

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.