Simultaneous aerodynamic analysis and design optimization (SAADO) for a 3-D rigid wing

The numerical method to solve simultaneous design and control optimization problems is considered. Firstly, the simultaneous optimization problem dealt with in this paper is defined, and this problem is transformed into a new formulated nonlinear programming problem. Considering that the nonlinear programming problem is solved by using a sequential quadratic programming method, the diagonal form of the Hessian matrix for the Lagrange function is introduced in a new formulation named Block Diagonal Hessian (BDH) method. Secondly, the BDH method is applied to the simultaneous spaceplane design and trajectory optimization problem, where the best size and its optimal trajectory are estimated to realize it. Through the several calculations, it is confirmed that the proposed BDH method is effective for simultaneous design and control optimization problems.

Simultaneous plant-controller design optimization of a two-link planar manipulator

<div>This article investigates the fuel savings potential of a series hybrid military truck using a simultaneous battery pack design and powertrain supervisory control optimization algorithm. The design optimization refers to the sizing of the lithium-ion battery pack in the hybrid configuration. The powertrain supervisory control optimization determines the most efficient way to split the power demand between the battery pack and the engine. Despite the available design and control optimization techniques, a generalized mathematical formulation and solution approach for combined design and control optimization is still missing in the literature. This article intends to fill that void by proposing a unified framework to simultaneously optimize both the battery pack size and power split control sequence. This is achieved through a combination of genetic algorithm (GA) and Pontryagin’s minimum principle (PMP) where the design parameters are integrated into the Hamiltonian function. As GA and PMP are global optimization methodologies under suitable conditions, the solution can be considered as a benchmark for the application under study. Five military drive cycles are used to evaluate the proposed approach. The simulation results show 5%-19% reduction in fuel consumption depending on the drive cycle compared to a baseline non-optimized case.</div>

Multidisciplinary aircraft design optimization and optimal trajectory analysis are both popular research topics, yet few studies have considered optimal flight-path analysis during the aircraft design process. This paper describes simultaneous aircraft and trajectory design optimization using the Optimizer-Based Decomposition (OBD) architecture for multidisciplinary optimization. The analysis used for this problem would be difficult to develop and run using a traditional unified architecture. The problem is decomposed both by discipline and by stages of the trajectory, creating a matrix of independent subproblems that each consist of a single discipline at one stage of the mission. This study, with high-fidelity analyses and many subproblems, demonstrates OBD as a practical, useful approach to real aerospace design problems.

While design optimization has been employed effectively in the past few years to address the integration of design and control, the implementation of advanced model-based control techniques in such a framework has largely been ignored despite their advantages. The reason is that the advanced control schemes involve solving an optimization problem on-line, leading to a drastic increase in the complexity of the design framework. In this work a novel method is presented for integrating advanced model-based predictive controllers in a simultaneous process and control design methodology. Parametric programming is used for the controller derivation, giving rise to an explicit controller structure and removing the need for solving an optimization problem online. The resulting parametric controller is readily incorporated in the design optimization framework, bringing about significant economic and operability benefits. The key features and advantages of our approach are highlighted via binary distillation and evaporator motivating examples.

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.

Implementation and utilisation of a Grid-enabled problem solving environment in Matlab

Chapter 11 - Mixed finite element–computational fluid dynamics method for nonlinear fluid–solid interactions

An investigation was conducted from October 1, 1990 to May 31, 1994 on the development of methodologies to improve the designs (more specifically, the shape) of aerodynamic surfaces of coupling optimization algorithms (OA) with Computational Fluid Dynamics (CFD) algorithms via sensitivity analyses (SA). The study produced several promising methodologies and their proof-of-concept cases, which have been reported in the open literature.

Simultaneous design and operational optimization for flexible carbon capture process using ionic liquids

Simultaneous design and part-load optimization of an industrial ammonia synthesis reactor

14 - A framework for synthesis of operable and intensified reactive separation systems

Simultaneous structure/control design synthesis for aero-servo-elastic system

The design of multidiscipli nary systems (such as aircraft, automobiles, and others) often requires an iterative cycle that includes a design initialization, a system analysis, a sensitivity analysis, and design optimization. This design cycle is standard in the field of Multidisciplinary Optimization (MDO) and has often been referred to in literature as the Multiple Discipline-Feasible (MDF) approach. The name stems from the fact that complete multidisciplinary feasibility is maintained each and every design cycle. The drawback of MDF is that it can be a timely and a costly procedure. Numerous researchers have developed alternate means for posing and subsequently solving the multidisciplinary design problem. One such solution procedure has been referred to both as Simultaneous Analysis and Design (SAND) and All-at-Once (AAO), and treats the entire multidisciplinary design cycle as one large optimization problem. Another alternate solution procedure lias been referred to as Individual-Discipline-Feasible (DDF); this procedure exhibits characteristics which lie in between the two extremes exemplified by MDF and AAO. IDF assures that each individual discipline is feasible on every design cycle, while driving the entire system (all disciplines) towards multidisciplinary feasibility. The present work will present a rigorous numerical comparison of these solution strategies over a wide variety of problem sizes and complexities. The multidisciplinary design test problems that are used for these comparisons are generated by a robust simulation tool called CASCADE. * Research Assistant, Student member, AIAA | Associate Professor, Member AIAA Background and Motivation Concurrent engineering is a systematic approach to the integrated, simultaneous design of products and their related processes. Many of the recently developed capabilities to address concurrent engineering have stemmed from the emerging area of Multidiscipli nary Optimization, or MDO. The origins of MDO can be traced back to the early 1980's, where Sobieski14 used a linear decomposition approach to subdivide the design of a large engineering system into a grouping of related and more manageable subsystems. However, such a decomposition often results in a grouping of subsystems which cannot be placed into a definitive top-down hierarchy. The resultant decomposition grouping is typically hybrid-hierarchic in nature as shown in the example decomposition of Figure 1.

Introduction: We examined the viability of pairing wire-obtained flow pressure data with computational fluid dynamic (CFD) analysis for accurate patient-specific 3D reconstruction of flow through cerebral vessels—an approach that provides comprehensive velocity data and other flow parameters to assist the preventative diagnosis of cerebral stenosis. Methods: Two physical phantom models of a cerebral stenosis—differing in regard to vascular morphology at and around the stenosis—were prepared, filled with physiological saline and connected individually to a flow pump (Vascular Simulations LLC Left Heart Replicator) applying pulsatile flow. Pressure measurements were taken upstream of the stenosis by microcatheter-guided wires to define an inlet boundary condition for CFD analysis. Pressure and velocity at 2 cm proximal to stenosis, the stenosis inlet, the stenosis outlet, and 2 cm distal to stenosis were measured to validate the CFD simulated flow. Results: Excellent agreement was observed between CFD and wire-measured time-dependent flow pressure, with the expected pulsatile behavior observed at each location. Between the two methods, differences in maximum systolic pressures ranged between |0.73±0.72|% and |3.90±0.74|%. Differences in minimum diastolic pressures ranged between |1.79±1.33|% and |9.08±1.42|%. Velocity data from wire measurements lacked pulsatile behavior and showed differences ranging between |2.93±5.20|% and |120.10±2.91|% from CFD-obtained measurements, which did show correct pulsatile behavior. Larger discrepancies were strongly associated with areas of higher CFD predicted velocities (r=0.86). Conclusion: Guidewire flow measurements provided CFD analysis with accurate inlet boundary conditions and validated CFD simulation results. CFD analysis allowed for detailed visualization of key flow parameters that may be difficult to physically measure—such as velocity—that are relevant for understanding risk factors associated with cerebral stenosis on a case-by-case basis.