Large Number Of Design Variables Research Articles

A gradient-based aero-stealth optimization design method for flying wing aircraft

Flying wing layout has both favorable aerodynamic performance and outstanding radar scattering characteristics, which is regarded as an ideal stealth aircraft layout. In this paper, a gradient-based aerodynamic and stealth optimization design method is established by coupling the Free-Form Deformation approach (FFD), Radial Basis Function algorithm (RBF), Computational Fluid Dynamics (CFD), Physical Optics (PO) and the Sequential Quadratic Programming algorithm (SQP). The gradient of the aerodynamic characteristic parameters could be obtained by solving the discrete adjoint equations. With PO code differentiated by the automatic differentiation tool, the Radar Cross Section (RCS) and its gradient would be collected. Based on the aero-stealth optimization design method, three kinds of optimization strategies are applied to design a certain flying wing layout aircraft: 1) The drag coefficient is chosen as the objective function while the RCS is set as a constraint condition; 2) The RCS is selected as the objective function while the drag coefficient is set as a constraint condition; 3) The objective function is composed of the drag coefficient and the RCS through the Weighted Sum method. The optimization results show that when choosing the first two strategies, the optimization algorithm would push single discipline performance to the edge. Alternatively, it would obtain several trade-off configurations when selecting the last strategy. The results demonstrate that the gradient-based aero-stealth optimization design method can deal with the multidisciplinary optimization problems which require a large number of design variables. Without a large number of function evaluations, this method could rapidly and efficiently obtain the shape that meets the requirements of aerodynamic and stealth performances.

Sound simulation-based design optimization of brass wind instruments.

A method for optimizing the inner shape of brass instruments using sound simulations is presented. This study considers different objective functions and constraints (representative of both the intonation and the spectrum of the instrument) for a relatively large number of design variables. A complete physics-based model, taking into account the instrument and the musician's embouchure, is used to simulate steady regimes of sounds by means of the harmonic balance technique, the instrument being represented by its input impedance. The design optimization variables are related to the geometrical dimensions of the resonator. The embouchure's parameters are varied during the optimization procedure to obtain an average behavior of the instrument. The objective and constraint functions of the optimization problem are evaluated using the physics-based simulation model, which is computationally expensive. Moreover, the gradients of the objective and constraint functions can be discontinuous, unavailable, or hard to approximate reliably. Therefore, a surrogate-assisted derivative-free optimization strategy using the mesh adaptive direct search algorithm was employed. One example of a B♭ trumpet's bore is used to demonstrate the effectiveness of the design optimization approach: the obtained results improve previously reported objective function values significantly.

Simulation-based evolutionary optimization for energy-efficient layout plan design of high-rise residential buildings

Buildings consume 40% of global energy, in which residential buildings account for a significant proportion of the total energy used. Previous studies have attempted to optimize the layout plan of residential buildings for minimizing the total energy usage, mainly focusing on low-rise houses of a regular shape and having a limited number of design variables. However, layout design for high-rise residential buildings involves the complicated interaction among a large number of design variables (e.g., different types of flats with varying configurations) under practical design constraints. The number of possible solutions may increase exponentially which calls for new optimization strategies. Therefore, this study aims to develop an energy performance-based optimization approach to identify the most energy-efficient layout plan design for high-rise residential buildings. A simulation-based optimization method applying the evolutionary genetic algorithm (GA) is developed to systematically explore the best layout design for maximizing the building energy efficiency. In an illustrative example, the proposed optimization approach is applied to generate the layout plan for a 40-storey public housing in Hong Kong. The results indicate that GA attempts to maximize the use of natural-occurring energy sources (e.g., wind-driven natural ventilation and sunlight) for minimizing 30–40% of the total energy consumption associated with air-conditioning and lighting. The optimization approach provides a decision support basis for achieving substantial energy conservation in high-rise residential buildings, thereby contributing to a sustainable built environment.

Reduced‐cost electromagnetic‐driven optimisation of antenna structures by means oftrust‐region gradient‐search with sparse Jacobian updates

Numerical optimisation plays more and more important role in the antenna design. Because of lack of design-ready theoretical models, electromagnetic (EM)-simulation-driven adjustment of geometry parameters is a necessary step of the design process. At the same time, traditional parameter sweeping cannot handle complex topologies and large number of design variables. On the other hand, high computational cost of the conventional optimisation routines can be reduced using, e.g., surrogate-assisted techniques. Still, direct optimisation of EM simulation antenna models is required at certain level of fidelity. This work proposes a reduced cost trust-region algorithm with sparse updates of the antenna response Jacobian, decided based on relocation of the design variable vector between algorithm iterations and the update history. Our approach permits significant reduction of the optimisation cost (∼40% as compared to the reference algorithm) without affecting the design quality in a significant manner. Robustness of the proposed technique is validated using a set of benchmark antennas, statistical analysis of the algorithm performance over multiple initial designs, as well as investigating the effects of its control parameters that permit control efficiency vs. design quality trade-off. Selected designs were fabricated and measured to validate the computational models utilised in the optimisation process.

Layout Optimization for Blended Wing Body Aircraft Structure

Structural layout design of blended wing body (BWB) aircraft in the preliminary design phase is a challenging optimization problem due to large numbers of design variables and various constraints. A two-loop optimization strategyis proposed to solve the BWB aircraft structural layout design problem considering constraints of the displacement, stress, strain, and buckling. The two-loop optimization consists of an inner loop and an outer loop. The inner loop is to optimize each stiffened panel of the BWB aircraft structure, and outer loop is to find the best layout design. To improve computational efficiency, an equivalent finite element model is applied to BWB aircraft structure analysis, and an analytical method is used for buckling and static analysis of the stiffened panels. The proposed method can efficiently solve the structural layout optimization problem of a notional BWB aircraft with acceptable computational burden. The result indicates the mass of main load-carrying structure of the BWB aircraft is reduced by 9.28% compared to that of the initial structural layout.

Impact of Building Design Parameters on Daylighting Metrics Using an Analysis, Prediction, and Optimization Approach Based on Statistical Learning Technique

Daylighting metrics are used to predict the daylight availability within a building and assess the performance of a fenestration solution. In this process, building design parameters are inseparable from these metrics; therefore, we need to know which parameters are truly important and how they impact performance. The purpose of this study is to explore the relationship between building design attributes and existing daylighting metrics based on a new methodology we are proposing. This methodology involves statistical learning. It is an emerging methodology that helps us to analyze a large quantity of output data and the impact of a large number of design variables. In particular, we can use these statistical methodologies to analyze which features are important, which ones are not, and the type of relationships they have. Using these techniques, statistical models may be created to predict daylighting metric values for different building types and design solutions. In this article we will outline how this methodology works, and analyze the building design features that have the strongest impact on daylighting performance.

Three-Level Hybrid Energy Storage Planning Under Uncertainty

In conventional hybrid energy storage systems, two storage units complement each other. One low-capacity and fast-response unit as a power supplier, and one high-capacity and low-response unit as an energy supplier. The power supplier mitigates fast fluctuations in generation or demand by transferring energy over seconds or minutes, and the energy supplier transfers energy over hours for managing energy. According to this concept, this paper presents a new model of hybrid energy storage systems, where three energy suppliers are considered as a three-level hybrid energy storage system. Energy storage at level 1 shifts energy from off-peak (or low-cost) hours to the on-peak (or high-cost) hours during one day, the storage unit at level 2 transfers energy from off-peak (or low-cost) days to the on-peak (or high-cost) days for the period of one week, and level 3 transfers energy from off-peak seasons to the on-peak seasons through one year. The proposed planning results in a large-scale optimization programming that optimizes large numbers of design variables at the same time. In order to increase the flexibility of the planning, the initial energy of the storage units is also modeled as a design variable and optimized. The uncertainty of loads is modeled and a stochastic planning is carried out to solve the problem. The introduced three-level hybrid energy storage planning is simulated on two test systems, and the results demonstrate that the proposed planning can reduce the planning cost by about 1.8%.

Methodology of evolutionary selection in energy efficient architecture Role of genetic algorithm in calculating the architectural spaces energy efficiency

The growing demand to energy impacted negatively on environment as result of burning fossil fuel, and because buildings are main energy consumer, studies have been called towards creating efficient buildings, In which digital simulation technology, presented with designer to select efficient design solutions, which is uneasy task, due to the large number of models to be evaluated by simulation, consuming time and effort, due to large number of design variables interacting and conflicting in influence. Limiting the effectiveness of (traditional computer simulation approach). And Emersion (genetic algorithm approach) as an effective alternative to assess and optimize design, by automated steps for exchanging design variables between efficient models and produce more efficient new models, away similar to genetic evolution of organisms. But, lake of what and how to adopting this approach limited its use in local architectural researches and practices, Highlighting a problem: "Lack of cognitive clarity about the genetic algorithms approach in creating high-efficiency domestic compared to traditional approach”, aiming to “determining the nature of the genetic algorithm approach and its application to create accurate and high efficiency domestic designs in less time and effort comparing to the traditional approach”. Research methodology was reviewing genetic algorithm mechanism in solving design problems, making a comparison between traditional simulation approach and genetic algorithm approach on virtual model within local environment, determining the most efficient approach which balances characteristics (windows to wall ratio) and (perfect orientation) to provide the highest rates of natural lighting with minimum solar thermal gain, as well as the time it takes to complete the work according to each of the two approaches. Reaching a number of conclusions about effectiveness to adopt genetic algorithm approach in terms of time reduction and results accuracy compared to the traditional approach. المستخلص: ولد تزاید الطلب على الطاقة الى تأثیرات سلبیة على البیئة نتیجة حرق الوقود الاحفوری، ولکون المبانی المستھلک الرئیس للطاقة، فقد دعی لإجراء الدراسات من اجل إنشاء مبانی کفؤة فی الاستھلاک، فکانت التکنولوجیا متمثلة بالأدوات الرقمیة للمحاکاة، حاضرة مع المصمم فی انتخاب الحلول التصمیمیة لتحقیق ھدف الوصول للتصمیم الاکفأ. وھو لیس بالأمر السھل، فی ظل صعوبات کم النماذج ذات الخصائص التصمیمیة المتعارضة التأثیر فیما بینھا، الواجب محاکاتھا وتقییمھا، بجھد ووقت کبیرین. ھی الأسباب التی حدت من فاعلیة تطبیق (منھج المحاکاة الحاسوبیة خصوصا للمشاریع الکبیرة، لیبرز (منھج الخوارزمیة الجینیة) کبدیل فعال لتقییم ً التقلیدی) التصامیم وتحسینھا، بخطوات آلیة لتبادل المتغیرات التصمیمیة بین النماذج الکفؤة وانتاج نماذج أکثر کفاءة بخطوات مماثلة لخطوات التطور الجینی للکائنات الحیة. الا ان ضعف الاغناء المعرفی ً المرتبطة لآلیة تطبیق ھذا المنھج غیبھ فی البحوث والممارسات المعماریة المحلیة، خصوصا بمجال البیئة، لتبرز المشکلة البحثیة: "ضعف الوضوح المعرفی عن طبیعة منھج الخوارزمیات الجینیة فی انشاء نماذج ذات حلول تصمیمیة عالیة الکفاءة مقارنة بالمنھج التقلیدی ضمن الواقع المحلی"، لیتحدد ھدف البحث: "الکشف عن طبیعة منھج الخوارزمیة الجینیة وتطبیقھ لإنشاء حلول تصمیمیة محلیة ذات کفاءة عالیة بأقل وقت وجھد مقارنة بالمنھج التقلیدی". اعتمد البحث منھج الکشف عن الآلیة التطبیقیة للخوارزمیة الجینیة فی حل المشکلات التصمیمیة وانشاء مقارنة بین تطبیق (منھج المحاکاة الحاسوبیة التقلیدی) ومنھج (الخوارزمیة الجینیة فی المحاکاة) على أنموذج افتراضی لفضاء معماری ضمن البیئة المحلیة، لتحدید المنھج الاکفأ لإنشاء تصمیم یحقق التوازن بین الخصائص التصمیمیة من حیث (نسبة مساحة الشباک الى مساحة الجدار) و(اختیار التوجیھ الامثل) بما یضمن توفیر اعلى معدلات للإضاءة الطبیعیة وبأقل مقدار للکسب الحراری الشمسی داخل الفضاء، ومقارنة الوقت المستغرق لإنجاز العمل على وفق کلا المنھجین. لیتوصل البحث الى عدد من الاستنتاجات تمحورت حول کفاءة اعتماد منھج الخوارزمیة الجینیة من حیث اختصار الوقت ودقة النتائج مقارنة بالمنھج التقلیدی.

High-Fidelity Design-Allocation Optimization of a Commercial Aircraft Maximizing Airline Profit

Traditionally, computational design optimization of commercial aircraft is performed by considering a small number of representative operating conditions. These conditions are based on the design Mach number, altitude, payload, and range for which the aircraft will be flown. However, the design also influences which routes and mission parameters are optimal, and so there is coupling that is ignored when using the traditional approach. Here, the aircraft design, mission profiles, and the allocation of aircraft to routes in an airline network are simultaneously optimized. This is a mixed-integer nonlinear programming problem that is reformulated as a nonlinear programming problem because of the large number of design variables. The reformulated problem is solved using a gradient-based optimization approach with a parallel computational framework that facilitates the multidisciplinary analysis and the derivative computation. A surrogate model is used for the computational fluid dynamics analysis that is retrained in each optimization iteration given the new set of shape design variables. The resulting optimization problem contains over 4,000 design variables and close to 14,000 constraints. The optimization results show a 2% increase in airline profit compared with the traditional multipoint optimization approach. The wing area increases to the upper bound, enabling a higher cruise altitude that improves propulsive efficiency. This study finds that simultaneously optimizing the allocation, mission, and design to maximize airline profit results in a different optimized wing design from that resulting from the multipoint optimization approach.

An ANN-exhaustive-listing method for optimization of multiple building shapes and envelope properties with maximum thermal performance

With increasing awareness of sustainability, demands on optimized design of building shapes with a view to maximize its thermal performance have become stronger. Current research focuses more on building envelopes than shapes, and thermal comfort of building occupants has not been considered in maximizing thermal performance in building shape optimization. This paper attempts to develop an innovative ANN (artificial neural network)-exhaustive-listing method to optimize the building shapes and envelope physical properties in achieving maximum thermal performance as measured by both thermal load and comfort hour. After verified, the developed method is applied to four different building shapes in five different climate zones in China. It is found that the building shape needs to be treated separately to achieve sufficient accuracy of prediction of thermal performance and that the ANN is an accurate technique to develop models of discomfort hour with errors of less than 1.5%. It is also found that the optimal solutions favor the smallest window-to-external surface area with triplelayer low-E windows and insulation thickness of greater than 90 mm. The merit of the developed method is that it can rapidly reach the optimal solutions for most types of building shapes with more than two objective functions and large number of design variables.

Aerodynamic inverse design using multifidelity models and manifold mapping

Aerodynamic inverse design is proposed using multifidelity models and the manifold mapping (MM) technique. Aerodynamic inverse design aims at achieving a target performance characteristic, such as a pressure coefficient distribution of an airfoil or local lift distribution of a wing. Due to the high computational cost of accurate aerodynamic models and the large number of design variables, the overall cost of inverse design can be prohibitive. The MM-based optimization algorithm leverages the speed of the low-fidelity model to accelerate the optimization process, but refers back to the high-fidelity model to ensure an accurate solution. In this work, the MM technique is applied to the characteristic distribution under consideration in each application. In particular, the pressure coefficient distribution is modeled with the MM technique in the case of airfoil inverse design, and the sectional lift distribution in the case of wing design. The proposed method is tested and evaluated on six airfoil inverse design cases and one rectangular wing inverse design case. In the two-dimensional cases, parameterized with eight design variables, direct aerodynamic inverse design using pattern search required 700 to 1200 high-fidelity model evaluations, which took 300 to 700 hours in total. The MM-based design algorithm required less than 20 high-fidelity simulations and 1000 to 2000 low-fidelity evaluations, which took 30 to 90 hours. In the three-dimensional case, parameterized with three design variables, direct aerodynamic inverse design took around 50 hours, whereas the MM-based design needed around six hours.

A surrogate assisted adaptive framework for robust topology optimization

This paper presents a novel approach for robust topology optimization (RTO). The proposed approach integrates the hybrid polynomial correlated function expansion (H-PCFE) with the deterministic topology optimization (DTO) algorithm, where H-PCFE is used for computing the moments associated with the objective function in robust design optimization (RDO). It is argued that re-training the H-PCFE model at every iteration will make the overall algorithm computationally expensive and time-consuming. To alleviate this issue, an adaptive algorithm is proposed that automatically decides whether to retrain the H-PCFE model or to use the available H-PCFE model. Since RTO involves a large number of design variables, we also argue that, from a computational point of view, gradient-based optimization algorithms are better suited over gradient-free optimization techniques. However, the bottleneck of using gradient-based optimization algorithms is associated with the computation of the gradients. To address this issue, we propose a procedure for computing the gradients of the objective function in RTO. The proposed approach is highly flexible and can be integrated with the already available DTO codes from literature. To illustrate the performance of the proposed approach, three well-known benchmark problems have been solved; two of them being on the 2D MBB beam and one more on a 3D cantilever beam. As compared to the results of the DTO, RTO yields conservative results which are visible from the additional limbs appearing in the optimized topology of the structure. Furthermore, to justify the use of H-PCFE as a surrogate within the proposed framework, a comparative assessment of the proposed approach against the popular surrogate models has also been presented. It is observed that H-PCFE outperforms other popular surrogate models such as Kriging, radial basis function and polynomial chaos expansion, in both accuracy and efficiency.

Acoustic topology optimization of porous material distribution based on an adjoint variable FMBEM sensitivity analysis

We develop an acoustic topology optimization approach in this work for the surface design of structures covered by porous materials. The fast multipole boundary element method (FMBEM) is employed for the sound scattering analysis. The acoustic absorption characteristics of porous materials are numerically modeled using the Delany–Bazley–Miki empirical model. Based on the solid isotropic material with penalization (SIMP) method, the optimization is performed by setting the artificial element densities of porous material as the design variables, minimizing the sound pressure or the dissipated sound power as the design objective. As a key treatment in this study, we develop a fast sensitivity analysis approach based on an adjoint variable method (AVM) and the fast multipole method (FMM) to calculate the sensitivities of the objective function with respect to a large number of design variables. The FMM is applied to accelerate the vector-matrix product required by the AVM. According to the gradient information, the method of moving asymptotes (MMA) is used for solving the optimization problem to find the optimal solution. We validate the proposed topology optimization approach through numerical examples of acoustic scattering over a single cylinder and multi cylinders, and demonstrate its ability to handle large-scale problems.

A wavelet-based scheme for impact identification of framed structures using combined genetic and water cycle algorithms

This paper presents a synthesis strategy for impact force localization and identification of framed structures in time-domain using a two-step wavelet-based fitness evaluation scheme in conjunction with genetic and water cycle algorithms. For this purpose, a straightforward approach is developed for sensitivity analysis of accelerations and spatial signal connecting the peak values. The proposed scheme is capable of using diverse scales of wavelet functions at considerably small sampling rates. A decimal genetic algorithm (GA) coding system and a recently developed water cycle algorithm (WCA) are improved to be used for impact localization and identification, respectively. The fitness evaluation is then modified for using the obtained results from sensitivity analysis rather than the acceleration itself. Numerical study is first conducted for a large space frame to evaluate the precision, convergence, and efficiency of the identified results using the proposed GA-WCA strategy to different measurement scenarios and initial estimates of the structural model. For comparison purposes, the robustness of WCA in identification step is thoroughly compared with other three state-of-the-art optimization algorithms i.e., particle swarm optimization (PSO), imperialist competitive algorithm (ICA), and differential evolution (DE). Afterwards, an experimental validation study is carried out on a laboratory scale truss bridge. It is concluded that the computational performance of the proposed method is significantly better than the existing methods with respect to fitness evaluation. Results show that, even for the cases with a rough knowledge on structural parameters, the impact force is successfully identified with an excellent precision. This demonstrates the superiority of WCA strategy in handling the global search over large design space with large number of design variables. It is also concluded that the impact localization step is accomplished very fast, thus providing a near real-time strategy in dealing with large-scaled frame and bridge structures.

Smooth size design for the natural frequencies of curved Timoshenko beams using isogeometric analysis

Curved thick beams with smoothly variable cross-section size are very common in practical engineering problems. Designing the variable size based on the traditional finite element methods often leads to non-smooth solutions. To guarantee the smoothness of size distribution, an isogeometric analysis (IGA)-based design approach is proposed in this work to optimize the cross-section size of curved Timoshenko beams for natural frequencies. Due to the geometric exactness and high-order continuity of IGA, there is high accuracy of natural frequency prediction and sensitivity analysis for design optimization. It is found that small numbers of design variables lead to parameterization-dependent solutions, while large numbers of design variables induce design fluctuation. To avoid the undesirable design fluctuation, a stability transformation method-based K-S aggregation constraint scheme is proposed to regularize the size distribution and also achieve the stable convergence of optimal solutions. Multiple design studies including the deterministic and reliability-based optimization problems are performed to demonstrate the applicability and effectiveness of the proposed approach.

Adjoint formulation of a steady multistage turbomachinery interface using automatic differentiation

The use of high-fidelity computational fluid dynamics (CFD) tools in turbomachinery design has seen a continuous increase as a result of computational power growth and numerical methods improvement. These tools are often used in optimization environments, where gradient-based optimization algorithms are the most common due to their efficiency. In cases where the optimization contains a large number of design variables, the adjoint approach for calculating the gradients is beneficial, as it provides a way of obtaining function sensitivities with a computational cost that is nearly independent of the number of design variables. The interaction between adjacent blade rows is of utmost importance for the performance of multistage turbomachines. The most commonly used method to address these effects (i.e. coupling in the simulation of multiple rows) is the mixing-plane treatment, that has become a standard industrial tool in the design environment. In this paper, the formulation and implementation of an adjoint solver for multistage turbomachinery applications are presented, namely the adjoint counterpart of the mixing-plane formulation used in the direct solver. The solver is developed using the discrete ADjoint approach, where the partial derivatives required for the assembly of the adjoint system of equations are obtained using automatic differentiation tools. The sensitivity of several performance metrics relative to neighbor blade/hub row geometry and boundary conditions are shown to highlight the physical coupling in multi-row turbomachines. The verification of the adjoint multistage solver against the finite-difference approach is performed successfully with relative differences below 1 %.

A Moving Morphable Component Based Topology Optimization Approach for Rib-Stiffened Structures Considering Buckling Constraints

Abstract Stiffened structures are widely used in industry. However, how to optimally distribute the stiffening ribs on a given base plate remains a challenging issue, partially because the topology and geometry of stiffening ribs are often represented in a geometrically implicit way in traditional approaches. This implicit treatment may lead to problems such as high computational cost (caused by the large number of design variables, geometry constraints in optimization, and large degrees-of-freedom (DOF) in finite element analysis (FEA)) and the issue of manufacturability. This paper presents a moving morphable component (MMC)-based approach for topology optimization of rib-stiffened structures, where the topology and the geometry of stiffening ribs are explicitly described. The proposed approach displays several prominent advantages, such as (1) both the numbers of design variables and DOF in FEA are reduced substantially; (2) the proper manufacture-related geometry requirements of stiffening ribs can be readily satisfied without introducing any additional constraint. The effectiveness of the proposed approach is further demonstrated with numerical examples on topology optimization of rib-stiffened structures with buckling constraints.

An isogeometric multimesh design approach for size and shape optimization of multidirectional functionally graded plates

This article firstly presents a novel numerical methodology to concurrently optimize material distribution (size) and thickness variation (shape) of multidirectional functionally graded (MFG) plates under free vibration within the isogeometric analysis (IGA) framework. An isogeometric multimesh design (IMD) approach is proposed to generate two distinct non-uniform rational B-spline (NURBS) surfaces via the k-refinement strategy. A finer analysis one relied upon a combination of the IGA and a generalized shear deformation theory (GSDT) is utilized for the unknown solution approximation in finite element analyses (FEAs). Whilst the other coarser design one is employed for the exact geometry representation as well as the optimal material and thickness depiction. Size and shape design variables are in turn the ceramic volume fraction and z-axis coordinate of the top side of the MFG plate coincidentally assigned to each of control points on this surface. Flexibly utilizing such two surfaces helps diminish a large number of design variables and considerably save the computational cost in optimization problems, yet still appropriately manifesting optimal material and thickness profiles. Additionally, this definition accurately simulates mechanical behavior of MFG plates in analysis ones as well. A recently developed derivative-free adaptive hybrid evolutionary firefly algorithm (AHEFA) is used to solve constrained frequency maximization problems. Several numerical examples are executed to verify the effectiveness and robustness of the present paradigm.

A data-driven modeling approach to zonal isolation of cemented gas wells

Gas leakage through the cemented section of a gas well, from a producing zone to other zones or to the open air, poses serious threats to safety and the environment. A number of design variables during drilling and cementing jobs may possibly contribute to such leakage. Decisions on these variables are best made during the design and well construction phase, as remedial operations after the well begins production have limited success rate. Therefore an approach that avoids the problem by ensuring robust zonal isolation during well construction jobs is more suitable. Such an approach involves decisions on a fairly large number of design variables. Building a model based on first principles to predict the effect of all of these variables on leakage is a formidable task. An alternative examined in this paper relies on using multivariate statistics to build an empirical model from available data. The model can then be used to make decisions on design variables such that leakage is avoided. The proposed approach is explained using data from 105 gas wells. The model built predicts leakage with about 75% accuracy in cross-validation tests. In addition, it ranks decision variables in the order of importance and suggests which ones need to receive more attention. The approach presented can be extended to include additional variables for which data is available.

Component-Based Geometry Manipulation for Aerodynamic Shape Optimization with Overset Meshes

Mesh generation for high-fidelity computational fluid dynamics simulations and aerodynamic shape optimization is a time-consuming task. Complex geometries can be accurately modeled using overset meshes, whereby multiple high-quality structured meshes corresponding to different aircraft components overlap to model the full aircraft configuration. However, from the standpoint of geometry manipulation, most methods operate on the entire geometry rather than on separate components, which diminishes the advantages of overset meshes. To address this issue, a geometry module is introduced that operates on individual components and automatically computes their intersections to update overset meshes during optimization. Reverse-mode automatic differentiation is applied to compute partial derivatives across this geometry module so that it fits into an optimization framework that uses a hybrid adjoint method (known as ADjoint) to efficiently compute gradients for a large number of design variables. By using these automatically updated meshes and the corresponding derivatives, the aerodynamic shape of the DLR-F6 geometry is optimized while allowing changes in the wing–fuselage intersection. Sixteen design variables control the fuselage shape, and 128 design variables determine the wing surface. Under transonic flight conditions, the optimization reduces drag by 15 counts (5%) as compared with the baseline design.