Pei Cao

A Multi-Objective Simulated Annealing Approach Towards 3D Packing Problems With Strong Constraints: CMOSA

This research concerns the packing problem frequently encountered in engineering design, where the volume and weight of a number of structural components such as valves and plumbing lines need to be minimized. Since in real applications the constraints are usually complex, the formulation of computationally tractable optimization becomes challenging. In this research, we propose a novel multiobjective simulated annealing (MOSA) approach towards the design optimization, i.e., optimizing the placement of valves under prescribed constraints to minimize the volume occupied, and the estimated plumbing line length. The objectives and constraints are described by analytical expressions. Our case study indicates that the new MOSA algorithm has relatively better performance towards 3D packing with strong constraints and the design can indeed be automated. The outcome of this research may benefit both existing manufacturing practice and future additive manufacturing.Copyright

Design for Additive Manufacturing: Optimization of Piping Network in Compact System with Enhanced Path-Finding Approach

In this research, we investigate the subject of path-finding. A pruned version of visibility graph based on Candidate Vertices is formulated, followed by a new visibility check technique. Such combination enables us to quickly identify the useful vertices and thus find the optimal path more efficiently. The algorithm proposed is demonstrated on various path-finding cases. The performance of the new technique on visibility graphs is compared to the traditional A* on Grids, Theta* and A* on Visibility Graphs in terms of path length, number of nodes evaluated, as well as computational time. The key algorithmic contribution is that the new approach combines the merits of grid-based method and visibility graph-based method and thus yields better overallThis research aims at unleashing the potential of additive manufacturing technology in industrial design that can produce structures/devices with irregular component geometries to reduce sizes/weights. We explore, by means of path-finding, the length minimization of freeform hydraulic piping network in compact space under given constraints. Previous studies on path-finding have mainly focused on enhancing computational efficiency due to the need to produce rapid results in such as navigation and video-game applications. In this research, we develop a new Focal Any-Angle A* approach that combines the merits of grid-based method and visibility graph-based method. Specifically, we formulate pruned visibility graphs preserving only the useful portion of the vertices, and then find the optimal path based on the candidate vertices using A*. The reduced visibility graphs enable us to outperform approximations and maintain the optimality of exact algorithms in a more efficient manner. The algorithm proposed is compared to the traditional A* on Grids, Theta* and A* on Visibility Graphs in terms of path length, number of nodes evaluated, as well as computational time. As demonstrated and validated through case studies, the proposed method is capable of finding the shortest path with tractable computational cost, which provides a viable design tool for the additive manufacturing of piping network systems.

A framework of a fast any-angle path finding algorithm on visibility graphs based on A∗ for plumbing design

In this research, we investigate the issue of compact design that involves finding the shortest connecting plumbing path between hydraulic components. An improved version of visibility graph called visibility graph of candidate is proposed followed by a new line-of-sight technique called visible-neighbors which is straightforward to expand to 3D applications. A new data structure associated with the techniques developed are proposed in order to address the inefficiency issue coming with A* algorithm on visibility graphs. The performance of the new technique on visibility graphs is compared to the traditional A* and Theta* algorithms on the aspect of path length, nodes explored as well as computational time.

Structural damage identification with multi-objective DIRECT algorithm using natural frequencies and single mode shape

Structural damage identification has been continuously pursued in engineering practices to facilitate diagnosis and prognosis in structural health monitoring (SHM) systems. In SHM, the changes of modal parameters are frequently used as inputs. In this research, we employ the multiple damage location assurance criterion (MDLAC) to characterize the correlation between predictions of both frequency changes and single mode shape change with the measured data. The damage locations and severities can be obtained by maximizing the MDLAC values. Thereafter, a multi-objective optimization problem based on their MDLAC values can be formulated and optimized by applying a newly devised multi-objective DIRECT approach. The proposed approach offers practical attractions of only requiring a short amount of computational time, and the results are conclusive and repeatable.

Application of model predictive control for optimal operation of wind turbines

For large-scale wind turbines, reducing maintenance cost is a major challenge. Model predictive control (MPC) is a promising approach to deal with multiple conflicting objectives using the weighed sum approach. In this research, model predictive control method is applied to wind turbine to find an optimal balance between multiple objectives, such as the energy capture, loads on turbine components, and the pitch actuator usage. The actuator constraints are integrated into the objective function at the control design stage. The analysis is carried out in both the partial load region and full load region, and the performances are compared with those of a baseline gain scheduling PID controller. The application of this strategy achieves enhanced balance of component loads, the average power and actuator usages in partial load region.

A Multi-Objective DIRECT Algorithm toward Structural Damage Identification with Limited Dynamic Response Information

A major challenge in Structural Health Monitoring (SHM) is to accurately identify both the location and severity of damage using the dynamic response information acquired. While in theory the vibration-based and impedance-based methods may facilitate damage identification with the assistance of a credible baseline finite element model since the changes of stationary wave responses are used in these methods, the response information is generally limited and the measurements may be heterogeneous, making an inverse analysis using sensitivity matrix difficult. Aiming at fundamental advancement, in this research we cast the damage identification problem into an optimization problem where possible changes of finite element properties due to damage occurrence are treated as unknowns. We employ the multiple damage location assurance criterion (MDLAC), which characterizes the relation between measurements and predictions (under sampled elemental property changes), as the vector-form objective function. We then develop an enhanced, multi-objective version of the DIRECT approach to solve the optimization problem. The underlying idea of the multi-objective DIRECT approach is to branch and bound the unknown parametric space to converge to a set of optimal solutions. A new sampling scheme is established, which significantly increases the efficiency in minimizing the error between measurements and predictions. The enhanced DIRECT algorithm is particularly suitable to solving for unknowns that are sparse, as in practical situations structural damage affect only a small number of finite elements. A number of test cases using vibration response information are executed to demonstrate the effectiveness of the new approach.

Efficient Uncertainty Quantification in Structural Dynamic Analysis Using Two-Level Gaussian Processes

Uncertainty quantification is an important aspect in structural dynamic analysis. Since practical structures are complex and oftentimes need to be characterized by large-scale finite element models, component mode synthesis (CMS) method is widely adopted for order-reduced modeling. Even with the model order-reduction, the computational cost for uncertainty quantification can still be prohibitive. In this research, we utilize a two-level Gaussian process emulation to achieve rapid sampling and response prediction under uncertainty, in which the low- and high-fidelity data extracted from CMS and full-scale finite element model are incorporated in an integral manner. The possible bias of low-fidelity data is then corrected through high-fidelity data. For the purpose of reducing the emulation runs, we further employ Bayesian inference approach to calibrate the order-reduced model in a probabilistic manner conditioned on multiple predicted response distributions of concern. Case studies are carried out to validate the effectiveness of proposed methodology.Copyright

Optimal Configuration of Valves and Plumbing Lines in Complex Housing

In engineering design, the volume and weight of a number of systems consisting of valves and plumbing lines often need to be minimized. In current practice, this is facilitated under empirical experience with trial and error, which is time-consuming and may not yield the optimal result. This problem is intrinsically difficult due to the challenge in the formulation of optimization problem that has to be computationally tractable. In this research, we choose a sequential approach towards the design optimization, i.e., first optimizing the placement of valves under prescribed constraints to minimize the volume occupied, and then identifying the shortest paths of plumbing lines to connect the valves. In the first part, the constraints are described by analytical expressions, and two approaches of valve placement optimization are reported, i.e., a two-phase method and a simulated annealing-based method. In the second part, a three-dimensional routing algorithm is explored to connect the valves. Our case study indicates that the design can indeed be automated and design optimization can be achieved under reasonable computational cost. The outcome of this research can benefit both existing manufacturing practice and future additive manufacturing.Copyright