Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

Acoustical and behavioral heuristics for fast interactive sound design

During their creative process, designers routinely seek the feedback of end users. Yet, the collection of perceptual judgments is costly and time-consuming, since it involves repeated exposure to the designed object under elementary variations. Thus, considering the practical limits of working with human subjects, randomized protocols in interactive sound design face the risk of inefficiency, in the sense of collecting mostly uninformative judgments. This risk is all the more severe that the initial search space of design variations is vast. In this paper, we propose heuristics for reducing the design space considered during an interactive optimization process. These heuristics operate by using an approximation model, called surrogate model, of the perceptual quantity of interest. As an application, we investigate the design of pleasant and detectable electric vehicle sounds using an interactive genetic algorithm. We compare two types of surrogate models for this task, one based on acoustical descriptors gathered from the literature and the other based on behavioral data. We find that reducing by a factor of up to 64 an original design space of 4096 possible settings with the proposed heuristics reduces the number of iterations of the design process by up to 2 to reach the same performance. The behavioral approach leads to the best improvement of the explored designs overall, while the acoustical approach requires an appropriate choice of acoustical descriptor to be effective. Our approach accelerates the convergence of interactive design. As such, it is particularly suitable to tasks in which exhaustive search is prohibitively slow or expensive.

Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning

Neural networks (NNs) are increasingly used in design to construct the objective functions and constraints, which leads to the needs of optimization of NN models with respect to design variables. A Neural Optimization Machine (NOM) is proposed for constrained single/multi-objective optimization by appropriately designing the NN architecture, activation function and loss function. The NN's built-in backpropagation algorithm conducts the optimization and is seamlessly integrated with the additive manufacturing (AM) process-property model. The NOM is tested using several numerical optimization problems. It is shown that the increase in the dimension of design variables does not increase the computational cost significantly. Next, a brief review of the physics-guided machine learning model for fatigue performance prediction of AM components is given. Finally, the NOM is applied to design processing parameters in AM to optimize the mechanical fatigue properties through the physics-guided NN under uncertainties. One novel contribution of the proposed methodology is that the constrained process optimization is integrated with physics/knowledge and the data-driven AM process-property model. Thus, a physics-compatible process design can be achieved. Another significant benefit is that the training and optimization are achieved in a unified NN model, and no separate process optimization is needed. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Hybrid IPSO-IAGA-BPNN algorithm-based rapid multi-objective optimization of a fully parameterized spaceborne primary mirror

The surface figure precision, weight, and dynamic performance of spaceborne primary mirrors depend on mirror structure parameters, which are usually optimized to improve the overall performance. To realize rapid multi-objective design optimization of a primary mirror with multiple apertures, a fully parameterized primary mirror structure is established. A surrogate model based on a hybrid of improved particle swarm optimization (IPSO), adaptive genetic algorithm (IAGA), and optimized back propagation neural network (IPSO-IAGA-BPNN) is developed to replace optomechanical simulation with its high computational cost. In this model, a self-adaptive inertia weight and a modified genetic operator are introduced into the particle swarm optimization (PSO) and adaptive genetic algorithm (AGA), respectively. The connection parameters of BPNN are optimized by the IPSO-IAGA algorithm for global searching capability. Further, the proposed IPSO-IAGA-BPNN, based on a rapid multi-objective optimization framework for a fully parameterized primary mirror structure, is established. Moreover, in addition to the proposed IPSO-IAGA-BPNN model, the Kriging, RSM, BPNN, GA-BPNN, PSO-BPNN, and PSO-GA-BPNN models are also analyzed as contrast models. The comparison results indicate that the predicted value obtained by IPSO-IAGA-BPNN is superior to the six other surrogate models since its mean absolute percentage error is less than 3% and its R² is more than 0.99. Finally, we present a Pareto-optimal primary mirror design and implement it through three optimization methods. The verification results show that the proposed method predicts mirror structural performance more accurately than existing surrogate-based methods, and promotes significantly superior computational efficiency compared to the conventional integration-based method.

Hypothesis derivation and its verification by a wholly automated many-objective evolutionary optimization system

This study has constructed a fully automated multidisciplinary and many-objective evolutionary design optimization system independent of computer environments to evaluate objective functions; the research applied it to a geometric design problem of a flyback booster for next-generation space transportation. In optimization involving objective functions to appraise the aero-/structural-dynamic performance with high fidelity, spatial discretization hinders the overall automation. This research has facilitated an efficient optimal design by wholly automating high-fidelity assessments, which designers had to implement manually, and has accomplished optimizations that directly contribute to real-world design problems. Moreover, this study would accumulate design knowledge for space transportation that the market is reviving. The total automated system yielded the embedding of geometric trait lines to ensure the discretization even for large curvature surfaces; the system innovated a robust automatic error-checking mechanism in the system’s preprocess. Consequently, the entirely automatized optimization procured nondominated solution sets for more precise data analyses in a pragmatic execution period. Design informatics, a framework combining optimization and data analysis, functioned usefully in real-world design on flyback-booster geometry by materializing smooth deriving and verifying a design hypothesis; eventually, the research gained a new design principle.

The State-of-the-Art on Framework of Vibration-Based Structural Damage Identification for Decision Making

Research on detecting structural damage at the earliest possible stage has been an interesting topic for decades. Among them, the vibration-based damage detection method as a global technique is especially pervasive. The present study reviewed the state-of-the-art on the framework of vibration-based damage identification in different levels including the prediction of the remaining useful life of structures and the decision making for proper actions. This framework consists of several major parts including the detection of damage occurrence using response-based methods, building reasonable structural models, selecting damage parameters and constructing objective functions with sensitivity analysis, adopting optimization techniques to solve the problem, predicting the remaining useful life of structures, and making decisions for the next actions. For each part, the commonly used methods were reviewed and the merits and drawbacks were summarized to give recommendations. This framework is aimed to guide the researchers and engineers to implement step by step the structure damage identification using vibration measurements. Finally, the future research work in this field is recommended.

Surrogate articular contact models for computationally efficient multibody dynamic simulations

Contact occurs in a wide variety of multibody dynamic systems, including the human musculoskeletal system. However, sensitivity and optimization studies of such systems have been limited by the high computational cost of repeated contact analyses. This study presents a novel surrogate modeling approach for performing computationally efficient three-dimensional elastic contact analyses within multibody dynamic simulations. The approach fits a computationally cheap surrogate contact model to data points sampled from a computationally expensive elastic contact model (e.g., a finite element or elastic foundation model) and resolves several unique challenges involved in applying surrogate modeling techniques to elastic contact problems. As an example application, we performed multibody dynamic simulations of a Stanmore wear simulator machine using surrogate and elastic foundation (EF) contact models of a total knee replacement. Accuracy was assessed by performing eleven dynamic simulations with both types of contact models utilizing large variations in motion and load inputs to the machine. Wear volumes predicted with the surrogate contact models were within 1.5% of those predicted with the EF contact models. Computational speed was assessed by performing five Monte Carlo analyses (over 1000 dynamic simulations each) with surrogate contact models utilizing realistic variations in motion and load inputs. Computation time was reduced from an estimated 284 h per analysis with the EF contact models to 1.4 h with the surrogate contact models (i.e., 17 min vs. 5 s per simulation), with higher wear sensitivity observed for motion variations than for load variations. The proposed surrogate modeling approach can significantly improve the computational speed of multibody dynamic simulations incorporating three-dimensional elastic contact models with general surface geometry.