A level set approach for optimal design of smart energy harvesters

The proliferation of Micro-Electro-Mechanical Systems (MEMS), portable electronics and wireless sensing networks has raised the need for a new class of devices with self-powering capabilities. Vibration-based piezoelectric energy harvesters provide a very promising solution, as a result of their capability of converting mechanical energy into electrical energy through the direct piezoelectric effect. However, the identification of fast, accurate methods and rational criteria for the design of piezoelectric energy harvesting devices still poses a challenge. In this work, a level set-based topology optimization approach is proposed to synthesize mechanical energy harvesting devices for self-powered micro systems. The energy harvester design problem is reformulated as a variational problem based on the concept of topology optimization, where the optimal geometry is sought by maximizing the energy conversion efficiency of the device. To ensure computational efficiency, the shape gradient of the energy conversion efficiency is analytically derived using the material time derivative approach and the adjoint variable method. A design velocity field is then constructed using the steepest descent method, which is further integrated into level set methods. The reconciled level set (RLS) method is employed to solve multi-material shape and topology optimization problems, using the Merriman–Bence–Osher (MBO) operator. Designs with both single and multiple materials are presented, which constitute improvements with respect to existing energy harvesting designs.     

An integrated approach to topology,sizing, and shape optimization

Topology optimization has become very popular in industrial applications, and most FEM codes have implemented certain capabilities of topology optimization. However, most codes do not allow simultaneous treatment of sizing and shape optimization during the topology optimization phase. This poses a limitation on the design space and therefore prevents finding possible better designs since the interaction of sizing and shape variables with topology modification is excluded. In this paper, an integrated approach is developed to provide the user with the freedom of combining sizing, shape, and topology optimization in a single process.     

A new overhang constraint for topology optimization of self-supporting structures in additive manufacturing

This work falls within the scope of computer-aided optimal design, and aims to integrate the topology optimization procedures and recent additive manufacturing technologies (AM). The elimination of scaffold supports at the topology optimization stage has been recognized and pursued by many authors recently. The present paper focuses on implementing a novel and specific overhang constraint that is introduced inside the topology optimization problem formulation along with the regular volume constraint. The proposed procedure joins the design and manufacturing processes into a integrated workflow where any component can directly be manufactured with no requirement of any sacrificial support material right after the topology optimization process. The overhang constraint presented in this work is defined by the maximum allowable inclination angle, where the inclination of any member is computed by the Smallest Univalue Segment Assimilating Nucleus (SUSAN), an edge detection algorithm developed in the field of image analysis and processing. Numerical results on some benchmark examples, along with the numerical performances of the proposed method, are introduced to demonstrate the capacities of the presented approach.     

Bridging topology optimization and additive manufacturing

Topology optimization is a technique that allows for increasingly efficient designs with minimal a priori decisions. Because of the complexity and intricacy of the solutions obtained, topology optimization was often constrained to research and theoretical studies. Additive manufacturing, a rapidly evolving field, fills the gap between topology optimization and application. Additive manufacturing has minimal limitations on the shape and complexity of the design, and is currently evolving towards new materials, higher precision and larger build sizes. Two topology optimization methods are addressed: the ground structure method and density-based topology optimization. The results obtained from these topology optimization methods require some degree of post-processing before they can be manufactured. A simple procedure is described by which output suitable for additive manufacturing can be generated. In this process, some inherent issues of the optimization technique may be magnified resulting in an unfeasible or bad product. In addition, this work aims to address some of these issues and propose methodologies by which they may be alleviated. The proposed framework has applications in a number of fields, with specific examples given from the fields of health, architecture and engineering. In addition, the generated output allows for simple communication, editing, and combination of the results into more complex designs. For the specific case of three-dimensional density-based topology optimization, a tool suitable for result inspection and generation of additive manufacturing output is also provided.     

Deep learning driven real time topology optimisation based on initial stress learning

Topology optimisation can facilitate engineers in proposing efficient and novel conceptual design schemes, but the traditional FEM based optimization demands significant computing power and makes the real time optimization impossible. Based on the convolutional neural network (CNN) method, a new deep learning approximate algorithm for real time topology optimisation is proposed. The algorithm learns from the initial stress (LIS), which is defined as the major principal stress matrix obtained from finite element analysis in the first iteration of classical topology optimisation. The initial major principal stress matrix of the structure is used to replace the load cases and boundary conditions of the structure as independent variables, which can produce topological prediction results with high accuracy based on a relatively small number of samples. Compared with the traditional topology optimisation method, the new method can produce a similar result in real time without repeated iterations. A classic short cantilever problem was used as an example, and the optimized topology of the cantilever structure is predicted successfully by the established approximate algorithm. By comparing the prediction results to the structural optimisation results obtained by the classical topology optimisation method, it is discovered that the two results are highly approximate, which verifies the validity of the established algorithm. Furthermore, a new algorithm evaluation method is proposed to evaluate the effects of using different methods to select samples on the prediction performance of the optimized topology, and the results were promising and concluded in the end.     

Ecogeography-based optimization: Enhancing biogeography-based optimization with ecogeographic barriers and differentiations

Biogeography-based optimization (BBO) is a bio-inspired metaheuristic based on the mathematics of island biogeography. The paper proposes a new variation of BBO, named ecogeography-based optimization (EBO), which regards the population of islands (solutions) as an ecological system with a local topology. Two novel migration operators are designed to perform effective exploration and exploitation in the solution space, mimicking the species dispersal under ecogeographic barriers and differentiations. Experimental results show that the EBO outperforms the basic BBO and several other popular evolutionary algorithms (EAs) on a set of well-known benchmark problems. We also present a real-world application of the proposed EBO to an emergency airlift problem in the 2013 Ya׳an–Lushan Earthquake, China.     

A new level-set based approach to shape and topology optimization under geometric uncertainty

Geometric uncertainty refers to the deviation of the geometric boundary from its ideal position, which may have a non-trivial impact on design performance. Since geometric uncertainty is embedded in the boundary which is dynamic and changes continuously in the optimization process, topology optimization under geometric uncertainty (TOGU) poses extreme difficulty to the already challenging topology optimization problems. This paper aims to solve this cutting-edge problem by integrating the latest developments in level set methods, design under uncertainty, and a newly developed mathematical framework for solving variational problems and partial differential equations that define mappings between different manifolds. There are several contributions of this work. First, geometric uncertainty is quantitatively modeled by combing level set equation with a random normal boundary velocity field characterized with a reduced set of random variables using the Karhunen–Loeve expansion. Multivariate Gauss quadrature is employed to propagate the geometric uncertainty, which also facilitates shape sensitivity analysis by transforming a TOGU problem into a weighted summation of deterministic topology optimization problems. Second, a PDE-based approach is employed to overcome the deficiency of conventional level set model which cannot explicitly maintain the point correspondences between the current and the perturbed boundaries. With the explicit point correspondences, shape sensitivity defined on different perturbed designs can be mapped back to the current design. The proposed method is demonstrated with a bench mark structural design. Robust designs achieved with the proposed TOGU method are compared with their deterministic counterparts.     

Texture mapping on surfaces of arbitrary topology using norm preserving-based optimization

A simple and yet highly efficient, high-quality texture mapping method for surfaces of arbitrary topology is presented. The new method projects the given surface from the 3D object space into the 2D texture space to identify the 2D texture structure that will be used to texture the surface. The object space to texture space projection is optimized to ensure minimum distortion of the texture mapping process. The optimization is achieved through a commonly used norm preserving minimization process on edges of the surface. The main difference here is, by using an initial value approach, the optimization problem can be set up as a quadratic programming problem and, consequently, solved by a linear least squares method. Three methods to choose a good initial value are presented. Test cases show that the new method works well on surfaces of arbitrary topology, with the exception of surfaces with exceptionally abnormal curvature distribution. Other advantages of the new method include uniformity and seamlessness of the texture mapping process. The new method is suitable for applications that do not require precise texture mapping results but demand highly efficient mapping process such as computer animation or video games.     

Topology synthesis of thermomechanical compliant mechanisms with geometrical nonlinearities using meshless method

The element-free Galerkin (EFG) method, one of the important meshless methods, is integrated into topology optimization and a new topology optimization method for designing thermomechanical actuated compliant mechanisms with geometrical nonlinearities is presented. The meshless method is employed to discretize the governing equations and the bulk density field. Using meshless method to analyze the thermomechanical model is better consistent with the natural behavior of large-displacement compliant mechanisms than using the standard finite element method (FEM). The optimization formulation is developed using the SIMP and meshless methods. The nonlinear design sensitivity analysis is performed by incorporating the adjoint approach into the meshless method. The filtering of the sensitivity developed corrects the topology including few discontinuous scattered points. The geometrically nonlinear design sensitivity analysis is performed by incorporating the adjoint approach into the meshless method. The availability of the proposed method is demonstrated by designing compliant actuators in which both linear and nonlinear modeling are considered.     

Modeling and simulation of injection control system on a four-stroke type diesel engine development platform using artificial neural networks

Efficiency, reliability and emission demands on fuel consumptions have directed us to develop a microcontroller-based electromechanical educational platform that emulates the basic injection process of common four-stroke type diesel engines. Modeling of a system provides rapid programming and implementation capabilities. This study focuses on modeling and simulation of the platform in order to observe the results of novel methods and development strategies. The model determines the injection time (IT) and injection order (IO) of the related pistons. Determination of the IO has standard steps, where of IT which directly affects the fuel consumption lets novel optimization methods. In traditional applications, IT is assigned by a lookup table, whose inputs are crankshaft speed (CS) and manifold absolute pressure (MAP) values. In this study, an alternative relation surface created by feedforward artificial neural networks (ANNs) is suggested to determine the IT. The novel method could interpolate precise intermediate values of IT which bring about optimization in fuel consumption. Performances of the traditional method and the ANNs method are compared.     

Single-loop system reliability-based topology optimization considering statistical dependence between limit-states

This paper presents a single-loop algorithm for system reliability-based topology optimization (SRBTO) that can account for statistical dependence between multiple limit-states, and its applications to computationally demanding topology optimization (TO) problems. A single-loop reliability-based design optimization (RBDO) algorithm replaces the inner-loop iterations to evaluate probabilistic constraints by a non-iterative approximation. The proposed single-loop SRBTO algorithm accounts for the statistical dependence between the limit-states by using the matrix-based system reliability (MSR) method to compute the system failure probability and its parameter sensitivities. The SRBTO/MSR approach is applicable to general system events including series, parallel, cut-set and link-set systems and provides the gradients of the system failure probability to facilitate gradient-based optimization. In most RBTO applications, probabilistic constraints are evaluated by use of the first-order reliability method for efficiency. In order to improve the accuracy of the reliability calculations for RBDO or RBTO problems with high nonlinearity, we introduce a new single-loop RBDO scheme utilizing the second-order reliability method and implement it to the proposed SRBTO algorithm. Moreover, in order to overcome challenges in applying the proposed algorithm to computationally demanding topology optimization problems, we utilize the multiresolution topology optimization (MTOP) method, which achieves computational efficiency in topology optimization by assigning different levels of resolutions to three meshes representing finite element analysis, design variables and material density distribution respectively. The paper provides numerical examples of two- and three-dimensional topology optimization problems to demonstrate the proposed SRBTO algorithm and its applications. The optimal topologies from deterministic, component and system RBTOs are compared with one another to investigate the impact of optimization schemes on final topologies. Monte Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach.     

Topology optimization using a reaction–diffusion equation

This paper presents a structural topology optimization method based on a reaction–diffusion equation. In our approach, the design sensitivity for the topology optimization is directly employed as the reaction term of the reaction–diffusion equation. The distribution of material properties in the design domain is interpolated as the density field which is the solution of the reaction–diffusion equation, so free generation of new holes is allowed without the use of the topological gradient method. Our proposed method is intuitive and its implementation is simple compared with optimization methods using the level set method or phase field model. The evolution of the density field is based on the implicit finite element method. As numerical examples, compliance minimization problems of cantilever beams and force maximization problems of magnetic actuators are presented to demonstrate the method’s effectiveness and utility.     

Deposition path planning-integrated structural topology optimization for 3D additive manufacturing subject to self-support constraint

This paper presents a novel level set-based topology optimization implementation, which addresses two main problems of design-for-additive manufacturing (AM): the material anisotropy and the self-support manufacturability constraint. AM material anisotropy is widely recognized and taking it into account while performing structural topology optimization could more realistically evaluate the structural performance. Therefore, both build direction and in-plane raster directions are considered by the topology optimization algorithm, especially for the latter, which is calculated through deposition path planning. The self-support manufacturability constraint is addressed through a novel multi-level set modeling. The related optimization problem formulation and solution process are demonstrated in detail. It is proved by several numerical examples that the manufacturability constraints are always strictly satisfied. Marginally, the recently popular structural skeleton-based deposition paths are also employed to assist the structural topology optimization, and its characteristics are discussed.     

A new multi-objective programming scheme for topology optimization of compliant mechanisms

This paper presents an alternative method in implementing multi-objective optimization of compliant mechanisms in the field of continuum-type topology optimization. The method is designated as “SIMP-PP” and it achieves multi-objective topology optimization by merging what is already a mature topology optimization method—solid isotropic material with penalization (SIMP) with a variation of the robust multi-objective optimization method—physical programming (PP). By taking advantages of both sides, the combination causes minimal variation in computation algorithm and numerical scheme, yet yields improvements in the multi-objective handling capability of topology optimization. The SIMP-PP multi-objective scheme is introduced into the systematic design of compliant mechanisms. The final optimization problem is formulated mathematically using the aggregate objective function which is derived from the original individual design objectives with PP, subjected to the specified constraints. A sequential convex programming method, the method of moving asymptotes (MMA) is then utilized to process the optimization evolvement based on the design sensitivity analysis. The main findings in this study include distinct advantages of the SIMP-PP method in various aspects such as computation efficiency, adaptability in convex and non-convex multi-criteria environment, and flexibility in problem formulation. Observations are made regarding its performance and the effect of multi-objective optimization on the final topologies. In general, the proposed SIMP-PP method is an appealing multi-objective topology optimization scheme suitable for “real world” problems, and it bridges the gap between standard topological design and multi-criteria optimization. The feasibility of the proposed topology optimization method is exhibited by benchmark examples.     

提高嵌入式Linux时钟精度的方法

突破Linux内核在实时应用方面的缺陷主要体现在增加Linux内核的可抢占性、细化时钟粒度和调度算法上。该文从时钟精度的角度出发，介绍了目前流行的嵌入式操作系统在实时性方面的改进方法，分析了MontaVista Linux采用的高精度定时器HRT机制的原理、HRT对Linux内核的改造方法及其在ARM平台上的实现方法等。     

Preprocessing of Spatial Query in Distributed GIS

This paper focuses on spatial query optimization in distributed GIS. A new qualitative spatial relation model and its consistency problem solution which is composed of topology, direction, distance and size, are proposed. Research integrating the four aspects has not appeared before. A new method to deduce the constraints of spatial query is given, so it saves the query process time in distributed GIS. Finally, the methods and theories are applied to a distributed GIS project, and the experiment result is satisfactory.     

基于多类网络模型的AI芯片测评方法研究

随着AI芯片制程不断升级以及结构设计不断优化,AI芯片在并行计算能力和泛化能力等方面展现出明显的优势,这些优势使得AI芯片在宇航领域得到越来越多的关注和应用。与传统的质量保证方法不同,AI芯片测评作为一项新的重要环节,对于AI芯片的选型和使用具有重要意义。本文针对面向宇航应用的AI芯片,研究了一种宇航用AI芯片的测评方法,该方法可以测评出AI芯片的精度、算力、功耗和量化支持能力,并基于该方法对AI芯片的性能和功能性指标进行了详细的分析和评价,最后针对宇航用AI芯片测评方法的进一步研究方向提出了相关建议。