Automatic Truss Design with Reinforcement Learning

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.Comment: IJCAI2023. The codes are available at https://github.com/StigLidu/AutoTrus

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044

Rationalization of trusses and yield-line patterns identified using layout optimization

To help engineers to design and analyse structures, various tools exist. However, many of them are complicated and difficult for engineers to master. In industry simple, accurate, and rapid tools are potentially very useful. The development of such tools has thus been the main focus of this thesis. One application is the design of lightweight truss structures. Although techniques have been available to identify efficient truss designs for more than half a century, these are not widely used in industry. A major problem is that the structures generated are often complex in form, so that manufacturing becomes problematic. To address this, the current research explores two rationalization techniques: (i) introducing joint lengths to control the number of joints that exist in the resulting structure; and (ii) utilising geometry optimization to adjust the locations of joints in a truss. The former involves a minor modification to the standard process such that it retains the linear nature of the original problem, while the latter solves a more challenging non-linear optimization problem that can simultaneously simplify (make less complicated) and improve (make lighter) a given truss layout. To ensure a rapid and reliable process for the latter, analytical expressions of functions and their derivatives are supplied to a general purpose non-linear optimizer and various practical issues are also considered. A number of benchmark problems are solved to show the efficacy of the two rationalization techniques. Another application is yield-line analysis of reinforced concrete slabs. Even in the modern computer age, with many engineering analysis procedures successfully computerized, a fully automated means of undertaking a yield-line analysis has been lacking, forcing engineers in industry to use hand-calculations in order to benefit from the power of the yield-line method. This thesis is therefore concerned with the development of techniques that automate this method. By utilising the novel discontinuity layout optimization (DLO) method, the process of yield-line analysis has been truly automated at last. In addition, motivated by the outcomes of the rationalization procedure developed for trusses, research has been conducted to rationalize yield-line patterns generated via DLO. Similar to the technique used in trusses, analytical expressions of functions and their derivatives are deduced and then supplied to a non-linear optimizer, leading to a rapid and reliable computational process. To make DLO and the rationalization ready for use in industry, various slab configurations found in practice are also considered, permitting challenging slab problems to be tackled using the method. A number of examples from the literature and industry are analysed to demonstrate the efficacy of DLO and the rationalization technique

Power System Simulation, Control and Optimization

This Special Issue “Power System Simulation, Control and Optimization” offers valuable insights into the most recent research developments in these topics. The analysis, operation, and control of power systems are increasingly complex tasks that require advanced simulation models to analyze and control the effects of transformations concerning electricity grids today: Massive integration of renewable energies, progressive implementation of electric vehicles, development of intelligent networks, and progressive evolution of the applications of artificial intelligence

Topology Optimization for Eigenvalue Problems with Applications to Phononic Crystals and Stochastic Dynamics

Topology optimization is the process of exploring the optimal layout of material within a design domain. It is a free-form technique as material can be added or removed from any location, making it more general than sizing and shape optimization. Although the rst topology optimization paper was written in late 1980s, it has experienced extremely rapid expansion over the last decade. It has been applied to nd optimal solutions for various engineering problems governed by diverse mechanics. However, only a relatively limited number of works have focused on problems governed by eigenvalues, and most of them have assumed deterministic eigenvalues and symmetric matrices. Therefore, this dissertation proposes topology optimization algorithms for general eigenvalue problems with and without considering uncertainties and applies them to the design of materials and structures. The topology optimization formulation for eigenvalue problems is rstly presented and the numerical challenges are subsequently discussed. Next, the sensitivity of complex eigenvalues and eigenvectors are derived using perturbation method. Then the proposed algorithm combined with a fast mixed variational eigenvalue solver and distributed Graphic Processing Unit computations developed by collaborators is used to reveal 3-D phononic structures which exhibit the largest normalized all-angle allmode band gaps reported to date. Uncertainties are considered in this dissertation for mitigating dynamic response under stochastic dynamic excitations. Stochastic equations are formulated in the standard manner by using second order di erential equations and state space in which they are described by rst order di erential equations. Later they are solved both in frequency domain and using state space analysis. It has been found that using state space formulation and further solving in frequency domain requires the least computational e ort. In addition, the by-product of this formulation is that it is capable of incorporating non-classical damping. Numerical results are presented to illustrate the comparisons between topologies optimized for stochastic ground motion loading and topologies optimized under free vibration. Lastly, this dissertation addresses the design of reinforced concrete structure by developing a stress-dependent truss-continuum topology optimization algorithm. Sti - ness is formulated such that truss elements carry only tensile forces and thus represent straight steel rebar, while the continuum elements carry only compression forces and thus represent concrete compression load paths. Constructability of reinforcement is also discussed by replacing the volume constraint with a total cost constraint

Challenges and Status on Design and Computation for Emerging Additive Manufacturing Technologies

The revolution of additive manufacturing (AM) has led to many opportunities in fabricating complex and novel products. The increase of printable materials and the emergence of novel fabrication processes continuously expand the possibility of engineering systems in which product components are no longer limited to be single material, single scale, or single function. In fact, a paradigm shift is taking place in industry from geometry-centered usage to supporting functional demands. Consequently, engineers are expected to resolve a wide range of complex and difficult problems related to functional design. Although a higher degree of design freedom beyond geometry has been enabled by AM, there are only very few computational design approaches in this new AM-enabled domain to design objects with tailored properties and functions. The objectives of this review paper are to provide an overview of recent additive manufacturing developments and current computer-aided design methodologies that can be applied to multimaterial, multiscale, multiform, and multifunctional AM technologies. The difficulties encountered in the computational design approaches are summarized and the future development needs are emphasized. In the paper, some present applications and future trends related to additive manufacturing technologies are also discussed

Towards Performance Portable Graph Algorithms

In today's data-driven world, our computational resources have become heterogeneous, making the processing of large-scale graphs in an architecture agnostic manner crucial. Traditionally, hand-optimized high-performance computing (HPC) solutions have been studied and used to implement highly efficient and scalable graph algorithms. In recent years, several graph processing and management systems have also been proposed. Hand optimized HPC approaches require high levels of expertise and graph processing frameworks suffer from expressibility and performance. Portability is a major concern for both approaches. The main thesis of this work is that block-based graph algorithms offer a compromise between efficient parallelism and architecture agnostic algorithm design for a wide class of graph problems. This dissertation seeks to prove this thesis by focusing the work on the three pillars; data/computation partitioning, block-based algorithm design, and performance portability. In this dissertation, we first show how we can partition the computation and the data to design efficient block-based algorithms for solving graph merging and triangle counting problems. Then, generalizing from our experiences, we propose an algorithmic framework, for shared-memory, heterogeneous machines for implementing block-based graph algorithms; PGAbB. PGAbB aims to maximally leverage different architectures by implementing a task-based execution on top of a block-based programming model. In this talk we will discuss PGAbB's programming model, algorithmic optimizations for scheduling, and load-balancing strategies for graph problems on real-world and synthetic inputs.Ph.D

A high-performance open-source framework for multiphysics simulation and adjoint-based shape and topology optimization

The first part of this thesis presents the advances made in the Open-Source software SU2, towards transforming it into a high-performance framework for design and optimization of multiphysics problems. Through this work, and in collaboration with other authors, a tenfold performance improvement was achieved for some problems. More importantly, problems that had previously been impossible to solve in SU2, can now be used in numerical optimization with shape or topology variables. Furthermore, it is now exponentially simpler to study new multiphysics applications, and to develop new numerical schemes taking advantage of modern high-performance-computing systems. In the second part of this thesis, these capabilities allowed the application of topology optimiza- tion to medium scale fluid-structure interaction problems, using high-fidelity models (nonlinear elasticity and Reynolds-averaged Navier-Stokes equations), which had not been done before in the literature. This showed that topology optimization can be used to target aerodynamic objectives, by tailoring the interaction between fluid and structure. However, it also made ev- ident the limitations of density-based methods for this type of problem, in particular, reliably converging to discrete solutions. This was overcome with new strategies to both guarantee and accelerate (i.e. reduce the overall computational cost) the convergence to discrete solutions in fluid-structure interaction problems.Open Acces