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This chapter questions the interpretability of numerical simulation tools such as finite element analysis for assisting human intelligence in architectural and engineering design. The intricacy of numerical code underlying simulation software can render the analysis output difficult to interpret into an intuitive format. In response, this chapter presents a machine learning (ML) based approach using Bayesian networks (BNs), to build interpretable representations of the simulation code, also referred to as a simulation metamodel or surrogate model. BNs are a probabilistic technique that lie at the intersection of statistics and ML, where input–output relationships are captured from data using ML techniques and subsequently represented in an interpretable form such that they can be explored intuitively using statistical techniques. A metamodel or surrogate model in the form of Bayesian Network facilitates the intuitive exploration of cause-effect relationships, in multi-simulation input, multi-simulation output scenarios. As a result, BNs absorb the cognitive effort to keep track of intricate input–output relationships while freeing-up the cognitive capacity to control design outcomes intuitively.

Lattice structures are frequently found in nature and engineering due to their myriad attractive properties, with applications ranging from molecular to architectural scales. Lattices have also become a key concept in additive manufacturing, which enables precise fabrication of complex lattices that would not be possible otherwise. While design and simulation tools for stiff lattices are common, here we present a digital design and nonlinear simulation approach for additive manufacturing of soft lattices structures subject to large deformations and instabilities, for which applications in soft robotics, healthcare, personal protection, energy absorption, fashion and design are rapidly emerging. Our framework enables design of soft lattices with curved members conforming to freeform geometries, and with variable, gradually changing member thickness and material, allowing the local control of stiffness. We model the lattice members as 3D curved rods and using a spline-based isogeometric method that allows the efficient simulation of nonlinear, large deformation behavior of these structures directly from the CAD geometries. Furthermore, we enhance the formulation with a new joint stiffening approach, which is based on parameters derived from the actual node geometries. Simulation results are verified against experiments with soft lattices realized by PolyJet multi-material polymer 3D printing, highlighting the potential for simulation-driven, digital design and application of non-uniform and curved soft lattice structures.

Three-dimensionally (3D) knitted technical textiles are spreading into industrial applications, since their geometric, structural and functional performance can be tailored and optimized on fibre-, yarn- and fabric levels by customizing yarn materials, knit patterns and geometric shapes. The ability to simulate their complex mechanical behaviour is thus an essential ingredient in the development of a digital workflow for optimal design and manufacture of 3D knitted textiles. Here, we present a multi-scale modelling and simulation framework for the prediction of the nonlinear orthotropic mechanical behaviour of single jersey knitted textiles and its experimental validation. On the meso-scale, representative volume elements (RVEs) of the fabric are modelled as single, interlocked yarn loops and their mechanical deformation behaviour is homogenized using periodic boundary conditions. Yarns are modelled as nonlinear 3D beam elements and numerically discretized using an isogeometric collocation method, where a frictional contact formulation is used to model inter-yarn interactions. On the macro-scale, fabrics are modelled as membrane elements with nonlinear orthotropic material behaviour, which is parameterized by a response surface constitutive model obtained from the meso-scale homogenization. The input parameters of the yarn-level simulation, i.e., mechanical properties of yarns and geometric dimensions of yarn loops in the fabrics, are determined experimentally and subsequent meso- and macro-scale simulation results are evaluated against reference results and mechanical tests of knitted fabric samples. Good agreement between computational predictions and experimental results is achieved for samples with varying stitch values, thus validating our novel computational approach combining efficient meso-scale simulation using 3D beam modelling of yarns with numerical homogenization and nonlinear orthotropic response surface constitutive modelling on the macro-scale.

We present the design of a new compliant ratchet mechanism using a multi-material additive manufacturing technology. The design enables the elimination of springs and global movement of the pawl or gear that typically exist in classical ratchets. It is also taking advantage of recent advances in 3D printing technology to replace traditional mechanisms with multi-material mechanisms. This design is evaluated with computational finite element (FE) simulations and verified with physical tests of motion in two opposite directions. We also present a parametric study of the geometric and material properties of our current design and discuss how this basic design concept can be customized for specific applications.

There are many ‘compelling possibilities’ for computational fluid dynamics (CFD) in architecture, as demonstrated by its successful adoption in the aerospace, automotive and product manufacturing industries. Sawako Kaijima, Roland Bouffanais, Karen Willcox and Suresh Naidu of ARCH-CFD, a research initiative at the International Design Centre established by the Singapore University of Technology and Design (SUTD) and the Massachusetts Institute of Technology (MIT), explore CFD's potential.

Panagiotis Michalatos and Sawako Kaijima describe how the Optimisation Design team at Adams Kara Taylor (AKT) work with mathematical algorithms to develop interactive software applicatons that help inform structural behaviour in the early parts of the design process. This is exemplified by the project-specific software they developed for Thomas Heatherwick's British Pavilion at the 2010 Shanghai Expo. Copyright © 2011 John Wiley & Sons, Ltd.