Computational Design Framework Research Articles

Clustering and optimization of nodes, beams and panels for cost-effective fabrication of free-form surfaces

Free-form surfaces are increasingly used in contemporary architectural designs for their unique and elegant shapes. However, fabricating these doubly curved surfaces using panel and frame systems presents challenges due to the shape variability of nodes, beams and panels. In this study, we propose a mesh-based computational design framework that clusters and optimizes these components together, reducing the shape variety of elements for free-form surfaces. Our method employs a vertex-based similarity metric to partition panels into user-defined groups and clusters beams based on edge lengths. A box-constrained optimization is introduced to achieve congruent faces and matching beam lengths while considering various functional constraints. Additionally, connection holes on node surfaces are clustered and optimized to allow their use at multiple locations. The practicality of our approach is demonstrated through the design and construction of a full-scale pavilion, resulting in a significant reduction in the shape variety of building elements.

A rapid and automated computational approach to the design of multistable soft actuators

We develop an automated computational modeling framework for rapid gradient-based design of multistable soft mechanical structures composed of non-identical bistable unit cells with appropriate geometric parameterization. This framework includes a custom isogeometric analysis-based continuum mechanics solver that is robust and end-to-end differentiable, which enables geometric and material optimization to achieve a desired multistability pattern. We apply this numerical modeling approach in two dimensions to design a variety of multistable structures, accounting for various geometric and material constraints. Our framework demonstrates consistent agreement with experimental results, and robust performance in designing for multistability, which facilities soft actuator design with high precision and reliability.

A fully automatic computational framework for beam structure design from continuum structural topology optimization

A fully automatic computational framework for beam structure design from continuum structural topology optimization

A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty

This paper introduces a scalable computational framework for optimal design under high-dimensional uncertainty, with application to thermal insulation components. The thermal and mechanical behaviors are described by continuum multi-phase models of porous materials governed by partial differential equations (PDEs), and the design parameter, material porosity, is an uncertain and spatially correlated field. After finite element discretization, these factors lead to a high-dimensional PDE-constrained optimization problem. The framework employs a risk-averse formulation that accounts for both the mean and variance of the design objectives. It incorporates two regularization techniques, the ℓ0-norm and phase field functionals, implemented using continuation numerical schemes to promote spatial sparsity in the design parameters. To ensure efficiency, the framework utilizes a second-order Taylor approximation for the mean and variance and exploits the low-rank structure of the preconditioned Hessian of the design objective. This results in computational costs that are determined by the rank of preconditioned Hessian, remaining independent of the number of uncertain parameters. The accuracy, scalability with respect to the parameter dimension, and sparsity-promoting abilities of the framework are assessed through numerical examples involving various building insulation components.

Arbitrary curvature programming of thermo-active liquid crystal elastomer via topology optimization

Plants can change their morphology upon environmental variations such as temperature. Inspired by plants’ morphological adaptability, we present a computational inverse design framework for systematically creating optimized thermo-active liquid crystal elastomers (LCEs) that spontaneously morph into arbitrary programmed geometries upon temperature changes. The proposed framework is based on multiphysics topology optimization and a statistical mechanics-based LCE model to realize arbitrary curvature programming for LCE composites under large deformations. We propose a curvature-based optimization formulation that enables rotation-invariant and size-insensitive programmability of LCE, accounting for its highly nonlinear deformed shape. We demonstrate that the programmed LCE composites can accurately morph into a wide range of complex target shapes and curvatures, such as those of numbers, letters, flowers, and various objects. The resulting optimized designs exhibit highly irregular material distributions, which surpass intuition-based designs, and precisely produce desired deformed geometries upon temperature increase. The computational inverse design technique holds promise for a wide array of applications requiring function- and performance-driven design of active materials.

Shape optimization of non-rigid origami leading to emerging bistability

Origami structures are a type of thin-walled flexible structures that undergo large deformation. Their inherent non-rigid characteristic empowers them with great potential for applications in space structures, metamaterials, and robotics. To understand the large deformation of origami structures, consideration of nonlinear mechanics is necessary. However, most origami structures are still designed by pure geometric approaches without considering their non-rigid behavior. In this work, we propose a computational design framework that incorporates nonlinear mechanics into the design procedure of origami. Guided by minimization of stored energy, under prescribed displacement boundary conditions, we optimize the configuration of origami structures. Difficulties arise due to the complex energy landscape of origami structures that inevitably induces bifurcation. We develop strategies to keep track of a stable deformation branch during the optimization process. A surprising outcome is that our approach naturally leads to self-emerging bistable structures, which is demonstrated by a series of numerical examples. We believe that our new approach would make substantial contribution to computational design of non-rigid origami structures, benefiting applications in origami-inspired solutions for science and engineering.

Computational redesign of cytochrome P450 CYP102A1 for highly stereoselective omeprazole hydroxylation by UniDesign

Cytochrome P450 CYP102A1 is a prototypic biocatalyst that has great potential in chemical synthesis, drug discovery, and biotechnology. CYP102A1 variants engineered by directed evolution and/or rational design are capable of catalyzing the oxidation of a wide range of organic compounds. However, it is difficult to foresee the outcome of engineering CYP102A1 for a compound of interest. In this work, we introduce UniDesign as a computational framework for enzyme design and engineering. We tested this method by redesigning CYP102A1 for stereoselective metabolism of omeprazole (OMP), a proton pump inhibitor, starting from an active but non-stereoselective triple mutant (TM: A82F/F87V/L188Q). To shift stereoselectivity toward (R)-OMP, we computationally scanned three active-site positions (75, 264, and 328) for mutations that would stabilize the binding of the transition state of (R)-OMP but destabilize that of (S)-OMP, and picked three variants, namely UD1 (TM/L75I), UD2 (TM/A264G), and UD3 (TM/A328V), for experimentation, based on computed energy scores and models. UD1, UD2, and UD3 exhibit high turnover rates of 55±4.7, 84±4.8, and 79±5.7 min-1, respectively, for (R)-OMP hydroxylation, whereas the corresponding rates for (S)-OMP are only 2.2±0.19, 6.0±0.68, and 14±2.8 min-1, yielding an enantiomeric excess value of 92, 87, and 70%, respectively. These results suggest the critical roles of L75I, A264G, and A328V in steering OMP in the optimal orientation for stereoselective oxidation and demonstrate the utility of UniDesign for engineering CYP102A1 to produce drug metabolites of interest. The results are discussed in the context of protein structures.

End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

Numerical simulations have revolutionized material design. However, although simulations excel at mapping an input material to its output property, their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability. Here, taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm, we introduce a computational inverse design framework that addresses these challenges, by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation. Thanks to its differentiability, the simulation is used to directly train a deep generative model, which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve. Importantly, this inverse design pipeline leverages the power of tensor processing units (TPU)—an emerging family of dedicated chips, which, although they are specialized in deep learning, are flexible enough for intensive scientific simulations. This approach holds promise to accelerate inverse materials design.

A Network Medical Framework based on Inflammatory Genes to Identify Drug Candidates for Abdominal Aortic Aneurysms.

Clinically, abdominal aortic aneurysms (AAA) can be treated with surgical intervention, but there is currently no effective drug for the disease. This study analyzed the biomedical data of single-cell RNA sequencing (scRNA-seq), RNA-seq and the network medical data of drug-target interaction as well as protein-protein interaction to identify key targets and potential drug compounds of AAA. Firstly, we identified 10 types of cells from AAA and nonaneurysmal control samples and screened monocyte, mast cell, smooth muscle cell and 327 genes showing significant differences between non-dilated PVATs and dilated PVATs. To further explore the association of three types of cells in AAA, we screened the common DEGs associated with the three types of cells and then identified 10 potential therapeutic targets for AAA. SLC2A3 and IER3 were the key targets that were the most closely related to immune score and significantly related to inflammatory pathways. We then designed a network-based proximity measure to identify potential drugs targeting SLC2A3. Finally, with computer simulation, we found that the compound with the highest affinity to SLC2A3 protein was DB08213, which was embedded into the SLC2A3 protein cavity and formed close contact with various amino acid residues, and was stable during the 100-ns MD simulation. This study provided a computational framework for drug design and development. It revealed key targets and potential therapeutic drug compounds for AAA, which might contribute to the drug development for AAA.

Robust multidisciplinary analysis and optimization for conceptual design of flexible aircraft under dynamic aeroelastic constraints

We present a computational framework for robust multidisciplinary design and optimization of flexible aircraft, suited for the conceptual design exploration phase. Constraints are representative of some dynamic aeroelastic effects, including flutter and gust-induced structural stresses. Uncertainties on wing structural parameters are considered, and reliability-based optimization under dynamic aeroelastic constraints is addressed by a Bayesian approach. This work details the implementation aspects, covering disciplinary software tools and the overall aircraft design suite, the optimization algorithm and the approach to propagate uncertainty efficiently. Results are thoroughly discussed for a reference aircraft, which is optimized in the study with respect to a few wing parameters including aspect ratio. The proposed approach is compared to a conventional design methodology assuming a rigid airframe. We show that when applied to slender and flexible wings, the latter can produce dangerous non-conservative results, as its predictions are too optimistic both with respect to efficiency and to aeroelastic safety.

Decoding CRISPR-Cas PAM recognition with UniDesign.

The critical first step in Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated (CRISPR-Cas) protein-mediated gene editing is recognizing a preferred protospacer adjacent motif (PAM) on target DNAs by the protein's PAM-interacting amino acids (PIAAs). Thus, accurate computational modeling of PAM recognition is useful in assisting CRISPR-Cas engineering to relax or tighten PAM requirements for subsequent applications. Here, we describe a universal computational protein design framework (UniDesign) for designing protein-nucleic acid interactions. As a proof of concept, we applied UniDesign to decode the PAM-PIAA interactions for eight Cas9 and two Cas12a proteins. We show that, given native PIAAs, the UniDesign-predicted PAMs are largely identical to the natural PAMs of all Cas proteins. In turn, given natural PAMs, the computationally redesigned PIAA residues largely recapitulated the native PIAAs (74% and 86% in terms of identity and similarity, respectively). These results demonstrate that UniDesign faithfully captures the mutual preference between natural PAMs and native PIAAs, suggesting it is a useful tool for engineering CRISPR-Cas and other nucleic acid-interacting proteins. UniDesign is open-sourced at https://github.com/tommyhuangthu/UniDesign.

Surrogate-based optimization of capture chromatography platforms for the improvement of computational efficiency

In this work, we discuss the use of surrogate functions and a new optimization framework to create an efficient and robust computational framework for process design. Our model process is the capture chromatography unit operation for monoclonal antibody purification, an important step in biopharmaceutical manufacturing. Simulating this unit operation involves solving a system of non-linear partial differential equations, which can have high computational cost. We implemented surrogate functions to reduce the computational time and make the framework more attractive for industrial applications. This strategy yielded accurate results with a 93% decrease in processing time. Additionally, we developed a new optimization framework to reduce the number of simulations needed to generate a solution to the optimization problem. We demonstrate the performance of our new framework, which uses MATLAB built-in tools, by comparing its performance against individual optimization algorithms for problems with integer, continuous, and mixed-integer variables.

A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design

We develop a fast and scalable computational framework to solve Bayesian optimal experimental design problems governed by partial differential equations (PDEs) with application to optimal sensor placement by maximizing expected information gain (EIG). Such problems are particularly challenging due to the curse of dimensionality for high-dimensional parameters and the expensive solution of large-scale PDEs. To address these challenges, we exploit two fundamental properties: (1) the low-rank structure of the Jacobian of the parameter-to-observable map, to extract the intrinsically low-dimensional data-informed subspace, and (2) a series of approximations of the EIG that reduce the number of PDE solves while retaining high correlation with the true EIG. Based on these properties, we propose an efficient offline-online decomposition for the optimization problem. The offline stage dominates the cost and entails precomputing all components that require PDE solves. The online stage optimizes sensor placement and does not require any PDE solves. For the online stage, we propose a new greedy algorithm that first places an initial set of sensors using leverage scores and then swaps the selected sensors with other candidates until certain convergence criteria are met, which we call a swapping greedy algorithm. We demonstrate the efficiency and scalability of the proposed method by both linear and nonlinear inverse problems. In particular, we show that the number of required PDE solves is small, independent of the parameter dimension, and only weakly dependent on the data dimension for both problems.

GEO: A Computational Design Framework for Automotive Exterior Facelift

Exterior facelift has become an effective method for automakers to boost the consumers’ interest in an existing car model before it is redesigned. To support the automotive facelift design process, this study develops a novel computational framework – Generator, Evaluator, Optimiser (GEO) , which comprises three components: a StyleGAN2-based design generator that creates different facelift designs; a convolutional neural network (CNN) -based evaluator that assesses designs from the aesthetics perspective; and a recurrent neural network (RNN) -based decision optimiser that selects designs to maximise the predicted profit of the targeted car model over time. We validate the GEO framework in experiments with real-world datasets and describe some resulting managerial implications for automotive facelift. Our study makes both methodological and application contributions. First, the generator’s mapping network and projection methods are carefully tailored to facelift where only minor changes are performed without affecting the family signature of the automobile brands. Second, two evaluation metrics are proposed to assess the generated designs. Third, profit maximisation is taken into account in the design selection. From a high-level perspective, our study contributes to the recent use of machine learning and data mining in marketing and design studies. To the best of our knowledge, this is the first study that uses deep generative models for automotive regional design upgrading and that provides an end-to-end decision-support solution for automakers and designers.

Inverse design of metal–organic frameworks for C2H4/C2H6 separation

Efficient separation of C2H4/C2H6 mixtures is of paramount importance in the petrochemical industry. Nanoporous materials, especially metal-organic frameworks (MOFs), may serve the purpose owing to their tailorable structures and pore geometries. In this work, we propose a computational framework for high-throughput screening and inverse design of high-performance MOFs for adsorption and membrane processes. High-throughput screening of the computational-ready, experimental (CoRE 2019) MOF database leads to materials with exceptionally high ethane-selective adsorption selectivity (LUDLAZ: 7.68) and ethene-selective membrane selectivity (EBINUA02: 2167.3). Moreover, the inverse design enables the exploration of broader chemical space and identification of MOF structures with even higher membrane selectivity and permeability. In addition, a relative membrane performance score (rMPS) has been formulated to evaluate the overall membrane performance relative to the Robeson boundary. The computational framework offers guidelines for the design of MOFs and is generically applicable to materials discovery for gas storage and separation.

Computational design and fabrication of active 3D-printed multi-state structures for shape morphing

Active structures can change their shape, properties, and functionality as a response to changing operational conditions, which makes them more versatile than their static counterparts. However, most active structures currently lack the capability to achieve multiple, different target states with a single input actuation or require a tedious material programming step. In this work, a computational design and fabrication framework is proposed to generate structures with multiple target states for one input actuation that do not require a separate training step. A material dithering scheme based on multi-material 3D printing is combined with locally applied copper coil heating elements and sequential heating patterns to control the thermo-mechanical properties of the structures and switch between the different deformation modes. A novel topology optimization approach based on power diagrams is used to encode different target states in the structure while ensuring the fabricability of the structures. The numerical and experimental results show that the optimization framework can produce structures that show the desired motion, but experimental accuracy is limited by current fabrication methods. The generality of the proposed method makes it suitable for the development of structures for applications in many different fields from aerospace to robotics to animated fabrication in computer graphics.

A Computational Framework for Design and Optimization of Risk-Based Soil and Groundwater Remediation Strategies

Soil and groundwater systems have natural attenuation potential to degrade or detoxify contaminants due to biogeochemical processes. However, such potential is rarely incorporated into active remediation strategies, leading to over-remediation at many remediation sites. Here, we propose a framework for designing and searching optimal remediation strategies that fully consider the combined effects of active remediation strategies and natural attenuation potentials. The framework integrates machine-learning and process-based models for expediting the optimization process with its applicability demonstrated at a field site contaminated with arsenic (As). The process-based model was employed in the framework to simulate the evolution of As concentrations by integrating geochemical and biogeochemical processes in soil and groundwater systems under various scenarios of remedial activities. The simulation results of As concentration evolution, remedial activities, and associated remediation costs were used to train a machine learning model, random forest regression, with a goal to establish a relationship between the remediation inputs, outcomes, and associated cost. The relationship was then used to search for optimal (low cost) remedial strategies that meet remediation constraints. The strategy was successfully applied at the field site, and the framework provides an effective way to search for optimal remediation strategies at other remediation sites.

DNA by Design: De novo Computational Framework for DNA Sequence Design and Nanotechnology

Chemical analysis of metalized DNA has made it quite clear that traditional models of DNA thermodynamics are insufficient to predict and control self-assembly in the context of orthogonally-paired nucleotides. Recently, there has been an increase in reports of Watson-Crick assembly of DNA wires and nanostructures [1–4]. The ability to add or remove pairing rules between nucleobases toward non-Watson-Crick, or orthogonal, self-assembly alters the fundamental language of DNA assembly: this change in behavior necessitates an accompanying shift in computational design. We begin by exploring the state-of-the-art in DNA modeling, and include both sequence analysis and sequence design practices. We then start from first principles and establish a mathematical basis for heterostructure and ‘nmer’ analysis in connected DNA networks that operates without assumptions about nucleobase parity. A generalized search algorithm is then constructed in Matlab and implemented using evolutionary techniques. We then discuss DNA nanostructure design criteria, operation efficiency in differentially-connected networks, and the application of computationally-aided sequence design for nanotechnological applications. We design a double crossover DNA motif with a silver base pair modification as a test case, and demonstrate successful model implementation. In sum, we present a novel computational framework for geometry-informed optimization of DNA networks. This tool is meant to enable design of both linear and nonlinear polynucleotide assemblies with inherent modularity for base parity, metalation, or more exotic nucleotide substitutions that may arise from advances in synthetic biology, nanomaterials and nanomedicine.

Computational Design of a Soft Robotic Myocardium for Biomimetic Motion and Function

AbstractSoft robotic devices containing multiple actuating elements have successfully recapitulated complex biological motion, leading to their utility in biomedical applications. However, there are inherent nonlinear mechanics associated with soft composite materials where soft actuators are embedded in elastomeric matrices. Predicting their overall behavior prior to fabrication and subsequent experimental characterization can therefore present a hurdle in the design process and in efficiently satisfying functional requirements and specifications. In this work, a computational design framework for optimizing the motion and function of biomimetic soft robotic composites is demonstrated by conducting a design case study of soft robotic cardiac muscle (myocardium) with a particular focus on applications including replicating and assisting cardiac motion and function. A finite element model of a soft robotic myocardium is built, in which actuators are prescribed with anisotropic strain to simulate local deformation, and various design parameters are investigated by evaluating the performance of each configuration in terms of ventricular twist, volumetric output, and pressure generation. Then, an optimized design is proposed that recapitulates the physiological motion and hemodynamics of the heart, and its thrombogenicity is further explored using a fluid‐structure interaction model. This framework has broader utility in predicting the performance of other soft robotic embedded composites.

Optoelectronic system and device integration for quantum-dot light-emitting diode white lighting with computational design framework

We propose a computational design framework to design the architecture of a white lighting system having multiple pixelated patterns of electric-field-driven quantum dot light-emitting diodes. The quantum dot of the white lighting system has been optimised by a system-level combinatorial colour optimisation process with the Nelder-Mead algorithm used for machine learning. The layout of quantum dot patterns is designed precisely using rigorous device-level charge transport simulation with an electric-field dependent charge injection model. A theoretical maximum of 97% colour rendering index has been achieved with red, green, cyan, and blue quantum dot light-emitting diodes as primary colours. The white lighting system has been fabricated using the transfer printing technique to validate the computational design framework. It exhibits excellent lighting performance of 92% colour rendering index and wide colour temperature variation from 1612 K to 8903 K with only the four pixelated quantum dots as primary.