Computational Design Methodology Research Articles

Multi-objective, multi-constraint high-throughput design, synthesis, and characterization of tungsten-containing refractory multi-principal element alloys

Refractory multi-principal element alloys (RMPEAs) have gained interest recently due to their superior properties at elevated temperatures, including outstanding yield and ultimate strengths, high thermal conductivity, and resistance to creep. RMPEAs can be designed to exhibit a wide range of properties by tailoring their composition. However, the vast chemical design space makes brute-force experimental screening inefficient and costly. In this work, we follow a closed-loop, iterative computational/experimental screening approach that combines computational alloy design methodologies with high-throughput synthesis and characterization tools to explore the vast RMPEAs space and design new RMPEAs that satisfy multiple objectives and constraints. In particular, we targeted compositions with yield strengths higher than 50 MPa at 2000 °C, W content of more than 30 at.% for high-temperature strength and operability up to 2000 °C, narrow solidification range for additive manufacturability, competitive ductility metrics, among other property constraints. We evaluated the mechanical properties and microstructure of 58 alloys designed in 5 batches, in both as-cast and homogenized conditions, synthesized using vacuum arc melting, utilizing scanning electron microscopy, X-ray diffraction, Vickers microhardness, nanoindentation, and high-temperature compression testing. Based on the microhardness screening experiments in each batch, the best-performing alloys were selected for scale-up. High-temperature compression at 1800 °C was performed in these alloys, demonstrating that the designed alloys exhibit up to five times higher yield strength than a pure tungsten benchmark. We conclude that W-containing RMPEAs designed in this study merit further consideration for next-generation structural materials for ultra-high temperature applications.

De novo designed proteins neutralize lethal snake venom toxins.

Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases.

Paradigm Shift in Architectural Pedagogy incorporating Artificial Intelligence

Abstract: The paradigm shift in architectural pedagogy encompasses various facets, each contributing to a holistic transformation of the educational landscape. From computational design methodologies to generative algorithms, AI empowers students to explore a vast array of design possibilities, pushing the boundaries of creativity and problem-solving. The goal of this study is to understand the paradigm shifts in the constantly changing fields of architectural education with the incorporation of Artificial Intelligence (AI). This change transforms architectural teaching fundamentally in addition to redefining architectural practice approaches. Now that we are at the crossroads of technology innovation and human creativity, it is critical to investigate how artificial intelligence (AI) might transform architecture education and promote creativity, sustainability, and adaptation. The primary exploration reveals a significant gap in the current teaching methods, with little emphasis on AI. The study emphasizes the urgency of incorporating certain approaches into architectural education along with AI which would enhance learning experience of the students.

Design and Extrusion-Based 3D Printing of Continuous Fiber Composites: Status, Challenges, and Opportunities.

This perspective article summarizes recent advancements in extrusion-based 3D printing of continuous fiber-reinforced polymers (CFRPs). It focuses on manufacturing techniques and computational design methodologies. While fused deposition modeling has been the primary method for printing thermoplastic CFRPs, recent innovations have enabled the printing of thermoset CFRPs using direct ink writing or similar techniques. These printing processes are also integrated with robotic arms to dramatically enhance manufacturing capabilities. Additionally, there has been notable progress in enhancing computational design methodologies to simultaneously optimize fiber distribution and topology of 3D printed CFRPs. The article also discusses future directions aimed at improving mechanical properties, scalability, multifunctionality, and predictability in CFRP 3D printing, which offer valuable perspectives for the development of this transformative manufacturing approach.

Systematic Mapping of Circular Economy in Structural Engineering

Facing increasing sustainability demands, the construction industry is at a turning point where the implementation of circular economy (CE) strategies plays an essential role in driving the necessary transformation aimed at reducing the environmental impact. To facilitate this shift, structural engineering must effectively integrate circular principles into building design. With the exponential growth of research articles within this field, it is crucial to map the evolution of the research area. The objective of this study is to detail the trends with, challenges to, and research contributions, integration, and material applications of CE principles within structural engineering. Consequently, a systematic mapping of the CE within the field of structural engineering has been conducted in this study. Initially, the mapping process began with the identification of relevant keywords, followed by searches across four databases. Each resulting article was carefully screened against content criteria, culminating in 91 publications that were thoroughly evaluated. The publications were then categorized and analyzed based on attributes such as research type, circular design, materials, and applications. The results are presented through informative figures and tables. The analysis of the research indicates a predominant focus on technical solutions for structural systems, with demountable connections designed to facilitate the future reuse of materials representing more than half of the literature reviewed. A significant portion of the literature also addresses designing from reclaimed elements; these articles reflect a transformation in engineering approaches, incorporating computational design and innovative methodologies. The focus on steel as a structural material is prominent in the reviewed literature. However, there is an increasing focus on timber, which signals a definitive shift toward sustainable structural systems. Recurring challenges identified in the literature regarding the transition to a circular economy (CE) in the construction industry include the need for industry-wide adoption, precise standardization, the integration of digital tools, and the overcoming of related obstacles in policy and market acceptances. Furthermore, the literature demonstrates a significant research gap: the absence of a comprehensive digital framework enabling an effective digital circular structural design workflow.

Molecular Motifs in Vascular Morphogenesis: Vascular Endothelial Growth Factor A (VEGFA) as the Leading Promoter of Angiogenesis.

Tissular hypoxia stimulates vascular morphogenesis. Vascular morphogenesis shapes the cell and, consecutively, tissue growth. The development of new blood vessels is intermediated substantially through the tyrosine kinase pathway. There are several types of receptors inferred to be located in the blood vessel structures. Vascular endothelial growth factor A (VEGF-A) is the leading protagonist of angiogenesis. VEGF-A's interactions with its receptors VEGFR1, VEGFR2, and VEGFR3, together with disintegrin and metalloproteinase with thrombospondin motifs 1 (ADAMTS1), connective tissue growth factor (CTGF), and neuropilin-1 (NRP1), independently, are studied computationally. Peripheral artery disease (PAD), which results in tissue ischemia, is more prevalent in the senior population. Presently, medical curatives used to treat cases of PAD-antiplatelet and antithrombotic agents, statins, antihypertensive remedies with ACE (angiotensin-converting enzyme) impediments, angiotensin receptor blockers (ARB) or β- blockers, blood glucose control, and smoking cessation-are not effective. These curatives were largely established from the treatment of complaint cases of coronary disease. However, these medical curatives do not ameliorate lower limb perfusion in cases of PAD. Likewise, surgical or endovascular procedures may be ineffective in relieving symptoms. Eventually, after successful large vessel revascularization, the residual microvascular circulation may well limit the effectiveness of curatives in cases of PAD. It would thus feel rational to attempt to ameliorate perfusion in PAD by enhancing vascular rejuvenescence and function. Likewise, stimulating specific angiogenesis in these cases (PAD) can ameliorate the patient's symptomatology. Also, the quality of life of PAD patients can be improved by developing new vasodilative and angiogenetic molecules that stimulate the tyrosine kinase pathway. In this respect, the VEGFA angiogenetic pathway was explored computationally. Docking methodologies, molecular dynamics, and computational molecular design methodologies were used. VEGFA's interaction with its target was primarily studied. Common motifs in the vascular morphogenesis pathway are suggested using conformational energy and Riemann spaces. The results show that interaction with VEGFR2 and ADAMTS1 is pivotal in the angiogenetic process. Also, the informational content of two VEGFA complexes, VEGFR2 and ADAMTS1, is crucial in the angiogenesis process.

Computational design exploration of a segmented concrete shell building floor system

The construction industry is responsible for nearly half of the UK's carbon dioxide emissions and the use of an extremely large volume of concrete – the world's most widely used man-made material – accounts for more than 7% of global carbon dioxide emissions. The scale of this problem spawned research to explore the potential for structurally efficient non-prismatic geometries to reduce the amount of concrete used in building elements substantially, thus also reducing their embodied carbon dioxide footprint. In particular, the research focused on segmented thin concrete shells as floor slabs, leveraging computational design and digital fabrication methodologies to automate their production off site. An important part of this research was the development of a computational framework for the design of thin concrete shells in order to make such a construction methodology accessible to building designers in practice. The framework combines solutions for parametric modelling, finite-element analysis, isogeometric analysis, form-finding and optimisation, along with embedded fabrication constraints specific to the project's automated manufacturing system. The application of the developed computational framework in the design of a 4.5 m × 4.5 m prototype is documented in this paper, illustrating how automating concrete construction can transform the industry towards net-zero.

COMPUTATIONAL DESIGN APPROACH FOR APPLYING NEURO-ARCHITECTURE PRINCIPLES IN HEALTHCARE FACILITIES

Neuroscience tools’ advancements have paved the way for neuroscience and architecture collaboration, spawning "Neuro-architecture." Neuro-architecture studies in various settings have been tentatively translated into design principles to improve the mood, perception, and satisfaction of healthcare facility users, as well as patient wellbeing and recovery rates. Integrating such principles into computational design methodologies should benefit users’ mental health and reduce the gap between computational design and human mental needs. The research was conducted in two phases: in the first phase, typologies used in evidence-based design research (EBD) were applied in order to determine neuro-architecture design principles for the interior environment of healthcare facilities. In the second phase, the research employed space syntax analysis to evaluate the incorporation of neuro-architecture principles into healthcare facility interiors. The method visualises and evaluates spatial qualities that reflect the application of planning and navigation neuro-interior principles for the inpatient ward of a healthcare facility

A Brief Review of Computational Product Design: A Brand Identity Approach

On the way to designing customized products as one of the core activities of Industry 4.0, the strategy of computational design emerges as a unique design process due to its flexibility and simplicity. More specifically, the aforementioned strategy is concerned with the study of brand identity and its description in the development of commercial industrial products. The proposed design approach is focused on the study of branded product forms following computational design methodologies, i.e., employing textual or/and visual programming languages. The paper presents an overview of in-depth research studies which deal with the systematic way of creation, evolution, and transformation of industrial products with modern digital tools. Through the review, 100 studies have been analyzed over the last 15 years. The background of this research includes definitions from the specific four pillars of the modern theory of industrial design, e.g., product design, digital design, visual representation, and product identity. Furthermore, the current paper combines the use of computational design with specific parameters of visual brand elements in order to develop a methodological tool for the mass customization of industrial products. Moreover, the proposed framework offers a great deal of flexibility in both design and manufacturing, while many design alternatives could become available in a very short time. Finally, the impact of this paper is the correlation between computational design techniques and the theoretical background of brand identity principles (i.e., shapes, geometries, styles, textures, colors, and materials) for inspiring novel ideas among engineers, designers, and marketers.

3-deazaadenosine: A promising novel p38γ antagonist with potential as a breast cancer therapeutic agent

Human p38γ protein kinase, or MAPK12, is a crucial signaling protein that is important in channelizing membrane signals to the nucleus in the MAPK cascade pathway, associated with breast and colorectal cancer, besides other forms of malignancies and atherosclerotic lesions too. P38γ has a significant contribution to the progression of breast carcinoma due to its multifaceted functions. Targeting p38γ for defining potent antagonists against p38γ can turn out to be an attractive and novel means of breast cancer therapeutics. Novel and potent lead molecules were designed utilizing computational drug design methodologies. Using high-throughput virtual screening, 1909 geometrically similar analogs of known inhibitors were generated, primarily using BIRB796, SB202190, ANP, CHEBI: 620708, and CHEBI: 524699. Chemical correctness was ensured using LigPrep for the standalone library, and Prep Wizard for p38γ using Maestro v.11.5. Using the Glide v5.5 flexible docking procedure on a standalone library of p38γ binding sites, we defined 18 potential leads and assessed their ADMET properties. Lead "1", among the proposed four p38γ antagonists with high-scoring and favorable interactions, was considered for 100 ns molecular dynamics simulations. Among the four proposed leads, Lead '1′ displayed consistent and stable bonding interactions with p38γ throughout the 100 ns molecular dynamics (MD) simulations. Additionally, it formed water bridges, contributing to its strong association with the protein. Notably, Lead '1′ (3-deazaadenosine) exhibited favorable root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) within the acceptable range of pharmacological properties. Thus, 3-deazaadenosine and its mimetic might be promising new directions for developing a novel class of antagonists for breast cancer treatment.

Computational Design of Porous Electrode Architecture for Electrochemical Flow Reactors and Redox Flow Batteries

Porous electrodes are utilized across electrochemical technologies including flow reactors and redox flow batteries. Controlling mass transport, conduction, and surface reaction in the electrodes is central to attaining peak device performance. Traditional methods often rely on manipulating the macroscopic electrode dimensions, device-level architecture, and flow distribution (e.g. via flow fields). However, beyond using traditional materials like felts or papers, engineering the internal electrode structure itself has recently emerged as a new design degree-of-freedom. These architected, porous electrodes are enabled by advanced and additive manufacturing techniques and are a promising route to provide greater control over the local transport and reactive environment, further improving device performance. New design tools will be necessary to fully exploit this expanded design space. Here we present a collection of computational design methodologies to automatically generate architected electrode design across scales. Resolved optimization techniques are used to determine optimal electrode architectures at the pore scale. Using homogenization, these techniques are then extended to scale-up the designs beyond typical benchtop dimensions. Using vanadium as a model chemistry, we demonstrate that the computationally designed electrodes yield improved performance across cost-functions and focus particularly on energy efficiency. Further, we analyze the resultant designs to highlight the structural features leading to improved performance.This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.LLNL-ABS-830166

Transcalar Design: An Approach to Biodesign in the Built Environment

Biodesign holds the potential for radically increasing the sustainability of the built environment and our material culture but comes with new challenges. One of these is the bridging of the vast differences of scale between microbiological processes and architecture. We propose that a transcalar design approach, which weaves together nonlinear dependencies using computational design tools and design methodologies through the biological generation of architectural components, is a way towards successful design implementations. Such design processes were explored in a laboratory-based fabrication and study of a column element. This column, named Protomycokion, serves to illustrate how design methodologies, particularly through the use of a demonstrator artefact, can serve to navigate the multiple scales, disciplines, and experiments that are necessary to engage the complexities of biodesign. Transcalar design processes embrace the adaptability, variability and interdependence of biological organisms and show possible gains with regard to material sustainability and increased performativity.

A Subspace-Inclusive Sampling Method for the Computational Design of Compositionally Graded Alloys

Abstract Compositionally graded alloys, a subclass of functionally graded materials (FGMs), utilize localized variations in composition with a single metal part to achieve higher performance than traditional single material parts. In previous work [Kirk, T., Galvan, E., Malak, R., and Arroyave, R., 2018, “Computational Design of Gradient Paths in Additively Manufactured Functionally Graded Materials,” J. Mech. Des., 140, p. 111410. 10.1115/1.4040816], the authors presented a computational design methodology that avoids common issues which limit a gradient alloy’s feasibility, such as deleterious phases, and optimizes for performance objectives. However, the previous methodology only samples the interior of a composition space, meaning designed gradients must include all elements in the space throughout the gradient. Because even small amounts of additional alloying elements can introduce new deleterious phases, this characteristic often neglects potentially simpler solutions to otherwise unsolvable problems and, consequently, discourages the addition of new elements to the state space. The present work improves upon the previous methodology by introducing a sampling method that includes subspaces with fewer elements in the design search. The new method samples within an artificially expanded form of the state space and projects samples outside the true region to the nearest true subspace. This method is evaluated first by observing the sample distribution in each subspace of a 3D, 4D, and 5D state space. Next, a parametric study in a synthetic 3D problem compares the performance of the new sampling scheme to the previous methodology. Lastly, the updated methodology is applied to design a gradient from stainless steel to equiatomic NiTi that has practical uses such as embedded shape memory actuation and for which the previous methodology fails to find a feasible path.

Assessing the Effect of Building Skin Adaptability on Energy Consumption in Hot Arid Regions

Building skins have a vital role in energy efficiency, particularly in terms of the conservation or consumption of energy. Many factors must be considered by designers to prevent wasting significant quantities of energy, to preserve and provide internal air conditioning and lighting, particularly in hot dry locations where the integration of sun protection systems is highly recommended. This pilot study looks at the challenge of developing energy-efficient building skins in hot regions like Biskra city by applying a natural daylight strategy represented by a parameterised moveable shading component to the skin of a hospital patient’s room. In this research, we aim to assess the adoption of building skin parameterisation as a beneficial technique for reducing energy consumption and improving internal temperature and lighting in this environment by developing and implementing a computational design methodology. Promising experimental results demonstrate the benefit of this proposal. The use of parameterisation in the design of patient’s room skins, with moveable, tightly folded morphology, providing self-shading, are essential and effective techniques for ensuring good natural lighting and reducing both temperature and energy consumption..

Computational design of microarchitected porous electrodes for redox flow batteries

Porous flow-through electrodes are used as the core reactive component across electrochemical technologies. Controlling the fluid flow, species transport, and reactive environment is critical to attaining high performance. However, conventional electrode materials like felts and papers provide few opportunities for precise engineering of the electrode and its microstructure. To address these limitations, architected electrodes composed of unit cells with spatially varying geometry determined via computational optimization are proposed. Resolved simulation is employed to develop a homogenized description of the constituent unit cells. These effective properties serve as inputs to a continuum model for the electrode when used in the negative half cell of a vanadium redox flow battery. Porosity distributions minimizing power loss are then determined via computational design optimization to generate architected porosity electrodes. The architected electrodes are compared to bulk, uniform porosity electrodes and found to lead to increased power efficiency across operating flow rates and currents. The design methodology is further used to generate a scaled-up electrode with comparable power efficiency to the bench-scale systems. The variable porosity architecture and computational design methodology presented here thus offers a novel pathway for automatically generating spatially engineered electrode structures with improved power performance.

Open-source platform for block polymer formulation design using particle swarm optimization.

Facile exploration of large design spaces is critical to the development of new functional soft materials, including self-assembling block polymers, and computational inverse design methodologies are a promising route to initialize this task. We present here an open-source software package coupling particle swarm optimization (PSO) with an existing open-source self-consistent field theory (SCFT) software for the inverse design of self-assembling block polymers to target bulk morphologies. To lower the barrier to use of the software and facilitate exploration of novel design spaces, the underlying SCFT calculations are seeded with algorithmically generated initial fields for four typical morphologies: lamellae, network phases, cylindrical phases, and spherical phases. In addition to its utility within PSO, the initial guess tool also finds generic applicability for stand-alone SCFT calculations. The robustness of the software is demonstrated with two searches for classical phases in the conformationally symmetric diblock system, as well as one search for the Frank-Kasper [Formula: see text] phase in conformationally asymmetric diblocks. The source code for both the initial guess generation and the PSO wrapper is publicly available.

Computational Design of Peptides with Improved Recognition of the Focal Adhesion Kinase FAT Domain.

We describe a two-stage computational protein design (CPD) methodology for the design of peptides binding to the FAT domain of the protein focal adhesion kinase. The first stage involves high-throughput CPD calculations with the Proteus software. The energies of the folded state are described by a physics-based energy function and of the unfolded peptides by a knowledge-based model that reproduces aminoacid compositions consistent with a helicity scale. The obtained sequences are filtered in terms of the affinity and the stability of the complex. In the second stage, design sequences are further evaluated by all-atom molecular dynamics simulations and binding free energy calculations with a molecular mechanics/implicit solvent free energy function.

A systematic design to develop high-performance sintered particulate copper-composite as heat spreader material

A computational framework built on an effective-medium theory and mean-field homogenization is used to design particulate copper (Cu) composite for high-performance heat spreaders for electronic devices. Several potential particulate types as a second-phase are considered based on their intrinsic properties to design various Cu-composite heat-spreaders leading to desirable properties such as low coefficient of thermal expansion (CTE), low density, and improved structural response without compromising on required thermal conductivity. Based on computational predictions, the thermal interface of matrix-particulate is found to be the dominant factor for maintaining required thermal conductivity (~300 W m−1 K−1), which suggested ceramic particles, particularly Beo, SiC, and AlN, as preferred candidates when loaded up to 30 vol%. Due to very high thermal conductivity and low CTE, diamond particles demonstrated the best results provided its wettability with copper is improved as a result of the surface coating. To validate the model results, numerous Cu composites with diamond as a second-phase are sintered using the spark plasma sintering technique, and the experimental data is found in close agreement with the predictions. The Cu composites containing Ni-coated diamond particles exhibited thermal conductivity more than the value of pure Cu at extremely low volume concentration (5%). The effect of particle size is also studied and it is found that the composites sintered with sub-micron particles resulted in more porosity as compared to micron-sized particles, which displayed direct bearing on thermal and mechanical properties as demonstrated by experimental data. The measured data on densification and particle–matrix interface condition is used in calibrating the models for establishing their effect on resulting thermal and structural properties. The presented integrated computational design methodology is validated using experimental data, which is expected to help the researchers and electronic industry develop high-performance heat spreaders with tailored properties.

Digital Twin-Driven Human-Centered Design Frameworks for Meeting Sustainability Objectives

Abstract This paper presents a manifesto for the future design of human-made systems and engage this reflection by promoting a computational human factors engineering (HFE) approach. In this paper, we concentrate on the fact that human well-being and sustainability objectives are inseparable. Thus, this paper’s primary focus is to demonstrate a simulation-based computational design methodology, digital human-in-the-loop (D-HIL), which utilizes the digital human modeling (DHM) platform to inject HFE design principles early in design. The D-HIL methodology enables human, product, and systems data to be coupled with computational toolkits; thus, bringing opportunities for facilitating ergonomics decision-making to be part of the digital twin (DT)-driven design. We presented two frameworks originating from our recent work in D-HIL research: (1) prototyping toolbox and (2) human error and functional failure reasoning (HEFFR) as means for realizing sustainability goals through DT-based design. While our preliminary studies show that both tools show promise in terms of realizing the sustainability goals, they are limited to only goals that relate to cost. In our future work, we plan to explore models and computational tools from non-traditional engineering domains to expand our frameworks’ scope to realizing sustainability goals that go beyond cost estimations.

Integrating human factors early in the design process using digital human modelling and surrogate modelling

Embedding ergonomics in the design process is often delayed as it requires building physical prototypes and human subject data collection. This causes retrofitting or expensive late design changes which increase overall cost and time-to-market. This paper presents a computational design methodology that employs Digital Human Modelling (DHM) research and the surrogate modelling technique to integrate ergonomics early in design. The goal of this methodology is to provide a proactive ergonomics assessment toolset that allows designers to identify the effects of design variables on human performance, computationally. In this research, a design space exploration study was conducted to evaluate the ergonomics of a concept aircraft cockpit model built based on a set of flight deck layout variables. Variability in the pilot's anthropometry was added through accommodating the extremities in population-based on computational human manikins. A genetic algorithm with the surrogate model was used to search the concept variants that meet ergonomics objectives. The results show that the proposed proactive ergonomics approach allows designers to explore the design space and evaluate trade-offs between the competing design objectives early in design. Our computational ergonomics approach, based on the cockpit packaging study, demonstrates the advantages of the DHM based surrogate modelling during product conceptualisation.