Design Space Research Articles

(Invited) Modeling of the Electric Double Layer (EDL) Structures of Localized High-Concentration Electrolytes (LHCE)

Various electrical double layer (EDL) models have been proposed but mainly for homogenous solutions. Recently, the so-called Localized high-concentration electrolytes (LHCE) have shown many benefits to high-capacity electrodes. Recently, we have proposed micelle-like structures to enable a complete understanding of microstructures in LHCE, which contains a salt, solvent, and non-ion-solvating diluent, analogical to the roles of oil, surfactant, and water as in micelles. [1] As the solid electrolyte interphase (SEI) formation is sensitive to the EDL structure, [2] it is critical to understand the EDL structures for LHCE with heterogeneous structures.Molecular dynamics simulations were performed in several electrolytes with heterogeneous structures on negatively charged surfaces. It was found the Stern model with an absorbed cation layer adsorption layer and a more diffused layer with cation, anion, solvent, and diluents, should be used to describe the EDL of this type of electrolyte.For the system consisting of LiFSI salt, DME solvent, and TFEO diluent at a concentration of LiFSI-1.2DME-2TFEO, the heterogeneous micelle-like structures in LHCE are maintained in the EDL, with the Li+-rich salt-solvent clusters and the Li+-poor diluent regions. The diluent region contains Li+ near the negatively charged surface but does not have any ions in the bulk of LHCE. This is because TFEO cannot form a complete solvation shell in the bulk electrolyte due to the steric effect, however, only a partial solvation shell is needed in the Stern layer in the EDL. The appearance of Li+ in the diluent is also necessary for the EDL, as TFEO alone cannot screen the charge.The chemical and concentration design space for the three components is very large. To obtain a more general picture, we also explored different salt-solvent ratios and different diluent-salt interactions. As the salt solvent ratio increases from low (LCE), to medium (MCE) to high concentration electrolytes (HCE), where all solvents are coordinated with ions, the salt-solvent structures evolve from fully solvated ions or ion pairs in LCE, 3D branched networks in MCE, to fully connected salt aggregates in HCE. The salt-diluent interactions and diluent concentration further modify the micelle-like structures. The EDL for both LHCE and LMCE are heterogenous. [1] Nature Materials 22, 1531–1539 (2023)[2] J. Am. Chem. Soc. 145, 4, 2473–2484 (2023)

Physically-Based Descriptors of the Nitrogen Reduction Reaction on Transition Metal Nitrides

Due to the high energy intensity and consequent carbon footprint of the Haber-Bosch process for ammonia production, researchers have been intensely studying the electrochemical nitrogen reduction reaction (NRR) as a means to directly produce ammonia using clean energy with lower emissions of carbon by-products. One exciting class of materials for NRR catalysis are transition metal nitrides (TMNs), as they have been shown to have good stability, flexible electronic structures, and decent electrochemical activity [1]. Previous studies using density functional theory (DFT) have explored the energetics of known NRR pathways (associative, dissociative, and Mars-van Krevelen) on the mononitrides of all naturally occurring d-block metals in rock salt and zincblende structures [2]. The most favorable NRR mechanism (determined to be the pathway that contains the smallest maximum potential change across all steps of the reaction) on these materials was shown to be the Mars-van Krevelen mechanism. Uncovering electronic, structural, and dynamic descriptors that correlate well with the energetics of this favorable NRR mechanism on TMNs will be useful in mitigating the need to run full DFT calculations when expanding the search space of potential catalyst candidates, such as ternary transition metal nitrides (TTMNs) and perovskite transition metal oxynitrides (TMONs). Previous literature has shown that the proton adsorption energy is a good descriptor for catalytic NRR activity on TMNs [3], oxygen vacancy content [4] and the oxygen p-band center of bulk perovskites [5] are good descriptors for catalytic oxygen evolution reaction performance, and the metal d-band center of pure transition metal catalysts is a good descriptor for reactivity and adsorption energies [6]. Inspired by these results, this work uses DFT on a set of TMNs to calculate a variety of potential physics-based descriptors, including: metal d and nitrogen p-band centers, hybridization between nitrogen p and metal d orbitals, nitrogen vacancy content, surface Bader charges, and phonon band centers. Since the energetics of the NRR can be modulated by the properties of either the atoms in the bulk or in the top surface layers, we calculate all of our descriptors from atoms in the TMN bulk form as well as from surface and subsurface atoms in the TMN slab form.Our results reveal several correlations between the descriptors and material attributes (such as adsorption energies and vacancy formation energies), which in turn govern the energetics of the NRR on the TMNs. We find that the N2 adsorption energy is negatively correlated with the metal d-band center, the N adsorption energy is positively correlated with the nitrogen p-band center, and both the proton adsorption energy and nitrogen vacancy formation energy are negatively correlated with nitrogen p and metal d orbital hybridization. Interestingly, we also find that for some attributes, the subsurface layer atoms dictate the attribute values and not the surface layer atoms. This suggests that heterogeneous catalysis reactivity may be modulated by atomic properties not purely from the bulk or purely from the surface, but rather from multiple subsurface layers as well.Overall, the insights gained from our descriptor analysis can be used to constrain the search and design space of future potential NRR catalysts, such as TTMNs and TMONs. In doing so, the descriptors can be used in future works in either a high-throughput screening or machine learning framework to allow for the inverse material design of optimal NRR catalysts. These optimal catalysts can in turn lead to the emergence of electrochemical ammonia synthesis in industrial production.References Qiao Z, Johnson D and Djire A. Cell Rep Phys Sci 2021; 2.Abghoui Y and Skúlason E. Catal Today 2017; 286: 69-77.Abghoui Y, Garden AL, Hlynsson VF, Björgvinsdóttir S, Ólafsdóttir H and Skúlason E. Physical Chemistry Chemical Physics 2015; 17: 4909-4918.Marelli E, Gazquez J, Poghosyan E, Müller E, Gawryluk DJ, Pomjakushina E, Sheptyakov D, Piamonteze C, Aegerter D, Schmidt TJ, Medarde M and Fabbri E. Angewandte Chemie - International Edition 2021; 60: 14609-14619.Jacobs R, Hwang J, Shao-Horn Y and Morgan D. Chemistry of Materials 2019; 31: 785-797.Nørskov JK, Abild-Pedersen F, Studt F and Bligaard T. Proc Natl Acad Sci U S A 2011; 108: 937-943.

Estimating Parameters of Physics-Based Battery Models – What Machine Learning Can and Cannot Do

With the widespread use of Lithium-ion (Li-ion) batteries in multiple industries, the design space for the electrochemical cells has increased drastically. Li-ion batteries’ design parameters and material properties, such as porosity, electrode thickness, active material loading, and solid phase diffusivities, typically vary substantially due to different design goals and variation in the manufacturing process. Due to the difficulty in obtaining these and other battery cell parameters, many battery management systems used to control and monitor Li-ion batteries opt for the simplified resistor-capacitor circuit-based battery cell representation over using physics-based battery models. However, if these parameters become obtainable, scientists could implement advanced battery management systems using physics-based battery models to capture precisely how the battery performs and degrades in a wide variety of applications. Multiple methods, such as genetic algorithms, statistics, and machine learning, have been attempted for the parametrization and characterization of battery cells to enable accurate battery simulations using physics-based models [1, 2]. However, there are inherent limitations to the type and number of parameters that can be estimated using these methods if only the standard battery cell cycling data is utilized for this purpose. The present work elucidates and quantifies these limitations specifically in the case of machine learning based approach and provides deeper insights into these limitations that may help develop more robust cell characterization protocols. In the present work, model parameters are varied in a continuum-level physics-based model and simulations are performed for: (1) constant current-constant voltage (CC-CV) charging and constant current discharge at different C-rates, and (2) electrochemical impedance spectroscopy (EIS) in the relevant frequency range at multiple states of charge (SOCs). The Single Particle Model (SPM) is used for performing these simulations due to its relative simplicity and small number of parameters. At first, the charge and discharge data are input as training data in a long short-term memory neural network (LSTM NN), and EIS data is input as training data in a convolutional neural network (CNN). Once trained, the machine learning (ML) model uses charge, discharge, and EIS data withheld from the original training data set to predict SPM parameters. The estimated parameters are compared with the true parameters used for SPM charge, discharge, and EIS simulations, and the error in parameter estimation is quantified. A rigorous grid convergence analysis is performed to highlight the importance of ensuring that the spatial discretization error of the model does not influence the parameter estimation accuracy. For the first error quantification, all parameters are varied concurrently to obtain the parameter estimation error that arises from a large, undetermined parameter set. Parameters are then analyzed individually and in clusters related to diffusion, kinetics, and stoichiometric limitations. Further analysis is performed on parameters that could not be estimated with sufficient accuracy, including theoretical explanations for the incurred error and the effect of this error on the internal state predictions.

Design Principles for Architected Battery Electrodes

The properties of rechargeable lithium-ion batteries are determined by the electrochemical and kinetic properties of their constituent materials as well as by their underlying microstructure. The discovery of 2D and 3D mesoscale architectures tailored to diverse applications remain an open challenge and it cannot be approached empirically. In this work, we leverage the design flexibility offered by several additive manufacturing methods (e.g. direct ink write, projection micro-stereolithography, etc.) to engineer the architecture of device electrodes. To explore this vast design space, we employ theoretical analyses and topology optimization techniques to determine printable electrode shapes. Our analyses produce general design guidelines, and three-dimensional CAD representations that optimize the battery architecture over specific performance metrics. In this talk, we show examples of electrode design driven by extreme operating conditions (e.g., low temperature) or by mechanical resilience. We also demonstrate an integrated workflow for model validation and guided experiments. For Li-metal anodes, our objective is to identify regimes of morphologically stable growth--where deleterious effects of dendrite growth might be avoided by microstructural design. For manganese-oxide cathodes, we develop physical models that capturethe pseudocapacitive response of the material and use them in inverse analyses to optimize the cathode architectures.

A designerly approach to a novel method for combining closed and semi-open spaces in a mid-rise office building envelope to improve energy efficiency and thermal comfort

This article introduces a new method of combining closed and semi-open spaces in a mid-rise office building. This method presented in the paper aims to improve energy efficiency and thermal comfort and emphasize the use of open spaces on all floors and in different directions. Usually, open spaces in buildings such as courtyards, balconies, atriums, and sky_court, despite their positive performance, have limitations in terms of geometry, location, enclosure, and connection with the building. However, this approach has tried to solve these limitations using a modular network in the plan production process. This approach allows the creation of open spaces with various geometries and areas on the floors. This study pays attention to the presence of semi-open space in mid-rise buildings and the creation of porosity in the volume, which reduces energy consumption and increases the comfort of employees. This article introduces an innovative approach to utilizing semi-open space on the floors of office buildings. This approach, by allocating a part of the area of each floor to semi-open space with two different parameters, i.e., “semi-open space with the most connection with the façade” and “semi-open space with the least connection with the facade,” is a framework for the design and optimal placement of the semi-open space in It offers office buildings. This research tries to generate all possible situations to achieve 40 % of open space in the building. By dividing the plan into 5 × 5 modules in the presented simulation model, 320 modules are available, and to convert 40 % of the building into semi-open space, 128 modules should be allocated for this purpose. The models simulated by Ladybug and Honeybee plugins show that in the volumes where the semi-open spaces in the building have a combination of the maximum connection with the facade and minimum connection with the facade, they perform better in energy consumption and thermal comfort. The research results show that a volume comprising closed and semi-open spaces with a suitable porosity ratio can perform best. In other words, the change in volume porosity significantly affects energy consumption and thermal comfort.

Challenges in illumination analysis and design for Martian surface-level habitation

It is needless to say that travel to and settlement on Mars are associated with extreme levels of scientific and engineering issues. This will only be amplified with the long-term duration of the mission, not only due to scarcity of resources, but also as the psychological aspects of the dynamics among the crew increase drastically. It should be emphasized that this is a scientific crew, who have undergone high levels of confinement during space travel to Mars, O (102 Earth days), are living in semi-solitude and partial confinement conditions for durations of O (103 Earth days), and even at the nominal termination of the mission, foresee a high-risk and arduous travel time of O (102 Earth days) back to the Earth. The mental weight of the described mission with its slow pace and tardy episodes, puts the crew under severe psychological issues. Minimal and conservative design of spaces, lack of constant access to the exterior, and social solitude are among major factors contributing to the psychological well-being of the crew. Furthermore, the overall lower levels of natural light, accompanied by the minimum possible area of transparent facades, protecting the crew from harmful radiations and cold exterior, burden the mental conditions of the crew even more. Given the limited availability of data from the surface of Mars, study of the effects linked to the lighting and illumination design of the habitats is challenging. The current manuscript hopes to shed light on the illumination and lighting design and simulation procedure, required data, assumptions, and final results for the surface-level habitats on Mars.•Mars / Sub orbital configuration allows for limited natural lighting, however, upon site-specific analysis, it might be considerable as a base passive source.•Current simulation tools are design based on Earth-bound design requirements. These need to be re-oriented to match available planetary data.

A review on integration of carbon fiber and polymer matrix composites in 3D printing technology

Three-dimensional (3D) printing applications obtained by combining the lightness, high strength, and durability of carbon fiber with polymer matrix composites provide various industrial advantages. These advantages offer new design and production opportunities for automotive, aviation, space, medical devices, and many other industrial fields. This review article discusses material innovations in 3D printing technology with a focus on the integration of carbon fiber and polymer matrix composites. After examining the current state and future potential of 3D printing technology, the properties and advantages of carbon fiber and polymer matrix composites and the difficulties encountered with their integration into the 3D printing process were examined. In conclusion, this review article comprehensively discusses the current status, advantages, challenges, and future directions on the integration of carbon fiber and polymer matrix composites in 3D printing technology. This article can be an important resource for industry professionals and researchers in materials science and engineering.

Nontrivial t-designs in polar spaces exist for all t

AbstractA finite classical polar space of rank n consists of the totally isotropic subspaces of a finite vector space over $$\mathbb {F}_q$$ F q equipped with a nondegenerate form such that n is the maximal dimension of such a subspace. A t-$$(n,k,\lambda )$$ ( n , k , λ ) design in a finite classical polar space of rank n is a collection Y of totally isotropic k-spaces such that each totally isotropic t-space is contained in exactly $$\lambda $$ λ members of Y. Nontrivial examples are currently only known for $$t\le 2$$ t ≤ 2 . We show that t-$$(n,k,\lambda )$$ ( n , k , λ ) designs in polar spaces exist for all t and q provided that $$k>\frac{21}{2}t$$ k > 21 2 t and n is sufficiently large enough. The proof is based on a probabilistic method by Kuperberg, Lovett, and Peled, and it is thus nonconstructive.

Space Design Protection of Traditional Villages for Smart City Under Sustainable Development Environment

Space Design Protection of Traditional Villages for Smart City Under Sustainable Development Environment

An Aero Engine Disk Sizing and Weight Estimation Approach with Improved Temperature Profiles

Abstract During the predesign phase of an aero engine, interdisciplinary parametric studies are crucial for identifying an optimum engine. As innovative engine concepts emerge, it is essential to study trends aiming to minimize climate impact and one of its key contributors, fuel burn. The comparatively heavy disks, subjected to high thermal and mechanical loads, are central to this analysis due to their high impact on the overall module mass. This paper proposes an approach to achieve a first lightweight mechanical design of disks in the predesign phase. Based on a performance and aerodynamic baseline, the design space and physical boundary conditions are set. Firstly, an initial mass optimized disk is derived using simplified temperature profiles and stress calculations under the Mechanical Design Point conditions. Subsequently, all the module disks are considered and an estimation for each bore temperature is achieved through a novel loop over the entire module and all available operating points. By evaluating the temperature and stress profiles across various operating conditions, the lowcyclefatigue life of each disk can be studied. As part of a highly modular engine predesign framework, this method allows for zooming capabilities of single disk calculations by prescribing individual boundary conditions. The proposed approach already yields an initial assessment of the rotors within a short runtime, facilitating simple iterations with the succeeding design phases due to method and tool commonalities. While the primary focus of the current work is on compressor design, the principles presented are adaptable and expandable to turbine disk applications as well.

Three-dimensional-printed femoral diaphysis for biomechanical testing-Optimization and validation.

Polylactic acid (PLA) models of normal human femoral diaphyses were designed using three-dimensional (3D) printing technology to create inexpensive, accessible, and reproducible specimens for flexural biomechanical studies. These models were subjected to three-point bending and their response to loading was characterized. The anisotropic mechanical behavior of the 3D-printed femurs and the influence of printing orientations, infill density, wall layers, resolution, and other printing parameters were explored to develop a design space. The objective of the design space was set to emulate the flexural biomechanical response of the normal human femur bones. Results show the 3D-printed PLA diaphyseal femurs with 5% infill density, two-four wall layers, and a resolution of 200 µm resulted in a flexural strength of 184.8 ± 8.18 MPa. Models with 20% infill density and six wall layers resulted in a flexural modulus of 18.54 ± 0.543 GPa. These results emulate the biomechanical response of the normal human femur, as determined by historical target values derived from prior cadaveric and 3D printing data. With further research, inclusive of modeling the proximal and distal femur and more comprehensive biomechanical testing, 3D-printed femurs may ultimately serve as a cheap, accessible biomechanical resource for surgeons and researchers.

Idea generation in exploitative and explorative business process redesign techniques

AbstractBusiness process redesign has received considerable attention in research and practice. An important task contributing to redesign is idea generation. It has been argued that the choice of technique influences redesign outcomes, but empirical evidence is largely missing. In this paper, we examine the role of idea generation in business process redesign by comparing two different kinds of techniques: exploitative and explorative redesign techniques. Often, exploitative techniques are expected to yield more appropriate solutions, while explorative techniques are associated with more creative outcomes. To investigate such propositions, we consider the Best Practices of Process Redesign as an example of an exploitative redesign technique and the Business Process Design Space as an explorative redesign technique. We conducted a free-simulation experiment to study the empirical impact of the choice of redesign techniques on outcomes. We find that the Business Process Design Space leads to a higher number of redesign ideas, which are also more diverse. These are more creative and novel than ideas produced by the Best Practices of Process Redesign. Against expectation, the best practices as an exploitative approach do not produce more appropriate nor impactful ideas in our task design. Since both approaches cover different areas of improvement, a key implication of our work is that they should not be seen as isolated approaches to redesign; rather, they should be purposefully combined to not only create novel but also operational business process redesigns.

Visually communicating pathological changes: A case study on the effectiveness of phong versus outline shading

In this paper, we investigate the suitability of different visual representations of pathological growth and shrinkage using surface models of intracranial aneurysms and liver tumors. By presenting complex medical information in a visually accessible manner, audiences can better understand and comprehend the progression of pathological structures. Previous work in medical visualization provides an extensive design space for visualizing medical image data. However, determining which visualization techniques are appropriate for a general audience has not been thoroughly investigated.We conducted a user study (n = 40) to evaluate different visual representations in terms of their suitability for solving tasks and their aesthetics. We created surface models representing the evolution of pathological structures over multiple discrete time steps and visualized them using illumination-based and illustrative techniques. Our results indicate that users’ aesthetic preferences largely coincide with their preferred visualization technique for task-solving purposes. In general, the illumination-based technique has been preferred to the illustrative technique, but the latter offers great potential for increasing the accessibility of visualizations to users with color vision deficiencies.

Analisis Perbandingan Biaya dan Waktu pada Penggunaan Duct Spacer dengan Prefabricated dan Hollow Baja Ringan Untuk Pekerjaan Underground Ducting

The Kediri Airport development project encountered challenges related to underground ducting work. The design of Kediri Airport adheres to international standards, which led to the use of materials not readily available in Indonesia, specifically pipes and duct spacers. Duct spacers are tools used to arrange ducting pipes according to their formation and prevent them from floating during concrete pouring due to buoyancy. The SKEP/114/VI/2002 serves as a reference for underground cable installation, involving direct burial of cables at a depth of 70 cm, covered with a 5 cm layer of sand above the cable surface. These two approaches significantly differ, necessitating problem-solving solutions. To address this issue, research was conducted to find suitable duct spacers in Indonesia. Two alternative materials were considered: PVC and lightweight hollow steel. The study compared costs and time associated with these materials. PVC duct spacers would be imported and be fabricated in Indonesia, while hollow steel duct spacers would be fabricated on-site. The research focused on three aspects: ducting work methods, duct spacer design, and cost-time analysis. Given tight installation spaces and busy schedules, duct spacers needed to be compact, simple, and adaptable to various pipe formations. The design for hollow steel duct spacers aimed for simplicity to reduce on-site construction work. However, care was taken to avoid potential injuries from exposed bolts on the hollow steel frame. The PVC duct spacer design utilized Polytam PF 1000 material, with a nominal shear strength of 399 kg and a nominal moment of 266 kg·cm—sufficient to withstand saturated soil loads up to 162 cm but not vehicular loads. Analyzing procurement time, PVC duct spacers from Indonesian manufacturers were the quickest, taking only 56 days due to no port clearance delays. The fastest completion time for the project was achieved using PVC duct spacers, taking 122 seconds—three times faster than hollow steel duct spacers. While lightweight steel was the most cost-effective option for on-site procurement, combining cost and time analysis favored PVC duct spacers. The minimal cost difference allowed for accelerated production, minimizing the risk of delays

Quantifying the Effects of Interwell Communication Using Dynamic Fluid-in-Place Calculations

Summary Multifractured horizontal wells (MFHWs) completed in the same reservoir layer, or different reservoir layers, commonly experience interwell communication through hydraulic fractures. For example, after a well is placed on production, its production performance can be impacted by communication with an offsetting well placed on production after it. The degree of communication between wells is important to quantify for the purposes of well production forecasting, reserves estimation, completions, and well spacing design optimization. In this study, dynamic fluid-in-place calculations, performed using the impacted producing well rates and flowing pressures, are applied to quantify the effect of communication with an offset-producing well on the impacted well-contacted fluid-in-place estimates. Agarwal (2010) demonstrated that pressure transient analysis theory can be used to derive the volume of fluid in place contacted by a well (CFIP) over time during constant rate, transient production. The method was later extended to variable-rate/pressure scenarios. However, all previous applications of Agarwal’s method were for single, isolated wells and assumed single-phase flow of oil and gas. To evaluate the usefulness of the method for modern development scenarios, it is extended to allow for quantification of interwell communication during flowback, for which single-phase flow of water before the breakthrough of formation fluids may precede multiphase flow of formation and fracturing fluids, and for analysis of multiphase data. Analysis of flowback data enables early-time identification and quantification of interference effects. Multiple numerical simulation cases are generated to simulate different degrees of communication for the case of a two-phase flow of oil and water. Wells are assumed to be communicating through a hydraulic fracture with a specified transmissibility multiplier (Tmult) used to adjust the amount of interwell communication. Corrections for multiphase flow in the CFIP method are performed using two different methods—the total volumetric flow rate (combined phase) approach and the multiphase pseudovariable approach. The CFIP diagnostic plot (i.e., log-log plot of CFIP vs. material balance time) is applied to the impacted producing well to evaluate the CFIP trend before and after offset well production and the magnitude of CFIP change. The practical application of the method is demonstrated with field cases. From the simulation cases, it is observed that, after the offset well is placed on production, a reduction of CFIP for the impacted producing well occurs (rapidly decreasing at first and then stabilizing after a transition period) proportional to productivity index reduction. The loss in CFIP for the impacted producing well can be determined simply by estimating its CFIP immediately before and after offset well production. For high connectivity (Tmult> 0.25) scenarios, application of the combined phase approach resulted in estimates of the impacted well CFIP reduction of ~46–50%, whereas application of the modified pseudovariable approach resulted in estimates of ~49–51%. For the low connectivity case (Tmult = 0.001), these estimates were ~11% and ~9%, respectively, for the two approaches. Therefore, for the simulation cases studied herein, the two approaches agreed within acceptable error. Numerical simulation was also used to verify the absolute change in CFIP using these two approaches for correcting for multiphase flow. The practical application of the modified CFIP method was demonstrated using two field cases with early-time production. Both field cases demonstrated that changes in CFIP for the impacted well can be unambiguously interpreted. In the first field case corresponding to early-time production data (gas and water) associated with Well 23 of the SPE data repository, the reduction in CFIP of the impacted producing well was estimated to be ~37% using the combined phase approach. In the second field case, for which a producing well completed in a low-permeability gas condensate reservoir is impacted by placing multiple offset wells on production at the same time, the reduction in CFIP of the impacted well was estimated to be ~20% using the combined phase approach. In this study, we demonstrate for the first time that CFIP calculations can be applied to quantify interwell communication between two wells during flowback or early-time production when multiphase flow occurs in the reservoir.

The Six Qualities of Great Urban Places framework: combining theory and practice to decode the components of places that deliver for people and planet

ABSTRACT The Six Qualities of Great Urban Places is a practice-based framework for designing cities and neighbourhoods that deliver for people and planet. Our approach is grounded in practical experience and a literature review, which aimed to identify the key factors that contribute to thriving ecological, economic, and social systems. We also drew inspiration from established design frameworks, such as the Government Architect of NSW’s ‘Better Placed’ design policy, The Scottish Government’s ‘Place Standard Tool’, and the ‘City Resilience Framework’ by the Resilient Cities Network. Moreover, we incorporated insights from Hassell’s experience in urban design and landscape architecture. From our research and practice, we identified six common characteristics of well-functioning places and ecosystems: 1) Beautifully Original, 2) Conveniently Compact, 3) Richly Diverse, 4) Openly Networked, 5) Playfully Dynamic, and 6) Naturally Regenerative. We discovered that what distinguishes these qualities from other design frameworks we have analysed is their dual manifestation in both the physical design of spaces and their social and economic fabric. In this article, we present these qualities and share reflections from our practical application of the framework. Our aim is to contribute to the ongoing dialogue about how to foster healthy urban environments.

Zonewise surrogate-based optimization of box-constrained systems

Complex physical or numerical systems may exhibit distinct behaviors in various zones of their design spaces. We present an algorithm that uses multiple cluster-based surrogates for optimizing such box-constrained systems. It partitions the design space into multiple clusters using K-means clustering and develops a separate surrogate for each cluster. It then uses these surrogates to sample additional points in the design space whose function evaluations guide the search for a global optimum. Clustering, surrogate construction, and smart sampling are employed iteratively to add sample points until a pre-defined threshold. The best solution from these points estimates a global optimum. An extensive test bed of 52 box-constrained functions was used to evaluate and compare the algorithm's performance and computational requirements with sixteen derivative-free optimization solvers. The best version of our algorithm surpassed all sixteen solvers in optimization accuracy for a fixed number of evaluations and demanded lower computational effort than fifteen.

Optimising shapes of multiple pin fins in a microchannel using deep reinforcement learning and mesh deformation techniques

The utilisation of advanced pin fin designs in microchannels is useful for enhancing cooling efficiency. Advancements in machine learning and processing power have sparked interest in shape optimisation techniques. This research employs a novel framework that integrates Deep Artificial Neural Networks and Reinforcement Learning with a Computational Fluid Dynamics (CFD) solver to optimise multiple pin fin shapes within a microchannel. By incorporating Radial Basis Function interpolation and Proximal Policy Optimisation alongside FLUENT, acting as the CFD environment, the reinforcement learning agent adeptly explores the design space to enhance the thermohydraulic performance factor (TPF), aiming to maximise Nusselt number while minimising pressure loss. Unlike previous heat transfer optimisation studies, which typically required mesh regeneration at each step, the proposed framework could bypass the meshing step and alter the geometry directly by relying on the RBF interpolation technique to deform the mesh directly. Three distinct scenarios investigated in this study are uniform deformation of all pin fins, deformation of all pin fins arranged in two rows, and individual deformation of each pin fin. Extensive simulations, exceeding 90,000 different cases, demonstrate that although the optimisation process for the individual deformation of each pin fin requires more iterations compared to others, it surpasses them in terms of TPF improvement. Notably, significant improvements are achieved, such as a 49 % enhancement in Nusselt number and a 33 % reduction in pressure drop, culminating in an impressive 63 % increase in TPF compared to the initial geometry.

Flutter Optimization of Carbon/Epoxy Plates Based on a Fast Tree Algorithm

This study focuses on optimizing carbon/epoxy laminate configurations to maximize the flutter speed of composite structures using a Fast Tree Regression algorithm. Initially, a seed dataset was created, using finite element method (FEM) modal analysis for common stack-ups used in composite fins and UAV components. The FEM analysis, based on the Lanczos algorithm for extracting modal frequencies in bending and torsion, was verified through experimental modal analysis using an AS-4/3501-6 composite system. Custom software was developed to interface with the FEA modal software, enabling the generation and augmentation of laminate dataset scenarios. The seed dataset was expanded until the coefficient of determination (R2) reached at least 0.95. Various regression algorithms, including Fast Forest Regression, Fast Tree Regression, Sdca Regression, and Lbfgs Poisson Regression, were evaluated. The Fast Tree Regression algorithm was selected for further analysis due to its superior performance. This algorithm was applied to a design space of nearly 2000 potential laminate candidates, focusing on symmetric lay-ups to avoid undesirable coupling between bending and torsion in UAV and missile control surfaces. The final optimized lay-ups, exhibit the highest Delta function values (the squared difference of modal frequencies in torsion and bending), indicating the expected highest flutter speeds. The results demonstrate the efficacy of tailored composite materials in achieving specific aerodynamic performance goals.

Multidisciplinary design analysis and optimization of the aerodynamic shape of the SpaceLiner passenger stage

The SpaceLiner is a futuristic concept of a suborbital space plane intended to provide ultra-fast intercontinental passenger transport, currently in pre-development at DLR-SART. Vehicles traveling at hypersonic speeds inevitably produce shock waves that are perceived at ground level as sonic booms, with associated disturbance of the overflown populations. The reduction of this disturbance, together with the decrease of the noise associated to the launch and ascent phase, is critical for a viable future operation of the SpaceLiner. The objective of this work is the redesign of the passenger stage aerodynamic shape in order to improve its atmospheric re-entry performance, in terms of a reduction of both the heat flux and the disturbance of the overflown populations. For this purpose, a MDAO methodology was developed with the tasks of (1) computing vehicle performance from a wing shape parametrization using fast estimation methods, (2) exploring the design space and (3) finding a new promising aerodynamic shape. First, a Python tool-chain was developed to compute vehicles performances from a wing shape parametrization using fast estimation methods. The tool-chain was then systematically exploited to explore the design space by means of parametric studies, whose results informed the implementation of the forthcoming optimization. Afterwards, the wing shape was optimized using a three-objective evolutionary algorithm that minimized the vehicle mass and maximized its lift-to-drag ratio and lift coefficient. Simplified trajectory optimizations were then run on the set of non-dominated solutions in order to identify the most performing configurations. Finally, multi-objective evolutionary trajectory optimizations were run for the most promising candidate along intercontinental point-to-point routes of interest. Comparisons with the previous design iteration showed a significant improvement in terms of a reduction of both reentry heat flux and population disturbance.