Performance-driven Design Research Articles

A novel forward performance-driven design method for gear parameters

Gear is one of the most crucial components of the transmission system, and the performance of gear directly affects the efficiency and reliability of the transmission system. Conventional methods for designing gear parameters involve several time-consuming and complex steps, which may not guarantee optimal performance. Therefore, we propose a new method for designing gear parameters that aims to improve efficiency and accuracy. First, the tooth surface equations of spur and helical involute gears suitable for symmetric and asymmetric teeth are deduced based on the gear-forming machining principle. Second, the performance evaluation models for load capacity, dynamic performance, efficiency, and power density of the gears are established based on the precise gear surface. The design objectives are standardized and evaluated comprehensively using a linear weighting method. Finally, a forward performance-driven design method of gear parameters is established. The proposed method is applied to a helical gear pair design case, and the results show that 90.7% of the individuals in the Pareto optimal front are asymmetric gears, with 9.3% being symmetric gears. This suggests that asymmetric gears have more opportunities to be optimal than symmetric gears. The highest-ranked gear designed using the proposed method is superior to the gear designed using conventional methods.

From layer to building: Multiscale modeling of thermo-optical properties in 3D-printed facades

The challenge of building sector decarbonization has driven an integral rethinking of the way we design and build facades. Recently, large scale 3D-printing has emerged as an alternative manufacturing technique for novel facade components aiming at high operational efficiency and low environmental impact. Focusing on translucent polymer 3DPFs, this study tackles the challenges of modeling thermal and optical effects in geometrically complex components where interactions across multiple domains and scales occur. In particular, we introduce a novel method for modeling the irregular thermo-optical properties of 3DPFs, capable of capturing relevant effects often out of the scope of traditional modeling approaches. Our model accounts for geometry-dependent physical effects ranging from millimeter-scale fabrication details that impact optical behavior to centimeter-scale geometric features influencing heat and radiation transfer, extending up to the meter-scale implications for the building application. By employing computational techniques such as ray-tracing, computational fluid dynamics, and finite element analysis, we establish a model that offers detailed thermal and optical analysis to support performance-driven design iterations. Finally, demonstrating this approach in an office building context, we show that 3DPFs can match the performance of double glazing with dynamic shading, providing effective solar and thermal management over the year. This is achieved in a single, mono-material component with no active control, suggesting 3DPFs are a promising direction for low-environmental impact facade design.

Advancements in Concrete Performance by Using Waste materials in the Mixture

There have been serious research advancements in concrete materials to make construction more sustainable. Specifically, this is true with regard to alternative ingredients used for self-compacting concrete (SCC) production. To improve its sustainability, this study is aimed at adding Fine Recycled Concrete Aggregate (FRCA) and Recycled Concrete Aggregate (RCA) into SCC. Crushed and reclaimed asphalt (RCA), on the other hand, has many benefits including lower environmental impact and cost-effectiveness. Nevertheless, there are several issues associated with contractors’ knowledge among others being variations in their properties. It’s also known that FRCA can imbibe water faster; hence, mineral admixtures may be introduced to further enhance mechanical behaviour of the concrete. Fly ash and geopolymer synthesis are examples of waste products from industry as well as by-products of coal combustion which are useful in making green concretes. The best results would be achieved if fly ash, a popular Supplementary Cementitious Material (SCM) that reduces Ordinary Portland Cement’s (OPC’s) need is divided according to its oxide content. In order for geopolymers to become an alternative for cement making materials such as aluminosilicate source or alkali reactants are applied. Engineered Cementitious Composite (ECC) enhances performance of concrete even more with strain-hardening properties, especially in terms of durability and tensile capacity. On the whole, manufacturing concrete from recycled and industrial waste products is cost effective and environmentally beneficial. These materials can be maximized if prope performance-driven design strategies and contemporary waste management systems are put into place. This paper emphasizes how essential and crucial it is to incorporate sustainable methods in the production of concrete to create a more environment friendly construction industry.

A Methodological Framework for Assessing the Influence of Process Parameters on Strand Stability and Functional Performance in Fused Filament Fabrication.

With an ever-increasing material and design space available for Fused Filament Fabrication (FFF) technology, fabrication of complex three-dimensional structures with functional performance offers unique opportunities for product customization and performance-driven design. However, ensuring the quality and functionality of FFF-printed parts remains a significant challenge, as material-, process-, and system-level factors introduce variability and potentially hinder the translation of bulk material properties in the respective FFF counterparts. To this end, the present study presents a methodological framework for assessing the influence of process parameters on FFF strand stability and functional performance through a systematic analysis of FFF structural elements (1D stacks of FFF strands and 3D blocks), in terms of dimensional deviation from nominal geometry and resistivity, corresponding to the printability and functionality attributes, respectively. The influence of printing parameters on strand stability was investigated in terms of dimensional accuracy and surface morphology, employing optical microscopy and micro-computed tomography (mCT) for dimensional deviation analysis. In parallel, electrical resistance measurements were carried out to assess the effect of different process parameter combinations and toolpath patterns on functional performance. In low-level structural elements, strand height (H) was found to induce the greatest influence on FFF strand dimensional accuracy and resistivity, with higher H values leading to a reduction in resistivity of up to 38% in comparison with filament feedstock; however, this occurred at the cost of increased dimensional deviation. At higher structural levels, the overall effect of process parameters was found to be less pronounced, indicating that the translation of 1D strand properties to 3D blocks is subject to a trade-off due to competing mechanisms that facilitate/hinder current flow. Overall, the proposed framework enables the quantification of the influence of process parameters on the selected response variables, contributing to the development of standard operating procedures and recommendations for selecting optimal process parameters to achieve the desired process stability and functional performance in FFF.

Advanced Digital Tools for Data-Informed and Performance-Driven Design: A Review of Building Energy Consumption Forecasting Models Based on Machine Learning

Cities and buildings represent the core of human life, the nexus of economic activity, culture, and growth. Although cities cover less than 10% of the global land area, they are notorious for their substantial energy consumption and consequential carbon dioxide (CO2) emissions. These emissions significantly contribute to reducing the carbon budget available to mitigate the adverse impacts of climate change. In this context, the designers’ role is crucial to the technical and social response to climate change, and providing a new generation of tools and instruments is paramount to guide their decisions towards sustainable buildings and cities. In this regard, data-informed digital tools are a viable solution. These tools efficiently utilise available resources to estimate the energy consumption in buildings, thereby facilitating the formulation of effective urban policies and design optimisation. Furthermore, these data-driven digital tools enhance the application of algorithms across the building industry, empowering designers to make informed decisions, particularly in the early stages of design. This paper presents a comprehensive literature review on artificial intelligence-based tools that support performance-driven design. An exhaustive keyword-driven exploration across diverse bibliographic databases yielded a consolidated dataset used for automated analysis for discerning the prevalent themes, correlations, and structural nuances within the body of literature. The primary findings indicate an increasing emphasis on master plans and neighbourhood-scale simulations. However, it is observed that there is a lack of a streamlined framework integrating these data-driven tools into the design process.

Impact of model detail on daylighting metrics in residential buildings

Daylight metrics that shall guide performance-driven design must be applicable to models that reflect the limited knowledge at the time of planning decisions. In particular in residential buildings, the furnishing is beyond the control of such decisions and therefore unknown in the planning phase. The impact of model detail on illuminance- and luminance-based metrics is tested on a residential unit in Singapore. At two elevations, representing a work-plane and the eye level of a standing inhabitant, the metrics are computed from a) a detailed model incorporating furniture and shading devices, b) a model with only shading devices, and c) a minimalist model representing only the design decisions of the architect. As a potential approximation to the detailed model, d) a variant of the minimalist model with wall reflectance reduced to 0.20 is tested. The results for the three levels of detail are compared and indicate the significant impact of detail on all assessed daylight metrics.

A method for design optimization of roof-integrated two-stage solar concentrators (TSSCs)

The optimal performance of two-stage solar concentrators (TSSCs) as building-integrated energy solutions is achievable through an integrative design of TSSCs and the building geometry. Such a design process reflects a multi-criteria decision-making problem involving different stakeholders having domain-specific concerns. To support this, in this study, a method to perform a two-step, design optimization of building and TSSCs engineered concerning building geometry is presented by developing a parametric model and applying, a multi-objective optimization algorithm (NSGA-II). The method allows for the creation of different design solutions in several iterations using a population set of individuals (decision variables) related to building design (i.e., roof type and slope, building orientation, length, width, and building type), and TSSC design (type, geometric ratio, mirrors' separation distance, modules number, and receiver area). Thus in several iterations, the method enables finding optimal solutions i.e., building with maximum direct normal irradiance (DNI) and TSSCs with maximum average load match index (av.LMI), and minimum average covered roof area (av.CRA). The application of the method is shown in a case study of four different, hypothetical residential buildings under the climate settings of Berlin. These buildings are illustrative (descriptive), which depict theoretical volumes as abstract representations of future constructions. Thus, a set of simulation runs are performed according to different model settings, to enable early-stage design exploration of future/new constructions which differ in size (varying floor levels) and energy demand. Results indicate that most solutions reflect buildings of high DNI (more than 125 kW/m2/month) and TSSC of av.LMI from 0.4 to 4.2, and av.CRA from 8% to 47%. Nevertheless, the performance of TSSCs depends on the building type. The method enables a performance-driven design approach, searches for the most feasible solutions, and helps to meaningfully support the decision-making process.

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.

Octahedral Distortions Generate a Thermally Activated Phonon-Assisted Radiative Recombination Pathway in Cubic CsPbBr3 Perovskite Quantum Dots.

Exciton-phonon interactions elucidate structure-function relationships that aid in the control of color purity and carrier diffusion, which is necessary for the performance-driven design of solid-state optical emitters. Temperature-dependent steady-state photoluminescence (PL) and time-resolved PL (TRPL) reveal that thermally activated exciton-phonon interactions originate from structural distortions related to vibrations in cubic CsPbBr3 perovskite quantum dots (PQDs) at room temperature. Exciton-phonon interactions cause performance-degrading PL line width broadening and slower electron-hole recombination. Structural distortions in cubic PQDs at room temperature exist as the bending and stretching of the PbBr6 octahedra subunit. The PbBr6 octahedral distortions cause symmetry breaking, resulting in thermally activated longitudinal optical (LO) phonon coupling to the photoexcited electron-hole pair that manifests as inhomogeneous PL line width broadening. At cryogenic temperatures, the line width broadening is minimized due to a decrease in phonon-assisted recombination through shallow traps. A fundamental understanding of these intrinsic exciton-phonon interactions gives insight into the polymorphic nature of the cubic phase and the origins of performance degradation in PQD optical emitters.

Parametric model of window-integrated planer Cassegrain concentrator-based shading system (PCSS)

This study proposes a performance-driven design method for a new and novel, window-integrated planer Cassegrain concentrator shading system (PCSS) for daylight and energy applications. The integration process is complex and needs to address multi-disciplinary concerns, where the PCSS’s performance depends on system design and building geometries ensuring good solar gains. The method is rigorous yet adaptable to inform decision-making involving multiple stakeholders and promote the use of PCSSs as, window-integrated technology. The method contributes to engineering PCSS with respect to windows in a parametric model. A total of 3,750 designs are generated by controlling parameters related to the building (i.e., orientation, window-to-wall ratio (WWR), louvers’ tilt and number), and PCSS (i.e., modules number, mirrors’ shape and dimensions). The method is validated in an illustrative case study of a residential building in Madrid (Spain), where the performance is assessed in terms of annually-averaged electrical load match index (av.LMIEl), thermal load match index (av.LMITh), continuous daylight autonomy (cDA(500 lx)), and useful daylight illuminance (UDI(100–2000 lx)). Further, a sensitivity analysis is performed to investigate the relative significance of parameters. The method enables a performance-driven design of buildings and PCSSs and facilitates stakeholders for informed decision-making in projects concerning energy transition in buildings.

Reinforcement learning for soft sensor design through autonomous cross-domain data selection

Data-driven soft sensors have been extensively applied in the process industry for quality variable estimation. It is challenging to build reliable soft sensors for complex industrial processes under new operating conditions where available data are limited. To overcome this issue, we leverage samples from source domains, formulate the sample selection and soft sensor problem of the target domain as a Markov decision process, and solve this cross-domain soft sensor problem by proposing a reinforcement learning framework. Specifically, we propose an asynchronous advantage selector-actor-critic method for cross-domain sample selection and soft sensor design. The transferability of source-domain samples to the target domain is determined by the proposed method. The correlation and estimation error metrics are incorporated into the reward function for the performance-driven design. An extension to feature data selection is also proposed. The applicability of the proposed methods is demonstrated via a simulation study and an industrial case study.

Workflow for applying optimization-based design exploration to early-stage architectural design – Case study based on EvoMass

The role of optimization-based design exploration in early-stage architectural design has been increasingly recognized and valued. It has been widely considered an effective approach to achieving performance-informed and performance-driven design. Nevertheless, there is little research into how such design exploration can be adapted to various early-stage architectural design tasks. With this motivation, this paper revolves around a computer-aided design workflow for early-stage building massing design optimization and exploration while presenting three workshop case studies to demonstrate how the workflow can be intertwined with the design process. The design workflow is based on EvoMass, an integrated building massing design generation and optimization tool in Rhino-Grasshopper. The case study illustrates task-specific applications of the design workflow for synthesizing building design, finding design precedents, and understanding the interrelationship between formal attributes and building performance. The paper concludes by discussing the relevant efficacy of the design workflow for architectural design.

Flow Table Security in SDN: Adversarial Reconnaissance and Intelligent Attacks

The performance-driven design of SDN architectures leaves many security vulnerabilities, a notable one being the communication bottleneck between the controller and the switches. Functioning as a cache between the controller and the switches, the flow table mitigates this bottleneck by caching flow rules received from the controller at each switch, but is very limited in size due to the high cost and power consumption of the underlying storage medium. It thus presents an easy target for attacks. Observing that many existing defenses are based on simplistic attack models, we develop a model of intelligent attacks that exploit specific cache-like behaviors of the flow table to infer its internal configuration and state, and then design attack parameters accordingly. Our evaluations show that such attacks can accurately expose the internal parameters of the target flow table and cause measurable damage with the minimum effort.

Performance-driven design methodology for habitation shell design in extreme conditions on Mars

The research project illustrates how performance-driven design tools can be conducted as an architectural design methodology that suggests an innovative approach to design a habitation shell in extreme environmental conditions without human assistance. This research study attempts to use environmental data revealed by NASA and its habitat design requirements to develop a conceptual design for an innovative habitation form and then simulate it with Mars conditions to analyze the habitation shell's structural behavior according to finite element analysis. In this regard, research phases, including layout configuration, form-finding, and structural analysis, have been conducted to explore a habitation concept implemented with generative design tools as a decision-maker in extreme conditions. In conclusion, two generated typologies of proposed habitation forms will be compared in terms of their structural performance under extreme loads of the martian environment. Within this research project, due to the numerous extreme challenges of design and construction of habitation in extreme conditions using conventional approaches, a performance-driven design methodology will provide a rational and sustainable design methodology to tackle extreme barriers to Mars's environment.

Design subspace learning: Structural design space exploration using performance-conditioned generative modeling

Designers increasingly rely on parametric design studies to explore and improve structural concepts based on quantifiable metrics, generally either by generating design variations manually or using optimization methods. Unfortunately, both of these approaches have important shortcomings: effectively searching a large design space manually is infeasible, and design optimization overlooks qualitative aspects important in architectural and structural design. There is a need for methods that take advantage of computing intelligence to augment a designer's creativity while guiding—not forcing—their search for better-performing solutions. This research addresses this need by integrating conditional variational autoencoders in a performance-driven design exploration framework. First, a sampling algorithm generates a dataset of meaningful design options from an unwieldy design space. Second, a performance-conditioned variational autoencoder with a low-dimensional latent space is trained using the collected data. This latent space is intuitive to explore by designers even as it offers a diversity of high-performing design options.

Proposal of a new automated workflow for the computational performance-driven design optimization of building energy need and construction cost

Delaying the analysis of the building performance to the final stages of the design process can often cause the actual results to differ from the expected ones, with the issue of significant redesign costs. The aim of this work is to improve the designer’s control on thermal and geometric variables of the building in the first stages of the design process, to obtain an optimized building from different points of view. An innovative computational performance-driven design optimization workflow is proposed. The integration of a parametric algorithmic modelling tool in a genetic algorithm powered performance optimization procedure allows to minimize energy need and construction cost of the building, and to optimize the natural illumination levels, affecting several geometric variables. This approach is tested on the early design stages for the new construction of an educational building located in the centre of Italy (L’Aquila). The results are post-processed in order to provide an innovative graphical visualization of the outputs to improve the decision-making process. Useful insights on the influence of different design choices on three key performance indicators can be drawn for the case study building, by comparison of the Pareto solutions: optimization results highlight the strongest influence of window-to-wall ratio, among the other variables, on energy need and useful daylight illuminance.

Furion: Engineering High-Quality Immersive Virtual Reality on Today's Mobile Devices

Despite the growing market penetration, today's high-end virtual reality (VR) systems remain tethered, which not only limits users' VR experience but also creates a safety hazard. In this paper, we perform a systematic design study of the “elephant in the room” facing the VR industry - is it feasible to enable high-quality VR apps on untethered mobile devices such as smartphones? Our quantitative, performance-driven design study makes two contributions. First, we show that the QoE achievable for high-quality VR applications on today's mobile hardware and wireless networks via local rendering or offloading is about 10X away from the acceptable QoE, yet waiting for future mobile hardware or next-generation wireless networks (e.g., 5G) is unlikely to help, because of power limitation and the higher CPU utilization needed for processing packets under higher data rate. Second, we present Furion, a VR framework that enables high-quality, immersive mobile VR on today's mobile devices and wireless networks. Furion exploits a key insight about the VR workload that foreground interactions and background environment have contrasting predictability and rendering workload, and employs a split renderer architecture running on both the phone and the server. Supplemented with video compression, use of panoramic frames, parallel decoding on multiple cores on the phone, and view-based bitrate adaptation we demonstrate Furion can support high-quality VR apps on today's smartphones over WiFi, with under 14 ms latency and 60 FPS (the phone display refresh rate).

Smart Self-Healing and Self-Sensing Cementitious Composites—Recent Developments, Challenges, and Prospects

Abstract The use of smart cementitious materials is becoming increasingly critical for the enhanced serviceability of structures. The addition of carbon fibers, carbon nanotubes, and various nano-powders such as nano-silica, carbon black, and graphite giving cementitious materials electrical properties that can be used for self-sensing has been known for almost two decades. Many sensing principles and techniques using smart materials have been successfully developed and applied mostly in laboratory testing over last few decades. The strong capacity of Fiber-Reinforced Cementitious Composites for autogenous healing in addition to crack control (especially in the case of Strain-Hardening Cementitious Composites) has been reported by many researchers. Similarly, the applications of different mineral and bio-additive materials to achieve the self-healing of cracks have been noted with great interest. Design for serviceability based on the durability of the materials used in concrete structures is often neglected. With durability performance testing becoming more sophisticated, detailed service life design is being demanded in the most important infrastructure projects. The present review is focused on identifying field applications and highlighting the Performance-Driven Design Approach for tailoring material solutions for the problems likely to be faced by civil infrastructures in the future. A real-life case study is presented to illustrate the minimal cost implications of adopting the latest smart material for an eco-friendly, durable, reliable, and resilient infrastructure. Identifying critical challenges faced by the industry and developing solutions for the same is going to help bridge the current gaps between research and adoption.

Towards nearly Zero Energy Buildings: Shape optimization of typical housing typologies in Ibero-American temperate climate cities from a holistic perspective

This research analyses the reduction of primary energy consumption (PE)11PE: primary energy. of slab and high-rise housing typologies through the optimization of their building shapes. The process considered applies passive strategies at the concept stage of the architectural design.A quasi-steady state method provides an estimation of building PE consumption in heating and cooling. Then, a multi-objective Genetic Algorithm (GA)22GA: Genetic algorithm. optimizes the building shapes with three competitive objective functions: minimization of PE consumption per m2 (PEI), and maximization of passive volume ratio (PVR)33PVR: Passive volume ratio is the ratio between the building passive and the useful volumes., and the sum of roofs and best oriented surfaces (RBOS)44RBOS: Best oriented surfaces are those looking South for the Northern Hemisphere and North for the Southern Hemisphere, and horizontal roofs.. The performance-driven design workflow shows the Pareto front solutions that reflect the influence of passive strategies on the building shapes. A performance simulation of the single tower with Energy Plus permits to evaluate the quasi-state method sensibility. Finally, a multivariate linear regression evaluates the interdependence among the optimized parameters.The methodology is tested in four cities with temperate climate: Resistencia and Buenos Aires in Argentina, and Seville and Madrid in Spain. It is suitable for the early stages of design since it allows designers to relate architectural morphologies with environmental issues to reduce energy consumption and Green-House Gasseś emissions within the current Climate Change scenario.

Inductive Design Exploration Method with Active Learning for Complex Design Problems

The design of multiscale materials and products has necessitated an inductive and robust design approach to ensure satisfying the performance goals for complex engineering problems. Inductive design exploration method is a performance-driven design approach that explores feasible design spaces while considering the effect of uncertainty that leads to performance variability. However, the existing design method suffers from high computational costs for pre-defined sample data, which sacrifices the accuracy of solution spaces. In this study, we present an improved implementation of the inductive design exploration method by applying the active learning algorithm that is mainly used in machine learning techniques. The purpose of this study is to minimize the sampling effort while maintaining reasonable accuracy in the exploration of design spaces, thereby alleviating computational burden. The capabilities of the improved method are highlighted and demonstrated via a design problem of the blast resistant sandwich panel.