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Consolidating dredged clay slurries using a combination of vacuum pressure and prefabricated horizontal drains (PHDs) is widely used in practice. This is a large strain problem, but there is no existing large strain theory for PHD induced consolidation. A method with explicit equations has been proposed to consider both mechanical and geometrical non-linearities in analyzing PHD induced consolidation. The method considers stepwise variation of properties using imaginary time concept. An imaginary time is determined by the condition of continuity of degree of consolidation before and after changing the properties. Then the method was applied to analyze a large scale model test of vacuum consolidation with PHD. Two analyses were conducted. One considered both the mechanical and geometrical non-linearity (large strain), and another only considered mechanical non-linearity (small strain). The results of large strain analysis agree with the measured settlement curve and excess pore pressures well. While the small strain analysis under-estimated the rate of consolidation significantly. The results from this study indicate that for analyzing PHD induced consolidation of clay slurries considering the effect of large strain is important, and the proposed method can be a useful design tool.

Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization (WHO), and it has about 219 million cases worldwide, with 435,000 of those mortal. The common malaria diagnosis approach is heavily reliant on highly trained experts, who use a microscope to examine the samples. Therefore, there is a need to create an automated solution for the diagnosis of malaria. One of the main objectives of this work is to create a design tool that could be used to diagnose malaria from the image of a blood sample. In this paper, we firstly developed a graphical user interface that could be used to help segment red blood cells and infected cells and allow the users to analyze the blood samples. Secondly, a Feed-forward Neural Network (FNN) is designed to classify the cells into two classes. The achieved results show that the proposed techniques can be used to detect malaria, as it has achieved 92% accuracy with a database that contains 27,560 benchmark images.

Inverse design has long been an efficient and powerful design tool in the aircraft industry. In this paper, a novel inverse design method for supercritical airfoils is proposed based on generative models in deep learning. A Conditional Variational AutoEncoder (CVAE) and an integrated generative network CVAE-GAN that combines the CVAE with the Wasserstein Generative Adversarial Networks (WGAN), are conducted as generative models. They are used to generate target wall Mach distributions for the inverse design that matches specified features, such as locations of suction peak, shock and aft loading. Qualitative and quantitative results show that both adopted generative models can generate diverse and realistic wall Mach number distributions satisfying the given features. The CVAE-GAN model outperforms the CVAE model and achieves better reconstruction accuracies for all the samples in the dataset. Furthermore, a deep neural network for nonlinear mapping is adopted to obtain the airfoil shape corresponding to the target wall Mach number distribution. The performances of the designed deep neural network are fully demonstrated and a smoothness measurement is proposed to quantify small oscillations in the airfoil surface, proving the authenticity and accuracy of the generated airfoil shapes.

Rapid decline in the cost of solar photovoltaic (PV) modules and associated electronics has elevated the significance of structural balance of systems (BOS) in the system cost composition. In this study tensegrity, a bio-inspired structure made of bars as compression members and strings as tensile members, is evaluated as an economical racking solution for solar photovoltaic arrays. For the first time, the electrical aspects of solar photovoltaic system design have been unified with the structural aspects of tensegrity through a novel algorithm. The end-to-end system design framework is validated through modelling of a practical system with System Advisor Model (SAM), a standard design tool by National Renewable Energy Lab (NREL), in congruence with the minimal mass planar bridge tensegrity model implemented in MATLAB. This algorithmic demonstration establishes tensegrity as a technically viable system design approach and assesses the significant reduction in racking material weight leading to maximum cost savings up to 77%. The technical compatibility, ease of installation, logistic efficiency and economic benefits of tensegrity-enabled racking would accelerate wide-scale adoption of solar energy across the world. It is also conducive to high-impact applications of solar energy such as water canal covering and shaded agriculture. Additionally, tensegrity configuration provisions the use of locally grown timbers like bamboo as the compression member which benefits developing regions through local sourcing, fast installation, carbon capture and farming incentive. Tensegrity also unlocks the research avenue for adding modularity and portability to solar PV systems leveraging its controllable dynamic properties.

Space is one of the most avant-garde sectors for additive manufacturing as the inherent characteristics of this technology allow the creation of complex and lightweight 3D structures. Using this brand-new design tool, it was possible to design the pumping system holder of the MicroMED instrument, a particle analyser developed for ExoMars 2022 mission. The geometry of the holder was optimized using a finite element approach. The effectiveness of the proposed solution was evaluated by comparing the performance of two different designs using the AlSi9Cu3 alloy. One solution was based on micro-milling and the other on Selective Laser Melting. In order to validate the designed SLMed holder, a mockup was produced and successfully tested in the mission's mechanical environment.

Fully metallic micrometer-scale 3D architectures can be fabricated via a hybrid additive methodology combining multi-photon lithography with electrochemical deposition of metals. The methodology – referred to as two-photon templated electrodeposition (2PTE) – has significant design freedom that enables the creation of complicated, traditionally difficult-to-make, high aspect ratio metallic structures such as microneedles. These complicated geometries, combined with their fully metallic nature, can enable precision surgical applications such as inner ear drug delivery or fluid sampling. However, the process involves electrochemical deposition of metals into complicated 3D lithography patterns thicker than 500 μm. This causes potential and chemical gradients to develop within the 3D template, creating limitations to what can be designed. These limitations can be explored, understood, and overcome via numerical modeling. Herein we introduce a numerical model as a design tool that can predict growth for manufacturing complicated 3D metallic geometries. The model is successful in predicting the geometric result of 2PTE, and enables extraction of insights about geometric constraints through exploration of its mechanics.

In the contemporary complex and fast-changing markets, there is persistent pressure for Small-and-Medium Enterprises (SMEs) to engage in business model innovation to promptly meet customer expectations and successfully compete for survival. In the last decade, multiple approaches to business model development and innovation have been explored. However, they have been primarily designed for large-sized companies, while SMEs display distinctive organizational attributes. Thus, SMEs require a tailored approach to design, experiment and innovate their business models, in order to frame the specific complexity of their value creation processes. This paper aims to explore how a Dynamic Business Modelling approach complies with inherent SME characteristics and serves as a lean strategy design tool for innovating associated business models. A case-study of a real SME currently involved in innovating its business model illustrates and discusses the empirical evidence emerging from the use of this approach.

Topology optimization has become a valuable design tool for structures to be fabricated by additive manufacturing (AM). However, during early stage design, parameters are frequently evolving, resulting in multiple similar TO runs. Especially when design for manufacturing principles expand the parameter space, this iterative process is computationally burdensome, and does not take advantage of redundant information in each study. We introduce a deep learning-based framework that learns latent similarities between runs in a training set to predict near optimal designs, enabling efficient wholistic understanding of the problem setup space, which includes both loading conditions and, for the first time in this study, manufacturing process configuration. Learning was achieved using a conditional generative adversarial network (cGAN) trained on a dataset of randomized boundary conditions, loadings, and AM build orientations, and the corresponding optimal structures obtained through overhang-filtered TO. cGAN predictions showed good agreement with true optima. For even greater accuracy, predictions can be post-processed by applying a small number of TO iterations. Manifold learning techniques were used to provide further insight, and we were able to conclude that the cGAN error generally increases with distance between the load and the boundary conditions or build plate. Interestingly, in 9% of test cases, the cGAN generated structures with compliances better than the corresponding TO-calculated structures, often by as much as 50 % with an average of 7.8 %. That some of these structures appeared qualitatively different in form suggests the potential value of the approach in other domains such as generative design, where a range of alternate near-optimal designs are used to guide the ideation process.

Recently, system level approach has been proposed as a novel design tool for large-scale cyber physical systems in the discrete time LTI framework. The main idea of the new paradigm is a shift from constructing and parametrizing the desired optimal controller to the parametrization of the achievable closed loop behaviours (closed loop shape). Then, the controller is synthesised from the system level data in a way that keep the imposed structural constraints intact. In this paper we lift the main idea of the system level parametrization to a general level, that also includes the class of LPV systems. We also provide a general scheme for the realization related to different Mobius transforms which are formulated in terms of the original data and also keep intact the structural information.

Conceptualizing a sound mechanical system exposed to dynamic loading conditions throughout its lifetime is a challenging task ill-supported by current design tooling. Current approaches typically use a Shock Response Spectrum (SRS) as design specification that describes the maximum dynamic response of a single subsystem component due to a given loading condition. However, for complex systems during the early stages of the design, there is a need to gain quick insights into the impact of dynamic loading conditions applied to system level as well as to subsystem and component levels simultaneously. Hereto, in this study, a coupled multi-level model has been developed that describes all essential elements of a system. The advantage of this approach is that rather than one single SRS, now upon each element an SRS can be prescribed. Multi-level shock response spectra can be computed in a time-efficient manner and, for the first time, peak acceleration and displacement for various combinations of natural frequencies may be displayed. Hence, effectively translating how subsystems and components within a multi-level dynamic system may be mounted or suspended constructively. Subsystem components and the system as a whole can be analysed simultaneously, and the sensitivity of subsystem conceptual design choices can be examined. This allows design engineers to get detailed overviews of (sub)system-level responses due to (critical) dynamic loading conditions and multi-level loading scenarios. Design insights can be evaluated within minutes aiding to find potential configurations that match the design requirements. Moreover, the developed design tool aids in both dealing with uncertainty of requirements during the project definition phase and designing a robust system against late-stage requirement updates.

The customer journey map is a user-centered design tool, which combines visual resources, and it takes the customer's point of view into account. A promising business model with relevant presence of customers' perspective, the product-service system has been widely studied since its first introduction in literature in 1999. However, only a few studies have approached the two research areas, and none of them has associated the characteristics of the visual tool of customer journey and the PSS business model. For this reason, this study proposes to explore how the customer journey map can support PSS Improvement, comparing a customer journey map from what the company imagines it occurs and what the customers have described. To achieve that, we have developed a case study with interviews of the PSS company and three different customers to avoid bias. After the collected data, we drew two customer journey maps, one representing the company's view and the other, the compilation of the customers' reports. For the analyses, we evaluated the relevant differences between the journey maps, discussed the customers feeling and emotions, and highlighted the impacts of the pandemic from Covid-19 in the customer journey. Finally, there was some recommendations suggested for the company, such as actions to improve influential touchpoints, to maintain critical services, and to prevent changes that negatively affect the user experience. © 2021 Elsevier B.V.. All rights reserved.

With the competitiveness at a global level, companies are always trying to develop improved and less costly solutions to join components. One of the current options to join components is adhesive bonding, with applications in the aerospace, aeronautical and automotive industries. The Finite Element Method (FEM) is the most commonly used tool to design bonded joints. A FEM variant, the Extended Finite Element Method (XFEM), is able to promote crack growth and predict the bonded joints’ strength, although its use is still limited. The present work aims to validate the XFEM design technique to predict the strength of T-peel joints under tensile loads with different configurations (purely welded, bonded and hybrid), considering steel adherends and a ductile adhesive. The study comprises several damage initiation criteria (stress and strain-based) and growth criteria, together with linear and exponential degradation laws. It was found that the joints’ performance highly depends on the joining methods, and that the XFEM can be a valuable design tool for specific modelling conditions.

In this article, torsional control has been studied in the plan-mass asymmetric vertical seismic isolation (VSI) system considering the three-dimensionally analytical model. In the 3D VSI, a structure is partitioned by vertical isolation planes into two inner and outer substructures interconnected by links for lateral response reduction. In an ideal analytical model of plan-mass asymmetric VSI, plan-wise distribution of damping and stiffness have been provided for more torsional control. Seismic responses of the non-classically 3D VSI were obtained by Runge-Kutta numerical method, considering a large number of isolation ratios. In this investigation, the mass and stiffness ratios of substructures are defined with respect to the original structure which provides a novel method of graphic presentation for analysis results. In this manner, contour graphs create comprehensive and worthwhile insight into the 3D VSI behavior and can also serve as a design tool for the preliminary design of the system. The results show that in the case that the inner substructure is the flexible one, torsional control is nearly effective in both substructures satisfactorily, regardless of plan-wise damping distribution. However, in the second case that is the outer flexible substructure, torsional control is nearly effective in the stiff substructure while the response in flexible substructure is satisfactory just in a limited range of isolation ratios.

Graphical abstract, The work proposes a synthesis method of capacitive fractional-order impedance element which is composed of homogenous distributed resistive-capacitive (RC) structures (lines). The method employs genetic algorithm and searches for optimal connection schemes and parameters of the partial RC structures. The synthesis algorithm is described in detail including the coding of the properties of the structures for the purpose of the genetic algorithm. The user interface of the design tool is introduced and the input and output parameters of the synthesis are explained. The algorithm was verified by computer simulations and particularly by measurements of element samples fabricated in thick-film technology. The results correspond to the required impedance characteristics, which confirm the validity of the synthesis method.

The U.S. Department of Energy (DOE) has developed the Framework model to simulate fuel cell-based light-duty vehicle operation for various hydrogen storage systems. This transient model simulates the performance of the storage system, fuel cell, and vehicle for comparison to DOE's Technical Targets using four drive cycles. Metal hydride hydrogen storage models have been developed for the Framework model. Despite the utility of this model, it requires that material researchers input system design specifications that cannot be easily estimated. To address this challenge, a design tool has been developed that allows researchers to directly enter physical and thermodynamic metal hydride properties into a simple sizing module that then estimates the systems parameters required to run the storage system model. This design tool can also be used as a standalone MS Excel model to estimate the storage system mass and volume outside of Framework and compare it to the DOE Technical Targets. This model will be explained and exercised with existing hydrogen storage materials.

Ceramic 3D printing has gained rapid development in recent years. Especially, desktop ceramic 3D printers get more and more popular for designers, artists, and makers to work with clays. As a highly viscous pseudoplastic liquid, the printing material clay produces featured aesthetics, and also indicates several manufacturing constraints. There are barely customized modeling tools for ceramic printing. Targeting for ceramic printing, we present a user-interactive modeling and fabricating framework using sweeping lines as the design tool. We integrate the ceramic printing constraints like self-supporting and collision-free in the modeling stage. Then we propose a customized method for generating the printing file, with the guarantee that the extruder traces a single continuous path without starts and stops. To fulfill this goal, we compute an adaptive zig-zag path along the sweeping surface. During the fabrication, our continuous path avoids the potential collision between the printing head and the physical model. Moreover, we optimize the extrusion amount along with the extruder movement, such that overlap between neighboring layers is appropriate in terms of both model stability and cost-effectiveness. We demonstrate the fabrication process and physical results to validate our method.

Among the few lessons learned presented in the literature, authors put in evidence the on-going need to investigate on station components and their integration. The specific power consumption of station units with on-site hydrogen generation is often subject to uncertainty, and it would have been desirable to find more details about the energy contribution of each component. To address this gap, this paper focuses on the development of a mathematical modeling as a dynamic and multi-physical design tool to predict the energy performance of hydrogen production systems. Particularly, the model aims to describe and analyze the energy performance of two different electrolyzer technologies (PEM and Alkaline), integrated with a compressor system and gaseous buffer storage. Multiple tank options and a switching strategy are investigated, as well as a control system to simulate a real infrastructure operation. Auxiliaries and components related to the thermal management system have been also included. A carbon-footprint analysis follows the energy one, focusing on the CO2 emission reduction. Comparisons between literature data and model show that the hydrogen system proposed model is suitable to evaluate systems with respect to energy efficiency and system performance. The model could be a powerful tool for exploring control strategies and understanding the contributions to the overall energy consumption from the various internal components as a guide to researchers aiming for improved performance.

To improve the efficiency and effectiveness of designing signage systems in buildings, we present AUTOSIGN – a design tool that supports user-in-the-loop and multi-criteria optimization of signage layouts in complex buildings. We formulate signage placement as a multi-objective optimization problem with competing objectives (i.e., total distance travelled, total number of turns, the centrality of decision points, path overlap, and number of decision taken) and constraints (i.e., user-specified sign location and orientation threshold), which we solve using a two-step approach. Firstly, an evolutionary method is used to optimize all combination of navigation paths based on cognitively inspired objective functions weighted by the designers. Secondly, a particle swarm optimization is used to optimize individual sign placement to maximize the exposure of wayfinding information (i.e., signage coverage area) from the optimized navigation graph generated. To evaluate the effectiveness of the tool, we apply it to the design of signage systems across two virtual 3D buildings. We generate signage layouts for both buildings and optimize each of them for user-defined criteria. Both optimized and non-optimized layouts are evaluated using an agent-based simulation. The simulation results demonstrate that even with fewer signs, the signage coverage area for the optimized layout increased by 18% on average. Finally, an expert-based VR walk-through and a System Usability Study is performed to further evaluate AUTOSIGN .

In the last couple of decades, there has been a growing concern in what effects fossil fuels are having on the environment, resulting in governments and governing organizations issuing stringent emission standards in an effort to curve their environmental damage. To meet these new standards, the transportation industry has been conducting research into alternative fuels, such as hydrogen, but one critical problem utilizing hydrogen is that there is almost no infrastructure. A network of hydrogen refueling stations similar to modern gasoline stations will be required to be constructed to meet future demand. The hydrogen refueling station model was created to aid in designing hydrogen facilities, thus accelerating their development while reducing design cost. A model was created using Simulink consisting of an electrolyzer that generates hydrogen, a compressor, numerous storage tanks, a dispensing unit that transfers hydrogen, and a vehicle component that consumes hydrogen fuel. The model was validated using data from existing hydrogen refueling stations, and the data obtained from testing the previous version of the hydrogen refueling station model to determine model accuracy and if the model has improved. The model has demonstrated that it can produce reasonable results for a station's performance and has improved compared to the previous version.

The Theory of Critical Distances (TCD) is a powerful design tool capable of estimating the strength of notched/cracked materials by post-processing the linear-elastic stress fields ahead of the relevant stress raisers. The purpose of this paper is to reformulate the TCD to make it suitable for predicting the static/dynamic strength of notched unreinforced concrete subjected to Mixed-Mode I/II loading. The accuracy and reliability of the new extension of the TCD were checked using a large number of experimental results generated by testing plain concrete containing different geometrical features and tested under different loading rates and loading multiaxility. The predictions based on the proposed approach were seen to be within an error interval of ±30%. This level of accuracy is acceptable because it is within the scattering level of the experimental results used to calibrate the approach. These findings are promising and proving that this new reformulation of the TCD can be used to design notched plain concrete by modelling concrete as a linear-elastic, homogeneous, and isotropic material.

peer-reviewed The Medical Device industry lags other industries such as automotive and aerospace in terms of the use of predictive modeling as a design tool. This has started to change with growing experience being established with metal scaffold type structures (stents, Transcatheter aortic valve structures etc.). However, these computational methods are generally used with structures that are composed of a single material type as with Finite Element Analysis (FEA). Composite interventional catheters generally features 3-layer composite structures (polymer layer A/Metal reinforcement layer B/polymer layer C) which offer different challenges than single material structures in terms of predictive modeling. The results achieved with two different Experimental Design or Design of Experiments (DOE) based predictive modeling methodologies will be compared. The Classic DOE approach is based on a full factorial DOE with center points. The Custom DOE approach is based on the full factorial approach but is augmented with a series of experiments to fill the internal design space more completely rather than rely on just taking sample points predominantly around the boundaries of the design space as in classic DOE. Results generated from both approaches relate to catheter performance criteria of value in early stage composite catheter design. Strengths and drawbacks of both modeling approaches are discussed.

Modern knitting machines allow the manufacturing of various textile products with complex surface structures and patterns. However, programming these machines requires expert knowledge due to constraints of the process and the programming language. We present a long short-term memory (LSTM) based deep learning model that generates low-level code of novel knitting patterns based on high-level style specifications. To be processable by our model, we describe knitting instructions as one-dimensional sequences of tokens, which diverts from image-based approaches reported in previous research. We integrate our model into a design tool, that allows to assemble the atomic patterns to bigger swatches or garments. To evaluate our approach quantitatively, we formalize the requirements for patterns to be syntactically correct and valid to manufacture. Although our generated patterns look more random and seem to resemble less to human patterns, our evaluation shows that their knittability is orders of magnitudes better than randomly generated patterns.

The tendency of modern designs towards optimal structures often leads to the lightest and best performing choice among a large set of design alternatives. In a similar scenario, the introduction of automated tools to further guide designers in achieving efficient solutions has been a recurrent topic for mechanical and structural engineers, over the past decades. Nowadays, topology optimization is considered a powerful preliminary design tool to determine the optimal material distribution in a design domain, i.e. the most effective configuration that satisfies a given set of prescribed constraints while reducing the consumption of structural material. Among different applications in the field of Civil Engineering, this work focuses on the definition of optimal layouts of lateral resisting systems for multi-storey steel building frameworks subject to lateral loads using topology optimization techniques. The objective of the research is to illustrate the benefits deriving from the introduction of automated routines within the preliminary design stage and establish reliable guidelines for performing accurate and objective optimization procedures. Since the optimal material distribution follows the load flow within the structure, optimal topologies are especially sensitive to the alteration of support and loading conditions: different loading scenarios naturally lead to distinct optimal layouts. In order to avoid the loss of objectivity and preserve the optimality of the results, the effects that preliminary modelling and loading assumptions produce on final layouts are investigated. Numerical applications to high-rise building models are presented and discussed.

In recent years, the Building Information Modeling (BIM) methodology has emerged as a pivotal approach for the design and management optimization of infrastructures. The need for fully integrated models has led to develop various tools to support the designers throughout the decision-making process of the entire life cycle of an infrastructure project. In particular, the design of transport infrastructures is a complex process that involves issues concerning all the different fields of civil engineering. In this framework, the BIM method can be efficiently applied to support the project of drainage systems capable of protecting railways and roadways from potential hydraulic risks. According to the above, this study presents an optimized BIM-based methodology for the integrated analysis of the flood risk and the design of the drainage system components. Specifically, the proposed method allows to implement hydraulics and hydrological numerical models as part of the different design phases. Such an approach holds two main advantages for the designer. On one hand, the computational modelling provides a decisive support in both defining water surface profiles over the designed area and identifying the spots exposed to major hydrological risks. On the other hand, the hydraulically guided modelling of each component of the drainage system allows for an increase of the overall productivity and efficiency of the project. A case study is presented to highlight the great potential of the proposed approach in ensuring both the integrity of datasets and the rapid clash detection of critical interferences between each system component. In conclusion, new perspectives about a fully integrated hydraulics analysis in the infrastructures design process are stressed out.

Topology optimization (TO) represents a free-form design approach that aims to identify optimized material distribution within the design domain. There are two main broad class of techniques which can be applied: discrete and continuum optimization of the structural system. In this work, TO is applied to structural systems modelled 1D finite elements (i.e. truss and frames) by using SAP2000 which is a well-known commercial software for analysis and design of structural systems that is equipped with an open application programming interface (OAPI). The optimization problem at hand is solved using the High-Performance Topology Optimization Computing Platform (HP-TOCP). The TO problem for the case of structures simulated with 1D finite elements is mainly divided in two stages. First, the generation of the initial design domain, usually called as “ground structures” and second, the formulation of the optimization problem. Most of the articles that can be found in literature and deal with the ground structure generation are applied in 2D structures and have many geometrical limitations. In this work, the Rhino’s plug-in Grasshopper is used, which is a well-known free form design tool with wide range of applications. An additional tool was also developed in C#, to transfer the initial geometry in SAP2000 and formulate the optimization problem. In this work, compliance represents the objective function of the problem while both volume of the final structure and the design code regulations represent the constraints. Without loss of the generality, circular and rectangle cross-sectional geometries are considered in both 2D and 3D test cases.

This paper presents a modification of an existing semi-probabilistic model that is used for estimating the residual service life of reinforced concrete structures subjected to carbonation. Generated MATLAB codes are used for the estimation of the residual service life for cases where the parameters considered are not necessarily normally distributed through the application of the theory of reliability. Monte Carlo simulation is also performed to validate the results of the modified model and to introduce a practical approach that can be used for the estimation of the residual service life of deteriorated reinforced concrete structures due to carbonation. The modified model is validated using existing data available in literature and is expected to serve as an efficient and user-friendly design tool that is able to deliver an estimation of the residual service life of deteriorated reinforced concrete structures due to carbonation.

Stainless steel reinforcement has become a very attractive option for reinforced concrete structures owing to its distinctive properties including outstanding corrosion resistance, excellent fire behaviour, long life cycle as well as low maintenance requirements. Additionally, stainless steel reinforcement offers exceptional ductility and strain hardening characteristics compared with other common materials, which are very desirable in design to avoid sudden collapse. However, most global design standards do not incorporate an appropriate design approach for reinforced concrete members with stainless steel. The substantial strain hardening characteristics of stainless steel are typically not represented in standardised material models and therefore this attractive characteristic is not exploited in design resulting in structural and economic inefficiencies. Hence, the aim of this paper is to propose and validate a new deformation-based design approach for stainless steel reinforced concrete beams based on the continuous strength method, with reference to the current design rules provided in Eurocode 2. This approach is shown to be an effective design tool that exploits the distinctive characteristics of stainless steel reinforcement in an efficient and reliable manner. It is shown to provide a more efficient design with less over-conservatism and greater accuracy, compared with other methods. A comprehensive parametric study is conducted using Abaqus software to study the influence that various geometric and material properties have on the capacity of the members. Moreover, the serviceability limit state is also explored through a detailed analysis of the deflection behaviour.

Landfills form an important link in the chain of waste management. Sound engineering design and effective gas collection can only be attained via a thorough understanding of flow in different parts of the landfill. This study addresses gas flow induced by perforated horizontal wells commonly used for landfill gas collection. Planar flow through a porous medium of varying properties is solved numerically, yielding a twofold insight: quantitative deviations establish the validity and limitations of theoretical models, whilst qualitative comparison illustrates the impact of various flow parameters. It is found that an accurate modelling of geometry and medium resistance is essential in determination of a correct pressure distribution that can then be used to optimise gas collection. Gravity, an often overlooked effect, is shown to be non-negligible. The error source and bounds are given and explained for different landfill configurations. By contrast, temperature is of a lesser importance by an order of magnitude, implying that comprehensive spatial modelling of the exothermal reactions generating the gas as well as temperature dependent fluid properties such as viscosity are of a secondary interest. The well radius of influence, i.e. the maximal collection distance, is a fundamental design parameter impacting the interchange of fluid with the atmosphere. The numerical solution permits to obtain optimal values for effective collection and minimise flux between the landfill and atmosphere. Based upon a comprehensive numerical simulation an analytical approximation is suggested as an accessible design tool. The formula enables the computation of pressure profiles within the landfill and involves a minimal number of required input parameters. The surface flux and radius of influence can also be determined thereby.

Recently, biomass has gained attention as a renewable energy resource due to low emissions and ample availability. Thermochemical conversion technologies to handle biomass are being developed rapidly. Due to complicated multiscale nature of biomass thermochemical conversion, numerical methods are playing important role in understanding detailed underlying mechanisms governing the processes. In this context, computational fluid dynamics (CFD) is playing an important role both as a research as well as design tool. There have been many publications in recent years using CFD to simulate systems involving biomass feedstock. In this work, we have tried to briefly summarize the CFD simulations of biomass systems especially after the year 2010 with focus on the drag models being used under the framework of Eulerian methodology. Drag modeling of biomass systems requires special attention due to non-spherical nature of particles both before the initiation of conversion process as well as during the process of thermochemical conversion. It is, therefore, this aspect that we have tried to highlight in this work with a brief review of recent relevant literature as well as a summary of some popular spherical and non-spherical drag models that can be or are being used to model systems containing biomass particles.

This paper proposes a new fractional-order multi-scrolls chaotic system. More complex systems and flexible ranges of the chaotic behavior are obtained due to the extra parameters added by the fractional-order. The proposed system has novel complex chaotic behaviors. The effect of changing the system parameters on the system behavior is investigated and their bifurcation diagrams have been provided. The MLE for the proposed system in integer and fractional domain has been discussed. It shows that the proposed chaotic system is richer in the case of fractional-order. A novel FPGA design automation tool for the proposed system is also proposed. It provides an optimized FPGA implementation of the system according to different system parameters. Additionally, it provides a facility and fast way to accelerate the design process for the developer to design their own module while applying the system in different applications. The proposed tool has been tested using Xilinx's Virtex-5 XC5VLX50T FPGA and verified using MATLAB. The experimental results are provided for different design peripherals options. Finally, a procedure to generalize the proposed tool to involve any other chaotic system is presented.

In this work, a novel approach based on artificial intelligence (AI) to assess the efficiency of wave energy converter (WEC) farms in coastal protection isdeveloped. We consider as a case study a beach subjected to severe erosion: Playa Granada (S Spain). More specifically, we analyse the changes in the dry beach area (quantified through the Pelnard-Considere equation) with and without wave farm protection by means of an Artificial Neural Network (ANN) model. The model is selected after a thorough comparative study involving forty ANN architectures, with one and two hidden layers, and two training algorithms (Levenberg-Marquadt and Bayesian regression). The best results are obtained with a [5-10-1] architecture trained with the Bayesian regression algorithm. Once validated, this ANN model is applied to optimize the design and position of the wave farm. The results confirm that ANN models are a useful design tool for hybrid wave farms.

[Abstract] An experimental campaign was carried out on the dynamic behaviour of timber-timber composite pieces under different loading conditions. This study compares the behaviour of non-tensioning and self-tensioning configurations under different stages of loading. The results show that the presence of an un-bonded prestressed bar hardly alters the eigenfrequency value in comparison with non-tensioned solutions, although it significantly reduces the damping ratio. An analytical methodology is presented that makes it possible to predict dynamic behaviour in terms of eigenfrequency with great exactitude. The simplicity of factors required by the analytical technique means that this method can be used as a design tool. FEM models were developed for each test configuration, and the different load states were analysed. The results show an extremely precise match with those obtained in the test campaign, indicating that this numerical methodology is suitable for the dynamic analysis of these structural elements.

The design, dimensioning and cost estimation of a district heating network poses many challenges. Especially for large networks, producing a network layout manually is complex and time-consuming. Custom-made spreadsheets allow automated hydraulic calculations and pipe selection, but can be error-prone if not properly used and are not suitable for large networks. The network deployment cost estimation is also typically calculated with yet other spreadsheets. In short, the different design aspects typically require different software tools and the process is usually not well-integrated. This makes the design process expensive and time-consuming. This paper discusses solutions to the challenges stated above provided by an automated, geographical information system (GIS) based planning tool. The tool has been developed as a plug-in to a GIS tool and includes optimized and automated network routing algorithms, including all aspects of a district heating network dimensioning as required for a feasibility study. A neighbourhood of Nijmegen used as a case study to demonstrate the tool, showing the effect of different design parameters such as network pressure level and substation size on total network dimensions, cost and performance. Several cases for future heat demand scenarios were simulated to analyze their impact on network cost and performance.

The goal of this study is to propose a rapid design approach for designing a minimum pressure loss U-bend in a turbine internal cooling passage using topology optimization. The total pressure loss in the bend region is critical, as the presence of U-bend dominates the pressure loss so that it demands increased coolant feed pressure. However, it is challenging to come up with general design rule for U-bend with minimum pressure loss. The proposed rapid design approach can be applied as a 3D U-bend geometry optimization tool. The minimization of the total pressure loss is achieved by implementing topology optimization that uses a continuous adjoint approach with steepest-descent method. An incompressible steady-state flow (low-fidelity simulation) solver is applied for the design space, which is modeled as a porous medium with variable porosity. A series of design optimization in 2D and 3D is conducted at a Reynolds number of 100,000 based on the bulk inlet velocity. 3D U-bend configurations are produced by the proposed rapid design approach in a few hours. High-fidelity computational simulations are also carried out on the optimized 2D and 3D U-bend configurations to evaluate the proposed rapid design approach. The computational studies are performed by solving the compressible, turbulent steady-state Reynolds-averaged Navier-Stokes (RANS) equations applying two turbulence models: Spalart-Allmaras and Shear Stress Transport (SST) model. The high-fidelity simulations predict a relative improvement of maximum 26.8% in total pressure drop with respect to the 3D baseline configuration. The results indicate that the proposed approach can be reliable fast design tool for optimizing U-bend geometry in incompressible flow regime.

In this work we introduce a new and systematic methodology for precise tracking control of normal-form nonlinear systems subject to non-vanishing/non-parametric uncertainties. We propose a general solution to achieving robust tracking with a rate of convergence that can be pre-assigned as fast as desired (e.g. super exponential or even closer to a pre-specified finite time). The key design tool is the utilization of a time-varying feedback gain through a time-varying scaling function that satisfies certain conditions. Our method also features with complete rejection (rather than partial attenuation) of uncertainties/disturbances and regulation of the tracking error to zero. A generic and analytical procedure to construct the time-varying rate function is provided, extending and generalizing the existing results in the literature.

External gear pumps (EGPs) are one of the most common choices in aerospace engines as a fuel delivery pump. These units usually include a pressure compensation system that defines the lubricating gap at the lateral side of the gears. This lubricating interface is a critical design aspect, which strongly affects the reliability as well as the mechanical and volumetric efficiencies of the pump. Major challenges in designing these lubricating gaps in aero engine fuel pumps, include high operating speeds, delivery pressures and low viscosity of the working fluids. Virtual prototyping methods present a fast and efficient design tool and have the potential to improve the EGP’s reliability and efficiency. This paper presents a study on the pressure compensation system of a fuel delivery EGP and shows how the presence of frictional forces opposing the motion of the compensating element significantly affect the lubricating performance of the unit. The presence of these frictional forces produces an effect of hysteresis on the axial balance system, so that the lubricating gaps that develop within the pump at a certain operating condition depend on the previous operating state. This effect was captured in an experimental setup purposely developed for this study at Rolls-Royce. Within this work, this behavior was also reproduced numerically, through a coupled fluid structure interaction – elastohydrodynamic (EHD) model that includes the modeling of these frictional forces. After detailing the implementation of the lateral gap model, this paper presents measurements from carefully conducted experiments which correlate with the simulated predictions of the influence of frictional forces on the performance of a reference EGP design under study.

Building energy systems, i.e. heating, ventilation, and air-conditioning (HVAC) systems, are essential for modern buildings. They provide a comfortable and healthy indoor environment. Design quality has significant impact on HVAC system efficiency. The typical building energy system design process involving several procedures is repetitive and time-consuming. It is often limited by the engineer's experience, capabilities, and time constraints; thus, the design in most cases barely satisfies building codes. In recent decades, computational intelligence (CI) has achieved substantial improvements in various fields. This paper presents a comprehensive review of using CI for HVAC system optimization design. Firstly, this paper analyzes seven procedures which constitute a typical HAVC system design process and finds that optimization problems encountered during design process can be divided into three categories: model estimation, decision making and uncertainty analysis. Then a brief introduction of CI techniques used to solve HVAC design optimization problems and detailed literature review of application examples are given. Though the design problem varies with each other, this paper outlines a typical workflow which is able to solve most HVAC optimization design problems. At last, a framework of an integrated HVAC automation and optimization design tool is proposed. The framework is developed based on building information modeling (BIM) and extracted typical design optimization workflow. It is able to connect various design stages by implementing structured information transfer between them and ultimately improve design efficiency and quality.

Producing assemblies that experience little to no rework during assembly fit-up is a perennial challenge in offsite construction. While the traditional response has been to solve geometric issues onsite at the expense of large rework costs, manufacturing optimisation techniques can mitigate these issues upstream. Tolerance analysis is one such method that is widely used in manufacturing to predict if problems will occur from the accumulation of tolerances and dimensional variability. This article demonstrates Monte Carlo tolerance simulation on a prefabricated construction assembly, where variations up to 37 mm are identified compared to as-built deviations which range up to 30 mm. Process optimisation is also explored, where risk of rework related to dimensional variability is reduced by 65.6% through selection of alternate fabrication processes. To compare the Monte Carlo method to traditional analysis methods, a simplified 1-D tolerance analysis is used. Compared to an as-built deviation of roughly 11 mm, the Monte Carlo method produces a conservative value of 15.4 mm, while other traditional methods are either overly conservative (worst-case tolerance chain has a deviation of 19.8 mm), or overly ambitious (root sum square tolerance chain has a deviation of 4.6 mm). Tolerance analysis through Monte Carlo simulation is shown to be a proactive design tool with several key advantages for prefabricated and offsite construction. First, complex three-dimensional geometric interactions can be readily modelled using very basic tolerance configurations. Secondly, potential misalignments at key connection points can be identified and quantified in terms of a probability distribution of variation. Finally, design improvements can be achieved by comparing alternate construction processes to mitigate the risk of assembly rework.

The effect of water must be considered during the different phases of the life cycle of infrastructures. When it is not properly managed, it can generate problems that may lead to infrastructure closure or even destruction. This paper presents a review of the different drainage systems existent in railway infrastructures. Current design criteria have become obsolete in many cases due to effects of climate change. Therefore, the purpose of this article is to serve as a starting point for new designs. To offer measures to address this problem and increase the resilience of the structures by means of a new design tool.

This paper deals with the design and FPGA implementation of a fractional order digital differentiator. It is realized using computationally efficient lattice wave digital filter (LWDF) which requires minimum multipliers in comparison to the direct form structure. A nature inspired ant lion optimization (ALO) algorithm is utilized to compute optimal coefficients of the LWDF based digital differentiator. The proposed LWDF based digital differentiator is compared with the existing literature in terms of magnitude response, percentage magnitude error and root mean square magnitude error. LWDF structure comprises of constant coefficient multipliers, adders and delay units which are implemented on FPGA with the help of Xilinx system generator for DSP design tool. For efficient implementation of multipliers, multiplier-less logic through digit recoding techniques such as canonic signed digit (CSD) representation and radix-2r encoding are described through Verilog HDL and incorporated in the implementation model as black box. Post-implementation results show that implementation based on the radix-2r encoding is found to be more efficient than that of the CSD encoding. The design and implementation results are also reported to highlight the improvements.

Mode II delamination testing for measuring the critical strain-energy release rate has reached a high level of maturity with the recent publication of ISO and ASTM standards. Cohesive zone modelling has also become a well-established design tool implemented in many commercial finite element analysis codes. The particularly large fracture process zone in mode II delamination increases the relevance of the traction-separation law. This paper proposes a fairly simple inverse method to evaluate an approximate trilinear traction-separation law from quasi-static end-notched flexure test results. The method employs an analytical beam cohesive zone model and consists of evaluating the parameters that fit the effective crack length versus strain-energy release rate curve. Preliminary studies showed that specific features of the latter curve were quite sensitive to some model parameters. Therefore, accurate approximate trilinear traction-separation laws could be obtained without the extensive numerical calculations typical of inverse methods.

Photovoltaic powered reverse osmosis systems are stand-alone water purification systems with the potential to provide a secure drinking water supply to remote off-grid communities. These systems are often operated intermittently with extended shut-down periods to accommodate the variability of renewable energy. This work presents a novel design tool for photovoltaic powered reverse osmosis installations which considers membrane fouling during intermittent operation to ensure more reliable and long-lasting systems. The tool uses a genetic algorithm coupled to a simulation model composed of a physical system model and a cost model to evaluate the system cost. The physical model determines the local energy production. The water system model uses an experimentally validated membrane fouling model to determine the water production. Several case studies (variable location, water production, and reliability) were used to demonstrate the method. In conclusion, the results show that incorporating the fouling model was essential to design reliable and cost-optimal systems. The method showed for smaller daily water demand, the system configuration did not vary with location, indicating they can be mass-produced. The design framework will aid in the configuration of low-cost photovoltaic powered reverse osmosis systems that are reliable throughout the system life.

We propose a new numerical tool for structural optimization design. To cut down the computational burden typical of the Solid Isotropic Material with Penalization (SIMP) method, we apply Proper Orthogonal Decomposition on SIMP snapshots computed on a fixed grid to construct a rough structure (predictor) which becomes the input of a SIMP procedure performed on an anisotropic adapted mesh (corrector). The benefit of the proposed design tool is to deliver smooth and sharp layouts which require a contained computational effort before moving to the 3D printing production phase.

peer-reviewed The stiffness-tailoring capability of Variable Angle Tow (VAT) laminates gives enhanced freedom to design thin-walled structures. One key advantage of tow steering is the ability to redistribute stresses improving buckling performance, leading to reduction in material weight and costs. The aim of this work is to optimise the initial postbuckling behaviour of a recently proposed VAT composite wingbox. The optimisation process is based on a fibre path parameterisation. It involves seeking the stacking sequence that minimises the displacements occurring in the postbuckling regime. This problem is solved by coupling the multi-modal Koiter asymptotic approach implemented with a solid-shell Finite Element environment through stochastic optimisation strategies. Results obtained regarding different optimisation scenarios show a much improved performance for the buckling and postbuckling response of the wingbox with respect to the initial VAT design. Additionally, manufacturing constraints are readily included in the optimisation program. The possibility of performing an efficient and robust optimisation process of a complex structure with a multi-modal Koiter asymptotic approach is demonstrated, showing its viability as a design tool for buckling dominated structures. A parametric study regarding the influence of steering radii shows that overcoming the current manufacturing constraint on minimum radius is worthy of investigation.

Reliable estimation of wave run-up is required for the effective and efficient design of coastal structures when flooding or wave overtopping volumes are an important consideration in the design process. In this study, a unified formula for the wave run-up on bermed structures has been developed using collected and existing data. As data on berm breakwaters was highly limited, physical model tests were conducted and the run-up was measured. Conventional governing parameters and influencing factors were then used to predict the dimensionless run-up level with 2% exceedance probability. The developed formula includes the effect of water depth which is required in understanding the influence of sea level rise and consequent changes of wave height to water depth ratio on the future hydraulic performance of the structures. The accuracy measures such as RMSE and Bias indicated that the developed formula is more accurate than the existing formulas. Additionally, the new formula was validated using field measurements and its superiority was observed when compared to the existing prediction formulas. Finally, the new design formula incorporating the partial safety factor was introduced as a design tool for engineers.

Solid oxide fuel cells (SOFC) are suitable for on-board electricity generation as Auxiliary Power Unit (APU) to support the electric power supply in heavy-duty vehicles. In order to satisfy the requirements of a lightweight fuel cell stack for mobile applications, thin-walled components must be used for the stack structure. This necessity is associated with material, process and design difficulties that must be solved in order to achieve a successful utilization. In this work a novel lightweight SOFC stack design with metal-supported cell was studied both numerically and experimentally. The metallic components are made from the Intermediate Temperature Metal (ITM), a high performance, high chromium ferritic stainless steels alloy. The multiphysics modeling approach (fluid dynamics, heat transfer, structural mechanics) was utilized in this work to predict the temperature distribution and the thermo-structural behavior of the new developed design. Geometric details of the fuel cell stack components as well as appropriate nonlinear, temperature and time-dependent constitutive models were developed to describe the material behavior. Experimental data were used to determine the material model parameters and validated the simulation results. The three-dimensional stress and deformation distributions in the individual stack components were evaluated and their maximum values for elements at risk were identified. Thus, the developed model enables the investigation of sustainability and serviceability of the structural elements to ensure a reliable operation of the stack. The developed computational model can be used as a design tool for parametric studies and optimization analysis to investigate the effects of process boundary conditions, material properties as well as geometrical design parameters and their variation on the induced thermal stresses.

Two-piece rigid molds are particularly amenable to mass-manufacturing. Arbitrary objects cannot be realized with such a mold, because the cast will collide with the mold during removal. Most shapes are far away from being castable, i.e., realizable with such a two-piece rigid mold. When starting with an arbitrary shape, large deformations are needed to produce castable shapes. Such large deformations require user interaction together with a design process that is aware of fabrication constraints. We present such a design tool to generate two-piece rigid molds separated by a planar cut for a wide variety of shapes. Our casts can be produced in one single piece from a rigid, reusable mold and do not require further assembly after casting. We use a castability energy that can be optimized with gradient-based methods combined with an elastoplastic deformation based on the as-rigid-as-possible method. The tool enables a designer to deform an input shape significantly and arrive at an output shape that is castable.

A current trend in the design of modern aero engines is the transition towards leaner combustion as a solution to satisfy increasingly stringent emission regulations. Lean combustion systems are often more susceptible to thermoacoustic instability and the fuel injector can play a critical role. This paper presents an analytical study on the unsteady air flow through a generic injector passage in response to incident acoustic waves. The injector passage is represented by a simplified geometry which comprises the main geometrical passage features. The unsteady flow through the passage is obtained by combining the elemental solutions for different parts of the passage. This enables the transfer impedance of the injector passage to be determined and the effects of different design parameters on the sensitivity of the air flow to acoustic perturbations to be examined. The convective wave associated with the unsteady swirl vane wakes is also visited and compared with the results from the numerical simulations obtained in previous works. In addition to helping derive design practices for injector passages from the perspective of thermoacoustic instability, the current analysis can also be applied as a preliminary design tool to assess the acoustic characteristics for an injector passage of the axial swirler type.

Self-healing structural materials are recognized as a cost effective and sustainable promising alternative to traditional maintenance methods of civil infrastructures. Great advances are being evidenced both in the synthesis/manufacturing and experimental characterization of these materials. However, there is limited information about reliable models which theoretically describe the observed behavior to be used as a useful design tool. In this paper a mechano-chemical-diffusive coupled model is introduced to represent the mechanical behavior of self-healing structural materials. Self-healing is restricted to precipitation of products within the matrix via an external agent, and is mathematically written by a set of reaction-diffusion equations. Mechanical behavior is modeled following classical continuous irreversible damage mechanics approaches. The overall model is numerically coupled and implemented in a finite element framework. The availability of the model is shown in the investigation of the self-healing mechanical behavior of a beam structure subjected to different regimes of cyclic loading – healing conditions.

At the ballistic limit velocity of a soft armor target pack, the impact response was shown to be decoupled in the thickness direction, with the initial few plies behaving in an inelastic fashion while the remaining plies dissipate energy via elastic strain modes. Since these initial plies only contribute to energy absorption via inelastic kinetic energy transfer, these plies may be replaced with another material with desirable properties, such as low costs or high shear strengths. The behavior of these diphasic armors has been shown in previous works to be varied depending on the type and the amount of strike face material that was replaced. However, there is a limited amount of published literature investigating this phenomenon. In this study, a framework is proposed to estimate the inelastic strike-face mass ratio as a function of the overall system areal density ratio to provide a preliminary design tool for diphasic armors.