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This research aimed to determine the differences and similarities in each pilot project to understand the primary design forms and concepts of sponge city concept (SCC) projects in China. It also aimed to examine ten pilot projects in Shanghai to extrapolate their main characteristics and the processes necessary for implementing SCC projects effectively.

A literature review and field survey case study were employed. Data were mostly collected through a field survey in Shanghai, focusing on both the projects and the surrounding environment. Based on these projects' examination, a comparative method was used to determine the characteristics of the ten pilot SCC projects and programs in Shanghai.

Six main types of SCC projects among 30 pilot cities were classified in this research to find differences and similarities among the pilot cities. Four sponge design methods were classified into ten pilot projects. After comparing each project size using the same geographical size, three geometrical types were categorized into both existing and new city areas. SCC project characteristics could be identified by combining four methods and three geometrical types and those of the SCC programs by comparing the change in land-use and the surrounding environment in ten pilot projects.

The results are valuable for implementing SCC projects in China and elsewhere and future research on the impact of SCC projects.

Design methods for medical rapid prototyping (RP) of personalized cranioplasty implants are presented in this paper. These methods are applicable to model cranioplasty implants for all types of the skull defects including beyond‐midline and multiple defects. The methods are based on two types of anatomical data, solid bone models (STereoLithography files – STL) and bone slice contours (Initial Graphics Exchange Specification – IGES and StrataSys Layer files – SSL). The bone solids and contours are constructed based on computed tomography scanning data, and these data are generated in medical image processing and STL slicing packages.

The purpose of this investigation was to study the function mechanisms of a corrosion inhibitor package used for martensitic stainless steel tubulars in acid solution at high temperatures.

The inhibition performance was evaluated by means of an acid corrosion test at high temperature and high pressure, and the functional mechanism of the inhibitor package at different temperatures was investigated using polarization curve and electrochemical impedance spectroscopy measurements.

The results showed that the corrosion inhibitor package chosen for high-temperature and high-pressure gas well applications was very suitable for use with 13Cr super martensitic stainless steel. At lower temperatures, the function mechanism of the corrosion inhibitor package was characterized as a type of negative catalytic effect. As the temperature was increased, the effect of the intensifier in the package became more significant and the function mechanism changed to be the geometric covering effect type.

This study has the important practical value for guiding the oil field to conduct reasonable screening and using the acidizing corrosion inhibitor for martensite stainless steel tubulars.

Determining whether a set of tolerances on a part can be met by a rapid prototyping (RP) machine is often difficult. To achieve a set of tolerances as closely as possible, relationships between part geometry, tolerances, and process variables must be understood quantitatively. This paper presents an empirical model for stereolithography apparatus (SLA) accuracy, as specified by geometric tolerances, and a process planning method based on response surface methodology and multiobjective optimization. Response surfaces are used to capture the relationships among part surfaces, tolerances, and process variables. These response surfaces were generated by extensive design‐of‐experiment studies for a variety of geometries. An annotated STL data format is also presented that enables the inclusion of tolerance and surface information in faceted representations. Application of the accuracy models and process planning method is illustrated on one part.

The geometric parameters of the compressor blade have a noteworthy influence on compressor stability, which should be meticulously designed. However, machining inaccuracies cause the blade geometric parameters to deviate from the ideal design, and the geometric deviation exhibits high randomness. Therefore, the purpose of this study is to quantify the uncertainty and analyze the sensitivity of the impact of blade geometric deviation on compressor stability.

In this work, the influence of blade geometric deviation is analyzed based on a subsonic compressor rotor stage, and three-dimensional numerical simulations are used to compute samples with different geometric features. A method of combining Halton sequence and non-intrusive polynomial chaos is adopted to carry out uncertainty quantitative analysis. Sobol’ index and Spearman correlation coefficient are used to analysis the sensitivity and correlation between compressor stability and blade geometric deviation, respectively.

The results show that the compressor stability is most sensitive to the tip clearance deviation, whereas deviations in the leading edge radius, trailing edge radius and chord length have minimal impact on the compressor stability. And, the effects of various blade geometric deviations on the compressor stability are basically independent and linearly superimposed.

This work provided a new approach for uncertainty quantification in compressor stability analysis. The conclusions obtained in this work provide some reference value for the manufacturing and maintenance of rotor blades.

The implied assembly constraints of a computer-aided design (CAD) model (e.g. hierarchical constraints, geometric constraints and topological constraints) represent an important basis for product assembly sequence intelligent planning. Assembly prior knowledge contains factual assembly knowledge and experience assembly knowledge, which are important factors for assembly sequence intelligent planning. This paper aims to improve monotonous assembly sequence planning for a rigid product, intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge is proposed.

A spatio-temporal semantic assembly information model is established. The internal data of the CAD model are accessed to extract spatio-temporal semantic assembly information. The knowledge system for assembly sequence intelligent planning is built using an ontology model. The assembly sequence for the sub-assembly and assembly is generated via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge. The optimal assembly sequence is achieved via a fuzzy comprehensive evaluation.

The proposed spatio-temporal semantic information model and knowledge system can simultaneously express CAD model knowledge and prior knowledge for intelligent planning of product assembly sequences. Attribute retrieval and rule reasoning of spatio-temporal semantic knowledge can be used to generate product assembly sequences.

The assembly sequence intelligent planning example of linear motor highlights the validity of intelligent planning of product assembly sequences based on spatio-temporal semantic knowledge.

The spatio-temporal semantic information model and knowledge system are built to simultaneously express CAD model knowledge and assembly prior knowledge. The generation algorithm via attribute retrieval and rule reasoning of spatio-temporal semantic knowledge is given for intelligent planning of product assembly sequences in this paper. The proposed method is efficient because of the small search space.

Facilitating the information exchange and interoperability between stakeholders during the life-cycle of an asset can be one of the fundamental necessities for developing an enhanced information exchange framework. Such a framework can also improve the successful accomplishment of building projects. This paper aims to use Semantic Web technologies for facilitating information exchange within existing building projects.

In real-world building projects, the construction industry’s information supply chain may initiate from near scratch when new building projects are started resulting in diverse data structures represented in unstructured data sources, like Excel spreadsheets and documents. Large-scale data generated throughout a building's life-cycle requires exchanging and processing during an asset's Operation and Maintenance (O&M) phase. Building information modelling (BIM) processes and related technologies can address some of the challenges and limitations of information exchange and interoperability within new building projects. However, the use of BIM in existing and retrofit assets has been hampered by the challenges surrounding the limitations of existing technologies.

The aim of this paper is twofold. Firstly, it briefly outlines the framework previously developed for generating semantically enriched 3D retrofit models. Secondly, a framework is proposed focussing on facilitating the information exchange and interoperability for existing buildings. Semantic Web technologies and standards, such as Web Ontology Language and existing AEC domain ontologies are used to enhance and improve the proposed framework.

The proposed framework is evaluated by implementing an example application and the Resource Description Framework data produced by the previously developed framework. The proposed approach makes a valuable contribution to the asset/facilities management (AM/FM) domain. It should be of interest to various FM practices for existing assets, such as the building information/knowledge management for design, construction and O&M stages of an asset’s life-cycle.

The purpose of this paper is to develop a numerical approach inspired by Geometrical Product Specifications (GPS) standards for the assessment of geometrical defects appearing during Additive Manufacturing (AM) by Laser Beam Melting (LBM).

The study is based on finite element (FE) simulations of thermal distortions, then an assessment of flatness defects (warping induced by the high-residual stresses appearing during the manufacturing) from the deformed surfaces provided by simulation, and finally the correction of the calculated flatness defects from preliminary comparison between simulated and experimental data.

For an elementary geometrical feature (a wall), it was possible to identify the variation in the flatness defect as a function of the dimensions. For a complex geometry exhibiting a significant flatness defect, it was possible to improve the geometric quality using the numerical tool.

To the best of the author’s knowledge, this work is the first attempt using a numerical approach inspired by GPS standards to identify variations in thermal distortions caused by LBM, which is an initial step toward optimization. This paper is mainly focused on flatness defect assessment, even though the approach is potentially applicable for all types of geometrical defects (shape, orientation or position defects).

The study opens prospects for the optimization of complex parts elaborated using LBM, based on the minimization of the geometric defects caused by thermal distortions.

The prospects in terms of shape optimization will extend the potential to benefit from the new possibilities offered by LBM additive manufacturing.

Unlike the usual approach, the proposed methodology does not require any artifacts or comparisons with the computer-aided-design (CAD) model for geometrical distortion assessment. The present approach opens up the possibility of performing metrology from FE simulation results, which is particularly promising in the AM field.

This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301.

In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function.

The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption.

The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

The purpose of this paper is to present a novel and applied method for optimum designing of plate-finned heat exchanger network. Considering the total annual cost as the objective function, a network of plate-finned heat exchanger is designed and optimized.

Accurate evaluation of plate-finned heat exchanger networks depends on different fin types with 10 different geometrical parameters of heat exchangers. In this study, fin numbers are considered as the main decision variables and geometrical parameters of fins are considered as the secondary decision variables. The algorithm applies heat transfer and pressure drop coefficients correction method and differential evolution (DE) algorithm to obtain the optimum results. In this paper, optimization and minimization of the total annual cost of heat exchanger network is considered as the objective function.

In this study, a novel and applied method for optimum designing of plate-finned heat exchanger network is presented. The comprehensive algorithm is applied into a case study and the results are obtained for both counter-flow and cross-flow plate-finned heat exchangers. The total annual cost and total area of the network with counter-flow heat exchangers were 12.5% and 23.27%, respectively, smaller than the corresponding values of the network with cross-flow heat exchanger.

In this paper, a reliable method is used to design, optimize parameters and the economic optimization of heat exchanger network. Taking into account the importance of plate-finned heat exchangers in industrial applications and the complexity in their geometry, the DE methodology is adopted to obtain an optimal geometric configuration. The total annual cost is chosen as the objective function. Applying this technique to a case study illustrates its capability to accurate design plate-finned heat exchangers to improve the objective function of the heat exchanger network from the economic viewpoint with the design of details.