Search results for: random dimer model

Authors: Chang-Jen Lan

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Current Level of Service (LOS) index adopted in Highway Capacity Manual (HCM) for signalized intersections on surface streets is based on the intersection average delay. The delay thresholds for defining LOS grades are subjective and is unrelated to critical traffic condition. For example, an intersection delay of 80 sec per vehicle for failing LOS grade F does not necessarily correspond to the intersection capacity. Also, a specific measure of average delay may result from delay minimization, delay equality, or other meaningful optimization criteria. To that end, a reliability version of the intersection critical degree of saturation (v/c) as the LOS index is introduced. Traditionally, the level of saturation at a signalized intersection is defined as the ratio of critical volume sum (per lane) to the average saturation flow (per lane) during all available effective green time within a cycle. The critical sum is the sum of the maximal conflicting movement-pair volumes in northbound-southbound and eastbound/westbound right of ways. In this study, both movement volume and saturation flow are assumed log-normal distributions. Because, when the conditions of central limit theorem obtain, multiplication of the independent, positive random variables tends to result in a log-normal distributed outcome in the limit, the critical degree of saturation is expected to be a log-normal distribution as well. Derivation of the risk index predictive limits is complex due to the maximum and absolute value operators, as well as the ratio of random variables. A fairly accurate functional form for the predictive limit at a user-specified significant level is yielded. The predictive limit is then compared with the designated LOS thresholds for the intersection critical degree of saturation (denoted as X

Keywords: reliability analysis, level of service, intersection critical degree of saturation, risk based index

Procedia PDF Downloads 130

Authors: Aref Ghafouri, Mohammad javad Mollakazemi, Farhad Asadi

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In this paper, model order reduction method is used for approximation in linear and nonlinearity aspects in some experimental data. This method can be used for obtaining offline reduced model for approximation of experimental data and can produce and follow the data and order of system and also it can match to experimental data in some frequency ratios. In this study, the method is compared in different experimental data and influence of choosing of order of the model reduction for obtaining the best and sufficient matching condition for following the data is investigated in format of imaginary and reality part of the frequency response curve and finally the effect and important parameter of number of order reduction in nonlinear experimental data is explained further.

Keywords: frequency response, order of model reduction, frequency matching condition, nonlinear experimental data

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Authors: Shan-Jen Cheng, Te-Jen Chang, Kuang-Hsiung Tan, Shou-Ling Kuo

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Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accuracy of NNARX model are tested by one step ahead relating output voltage to input current from measured experimental of PEMFC. The results show that the obtained nonlinear NNARX model can efficiently approximate the dynamic mode of the PEMFC and model output and system measured output consistently.

Keywords: PEMFC, neural network, nonlinear modeling, NNARX

Procedia PDF Downloads 380

Authors: Edlira Donefski, Lorenc Ekonomi, Tina Donefski

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Edgeworth approximation is one of the most important statistical methods that has a considered contribution in the reduction of the sum of standard deviation of the independent variables’ coefficients in a Quantile Regression Model. This model estimates the conditional median or other quantiles. In this paper, we have applied approximating statistical methods in an economical problem. We have created and generated a quantile regression model to see how the profit gained is connected with the realized sales of the cosmetic products in a real data, taken from a local business. The Linear Regression of the generated profit and the realized sales was not free of autocorrelation and heteroscedasticity, so this is the reason that we have used this model instead of Linear Regression. Our aim is to analyze in more details the relation between the variables taken into study: the profit and the finalized sales and how to minimize the standard errors of the independent variable involved in this study, the level of realized sales. The statistical methods that we have applied in our work are Edgeworth Approximation for Independent and Identical distributed (IID) cases, Bootstrap version of the Model and the Edgeworth approximation for Bootstrap Quantile Regression Model. The graphics and the results that we have presented here identify the best approximating model of our study.

Keywords: bootstrap, edgeworth approximation, IID, quantile

Procedia PDF Downloads 158

Authors: Amir Mohammad Fathollahi Fard, Mostafa Hajiaghaei-Keshteli, Mohammad Mahdi Paydar

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With increasing life expectancy in developed countries, the role of home care services is highlighted by both academia and industrial contributors in Home Health Care Supply Chain (HHCSC) companies. The main decisions in such supply chain systems are the location of pharmacies, the allocation of patients to these pharmacies and also the routing and scheduling decisions of nurses to visit their patients. In this study, for the first time, an integrated model is proposed to consist of all preliminary and necessary decisions in these companies, namely, location-allocation-routing model. This model is a type of NP-hard one. Therefore, an Imperialist Competitive Algorithm (ICA) is utilized to solve the model, especially in large sizes. Results confirm the efficiency of the developed model for HHCSC companies as well as the performance of employed ICA.

Keywords: home health care supply chain, location-allocation-routing problem, imperialist competitive algorithm, optimization

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Authors: Hamdi Sana, Emna Bouazizi, Sami Faiz

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In recent years, there is a huge increase in the use of spatio-temporal applications where data and queries are continuously moving. As a result, the need to process real-time spatio-temporal data seems clear and real-time stream data management becomes a hot topic. Sliding window model and frequent itemset mining over dynamic data are the most important problems in the context of data mining. Thus, sliding window model for frequent itemset mining is a widely used model for data stream mining due to its emphasis on recent data and its bounded memory requirement. These methods use the traditional transaction-based sliding window model where the window size is based on a fixed number of transactions. Actually, this model supposes that all transactions have a constant rate which is not suited for real-time applications. And the use of this model in such applications endangers their performance. Based on these observations, this paper relaxes the notion of window size and proposes the use of a timestamp-based sliding window model. In our proposed frequent itemset mining algorithm, support conditions are used to differentiate frequents and infrequent patterns. Thereafter, a tree is developed to incrementally maintain the essential information. We evaluate our contribution. The preliminary results are quite promising.

Keywords: real-time spatial big data, frequent itemset, transaction-based sliding window model, timestamp-based sliding window model, weighted frequent patterns, tree, stream query

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Authors: Mehrnaz Alibeikloo, Hadi Khabbaz, Behzad Fatahi

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Probabilistic analysis has become one of the most popular methods to quantify and manage geotechnical risks due to the spatial variability of soil input parameters. The correlation length is one of the key factors of quantifying spatial variability of soil parameters which is defined as a distance within which the random variables are correlated strongly. This paper aims to assess the impact of correlation length on the long-term settlement of soft soils improved with preloading. The concept of 'worst-case' spatial correlation length was evaluated by determining the probability of failure of a real case study of Vasby test fill. For this purpose, a finite difference code was developed based on axisymmetric consolidation equations incorporating the non-linear elastic visco-plastic model and the Karhunen-Loeve expansion method. The results show that correlation length has a significant impact on the post-construction settlement of soft soils in a way that by increasing correlation length, probability of failure increases and the approach to asymptote.

Keywords: Karhunen-Loeve expansion, probability of failure, soft soil settlement, 'worst case' spatial correlation length

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Authors: Vivek Verma, Sanjeev Kumar

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This study presents a novel quantum image encryption algorithm, using a 3D chaotic map and controlled qubit-level scrambling operations. The newly proposed 3D-BNM chaotic map effectively reduces the degradation of chaotic dynamics resulting from the finite word length effect. It facilitates the generation of highly unpredictable random sequences and enhances chaotic performance. The system’s efficacy is additionally enhanced by the inclusion of a SHA-256 hash function. Initially, classical plain images are converted into their quantum equivalents using the Novel Enhanced Quantum Representation (NEQR) model. The Generalized Quantum Arnold Transformation (GQAT) is then applied to disrupt the coordinate information of the quantum image. Subsequently, to diffuse the pixel values of the scrambled image, XOR operations are performed using pseudorandom sequences generated by the 3D-BNM chaotic map. Furthermore, to enhance the randomness and reduce the correlation among the pixels in the resulting cipher image, a controlled qubit-level scrambling operation is employed. The encryption process utilizes fundamental quantum gates such as C-NOT and CCNOT. Both theoretical and numerical simulations validate the effectiveness of the proposed algorithm against various statistical and differential attacks. Moreover, the proposed encryption algorithm operates with low computational complexity.

Keywords: 3D Chaotic map, SHA-256, quantum image encryption, Qubit level scrambling, NEQR

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

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This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Jung–Min Yang

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Solvability of the model matching problem for input/output switched asynchronous sequential machines is discussed in this paper. The control objective is to determine the existence condition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the desired input/output behavior. A matrix expression is presented to address reachability of switched asynchronous sequential machines with output equivalence with respect to a model. The presented reachability condition for the controller design is validated in a simple example.

Keywords: asynchronous sequential machines, corrective control, model matching, input/output control

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Authors: Hycham Aboutaleb, Bruno Monsuez

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Current systems complexity has reached a degree that requires addressing conception and design issues while taking into account all the necessary aspects. Therefore, one of the main challenges is the way complex systems are specified and designed. The exponential growing effort, cost and time investment of complex systems in modeling phase emphasize the need for a paradigm, a framework and a environment to handle the system model complexity. For that, it is necessary to understand the expectations of the human user of the model and his limits. This paper presents a generic framework for designing complex systems, highlights the requirements a system model needs to fulfill to meet human user expectations, and defines the refined functional as well as non functional requirements modeling tools needs to meet to be useful in model-based system engineering.

Keywords: system modeling, modeling language, modeling requirements, framework

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Authors: Faramarz Khosravi, Gokhan Izbirak

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A weighted statistical stochastic based Analytical Hierarchy Process (AHP) model for modeling the potential barriers and enablers of sustainability for measuring and assessing the sustainability level is proposed. For context-dependent potential barriers and enablers, the proposed model takes the basis of the properties of the variables describing the sustainability functions and was developed into a realistic analytical model for the sustainable behavior of an organization. This thus serves as a means for measuring the sustainability of the organization. The main focus of this paper was the application of the AHP tool in a statistically-based model for measuring sustainability. Hence a strong weighted stochastic AHP based procedure was achieved. A case study scenario of a widely reported major Canadian electric utility was adopted to demonstrate the applicability of the developed model and comparatively examined its results with those of an equal-weighted model method. Variations in the sustainability of a company, as fluctuations, were figured out during the time. In the results obtained, sustainability index for successive years changed form 73.12%, 79.02%, 74.31%, 76.65%, 80.49%, 79.81%, 79.83% to more exact values 73.32%, 77.72%, 76.76%, 79.41%, 81.93%, 79.72%, and 80,45% according to priorities of factors that have found by expert views, respectively. By obtaining relatively necessary informative measurement indicators, the model can practically and effectively evaluate the sustainability extent of any organization and also to determine fluctuations in the organization over time.

Keywords: AHP, sustainability fluctuation, environmental indicators, performance measurement

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Authors: Nikolaos Reppas, Yilin Gui, Ben Wetenhall, Colin Davie

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Injection of CO2 inside wellbore can induce different kind of loadings that can lead to thermal, hydraulic, and mechanical changes on the surrounding rock. A dual-porosity theoretical constitutive model will be presented for the stability analysis of the wellbore during CO2 injection. An elastoplastic damage response will be considered. A bounding yield surface will be presented considering damage effects on sandstone. The main target of the research paper is to present a theoretical constitutive model that can help industries to safely store CO2 in geological rock formations and forecast any changes on the surrounding rock of the wellbore. The fully coupled elasto-plastic damage Thermo-Hydraulic-Mechanical theoretical model will be validated from existing experimental data for sandstone after simulating some scenarios by using FEM on MATLAB software.

Keywords: carbon capture and storage, rock mechanics, THM effects on rock, constitutive model

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning

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Authors: Mohammad Reza Oftadeh

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The purpose of the article is to describe project objective structure (POS) concept that was developed on research activities and experiences about project management, Balanced Scorecard (BSC) and European Foundation Quality Management Excellence Model (EFQM Excellence Model). Furthermore, this paper tries to define a balanced, systematic, and integrated measurement approach to meet project objectives and project strategic goals based on a process-oriented model. In this paper, POS is suggested in order to measure project performance in the project life cycle. After using the POS model, the project manager can ensure in order to achieve the project objectives on the project charter. This concept can help project managers to implement integrated and balanced monitoring and control project work.

Keywords: project objectives, project performance management, PMBOK, key performance indicators, integration management

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Authors: Rupinder Pal Kaur, Vishal Goyal

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In this research, a systematic and quantitative engineering-based approach is followed by applying well-known international standards and guidelines to develop a web quality model (PH.WQT- Punjabi and Hindi Website Quality Tester) to measure external quality for websites of government domain that are developed in Punjabi and Hindi. Correspondingly, the model can be used for websites developed in other languages also. The research is valuable to researchers and practitioners interested in designing, implementing and managing websites of government domain Also, by implementing PH.WQT analysis and comparisons among web sites of government domain can be performed in a consistent way.

Keywords: external quality, PH.WQT, indian languages, punjabi and hindi, quality model, websites of government

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Authors: João Paulo Pascon

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A constitutive model accounting for large strains, thermoviscoplasticity, and ductile damage evolution is proposed in the present work. To this end, a fully Lagrangian framework is employed, considering plane stress conditions and multiplicative split of the deformation gradient. The full model includes Gurson’s void growth, nucleation and coalescence, plastic work heating, strain and strain-rate hardening, thermal softening, and heat conductivity. The contribution of the work is the combination of all the above-mentioned features within the finite-strain setting. The model is implemented in a computer code using triangular finite elements and nonlinear analysis. Two mechanical examples involving ductile damage and finite strain levels are analyzed: an inhomogeneous tension specimen and the necking problem. Results demonstrate the capabilities of the developed formulation regarding ductile fracture and large deformations.

Keywords: ductile damage model, finite element method, large strains, thermoviscoplasticity

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Authors: Khadija Mushtaq, Muhammad Umar

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The research aims to study the association between job satisfaction, motivation and the five factors of organizational citizenship behavior (i.e. Altruism, Conscientiousness, Sportsmanship, Courtesy and Civic virtue) among Public Sector Employees in Pakistan.In this research Structure Equation Modeling with confirmatory factor analysis was used to test the relationship between two independent and five dependent variables. Data was collected through questionnaire survey from 152 Public Servants Working in Gujrat District-Pakistan in different capacities. Stratified Random Sampling Technique was used to conduct this survey. The results of the study indicate that five factors of OCB have positive significant relation with both motivation and job satisfaction except the relationship of Civic Virtue with Motivation.The research findings implicate that factors other than motivation and job satisfaction may also affect OCB. Likewise, all the five factors of OCB may not be present in all populations. Thus, Managers must concentrate on increasing motivation and job satisfaction to increase OCB. Furthermore, the present research gives a direction to future researchers to use more independent variables (e.g. Culture, leadership, workplace environment, various job attitudes, types of motivation, etc.) on different types of populations with larger sample size in order to find the reasons behind insignificant relationship of civic virtue with Motivation in the research in hand and to generalize the tested model.

Keywords: five factors of organizational citizenship behavior (OCB), motivation, job satisfaction, public sector employees in Pakistan

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Authors: Chattrarat Hotrawaisaya

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This research aims to formulate the logistics collaborative model which is the management tool for orchid flower exporter. The researchers study logistics activities in orchid supply chain that stakeholders can collaborate and develop, including demand forecasting, inventory management, warehouse and storage, order-processing, and transportation management. The research also explores logistics collaboration implementation into orchid’s stakeholders. The researcher collected data before implementation and after model implementation. Consequently, the costs and efficiency were calculated and compared between pre and post period of implementation. The research found that the results of applying the logistics collaborative model to orchid exporter reduces inventory cost and transport cost. The model also improves forecasting accuracy, and synchronizes supply chain of exporter. This research paper contributes the uniqueness logistics collaborative model which value to orchid industry in Thailand. The orchid exporters may use this model as their management tool which aims in competitive advantage.

Keywords: logistics, orchid, supply chain, collaboration

Procedia PDF Downloads 435

Authors: Jayanta Kumar Ghosh, Swastik Sunil Goriwale, Himangshu Sarkar

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Groundwater is one of the most valuable natural resources that society consumes for its domestic, industrial, and agricultural water supply. Its bulk and indiscriminate consumption affects the groundwater resource. Often, it has been found that the groundwater recharge rate is much lower than its demand. Thus, to maintain water and food security, it is necessary to monitor and management of groundwater storage. However, it is challenging to estimate groundwater storage (GWS) by making use of existing hydrological models. To overcome the difficulties, machine learning (ML) models are being introduced for the evaluation of groundwater level (GWL). Thus, the objective of this research work is to develop an ML-based model for the prediction of GWL. This objective has been realized through the development of an artificial neural network (ANN) model based on hydro-gravimetry. The model has been developed using training samples from field observations spread over 8 months. The developed model has been tested for the prediction of GWL in an observation well. The root means square error (RMSE) for the test samples has been found to be 0.390 meters. Thus, it can be concluded that the hydro-gravimetric-based ANN model can be used for the prediction of GWL. However, to improve the accuracy, more hydro-gravimetric parameter/s may be considered and tested in future.

Keywords: machine learning, hydro-gravimetry, ground water level, predictive model

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Authors: Simin Zarea Karizi, Somaye Fatahi, Amirhossein Hosseni

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Background: There are conflicting findings regarding the effect of vitamin D supplementation on obesity-related factors. This study aimed to investigate the effect of vitamin D supplementation on changes in anthropometric indicators of adiposity and fat distribution in children and adolescents. Methods: Original databases were searched using standard keywords to identify all controlled trials investigating the effects of vitamin D supplementation on obesity-related factors in children and adolescents. Pooled weighted mean difference and 95% confidence intervals were achieved by random-effects model analysis. Results: Fourteen treatment arms were included in this systematic review and meta-analysis. The quantitative meta-analysis revealed no significant effect of vitamin D supplement on BMI (-0.01 kg/m2; 95% CI: -0.09, 0.12; p= 0.74; I2=0.0%), BMI z score (0.02; 95% CI: -0.04, 0.07; p= 0.53; I2=0.0%) and fat mass (0.07%; 95% CI: -0.09 to 0.24; p= 0.38; I2=31.2%). However, the quantitative meta-analysis displayed a significant effect of vitamin D supplementation on WC compared with the control group (WMD=-1.17 cm, 95% CI: -2.05, -0.29, p=0.009; I2=32.0 %). It seems that this effect was greater in healthy children with duration>12 weeks, dose<=400 IU and baseline less than 50 nmol/l vitamin D than others. Conclusions: Our findings suggest that vitamin D supplementation may be a protective factor of abdominal obesity and should be evaluated on an individual basis in clinical practice.

Keywords: weight loss, vitamin D, anthropometry, children, adolescent

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Authors: Jitendra Singh, Vivek Kumar

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In this paper, a susceptible infective susceptible mathematical model is proposed and analyzed where the migration of human population is given by migration function. It is assumed that the disease is transmitted by direct contact of susceptible and infective populations with constant contact rate. The equilibria and their stability are studied by using the stability theory of ordinary differential equations and computer simulation. The model analysis shows that the spread of infectious disease increases when human population immigration increases in the habitat but it decreases if emigration increases.

Keywords: SIS (Susceptible Infective Susceptible) model, migration function, susceptible, stability

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Authors: Tarek Sayed Taha Ali

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The dependence of the axial-vector coupling constant gA on the quark masses has been investigated in the frame work of the extended linear sigma model. The field equations have been solved in the mean-field approximation. Our study shows a better fitting to the experimental data compared with the existing models.

Keywords: extended linear sigma model, nucleon properties, axial coupling constant, physic

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Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

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Authors: Autcha Araveeporn

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This paper presents a study about a nonparametric regression model consisting of a smoothing spline method and a penalized spline regression method. We also compare the techniques used for estimation and prediction of nonparametric regression model. We tried both methods with crude oil prices in dollars per barrel and the Stock Exchange of Thailand (SET) index. According to the results, it is concluded that smoothing spline method performs better than that of penalized spline regression method.

Keywords: nonparametric regression model, penalized spline regression method, smoothing spline method, Stock Exchange of Thailand (SET)

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Authors: Hanjong Kim, Changwan Han, Seonghun Park

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Heat exchanger manifolds in aircraft play an important role in evenly distributing the fluid entering through the inlet to the heat transfer unit. In order to achieve this requirement, the manifold should be designed to have a light weight by withstanding high internal pressure. Therefore, this study aims at minimizing the weight of the heat exchanger manifold through topology optimization. For topology optimization, the initial design space was created with the inner surface extracted from the currently used manifold model and with the outer surface having a dimension of 243.42 mm of X 74.09 mm X 65 mm. This design space solid model was transformed into a finite element model with a maximum tetrahedron mesh size of 2 mm using ANSYS Workbench. Then, topology optimization was performed under the boundary conditions of an internal pressure of 5.5 MPa and the fixed support for rectangular inlet boundaries by SIMULIA TOSCA. This topology optimization produced the minimized finial volume of the manifold (i.e., 7.3% of the initial volume) based on the given constraints (i.e., 6% of the initial volume) and the objective function (i.e., maximizing manifold stiffness). Weight of the optimized model was 6.7% lighter than the currently used manifold, but after smoothing the topology optimized model, this difference would be bigger. The current optimized model has uneven thickness and skeleton-shaped outer surface to reduce stress concentration. We are currently simplifying the optimized model shape with spline interpolations by reflecting the design characteristics in thickness and skeletal structures from the optimized model. This simplified model will be validated again by calculating both stress distributions and weight reduction and then the validated model will be manufactured using 3D printing processes.

Keywords: topology optimization, manifold, heat exchanger, 3D printing

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Authors: H. Hosseini, B. V. Berdyshev, I. Iskopintsev

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This paper presents a rheological model for producing shape-memory thermoplastic polymers. Shape-memory occurs as a result of internal rearrangement of the structural elements of a polymer. A non-linear viscoelastic model was developed that allows qualitative and quantitative prediction of the stress-strain behavior of shape-memory polymers during heating. This research was done to develop a technique to determine the maximum possible change in size of heat-shrinkable products during heating. The rheological model used in this work was particularly suitable for defining process parameters and constructive parameters of the processing equipment.

Keywords: elastic deformation, heating, shape-memory polymers, stress-strain behavior, viscoelastic model

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Authors: Zuodong Liang, Dong-Sheng Jeng

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Seabed instability around an offshore pipeline is one of key factors that need to be considered in the design of offshore infrastructures. Unlike previous investigations, a three-dimensional numerical model for the wave-induced soil response around an offshore pipeline is proposed in this paper. The numerical model was first validated with 2-D experimental data available in the literature. Then, a parametric study will be carried out to examine the effects of wave, seabed characteristics and confirmation of pipeline. Numerical examples demonstrate significant influence of wave obliquity on the wave-induced pore pressures and the resultant seabed liquefaction around the pipeline, which cannot be observed in 2-D numerical simulation.

Keywords: pore pressure, 3D wave model, seabed liquefaction, pipeline

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Authors: S. Art-in

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This research was a survey research. The objective of this study was to study current situation and need in learning management for developing the analytical thinking of teachers in basic education of Thailand. The target group consisted of 400 teachers teaching in basic education level. They were selected by multi-stage random sampling. The instrument used in this study was the questionnaire asking current situation and need in learning management for developing the analytical thinking, 5 level rating scale. Data were analyzed by calculating the frequency, mean, standard deviation, percentage and content analysis. The research found that: 1) For current situation, the teachers provided learning management for developing analytical thinking, in overall, in “high” level. The issue with lowest level of practice: the teachers had competency in designing and establishing the learning management plan for developing the students’ analytical thinking. Considering each aspect it was found that: 1.1) the teacher aspect; the issue with lowest level of practice was: the teachers had competency in designing and establishing the learning management plan for developing the students’ analytical thinking, and 1.2) the learning management aspect for developing the students’ analytical thinking, the issue with lowest level of practice was: the learning activities provided opportunity for students to evaluate their analytical thinking process in each learning session. 2) The teachers showed their need in learning management for developing the analytical thinking, in overall, in “the highest” level. The issue with highest level of the need was: to obtain knowledge and competency in model, technique, and method for learning management or steps of learning management for developing the students’ analytical thinking. Considering each aspect it was found that: 2.1) teacher aspect; the issue with highest level of the need was: to obtain knowledge and comprehension in model, technique, and method for learning management or steps of learning management for developing the students’ analytical thinking, and 2.2) learning management aspect for developing the analytical thinking, the issue with highest level of need consisted of the determination of learning activities as problem situation, and the opportunity for students to comprehend the problem situation as well as practice their analytical thinking in order to find the answer.

Keywords: current situation and need, learning management, analytical thinking, teachers in basic education level, Thailand

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Authors: Zheng Junchao, Wang Zhu

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With the advancement of China's urbanization process, the Chinese government has put forward the strategy of rural revitalization which is aiming at realizing the comprehensive integration of urban and rural areas and the comprehensive revitalization of rural areas. The path of rural revitalization in Zhejiang province put forward a typical model from four dimensions: suburban area, plain, island and mountain area. Research methods include on-the-spot investigation, visiting a number of successful demonstration villages in Zhejiang and interviewing village officials. Based on the location conditions, resource endowments, industrial forms and development foundations of Zhejiang Province, this paper introduces in detail the model of rural revitalization in Zhejiang Province and the challenges it encounters, as well as the role of building construction. The rural development model of Zhejiang province makes the rural culture flourish. Taking the construction of rural scenic spots as the carrier, the rural culture, and natural landscape are constantly improved. It provides a model and template for the country's rural revitalization. The promotion of Zhejiang rural revitalization model will improve the current rural landscape, living standard and industrial structure, which will narrow the urban-rural gap greatly.

Keywords: comprehensive rural revitalization, Zhejiang model, reproducible, comprehensive integration

Procedia PDF Downloads 197