Search results for: integrative model of behavior prediction

Authors: Mohammed T. Hayajneh

Abstract:

Machining of Metal Matrix Composites (MMCs) is very significant process and has been a main problem that draws many researchers to investigate the characteristics of MMCs during different machining process. The poor machining properties of hard particles reinforced MMCs make drilling process a rather interesting task. Unlike drilling of conventional materials, many problems can be seriously encountered during drilling of MMCs, such as tool wear and cutting forces. Cutting tool wear is a very significant concern in industries. Cutting tool wear not only influences the quality of the drilled hole, but also affects the cutting tool life. Prediction the cutting tool life during drilling is essential for optimizing the cutting conditions. However, the relationship between tool life and cutting conditions, tool geometrical factors and workpiece material properties has not yet been established by any machining theory. In this research work, fuzzy subtractive clustering system has been used to model the cutting tool life in drilling of Al₂O₃ particle reinforced aluminum alloy composite to investigate of the effect of cutting conditions on cutting tool life. This investigation can help in controlling and optimizing of cutting conditions when the process parameters are adjusted. The built model for prediction the tool life is identified by using drill diameter, cutting speed, and cutting feed rate as input data. The validity of the model was confirmed by the examinations under various cutting conditions. Experimental results have shown the efficiency of the model to predict cutting tool life.

Keywords: composite, fuzzy, tool life, wear

Procedia PDF Downloads 295

Authors: Amit Kumar Verma

Abstract:

The present study deals with fire prediction model in Terai Arc Landscape, one of the most dramatic ecosystems in Asia where large, wide-ranging species such as tiger, rhinos, and elephant will thrive while bringing economic benefits to the local people. Forest fires cause huge economic and ecological losses and release considerable quantities of carbon into the air and is an important factor inflating the global burden of carbon emissions. Forest fire is an important factor of behavioral cum ecological habit of tiger in wild. Post fire changes i.e. micro and macro habitat directly affect the tiger habitat or land. Vulnerability of fire depicts the changes in microhabitat (humus, soil profile, litter, vegetation, grassland ecosystem). Microorganism like spider, annelids, arthropods and other favorable microorganism directly affect by the forest fire and indirectly these entire microorganisms are responsible for the development of tiger (Panthera tigris) habitat. On the other hand, fire brings depletion in prey species and negative movement of tiger from wild to human- dominated areas, which may leads the conflict i.e. dangerous for both tiger & human beings. Early forest fire prediction through mapping the risk zones can help minimize the fire frequency and manage forest fires thereby minimizing losses. Satellite data plays a vital role in identifying and mapping forest fire and recording the frequency with which different vegetation types are affected. Thematic hazard maps have been generated by using IDW technique. A prediction model for fire occurrence is developed for TAL. The fire occurrence records were collected from state forest department from 2000 to 2014. Disciminant function models was used for developing a prediction model for forest fires in TAL, random points for non-occurrence of fire have been generated. Based on the attributes of points of occurrence and non-occurrence, the model developed predicts the fire occurrence. The map of predicted probabilities classified the study area into five classes very high (12.94%), high (23.63%), moderate (25.87%), low(27.46%) and no fire (10.1%) based upon the intensity of hazard. model is able to classify 78.73 percent of points correctly and hence can be used for the purpose with confidence. Overall, also the model works correctly with almost 69% of points. This study exemplifies the usefulness of prediction model of forest fire and offers a more effective way for management of forest fire. Overall, this study depicts the model for conservation of tiger’s natural habitat and forest conservation which is beneficial for the wild and human beings for future prospective.

Keywords: fire prediction model, forest fire hazard, GIS, landsat, MODIS, TAL

Procedia PDF Downloads 352

Authors: Yingkun Huang, Li Guo, Zekang Lan, Kai Tian

Abstract:

Hard disk drives (HDD) failure of the exascale supercomputing system may lead to service interruption and invalidate previous calculations, and it will cause permanent data loss. Therefore, initiating corrective actions before hard drive failures materialize is critical to the continued operation of jobs. In this paper, a highly accurate analysis model based on CNN-LSTM and oversampling technique was proposed, which can correctly predict the necessity of a disk replacement even ten days in advance. Generally, the learning-based method performs poorly on a training dataset with long-tail distribution, especially fault prediction is a very classic situation as the scarcity of failure data. To overcome the puzzle, a new oversampling was employed to augment the data, and then, an improved CNN-LSTM with the shortcut was built to learn more effective features. The shortcut transmits the results of the previous layer of CNN and is used as the input of the LSTM model after weighted fusion with the output of the next layer. Finally, a detailed, empirical comparison of 6 prediction methods is presented and discussed on a public dataset for evaluation. The experiments indicate that the proposed method predicts disk failure with 0.91 Precision, 0.91 Recall, 0.91 F-measure, and 0.90 MCC for 10 days prediction horizon. Thus, the proposed algorithm is an efficient algorithm for predicting HDD failure in supercomputing.

Keywords: HDD replacement, failure, CNN-LSTM, oversampling, prediction

Procedia PDF Downloads 80

Authors: Yuhang Wang, Jorg Schluter, Sergiy Shelyag

Abstract:

The high cost associated with high-resolution computational fluid dynamics (CFD) is one of the main challenges that inhibit the design, development, and optimisation of new combustion systems adapted for renewable fuels. In this study, we propose a physics-guided CNN-based model to predict turbulence evolution and mixing without requiring a traditional CFD solver. The model architecture is built upon U-Net and the inception module, while a physics-guided loss function is designed by introducing two additional physical constraints to allow for the conservation of both mass and pressure over the entire predicted flow fields. Then, the model is trained on the Large Eddy Simulation (LES) results of a natural turbulent mixing layer with two different Reynolds number cases (Re = 3000 and 30000). As a result, the model prediction shows an excellent agreement with the corresponding CFD solutions in terms of both spatial distributions and temporal evolution of turbulent mixing. Such promising model prediction performance opens up the possibilities of doing accurate high-resolution manifold-based combustion simulations at a low computational cost for accelerating the iterative design process of new combustion systems.

Keywords: computational fluid dynamics, turbulence, machine learning, combustion modelling

Procedia PDF Downloads 91

Authors: S. A. Tabatabaei, S. Aarabi

Abstract:

Predicting the hot mix asphalt (HMA) response and performance is a challenging task because of the subjectivity of HMA under the complex loading and environmental condition. The behavior of HMA is a function of temperature of loading and also shows the time and rate-dependent behavior directly affecting design criteria of mixture. Velocity of load passing make the time and rate. The viscoelasticity illustrates the reaction of HMA under loading and environmental conditions such as temperature and moisture effect. The behavior has direct effect on design criteria such as tensional strain and vertical deflection. In this paper, the computational framework for viscoelasticity and implementation in 3D dimensional HMA model is introduced to use in finite element method. The model was lied under various repeated loading conditions at constant temperature. The response of HMA viscoelastic behavior is investigated in loading condition under speed vehicle and sensitivity of behavior to the range of speed and compared to HMA which is supposed to have elastic behavior as in conventional design methods. The results show the importance of loading time pulse, unloading time and various speeds on design criteria. Also the importance of memory fading of material to storing the strain and stress due to repeated loading was shown. The model was simulated by ABAQUS finite element package

Keywords: viscoelasticity, finite element method, repeated loading, HMA

Procedia PDF Downloads 398

Authors: Sungzoon Cho, Hye Jin Lee, Sungwhan Jeon, Dongyoung Min, Sungwon Lyu

Abstract:

Major public corporations worldwide use conference calls to report their quarterly earnings. These 'earnings' conference calls allow for questions from stock analysts. We investigated if it is possible to identify sentiment from the call script and use it to predict stock price movement. We analyzed call scripts from six companies, two each from Korea, China and Indonesia during six years 2011Q1 – 2017Q2. Random forest with Frequency-based sentiment scores using Loughran MacDonald Dictionary did better than control model with only financial indicators. When the stock prices went up 20 days from earnings release, our model predicted correctly 77% of time. When the model predicted 'up,' actual stock prices went up 65% of time. This preliminary result encourages us to investigate advanced sentiment scoring methodologies such as topic modeling, auto-encoder, and word2vec variants.

Keywords: earnings call script, random forest, sentiment analysis, stock price prediction

Procedia PDF Downloads 292

Authors: David Percy

Abstract:

Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

Procedia PDF Downloads 66

Authors: Shurui Bai, Biyun Huang, Khe Foon Hew

Abstract:

Gamification is defined as the use of game design elements in non-game contexts. The primary purpose of using gamification in an educational context is to engage students in school activities such that their likelihood of completion is increased. But how actually effective is gamification in improving student learning? In order to answer this question, this paper provides a systematic review of prior research studies on gamification in K-12 and university contexts limited to computer science discipline. Unlike other published gamification review works, we specifically analyzed comparison-based studies in quasi-experiment, historical control, and randomization rather than studies with mere anecdotal or phenomenological results. The main purpose for this is to discuss possible causal effects of gamified practices on student performance, behavior change, and perceptual skills following an integrative model. Implications for practice are discussed, along with several suggestions for future research studies.

Keywords: computer science, gamification, learning performance, systematic review

Procedia PDF Downloads 131

Authors: Lule Basha, Eralda Gjika

Abstract:

This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

Procedia PDF Downloads 92

Authors: Anatoliy A. Alabugin, Nikolay K. Topuzov, Sergei V. Aliukov

Abstract:

It is well known, that while we do a quantitative evaluation of the quality control of some economic processes (in particular, resource saving) with help innovation factors, there are three groups of problems: high uncertainty of indicators of the quality management, their considerable ambiguity, and high costs to provide a large-scale research. These problems are defined by the use of contradictory objectives of enhancing of the quality control in accordance with innovation factors and preservation of economic stability of the enterprise. The most acutely, such factors are felt in the countries lagging behind developed economies of the world according to criteria of innovativeness and effectiveness of management of the resource saving. In our opinion, the following two methods for reconciling of the above-mentioned objectives and reducing of conflictness of the problems are to solve this task most effectively: 1) the use of paradigms and concepts of evolutionary improvement of quality of resource-saving management in the cycle "from the project of an innovative product (technology) - to its commercialization and update parameters of customer value"; 2) the application of the so-called integrative-cyclical approach which consistent with complexity and type of the concept, to studies allowing to get quantitative assessment of the stages of achieving of the consistency of these objectives (from baseline of imbalance, their compromise to achievement of positive synergies). For implementation, the following mathematical tools are included in the integrative-cyclical approach: index-factor analysis (to identify the most relevant factors); regression analysis of relationship between the quality control and the factors; the use of results of the analysis in the model of fuzzy sets (to adjust the feature space); method of non-parametric statistics (for a decision on the completion or repetition of the cycle in the approach in depending on the focus and the closeness of the connection of indicator ranks of disbalance of purposes). The repetition is performed after partial substitution of technical and technological factors ("hard") by management factors ("soft") in accordance with our proposed methodology. Testing of the proposed approach has shown that in comparison with the world practice there are opportunities to improve the quality of resource-saving management using innovation factors. We believe that the implementation of this promising research, to provide consistent management decisions for reducing the severity of the above-mentioned contradictions and increasing the validity of the choice of resource-development strategies in terms of parameters of quality management and sustainability of enterprise, is perspective. Our existing experience in the field of quality resource-saving management and the achieved level of scientific competence of the authors allow us to hope that the use of the integrative-cyclical approach to the study and evaluation of the resulting and factor indicators will help raise the level of resource-saving characteristics up to the value existing in the developed economies of post-industrial type.

Keywords: integrative-cyclical approach, quality control, evaluation, innovation factors. economic sustainability, innovation cycle of management, disbalance of goals of development

Procedia PDF Downloads 245

Authors: Shayla He

Abstract:

Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

Procedia PDF Downloads 121

Authors: Saule Shazhanbayeva, Denise van der Merwe

Abstract:

Study hypothesis: Nazarbayev Intellectual School of Kyzylorda’s Biology teachers can use STEM-integrated learning to improve students' problem-solving ability and responsibility as global citizens. The significance of this study is to indicate how the use of STEM integrative learning during Biology lessons could contribute to forming globally-minded students who are responsible community members. For the purposes of this study, worldview is defined as a view that is broader than the country of Kazakhstan, allowing students to see the significance of their scientific contributions to the world as global citizens. The context of worldview specifically indicates that most students have never traveled outside of their city or region within Kazakhstan. In order to broaden student understanding, it is imperative that students are exposed to different world views and contrasting ideas within the educational setting of Biology as the science being used for the research. This exposure promulgates students understanding of the significance they have as global citizens alongside the obligations which would rest on them as scientifically minded global citizens. Integrative learning should be Biological Science - with Technology and engineering in the form of problem-solving, and Mathematics to allow improved problem-solving skills to develop within the students of Nazarbayev Intellectual School (NIS) of Kyzylorda. The school's vision is to allow students to realise their role as global citizens and become responsible community members. STEM allows integrations by combining four subject skills to solve topical problems designed by educators. The methods used are based on qualitative analysis: for students’ performance during a problem-solution scenario; and Biology teacher interviews to ascertain their understanding of STEM implementation and willingness to integrate it into current lessons. The research indicated that NIS is ready for a shift into STEM lessons to promote globally responsible students. The only additional need is for proper STEM integrative lesson method training for teachers.

Keywords: global citizen, STEM, Biology, high-school

Procedia PDF Downloads 72

Authors: Akash K. Walunj, Dipendu Bhunia, Samarth Gupta, Prabhat Gupta

Abstract:

Coupled shear walls consist of two shear walls connected intermittently by beams along the height. The behavior of coupled shear walls is mainly governed by the coupling beams. The coupling beams are designed for ductile inelastic behavior in order to dissipate energy. The base of the shear walls may be designed for elastic or ductile inelastic behavior. The amount of energy dissipation depends on the yield moment capacity and plastic rotation capacity of the coupling beams. In this paper, an analytical model of coupling beam was developed to calculate the rotations and moment capacities of coupling beam with conventional reinforcement.

Keywords: design studies, computational model(s), case study/studies, modelling, coupling beam

Procedia PDF Downloads 476

Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

Abstract:

In multi-echelon supply chain networks, optimal supplier selection significantly depends on the accuracy of suppliers’ performance prediction. Different methods of multi criteria decision making such as ANN, GA, Fuzzy, AHP, etc have been previously used to predict the supplier performance but the “black-box” characteristic of these methods is yet a major concern to be resolved. Therefore, the primary objective in this paper is to implement an artificial intelligence-based gene expression programming (GEP) model to compare the prediction accuracy with that of ANN. A full factorial design with %95 confidence interval is initially applied to determine the appropriate set of criteria for supplier performance evaluation. A test-train approach is then utilized for the ANN and GEP exclusively. The training results are used to find the optimal network architecture and the testing data will determine the prediction accuracy of each method based on measures of root mean square error (RMSE) and correlation coefficient (R2). The results of a case study conducted in Supplying Automotive Parts Co. (SAPCO) with more than 100 local and foreign supply chain members revealed that, in comparison with ANN, gene expression programming has a significant preference in predicting supplier performance by referring to the respective RMSE and R-squared values. Moreover, using GEP, a mathematical function was also derived to solve the issue of ANN black-box structure in modeling the performance prediction.

Keywords: Supplier Performance Prediction, ANN, GEP, Automotive, SAPCO

Procedia PDF Downloads 419

Authors: Habtamu Tkubet Ebuy

Abstract:

Purpose Conventional dynamic energy simulation tools go beyond the static dimension of simplified methods by providing better and more accurate prediction of building performance. However, their ability to forecast actual performance is undermined by a low representation of human interactions. The purpose of this study is to examine the potential benefits of incorporating information on occupant diversity into occupant behavior models used to simulate building performance. The co-simulation of the stochastic behavior of the occupants substantially increases the accuracy of the simulation. Design/methodology/approach In this article, probabilistic models of the "opening and closing" behavior of the window of inhabitants have been developed in a separate multi-agent platform, SimOcc, and implemented in the building simulation, TRNSYS, in such a way that the behavior of the window with the interconnectivity can be reflected in the simulation analysis of the building. Findings The results of the study prove that the application of complex behaviors is important to research in predicting actual building performance. The results aid in the identification of the gap between reality and existing simulation methods. We hope this study and its results will serve as a guide for researchers interested in investigating occupant behavior in the future. Research limitations/implications Further case studies involving multi-user behavior for complex commercial buildings need to more understand the impact of the occupant behavior on building performance. Originality/value This study is considered as a good opportunity to achieve the national strategy by showing a suitable tool to help stakeholders in the design phase of new or retrofitted buildings to improve the performance of office buildings.

Keywords: occupant behavior, co-simulation, energy consumption, thermal comfort

Procedia PDF Downloads 104

Authors: Senol Dogan, Gunay Karli

Abstract:

Identification of relevant publications based on a Medline query is time-consuming and error-prone. An all based process has the potential to solve this problem without any manual work. To the best of our knowledge, our study is the first to investigate the ability of machine learning to identify relevant articles accurately. 5 different machine learning algorithms were tested using 23 predictors based on several metadata fields attached to publications. We find that the Boosted model is the best-performing algorithm and its overall accuracy is 96%. In addition, specificity and sensitivity of the algorithm is 97 and 93%, respectively. As a result of the work, we understood that we can apply the same procedure to understand cancer gene expression big data.

Keywords: prediction of publications, machine learning, algorithms, bariatric surgery, comparison of algorithms, boosted, tree, logistic regression, ANN model

Procedia PDF Downloads 210

Authors: Muhammad Awais Shafique, Eiji Hato, Hideki Yaginuma

Abstract:

Recently GPS data is used in a lot of studies to automatically reconstruct travel patterns for trip survey. The aim is to minimize the use of questionnaire surveys and travel diaries so as to reduce their negative effects. In this paper data acquired from GPS and accelerometer embedded in smart phones is utilized to predict the mode of transportation used by the phone carrier. For prediction, Support Vector Machine (SVM) and Adaptive boosting (AdaBoost) are employed. Moreover a unique method to improve the prediction results from these algorithms is also proposed. Results suggest that the prediction accuracy of AdaBoost after improvement is relatively better than the rest.

Keywords: accelerometer, AdaBoost, GPS, mode prediction, support vector machine

Procedia PDF Downloads 359

Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

Abstract:

Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: surface roughness, fused deposition modelling (FDM), adaptive neuro fuzzy inference system (ANFIS), orientation

Procedia PDF Downloads 460

Authors: Sumit Sen, Sohan Khedekar, Umang Shinde, Shivam Bhargava

Abstract:

This study has been undertaken to investigate whether the deep learning models are able to predict the future stock prices by training the model with the historical stock price data. Since this work required time series analysis, various models are present today to perform time series analysis such as Recurrent Neural Network LSTM, ARIMA and Facebook Prophet. Applying these models the movement of stock price of stocks are predicted and also tried to provide the future prediction of the stock price of a stock. Final product will be a stock price prediction web application that is developed for providing the user the ease of analysis of the stocks and will also provide the predicted stock price for the next seven days.

Keywords: Autoregressive Integrated Moving Average, Deep Learning, Long Short Term Memory, Time-series

Procedia PDF Downloads 142

Authors: Ejaz Ahmed, Huang Yong

Abstract:

The operation of aero engines has a critical importance in the vicinity of lean blowout (LBO) limits. Lefebvre’s model of LBO based on empirical correlation has been extended to flame volume concept by the authors. The flame volume takes into account the effects of geometric configuration, the complex spatial interaction of mixing, turbulence, heat transfer and combustion processes inside the gas turbine combustion chamber. For these reasons, flame volume based LBO predictions are more accurate. Although LBO prediction accuracy has improved, it poses a challenge associated with Vf estimation in real gas turbine combustors. This work extends the approach of flame volume prediction previously based on fuel iterative approximation with cold flow simulations to reactive flow simulations. Flame volume for 11 combustor configurations has been simulated and validated against experimental data. To make prediction methodology robust as required in the preliminary design stage, reactive flow simulations were carried out with the combination of probability density function (PDF) and discrete phase model (DPM) in FLUENT 15.0. The criterion for flame identification was defined. Two important parameters i.e. critical injection diameter (Dp,crit) and critical temperature (Tcrit) were identified, and their influence on reactive flow simulation was studied for Vf estimation. Obtained results exhibit ±15% error in Vf estimation with experimental data.

Keywords: CFD, combustion, gas turbine combustor, lean blowout

Procedia PDF Downloads 268

Authors: Bothinah Altaf, Gary Montague, Elaine B. Martin

Abstract:

This study involves the modeling and monitoring of an ammonia synthesis fixed-bed reactor using partial least squares (PLS) and its variants. The process exhibits complex dynamic behavior due to the presence of heat recycling and feed quench. One limitation of static PLS model in this situation is that it does not take account of the process dynamics and hence dynamic PLS was used. Although it showed, superior performance to static PLS in terms of prediction, the monitoring scheme was inappropriate hence adaptive PLS was considered. A limitation of adaptive PLS is that non-conforming observations also contribute to the model, therefore, a new adaptive approach was developed, robust adaptive dynamic PLS. This approach updates a dynamic PLS model and is robust to non-representative data. The developed methodology showed a clear improvement over existing approaches in terms of the modeling of the reactor and the detection of faults.

Keywords: ammonia synthesis fixed-bed reactor, dynamic partial least squares modeling, recursive partial least squares, robust modeling

Procedia PDF Downloads 393

Authors: Aasheesh Raturi, Vimal Kothiyal, P. D. Semalty

Abstract:

We studied mechanical properties of Eucalyptus tereticornis using FT-NIR spectroscopy. Firstly, spectra were pre-processed to eliminate useless information. Then, prediction model was constructed by partial least squares regression. To study the influence of pre-processing on prediction of mechanical properties for NIR analysis of wood samples, we applied various pretreatment methods like straight line subtraction, constant offset elimination, vector-normalization, min-max normalization, multiple scattering. Correction, first derivative, second derivatives and their combination with other treatment such as First derivative + straight line subtraction, First derivative+ vector normalization and First derivative+ multiplicative scattering correction. The data processing methods in combination of preprocessing with different NIR regions, RMSECV, RMSEP and optimum factors/rank were obtained by optimization process of model development. More than 350 combinations were obtained during optimization process. More than one pre-processing method gave good calibration/cross-validation and prediction/test models, but only the best calibration/cross-validation and prediction/test models are reported here. The results show that one can safely use NIR region between 4000 to 7500 cm-1 with straight line subtraction, constant offset elimination, first derivative and second derivative preprocessing method which were found to be most appropriate for models development.

Keywords: FT-NIR, mechanical properties, pre-processing, PLS

Procedia PDF Downloads 362

Authors: Quentin C.P. Bourgogne, Vanessa Bouchart, Pierre Chevrier, Emmanuel Dattoli

Abstract:

This study presents an experimental and theoretical work conducted on a PolyPhenylene Sulfide reinforced with 40%wt of short glass fibers (PPS GF40) and its matrix. Thermoplastics are widely used in the automotive industry to lightweight automotive parts. The replacement of metallic parts by thermoplastics is reaching under-the-hood parts, near the engine. In this area, the parts are subjected to high temperatures and are immersed in cooling liquid. This liquid is composed of water and glycol and can affect the mechanical properties of the composite. The aim of this work was thus to quantify the evolution of mechanical properties of the thermoplastic composite, as a function of temperature and liquid aging effects, in order to develop a reliable design of parts. An experimental campaign in the tensile mode was carried out at different temperatures and for various glycol proportions in the cooling liquid, for monotonic and cyclic loadings on a neat and a reinforced PPS. The results of these tests allowed to highlight some of the main physical phenomena occurring during these solicitations under tough hydro-thermal conditions. Indeed, the performed tests showed that temperature and liquid cooling aging can affect the mechanical behavior of the material in several ways. The more the cooling liquid contains water, the more the mechanical behavior is affected. It was observed that PPS showed a higher sensitivity to absorption than to chemical aggressiveness of the cooling liquid, explaining this dominant sensitivity. Two kinds of behaviors were noted: an elasto-plastic type under the glass transition temperature and a visco-pseudo-plastic one above it. It was also shown that viscosity is the leading phenomenon above the glass transition temperature for the PPS and could also be important under this temperature, mostly under cyclic conditions and when the stress rate is low. Finally, it was observed that soliciting this composite at high temperatures is decreasing the advantages of the presence of fibers. A new phenomenological model was then built to take into account these experimental observations. This new model allowed the prediction of the evolution of mechanical properties as a function of the loading environment, with a reduced number of parameters compared to precedent studies. It was also shown that the presented approach enables the description and the prediction of the mechanical response with very good accuracy (2% of average error at worst), over a wide range of hydrothermal conditions. A temperature-humidity equivalence principle was underlined for the PPS, allowing the consideration of aging effects within the proposed model. Then, a limit of improvement of the reachable accuracy was determinate for all models using this set of data by the application of an artificial intelligence-based model allowing a comparison between artificial intelligence-based models and phenomenological based ones.

Keywords: aging, analytical modeling, mechanical testing, polymer matrix composites, sequential model, thermomechanical

Procedia PDF Downloads 116

Authors: Naresh Reddy Kolanu

Abstract:

The stability and collapse behavior of thin-walled composite structures, particularly carbon fiber-reinforced plastic (CFRP) panels, are paramount concerns for structural designers. Accurate prediction of collapse loads necessitates precise modeling of damage evolution in the post-buckling regime. This study conducts a comparative assessment of various finite element (FE) methodologies employed in predicting post-buckling collapse in stiffened CFRP panels. A systematic approach is adopted, wherein FE models with various damage capabilities are constructed and analyzed. The study investigates the influence of interacting intra- and interlaminar damage modes on the post-buckling response and failure behavior of the stiffened CFRP structure. Additionally, the capabilities of shell and brick FE-based models are evaluated and compared to determine their effectiveness in capturing the complex collapse behavior. Conclusions are drawn through quantitative comparison with experimental results, focusing on post-buckling response and collapse load. This comprehensive evaluation provides insights into the most effective FE methodologies for accurately predicting the collapse behavior of stiffened CFRP panels, thereby aiding structural designers in enhancing the stability and safety of composite structures.

Keywords: CFRP stiffened panels, delamination, Hashin’s failure, post-buckling, progressive damage model

Procedia PDF Downloads 42

Authors: Arvinder Kaur, Deepti Chopra

Abstract:

Software Entropy Metrics for bug prediction have been validated on various software systems by different researchers. In our previous research, we have validated that Software Entropy Metrics calculated for Mozilla subsystem’s predict the future bugs reasonably well. In this study, the Software Entropy metrics are calculated for a subsystem of Android and it is noticed that these metrics are not suitable for bug prediction. The results are compared with a subsystem of Mozilla and a comparison is made between the two software systems to determine the reasons why Software Entropy metrics are not applicable for Android.

Keywords: android, bug prediction, mining software repositories, software entropy

Procedia PDF Downloads 578

Authors: M. Azarbarmas, J. M. Cabrera, J. Calvo, M. Aghaie-Khafri

Abstract:

The modeling of hot deformation behavior for unseen conditions is important in metal-forming. In this study, the hot deformation of IN718 has been characterized in the temperature range 950-1100 and strain rate range 0.001-0.1 s-1 using hot compression tests. All stress-strain curves showed the occurrence of dynamic recrystallization. These curves were implemented quantitatively in mathematics, and then constitutive equation indicating the relationship between the flow stress and hot deformation parameters was obtained successfully.

Keywords: compression test, constitutive equation, dynamic recrystallization, hot working

Procedia PDF Downloads 425

Authors: Juan Huang, Hugo Ninanya

Abstract:

Oil has been used as a source of energy and supply to make materials, such as asphalt or rubber for many years. This is the reason why new technologies have been implemented through time. However, research still needs to continue increasing due to new challenges engineers face every day, just like unconventional reservoirs. Various numerical methodologies have been applied in petroleum engineering as tools in order to optimize the production of reservoirs before drilling a wellbore, although not all of these have the same efficiency when talking about studying fracture propagation. Analytical methods like those based on linear elastic fractures mechanics fail to give a reasonable prediction when simulating fracture propagation in ductile materials whereas numerical methods based on the cohesive zone method (CZM) allow to represent the elastoplastic behavior in a reservoir based on a constitutive model; therefore, predictions in terms of displacements and pressure will be more reliable. In this work, a hydro-geomechanical coupled model of horizontal wells in fractured rock was developed using ABAQUS; both extended element method and cohesive elements were used to represent predefined fractures in a model (2-D). A power law for representing the rheological behavior of fluid (shear-thinning, power index <1) through fractures and leak-off rate permeating to the matrix was considered. Results have been showed in terms of aperture and length of the fracture, pressure within fracture and fluid loss. It was showed a high infiltration rate to the matrix as power index decreases. A sensitivity analysis is conclusively performed to identify the most influential factor of fluid loss.

Keywords: fracture, hydro-geomechanical model, non-Newtonian fluid, numerical analysis, sensitivity analysis

Procedia PDF Downloads 206

Authors: M. Khoshab, M. J. Sedigh

Abstract:

Dynamic systems, which in mathematical point of view are those governed by differential equations, are much more difficult to study and to predict their behavior in comparison with static systems which are governed by algebraic equations. Economical systems such as market are among complicated dynamic systems. This paper tries to adopt a very simple mathematical model for market and to study effect of supply and demand function on behavior of the market while the supply side experiences a lag due to production restrictions.

Keywords: dynamic system, lag on supply demand, market stability, supply demand model

Procedia PDF Downloads 295

Authors: Wei Lu

Abstract:

With the development of information technology in China, network consumption is becoming more and more popular. As a special group, college students have a high degree of education and distinct opinions and personalities. In the future, the key groups of network consumption have gradually become the focus groups of network consumption. Studying college students’ online consumption behavior has important theoretical significance and practical value. Based on the Stimulus-Organism-Response (SOR) driving model and the structural equation model, this paper establishes the influencing factors model of College students’ online consumption behavior, evaluates and amends the model by using SPSS and AMOS software, analyses and determines the positive factors of marketing college students’ consumption, and provides an effective basis for guiding and promoting college student consumption.

Keywords: college students, online consumption, stimulate-organism-reaction driving model, structural equation model

Procedia PDF Downloads 153

Authors: Chang Su Woo, Hyun Sung Park

Abstract:

Useful lifetime evaluation of chevron rubber spring was very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of chevron rubber spring. In this study, we performed characteristic analysis and useful lifetime prediction of chevron rubber spring. Rubber material coefficient was obtained by curve fittings of uni-axial tension, equi bi-axial tension and pure shear test. Computer simulation was executed to predict and evaluate the load capacity and stiffness for chevron rubber spring. In order to useful lifetime prediction of rubber material, we carried out the compression set with heat aging test in an oven at the temperature ranging from 50°C to 100°C during a period 180 days. By using the Arrhenius plot, several useful lifetime prediction equations for rubber material was proposed.

Keywords: chevron rubber spring, material coefficient, finite element analysis, useful lifetime prediction

Procedia PDF Downloads 568