Pub Date : 2023-07-01DOI: 10.1016/j.compchemeng.2023.108363
Hector D. Perez, Kyle C. Harshbarger, J. Wassick, I. Grossmann
{"title":"Integrating information, financial, and material flows in a chemical supply chain","authors":"Hector D. Perez, Kyle C. Harshbarger, J. Wassick, I. Grossmann","doi":"10.1016/j.compchemeng.2023.108363","DOIUrl":"https://doi.org/10.1016/j.compchemeng.2023.108363","url":null,"abstract":"","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"40 1","pages":"108363"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88210032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tabish Ali, Waseem Haider, Muhammad Haziq, Muhammad Omar Khan, Arif Hussain
As a country, Pakistan is mostly dependent on fossil fuels for fulfilling its energy demand, which is expensive, as well as being environmentally unfriendly. It is high time that the country decides to shift from fossil fuels to renewable energy resources like geothermal, wind, solar, etc., to cater for global warming issues. Pakistan has a lot of potential geothermal sites, as the location of Pakistan lies on several fault lines and hot springs, thus making it very easy to extract the temperature from deep inside the earth and harness it for Geothermal Energy. Also, a sound knowledge of ground temperature is essential to use geothermal energy, which is obtained by drilling boreholes and putting in sensors. However it becomes a very expensive and labor intensive procedure. Therefore, to avoid the huge cost for drilling boreholes, particularly for ground temperature analysis, a numerical approach has been considered for determining ground temperature. Furthermore, correlation charts between air and ground temperatures have been developed, as there were no proper studies on the ground temperature of Pakistan. Then, with the help of a boreholes drilled in the National University of Sciences and Technology, Islamabad, Pakistan, the actual ground and numerically calculated temperatures have been compared. The results show a temperature error margin in the range between 0.27% for higher depths of about 5.6 m and 7.3% near the surface of about 2.7 m. Thus, it is shown that the proposed method is easy to implement and better than large scale testing methods for the depths at which geothermal energy is extracted.
{"title":"The Development and Validation of Correlation Charts to Predict the Undisturbed Ground Temperature of Pakistan: A Step towards Potential Geothermal Energy Exploration","authors":"Tabish Ali, Waseem Haider, Muhammad Haziq, Muhammad Omar Khan, Arif Hussain","doi":"10.3390/eng4030104","DOIUrl":"https://doi.org/10.3390/eng4030104","url":null,"abstract":"As a country, Pakistan is mostly dependent on fossil fuels for fulfilling its energy demand, which is expensive, as well as being environmentally unfriendly. It is high time that the country decides to shift from fossil fuels to renewable energy resources like geothermal, wind, solar, etc., to cater for global warming issues. Pakistan has a lot of potential geothermal sites, as the location of Pakistan lies on several fault lines and hot springs, thus making it very easy to extract the temperature from deep inside the earth and harness it for Geothermal Energy. Also, a sound knowledge of ground temperature is essential to use geothermal energy, which is obtained by drilling boreholes and putting in sensors. However it becomes a very expensive and labor intensive procedure. Therefore, to avoid the huge cost for drilling boreholes, particularly for ground temperature analysis, a numerical approach has been considered for determining ground temperature. Furthermore, correlation charts between air and ground temperatures have been developed, as there were no proper studies on the ground temperature of Pakistan. Then, with the help of a boreholes drilled in the National University of Sciences and Technology, Islamabad, Pakistan, the actual ground and numerically calculated temperatures have been compared. The results show a temperature error margin in the range between 0.27% for higher depths of about 5.6 m and 7.3% near the surface of about 2.7 m. Thus, it is shown that the proposed method is easy to implement and better than large scale testing methods for the depths at which geothermal energy is extracted.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87686799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estimation of the potential consequences from events occurring downstream of a dam is part of the risk assessment needed during the installation phase of a new dam. In the case of specific natural or man-made ongoing or prospected events, it may also be important to carry out fast computations that can provide information on the areas at risk either because the original design analyses are not available or because the parameters needed are different. This study aimed to develop a procedure that strongly facilitates the preparation of the input deck and the derivation of the output quantities to allow a fast analysis of a dam break event using a shallow water model, NAMI DANCE, as the analysis tool. The analysis shows that in a few minutes, it is possible to obtain the input deck for a new case. This makes it possible to include the prospected methods into automatic routines in analytical tools such as the Global Disasters Alerts and Coordination System (GDACS) to have a quick overview of the expected flood due to a dam break event.
{"title":"Modeling Dam Break Events Using Shallow Water Model","authors":"A. Annunziato, Gozde Guney Dogan, A. Yalciner","doi":"10.3390/eng4030105","DOIUrl":"https://doi.org/10.3390/eng4030105","url":null,"abstract":"Estimation of the potential consequences from events occurring downstream of a dam is part of the risk assessment needed during the installation phase of a new dam. In the case of specific natural or man-made ongoing or prospected events, it may also be important to carry out fast computations that can provide information on the areas at risk either because the original design analyses are not available or because the parameters needed are different. This study aimed to develop a procedure that strongly facilitates the preparation of the input deck and the derivation of the output quantities to allow a fast analysis of a dam break event using a shallow water model, NAMI DANCE, as the analysis tool. The analysis shows that in a few minutes, it is possible to obtain the input deck for a new case. This makes it possible to include the prospected methods into automatic routines in analytical tools such as the Global Disasters Alerts and Coordination System (GDACS) to have a quick overview of the expected flood due to a dam break event.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"552 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78921607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marcelo Romanssini, P. D. de Aguirre, Lucas Compassi-Severo, A. Girardi
Machine failure in modern industry leads to lost production and reduced competitiveness. Maintenance costs represent between 15% and 60% of the manufacturing cost of the final product, and in heavy industry, these costs can be as high as 50% of the total production cost. Predictive maintenance is an efficient technique to avoid unexpected maintenance stops during production in industry. Vibration measurement is the main non-invasive method for locating and predicting faults in rotating machine components. This paper reviews the techniques and tools used to collect and analyze vibration data, as well as the methods used to interpret and diagnose faults in rotating machinery. The main steps of this technique are discussed, including data acquisition, data transmission, signal processing, and fault detection. Predictive maintenance through vibration analysis is a key strategy for cost reduction and a mandatory application in modern industry.
{"title":"A Review on Vibration Monitoring Techniques for Predictive Maintenance of Rotating Machinery","authors":"Marcelo Romanssini, P. D. de Aguirre, Lucas Compassi-Severo, A. Girardi","doi":"10.3390/eng4030102","DOIUrl":"https://doi.org/10.3390/eng4030102","url":null,"abstract":"Machine failure in modern industry leads to lost production and reduced competitiveness. Maintenance costs represent between 15% and 60% of the manufacturing cost of the final product, and in heavy industry, these costs can be as high as 50% of the total production cost. Predictive maintenance is an efficient technique to avoid unexpected maintenance stops during production in industry. Vibration measurement is the main non-invasive method for locating and predicting faults in rotating machine components. This paper reviews the techniques and tools used to collect and analyze vibration data, as well as the methods used to interpret and diagnose faults in rotating machinery. The main steps of this technique are discussed, including data acquisition, data transmission, signal processing, and fault detection. Predictive maintenance through vibration analysis is a key strategy for cost reduction and a mandatory application in modern industry.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"58 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74356897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Solar photovoltaic system parameter identification is crucial for effective performance management, design, and modeling of solar panel systems. This work presents the Subtraction-Average-Based Algorithm (SABA), a unique, enhanced evolutionary approach for solving optimization problems. The conventional SABA works by subtracting the mean of searching solutions from the position of those in the population in the area of search. In order to increase the search capabilities, this work proposes an Augmented SABA (ASABA) that incorporates a method of collaborative learning based on the best solution. In accordance with manufacturing, the suggested ASABA is used to effectively estimate Photovoltaic (PV) characteristics for two distinct solar PV modules, RTC France and Kyocera KC200GT PV modules. Through the adoption of the ASABA approach, the simulation findings improve the electrical characteristics of PV systems. The suggested ASABA outperforms the regular SABA in terms of efficiency and effectiveness. For the R.T.C France PV system, the suggested ASABA approach outperforms the traditional SABA technique by 90.1% and 87.8 for the single- and double-diode models, respectively. Also, for the Kyocera KC200GT PV systems, the suggested ASABA approach outperforms the traditional SABA technique by 99.1% and 99.6 for the single- and double-diode models, respectively. Furthermore, the suggested ASABA method is quantitatively superior to different current optimization algorithms.
{"title":"Parameter Identification of Solar Photovoltaic Systems Using an Augmented Subtraction-Average-Based Optimizer","authors":"G. Moustafa","doi":"10.3390/eng4030103","DOIUrl":"https://doi.org/10.3390/eng4030103","url":null,"abstract":"Solar photovoltaic system parameter identification is crucial for effective performance management, design, and modeling of solar panel systems. This work presents the Subtraction-Average-Based Algorithm (SABA), a unique, enhanced evolutionary approach for solving optimization problems. The conventional SABA works by subtracting the mean of searching solutions from the position of those in the population in the area of search. In order to increase the search capabilities, this work proposes an Augmented SABA (ASABA) that incorporates a method of collaborative learning based on the best solution. In accordance with manufacturing, the suggested ASABA is used to effectively estimate Photovoltaic (PV) characteristics for two distinct solar PV modules, RTC France and Kyocera KC200GT PV modules. Through the adoption of the ASABA approach, the simulation findings improve the electrical characteristics of PV systems. The suggested ASABA outperforms the regular SABA in terms of efficiency and effectiveness. For the R.T.C France PV system, the suggested ASABA approach outperforms the traditional SABA technique by 90.1% and 87.8 for the single- and double-diode models, respectively. Also, for the Kyocera KC200GT PV systems, the suggested ASABA approach outperforms the traditional SABA technique by 99.1% and 99.6 for the single- and double-diode models, respectively. Furthermore, the suggested ASABA method is quantitatively superior to different current optimization algorithms.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77738402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. B. Santos, C. Freitas, L. Bacelar, Jaqueline A. J. P. Soares, Michael M. Diniz, G. R. T. Lima, S. Stephany
Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.
{"title":"A Neural Network-Based Hydrological Model for Very High-Resolution Forecasting Using Weather Radar Data","authors":"L. B. Santos, C. Freitas, L. Bacelar, Jaqueline A. J. P. Soares, Michael M. Diniz, G. R. T. Lima, S. Stephany","doi":"10.3390/eng4030101","DOIUrl":"https://doi.org/10.3390/eng4030101","url":null,"abstract":"Many hydro-meteorological disasters in small and steep watersheds develop quickly and significantly impact human lives and infrastructures. High-resolution rainfall data and machine learning methods have been used as modeling frameworks to predict those events, such as flash floods. However, a critical question remains: How long must the rainfall input data be for an empirical-based hydrological forecast? The present article employed an artificial neural network (ANN)hydrological model to address this issue to predict river levels and investigate its dependency on antecedent rainfall conditions. The tests were performed using observed water level data and high-resolution weather radar rainfall estimation over a small watershed in the mountainous region of Rio de Janeiro, Brazil. As a result, the forecast water level time series only archived a successful performance (i.e., Nash–Sutcliffe model efficiency coefficient (NSE) > 0.6) when data inputs considered at least 2 h of accumulated rainfall, suggesting a strong physical association to the watershed time of concentration. Under extended periods of accumulated rainfall (>12 h), the framework reached considerably higher performance levels (i.e., NSE > 0.85), which may be related to the ability of the ANN to capture the subsurface response as well as past soil moisture states in the watershed. Additionally, we investigated the model’s robustness, considering different seeds for random number generating, and spacial applicability, looking at maps of weights.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74023207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kenia Yadira Gómez Gómez Díaz, Susana Estefany de León Aldaco, Jesus Aguayo Aguayo Alquicira, L. G. Vela Valdés
This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem.
{"title":"THD Minimization in a Seven-Level Multilevel Inverter Using the TLBO Algorithm","authors":"Kenia Yadira Gómez Gómez Díaz, Susana Estefany de León Aldaco, Jesus Aguayo Aguayo Alquicira, L. G. Vela Valdés","doi":"10.3390/eng4030100","DOIUrl":"https://doi.org/10.3390/eng4030100","url":null,"abstract":"This paper presents the minimization of total harmonic distortion in a seven-level cascaded H-bridge multilevel inverter with resistive load using the teaching–learning-based optimization algorithm. The minimization of Total Harmonic Distortion (THD)is a challenging optimization problem due to the fact that nonlinear equations are involved. Recently, bio-inspired algorithms have become very popular approaches to solving various optimization problems in different areas of engineering. For this reason, the results obtained with the Teaching–Learning-Based Optimization (TLBO)algorithm were compared with three other popular bio-inspired algorithms, the genetic algorithm, differential evolution, and particle swarm optimization. The comparative analysis, conducted by sweeping the modulation index, made it possible to obtain graphs and data on the behavior of the four analyzed algorithms. Finally, it was concluded that the TLBO algorithm is very effective and is able to solve the THD-minimization problem. Its main advantage over the other algorithms is the fact that it does not require control parameters for its correct operation in the solution of the problem.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"38 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81669288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a valuable mineral resource, uranium is extensively utilized in nuclear power generation, radiation therapy, isotope labeling, and tracing. In order to achieve energy structure diversification, reduce dependence on traditional fossil fuels, and promote the sustainable development of energy production and consumption, research on the metallogenic mechanisms and related development technologies of uranium resources has been one of the focuses of China’s energy development. Sandstone-type uranium deposits make up approximately 43% of all deposits in China, making them the most prevalent form of uranium deposit there. Sandstone-type uranium deposits and hydrocarbon resources frequently coexist in the same basin in China. Therefore, this study summarizes the spatial and chronological distribution, as well as the geological characteristics, of typical sandstone-type uranium deposits in China’s hydrocarbon-bearing basins. From the perspectives of fluid action, geological structure, and sedimentary environment, the metallogenic mechanisms of sandstone-type uranium deposits in hydrocarbon-bearing basins are explored. According to the research, the rapid reduction effect of oil and gas in the same basin is a major factor in the generation of relatively large uranium deposits. Additionally, ions such as CO32− and HCO3− in hydrothermal fluids of hydrocarbon-bearing basins, which typically originate from dispersed oil and gas, are more conducive to uranium enrichment and sedimentation. This study provides guidance for efficient sandstone-type uranium deposit exploration and production in hydrocarbon-bearing basins and helps to achieve significant improvements in uranium resource exploitation efficiency.
{"title":"A Review of the Metallogenic Mechanisms of Sandstone-Type Uranium Deposits in Hydrocarbon-Bearing Basins in China","authors":"Guiheng Li, Jiachen Yao, Yiming Song, Jieyun Tang, Hongdou Han, Xiangdong Cui","doi":"10.3390/eng4020098","DOIUrl":"https://doi.org/10.3390/eng4020098","url":null,"abstract":"As a valuable mineral resource, uranium is extensively utilized in nuclear power generation, radiation therapy, isotope labeling, and tracing. In order to achieve energy structure diversification, reduce dependence on traditional fossil fuels, and promote the sustainable development of energy production and consumption, research on the metallogenic mechanisms and related development technologies of uranium resources has been one of the focuses of China’s energy development. Sandstone-type uranium deposits make up approximately 43% of all deposits in China, making them the most prevalent form of uranium deposit there. Sandstone-type uranium deposits and hydrocarbon resources frequently coexist in the same basin in China. Therefore, this study summarizes the spatial and chronological distribution, as well as the geological characteristics, of typical sandstone-type uranium deposits in China’s hydrocarbon-bearing basins. From the perspectives of fluid action, geological structure, and sedimentary environment, the metallogenic mechanisms of sandstone-type uranium deposits in hydrocarbon-bearing basins are explored. According to the research, the rapid reduction effect of oil and gas in the same basin is a major factor in the generation of relatively large uranium deposits. Additionally, ions such as CO32− and HCO3− in hydrothermal fluids of hydrocarbon-bearing basins, which typically originate from dispersed oil and gas, are more conducive to uranium enrichment and sedimentation. This study provides guidance for efficient sandstone-type uranium deposit exploration and production in hydrocarbon-bearing basins and helps to achieve significant improvements in uranium resource exploitation efficiency.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84812887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marzieh Zamani Kouhpangi, S. Yaghoubi, A. Torabipour
Structural health monitoring (SHM) is crucial for ensuring the safety and performance of offshore platforms. SHM uses advanced sensor systems to detect and respond to negative changes in structures, improving their reliability and extending their life cycle. Model updating methods are also useful for sensitivity analysis. It is feasible to discuss and introduce established techniques for detecting damage in structures by utilizing their mode shapes. In this research, by considering reducing the stiffness of elements in the damage scenarios, we conducted simulations of the models in MATLAB, including both two-dimensional and three-dimensional structures, to update the method suggested by Wang. Wang’s method was improved to produce a sensitivity equation for the damaged structures. The sensitivity equation solution using a subset of mode shapes data was found to evaluate structural parameter changes. Comparing the updated results for Wang’s method and the suggested method in the two- and three-dimensional frames showed a noticeable modification in damage recognition. Furthermore, the suggested method can update a model containing measurement errors. Since Wang’s damage detection formulation is suitable only for 2D structures, this modified framework provides a more accurate decision-making tool for damage detection of structures, regardless of whether a 2D or 3D formulation is used.
{"title":"Improved Structural Health Monitoring Using Mode Shapes: An Enhanced Framework for Damage Detection in 2D and 3D Structures","authors":"Marzieh Zamani Kouhpangi, S. Yaghoubi, A. Torabipour","doi":"10.3390/eng4020099","DOIUrl":"https://doi.org/10.3390/eng4020099","url":null,"abstract":"Structural health monitoring (SHM) is crucial for ensuring the safety and performance of offshore platforms. SHM uses advanced sensor systems to detect and respond to negative changes in structures, improving their reliability and extending their life cycle. Model updating methods are also useful for sensitivity analysis. It is feasible to discuss and introduce established techniques for detecting damage in structures by utilizing their mode shapes. In this research, by considering reducing the stiffness of elements in the damage scenarios, we conducted simulations of the models in MATLAB, including both two-dimensional and three-dimensional structures, to update the method suggested by Wang. Wang’s method was improved to produce a sensitivity equation for the damaged structures. The sensitivity equation solution using a subset of mode shapes data was found to evaluate structural parameter changes. Comparing the updated results for Wang’s method and the suggested method in the two- and three-dimensional frames showed a noticeable modification in damage recognition. Furthermore, the suggested method can update a model containing measurement errors. Since Wang’s damage detection formulation is suitable only for 2D structures, this modified framework provides a more accurate decision-making tool for damage detection of structures, regardless of whether a 2D or 3D formulation is used.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77131796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Felipe Augusto Fiorin, L. Sartori, María Verónica González Méndez, Christiane Henriques Ferreira, Maria Bernadete de Morais França, E. Krueger
The use of assistive technologies, such as a non-invasive interface for neuroelectrical signal and functional electrical stimulation (NESs-FESs), can mitigate the effects of spinal cord injury (SCI), including impairment of motor, sensory, and autonomic functions. However, it requires an adaptation process to enhance the user’s performance by tuning the learning curve to a point of extreme relevance. Therefore, in this pilot study, the learning curves of two people with complete SCI (PA: paraplegic-T6, and PB: quadriplegic-C4) were analyzed, with results obtained on the accuracy of the classifier (AcCSP−LDA), repetitions of intra-day training, and number of hits and misses in the activation of FESs for sixteen interventions using the NESs-FESs interface. We assumed that the data were non-parametric and performed the Spearman’s ρ test (and p-value) for correlations between the data. There was variation between the learning curves resulting from the training of the NESs-FESs interface for the two participants, and the variation was influenced by factors both related and unrelated to the individual users. Regardless of these factors, PA improved significantly in its learning curve, as it presented lower values in all variables in the first interventions compared to the PB, although only PA showed statistical correlation (on AcCSP−LDA values in RLL). It was concluded that despite the variations according to factors intrinsic to the user and the functioning of the equipment used, sixteen interventions were sufficient to achieve a good learning effect to control the NESs-FESs interface.
{"title":"The Learning Curve of People with Complete Spinal Cord Injury Using a NESs-FESs Interface in the Sitting Position: Pilot Study","authors":"Felipe Augusto Fiorin, L. Sartori, María Verónica González Méndez, Christiane Henriques Ferreira, Maria Bernadete de Morais França, E. Krueger","doi":"10.3390/eng4020097","DOIUrl":"https://doi.org/10.3390/eng4020097","url":null,"abstract":"The use of assistive technologies, such as a non-invasive interface for neuroelectrical signal and functional electrical stimulation (NESs-FESs), can mitigate the effects of spinal cord injury (SCI), including impairment of motor, sensory, and autonomic functions. However, it requires an adaptation process to enhance the user’s performance by tuning the learning curve to a point of extreme relevance. Therefore, in this pilot study, the learning curves of two people with complete SCI (PA: paraplegic-T6, and PB: quadriplegic-C4) were analyzed, with results obtained on the accuracy of the classifier (AcCSP−LDA), repetitions of intra-day training, and number of hits and misses in the activation of FESs for sixteen interventions using the NESs-FESs interface. We assumed that the data were non-parametric and performed the Spearman’s ρ test (and p-value) for correlations between the data. There was variation between the learning curves resulting from the training of the NESs-FESs interface for the two participants, and the variation was influenced by factors both related and unrelated to the individual users. Regardless of these factors, PA improved significantly in its learning curve, as it presented lower values in all variables in the first interventions compared to the PB, although only PA showed statistical correlation (on AcCSP−LDA values in RLL). It was concluded that despite the variations according to factors intrinsic to the user and the functioning of the equipment used, sixteen interventions were sufficient to achieve a good learning effect to control the NESs-FESs interface.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87384023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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