Current Issue : January-March Volume : 2025 Issue Number : 1 Articles : 5 Articles
Microscale modeling plays a critical role in fuel cell development, offering deep insights into the microscale transport phenomena and electrochemical reactions. This level of detail is essential for optimizing the performance of a single fuel cell, enabling the precise design and improvement of materials and structures at the microscale and consequently enhancing the overall efficiency of a stack. Here, we show a comprehensive transition from white-box models, characterized by their reliance on physical laws, to black-box models exemplified by neural networks, which excel in pattern recognition from provided data without necessitating a clear understanding of the underlying processes. This spectrum encompasses the inherent challenges and merits of both methodologies. While white-box models are recognized for their reliability due to their foundation in mathematical equations that describe physical phenomena, they often require the integration of empirical parameters and are susceptible to experimental errors, much like their black-box counterparts. The core novelty in this study lies in the synergistic integration of these two paradigms, specifically tailored for enhancing the predictive accuracy in solid oxide fuel cell modeling. In this approach, the neural network is employed to replace different parts of the mathematical model, from refining empirical parameters in the electrochemical model to replacing the entire electrochemical model. The adjustment of parameters is conducted by an evolutionary strategy based on the outputs of the mathematical model. The results underscore the superiority of the gray box in achieving higher prediction accuracy and in minimizing the requisite data volume for network training. This presented approach not only bridges the gap between the deterministic clarity of white-box models and the data-driven insights of black-box models but also strategically distributes the computational load between them, thereby offering a promising solution to the prevalent challenges in solid oxide fuel cell modeling....
Municipal solid waste generation is strongly linked to rising human population and expanding urban areas, with significant implications on urban metabolism as well as space and place values redefinition. Effective management performance of municipal solid waste management underscores the interdisciplinarity strategies. Such knowledge and skills are paramount to uncover the sources of waste generation as well as means of waste storage, collection, recycling, transportation, handling/treatment, disposal, and monitoring. This study was conducted in Dar es Salaam city. Driven by the curiosity model of the solid waste minimization performance at source, study data was collected using focus group discussion techniques to ward-level local government officers, which was triangulated with literature and documentary review. The main themes of the FGD were situational factors (SFA) and local government bylaws (LGBY). In the FGD session, sub-themes of SFA tricked to understand how MSW minimization is related to the presence and effect of services such as land use planning, availability of landfills, solid waste transfer stations, material recovery facilities, incinerators, solid waste collection bins, solid waste trucks, solid waste management budget and solid waste collection agents. Similarly, FGD on LGBY was extended by sub-themes such as contents of the bylaw, community awareness of the by-law, and by-law enforcement mechanisms. While data preparation applied an analytical hierarchy process, data analysis applied an ordinary least square (OLS) regression model for sub-criteria that explain SFA and LGBY; and OLS standard residues as variables into geographically weighted regression with a resolution of 241 × 241 meter in ArcMap v10.5. Results showed that situational factors and local government by-laws have a strong relationship with the rate of minimizing solid waste dumping in water bodies (local R square = 0.94)....
This work aims to study the modeling and sizing of a floor reinforced by ballasted columns. We are studying the system of reinforcement by ballasted columns because this technique is able to replace deep foundations that are technically difficult to realize and their cost is higher. The modelling and dimensioning of foundations on a ballasted column will be an important contribution to the state of the art of this method because it will highlight the mode of transfer of loads, and will expose the induced deformations by also allowing to verification criteria of bearing capacity and allowable settlement according to geometric information of the model. The columns on a substrate located at 9 m have a length of 9 m and a diameter of 40 cm and were obtained by incorporating ballast of granular class 0/31.5 of internal friction angle of 38˚ and a density weight of 21 kN/m3. The choice of this method is based on the geotechnical characteristics of the initial soil. Thus, identification and characterization tests were carried out to estimate the bearing capacity and the settlement giving respectively 125 kPa and 57 cm. These results show the ground does not have sufficient mechanical properties to withstand the loads transmitted by the tank. By adopting the reinforcement of the soil with ballasted columns, numerical calculations show that after applying a load equal to 265.1 KPa, 20 cm vertical settlement and 17 cm horizontal displacement were obtained. This is in the tolerable deformation range for our tank, namely, less than 20 cm. Analytically, in addition to reducing settlement, ballasted columns, Due to their high stiffness, they have effectively contributed to the increase of the permissible soil stress up to 257 kPa....
In this work, we present numerical modelling of coupled heat and mass transfer within porous materials. Our study focuses on cinder block bricks generally used in building construction. The material is assumed to be placed in air. Moisture content and temperature have been chosen as the main transfer drivers and the equations governing these transfer drivers are based on the Luikov model. These equations are solved by an implicit finite difference scheme. A Fortran code associated with the Thomas algorithm was used to solve the equations. The results show that heat and mass transfer depend on the temperature of the air in contact with the material. As this air temperature rises, the temperature within the material increases, and more rapidly at the material surface. Also, thermal conductivity plays a very important role in the thermal conduction of building materials and influences heat and mass transfer in these materials. Materials with higher thermal conductivity diffuse more heat....
In order to improve the efficiency and accuracy of the modeling and design work of the sluice gate project, this paper proposes an automatic generation template of the sluice gate project with customized semantics and project layout scheme, aiming at realizing the rapid assembling of all kinds of components of the sluice gate project. In the construction process, this paper first starts from basic parametric modeling and proposes constraints as the basis of modeling. Subsequently, a template library framework is developed based on the constraints to ensure that the generated templates have a high degree of standardization and consistency. Finally, an efficient and flexible template library is successfully constructed by using the customized classes and functions of Revit API, which provides powerful technical support for the modeling and design work of sluice gate engineering. This achievement helps to promote the informationization and intelligent development of the water conservancy engineering industry, and its versatility and scalability also make it have a wide range of application prospects in other water conservancy engineering fields....
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