The stress-strain behavior of carbon fiber reinforced polymer matrix composites (CFRPs) is highly influenced by thermal expansion mismatches, particularly due to their anisotropic nature and directional mechanical properties. While prior research has explored thermal and mechanical loading separately, a critical gap remains in understanding their combined effects concerning fiber orientation and load application. This study integrates finite element modeling (FEM) with optimized coefficient of thermal expansion (CTE) selection to improve stress-strain prediction accuracy under coupled thermal-mechanical loading for various fiber orientation. Experimental tensile tests at-53 degrees C, 82 degrees C, and room temperature reveal that improper CTE selection significantly impacts transverse loading, causing premature matrix cracking and interfacial debonding. The proposed FEM framework captures thermally induced residual stresses, improving predictive accuracy and reinforcing the need for thermo-mechanical coupling in aerospace applications to ensure long-term material reliability.
Bottom plate leakage is a critical failure mode in storage tank engineering, often causing environmental contamination and high remediation costs. To improve structural integrity and leak detection, double-layer steel bottom systems have been widely adopted and standardized in API 650 and EN 14015. This study evaluates five typical configurations (Structures A-E) using finite element analysis with both 2D and 3D models under hydrostatic loading. Structure D shows the most uniform stress distribution, while Structure E achieves minimal deformation with greater complexity. Parametric studies investigate the effect of rubber pad thickness and elastic modulus on stress and deformation, revealing limited influence on overall performance. A modulus-material mapping framework is established, correlating elastic modulus to commercially available elastomers such as EPDM, polyurethane, and HDPE. Polyurethane materials with elastic moduli between 10-50 MPa are identified as offering the best balance between mechanical performance and economic feasibility. This research provides theoretical and practical insights for optimizing double-layer tank bottom structures, supporting better material selection, safer designs, and more efficient long-term operation of liquid storage systems.
A key goal of engineers working on industrial fluid management systems is to balance cost and performance. This research presents a methodology for redesign of a liquid dispersion unit using value engineering (VE) concepts and simulation-based analysis. The approach identifies high-cost, low-value components through Function-Cost-Worth Analysis (FCWA) and evaluates their impact with the Function Analysis System Technique (FAST) and evaluation matrices. Results showed that the nozzle subsystem was the main limitation, as it did not cover the spray area well and fluid dynamics were unsatisfactory. To address this, several nozzle shapes and materials were examined during the creative phase. A complete cone nozzle proved the best design. Computational Fluid Dynamics (CFD) confirmed improved flow properties, while Finite Element Analysis (FEA) showed structural adequacy under operational pressures. Polypropylene was selected as the best material since it is secure and significantly less costly compared to metals like gunmetal and stainless steel. The changes increased system efficiency from 63.75% to 75.25%, an 11.5% improvement, while overall cost decreased by 2.52%. This demonstrates that integrating VE with CAD-based simulations can generate innovative, scalable designs for fluid-based industrial systems.
Bio-Inspired Structures have become a significant research direction for developing sustainable Structural components with lightweight yet damage tolerant, and energy efficient designs. Among these Structures, one of the most prominent ones is the Bouligand-type Helicoidal structures which are characterized by their linear and gradual rotation of layers between each layer. They've shown a high resistance to fracture and improvised energy dissipation capabilities. while significant number of studies were conducted to examine their high strain-rate impact behavior, their response to gradual loading are has not been full studied. To address this gap, the present work involves in performing an investigation of Bouligand-type structures under quasi static loading using Ansys. In this study, four such Bio-inspired structures were studied out of those four, three of them are helicoidal structures (Bouligand-type) with different angle of layer rotations and the other one is Honeycomb Structure. All these structures are assigned epoxy-carbon (395 GPa) composite material to ensure consistent mechanical assumptions across all the cases. All of those structures are designed and sandwiched between two plates. A Controlled displacement ranging from 0-10 mm was applied to the top surface which is generally the plate in a quasi-static Approach, while the bottom plate is fixed. In addition to compression, a displacement-controlled flexural analysis was conducted to evaluate the structural performance under bending action and to represent more realistic service loading conditions. Frictional effects were neglected to maintain computational stability and to solely focus on the structural response.
This research presents a machine learning-based approach for monitoring an industrial battery production system using a Naive Bayesian Network, a probabilistic model widely recognized for its ability to handle uncertainty. The proposed framework infers system states from observed operational conditions and event data, providing predictive insights into machine behavior. Real-world production data were employed to train and validate the model, ensuring both accuracy and practical applicability. Through probabilistic inference, the model anticipates potential failures or abnormal behaviors, enabling timely maintenance interventions and minimizing downtime. Evaluation results demonstrate that the Naive Bayesian Network offers a robust and interpretable solution for industrial monitoring, with strong potential to enhance predictive maintenance strategies and improve the overall reliability and efficiency of battery manufacturing operations.
