Porous metal structures have emerged as a promising solution in repairing and replacing damaged bone in biomedical applications. With the advent of additive manufacturing technology, fabrication of porous scaffold architecture of different unit cell types with desired parameters can replicate the biomechanical properties of the natural bone, thereby overcoming the issues, such as stress shielding effect, to avoid implant failure. The purpose of this research was to investigate the influence of cube and gyroid unit cell types, with pore size ranging from 300 to 600 µm, on porosity and mechanical behavior of titanium alloy (Ti6Al4V) scaffolds. Scaffold samples were modeled and analyzed using finite element analysis (FEA) following the ISO standard (ISO 13314). Selective laser melting (SLM) process was used to manufacture five samples of each type. Morphological characterization of samples was performed through micro CT Scan system and the samples were later subjected to compression testing to assess the mechanical behavior of scaffolds. Numerical and experimental analysis of samples show porosity greater than 50% for all types, which is in agreement with desired porosity range of natural bone. Mechanical properties of samples depict that values of elastic modulus and yield strength decreases with increase in porosity, with elastic modulus reduced up to 3 GPa and yield strength decreased to 7 MPa. However, while comparing with natural bone properties, only cube and gyroid structure with pore size 300 µm falls under the category of giving similar properties to that of natural bone. Analysis of porous scaffolds show promising results for application in orthopedic implants. Application of optimum scaffold structures to implants can reduce the premature failure of implants and increase the reliability of prosthetics.

Complex composite structures manufactured using a low-pressure vacuum bag-only (VBO) method are more susceptible to defects than flat laminates because of the presence of complex compaction conditions at corners. This study investigates the contribution of multivariate processing parameters such as bagging techniques, curing profiles, and laminate structures on laminates’ shape conformation. Nine sets of laminates were produced with a concave corner and another nine sets with a convex corner, both with a 45° inclined structure. Three-way analysis of variance (ANOVA) was performed to quantify thickness variation and spring effect of laminated composites. The analysis for concave and convex corners showed that the bagging techniques is the main factor in controlling the laminate thickness for complex shape applications. The modified (single) vacuum-bag-only (MSVB) technique appeared to be superior when compared to other bagging techniques, exhibiting the least coefficients of variation of 0.015 and 0.016 in composites with concave and convex corners, respectively. Curing profiles and their interaction with bagging techniques showed no statistical significance in the contribution toward laminate thickness variation. The spring effect of laminated composites was investigated by calculating the coefficient of determination (R²) relative to that of the mold. The specimens exhibited a good agreement with R² values ranging from 0.9824 to 0.9946, with no major data offset. This study provides guidelines to reduce thickness variations and spring effect in laminated composites with complex shapes by the optimum selection of processing parameters for prepreg processing.

Two-phase flow is commonly encountered in various engineering systems. Momentum fluctuation in two-phase flow can create undesirable and destructive vibrations. These vibrations are known as flow-induced vibrations, which are a fundamental phenomenon in fluid–structure interactions, and have been the center of this type of research in the past few decades. Flow-induced vibrations due to the multiphase flow are a complex phenomenon and its understanding is still immature. Various accidents related to flow-induced vibrations have been reported in heat exchangers and piping systems and it is very important to develop a deeper understanding of flow-induced vibrations in multiphase flow. The present review article aims to discuss the literature related to flow-induced vibrations, with special focus on factors affecting flow-induced vibrations in internal two-phase flow. Various factors affecting the magnitude and dominant frequency of forces are narrated and the correlations previously developed to estimate these quantities are discussed. Dimensionless forces are extracted from the literature and plotted against Weber number, to provide a database for comparison and to serve as a validation tool for any studies conducted using computational fluid dynamics. Furthermore, some important literature on flow-induced vibrations under different conditions is presented in tabular form to better understand these findings. Finally, some concluding remarks and comments on future research prospects and challenges are outlined.

Gas–liquid two-phase flow is very common in industrial pipelines. Flow regime identification is the first step to design, analyze, and operate the gas–liquid system successfully. The purpose of this study is to develop a methodology for identification of a two-phase flow regime using post signal processing techniques, namely Fast Fourier Transform (FFT) and Probabilistic Density Function (PDF). Three different flow regimes were simulated in a 6 m horizontal pipe with a 0.050 m inner diameter. A Level-Set (LS) method coupled with Volume of Fluid (VOF) method is used to model the air–water interface. After validation of the numerical method, dynamic pressure readings were collected with the intent to identify the associated flow regimes by post-processing of these signals. It was concluded that dynamic pressure signals of different flow regimes show different characteristics (like dominant frequency, FFT amplitude, PDF location and PDF magnitude) in the time and frequency domains. These characteristics can be potentially used as differentiating factors to distinguish different flow regimes. This research is limited to stratified, slug, and annular flow in the horizontal pipe. This paper uses a new approach to identify the flow regime in a horizontal pipe by Fast Fourier Transform and Probability Density Function of dynamic pressure readings obtained by using numerical simulation.

Two-phase flow is of great importance in various industrial processes. A characteristic feature of two-phase flow is that it can acquire various spatial distribution of phases to form different flow patterns/regimes. The knowledge of flow regime is very important for quantifying the pressure drop, the stability and safety of two-phase flow systems and it holds great significance in petrochemical and thermonuclear industries today. The objective of this study is to develop a methodology for identification of flow regime using dynamic pressure signals and deep learning techniques. Stratified, slug and annular flow regimes were simulated using a Level-Set (LS) method coupled with Volume of Fluid (VOF) method in a 6 m horizontal pipe with 0.050 m inner diameter. Dynamic pressure signals were collected at a strategic location. These signals were converted to scalograms and used as inputs in deep learning architectures like ResNet-50 and ShuffleNet. Both architectures were effective in classifying different flow regime and recorded testing accuracies of 85.7% and 82.9% respectively. According to our knowledge no similar research has been reported in literature, where various Convolutional Neural Networks are used along with dynamic pressure signals to identify flow regime in horizontal pipe. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This research provides a benchmark for future research to use dynamic pressure for identification of two-phase flow regimes. This study can be extended by collecting data over broader range of flow parameters and different geometries.