Search

Global cashew nut production is nearly 4 million tons per year, valued at 7 billion US dollars. Remarkably, almost the entire cashew apple crop, amounting to 20 million tons annually, goes to waste. However, the cashew apple contains valuable nutraceutical compounds, including tannins, polyphenols, and carotenoids, estimated to be worth 150 million US dollars annually. Due to the highly perishable nature of cashew apples, degradation is a significant issue. In response, the current work has established drying as an effective preservation technique for these bioactive components. The effect of drying temperature on bioactive compounds has been thoroughly investigated. The non‐random two liquid (NRTL) activity coefficient model effectively captures the thermodynamics of the drying process. To facilitate the selection and design of drying equipment, two mechanistic mass transfer models were developed. The first model employs the Maxwell‐Stefan framework to account for internal diffusion, with external mass transfer resistance appearing as a boundary condition. While this model works well for products like grapes, it proved inadequate for explaining the drying behaviour of cashew apples. Consequently, a second model was developed, postulating rapid moisture transport by capillary action within the cashew apple. This model effectively captures the effects of a wide range of operating conditions, using only external mass transfer resistance as the tuneable kinetic parameter. This mechanistic model is more suitable for dryer design compared to conventional phenomenological models like the logarithmic model and the two‐term exponential model. [ABSTRACT FROM AUTHOR]

The accurate estimation of the power number for closed clearance impellers holds significant importance in industries such as chemical, biochemical, paper and pulp, as well as paints, pigments, and polymers. Existing state‐of‐the‐art correlations for predicting power numbers, however, are inaccurate for impeller Reynolds number ReI>100. In this study, we compiled a dataset of 1470 data points from 15 research articles in the open literature, covering five types of impellers: (i) anchor; (ii) gate; (iii) single helical ribbon; (iv) double helical ribbon; and (v) helical ribbon with screw. Six machine learning models, namely artificial neural networks (ANN), CatBoost regressor, extra tree regressor, support vector regressor, random forest, and XGBoost regressor, were developed and compared. The results revealed that ANN emerged as the most efficient model, demonstrating the highest testing R2 value of 0.99 and the lowest testing MAPE of 7.3%. Further, we used the ANN model to develop a novel set of process correlations to estimate impeller power numbers for the industrially important anchor and double helical ribbon impellers: which significantly outperform the existing state‐of‐the‐art correlations available in literature. [ABSTRACT FROM AUTHOR]

In some nuclear reactors, under accidental conditions, core debris forms a molten pool, which is later located in a core catcher. The core catcher proposed by the authors uses special refractory material to absorb enthalpy of corium so that temperatures are within 1500 K, which is possible to cool with side cooling and top flooding. Since performing a full-scale prototypic experiment is extremely challenging and complex because of the involvement of very high temperatures and the presence of radioactive materials, it is important to develop a Computational Fluid Dynamics (CFD) model capable of simulating coolability of the melt pool with the above cooling strategy. In the present work, a CFD model was developed for the above purpose and was benchmarked with experiments conducted under simulated conditions by the authors. The experiment involved the melting of about 25 L of sodium borosilicate glass at about 1473 K and cooling it in a scaled-down core catcher model. In the presence of decay heat inside the melt pool, turbulent natural convection plays an important role in the temperature distribution inside the melt pool and on the vessel walls. For this, we used different turbulence models. Comparisons among the Standard k-ε, Shear Stress Transport (SST) k-ω, and two-dimensional (2D) Large Eddy Simulation (LES) turbulence models show that SST k-ω and 2D LES turbulences are found to be in good agreement with the experimental results for the temperature distribution in the melt pool, and SST k-ω is found to be computationally less expensive than 2D LES. In general, the CFD model is capable of simulating heat transfer with phase changes inside the heat-generating melt pool. In view of this, the model can be further extended to include cooling of the melt pool in the prototype core catcher. The evolution of crust formation has been investigated in detail using a CFD model. [ABSTRACT FROM AUTHOR]

In multiphase particulate systems, the turbulence of the continuous phase (gas or liquid) is modulated due to interactions between the continuous phase and the suspended particles. Such phenomena are non-trivial in the essence that addition of a dispersed phase to a turbulent flow complicates the existing flow patterns depending on the physical properties of the particles leading to either augmentation or attenuation of continuous phase turbulence. In the present study, this aspect has been comprehensively analysed based on the available experimental data obtained from the well-studied turbulent flow systems such as channel and pipes, free jets and grids. Relevant non-dimensional parameters such as particle diameter to integral length scale ratio, Stokes number, particle volume fraction, particle momentum number, and particle Reynolds number have been utilised to characterise the reported turbulence modulation behavior. Some limitations of these commonly used dimensionless parameters to characterise turbulence modulation are discussed, and possible improvements are suggested. [ABSTRACT FROM AUTHOR]