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Grids such as perforated plates are of fundamental importance in flow turbulence study and are commonly utilised to promote mixing. An orificed perforated plate (OPP) and its reversed counterpart, the converging-nozzle perforated plate (CNPP), were applied to produce quasi-isotropic turbulence inside a wind tunnel. The three orthogonal velocity components were measured using a triple hotwire at 10D downstream of the perforated plate for Reynolds numbers, ReD, 18,700 and 28,400, where D is the diameter of the perforated holes. The statistics of the grid-generated turbulence was analysed using the time-averaged local velocity profile and turbulence intensity, which revealed a more homogeneous distribution of the flow field with a higher level of turbulence for the OPP. Fourier and wavelet analyses were employed to investigate the energy of the eddies as a function of frequency and multiscale characteristics of the fluctuating velocity, respectively. At ReD = 18,700, the turbulent energy remains prominently with large-scale vortical structures which are non-intermittently present in the flow for both perforated plates. The thickness of the converging channels of the CNPP appears to provide the venue for spawning intermittent fluctuations. At higher ReD 28,400, the effect of this intermittent behaviour becomes evident for the CNPP, leading to a multiscale distribution of turbulent energy. [ABSTRACT FROM AUTHOR]

Bubble columns are widely used in many industrial applications. Gas holdup knowledge is an essential element in bubble column design and process optimization. Despite the simple structure of bubble columns, the behaviour of the bubbly flow is still not well understood owing to the complexity of gas–liquid interactions and coupling between the phases. Variation of gas density and initial liquid height are the most likely reasons for gas holdup changes in bubble columns. These parameters affect bubble breakup and coalescence as well as the rise velocity of small bubbles, which causes change to bubble size distribution that finally affect the gas holdup. In this work, through the application of computational fluid dynamics (CFD) and by investigating the effect of gas density and the initial liquid height on the bubbly flow condition, a new correlation for deducing the total gas holdup is developed. To achieve this, a wide range of bubbly flows are modelled, and gas holdup values are determined for different heights and operating pressures. Ranges of the normalized pressure (P/P0), superficial Reynolds number (Resup), and the aspect ratio of the bubble column (H0/D) were 1 ≤ P/P0 ≤ 15, 3000 ≤ Resup ≤ 57000, and1.5 ≤ H0/D ≤ 8, respectively. The proposed correlation has been found to predict the experimental as well as CFD gas holdup data fairly well. The results of this research provide a fast and accurate method for predicting gas holdup in bubble columns that work either in atmospheric or high‐pressure conditions. [ABSTRACT FROM AUTHOR]

Forecasting crop water demand is a critical part of any greenhouse's day-to-day operations. This study focuses on a region located in Essex County, Ontario Canada where water demand is dominated by commercial greenhouse operations (78% of capacity). Development of complex and elaborate forecasting methods such as artificial neural networks (ANN) can be costly to develop and implement, especially with the limited resources available to greenhouses. This study proposes simplified forecasting methods that would be used in conjunction with a more complex base model architecture. These simplified methods use one crop water usage as an indicator of another's, and is titled crop-to-crop forecasting (C2C). In this study, tomatoes and peppers were evaluated, and three C2C models were developed along with an ANN base model to provide a basis for evaluation. The models were created using a dataset containing hourly watering data along with climatic and temporal data for the period between June 2015 and August 2016. The three C2C architectures used were linear regression (LR), quotient method (QM), and feed-forward neural network (FFNN), compared with the (ANN) model, which is a feed-forward neural network with extra inputs (FFNN-EI). Each model was evaluated using the root mean squared error (RMSE) and the normalized root mean squared error (NRMSE). The results show that all C2C methods have higher RMSE and NRMSE than that of the base model, with an average RMSE increase of 12% for peppers and 29% for tomatoes. [ABSTRACT FROM AUTHOR]