Large Eddy Simulation of Turbulent Channel Flows by the Rational LES Model

The rational large eddy simulation (RLES) model is applied to turbulent channel flows. This approximate deconvolution model is based on a rational (subdiagonal Pade') approximation of the Fourier transform of the Gaussian filter and is proposed as an alternative to the gradient (also known as the nonlinear or tensor-diffusivity) model. We used a spectral element code to perform large eddy simulations of incompressible channel flows at Reynolds numbers based on the friction velocity and the channel half-width Re{sub tau} = 180 and Re{sub tau} = 395. We compared the RLES model with the gradient model. The RLES results showed a clear improvement over those corresponding to the gradient model, comparing well with the fine direct numerical simulation. For comparison, we also present results corresponding to a classical subgrid-scale eddy-viscosity model such as the standard Smagorinsky model.Comment: 31 pages including 15 figure

Numerical and experimental analysis of vertical spray control patternators

The experimental vertical spray control walls have the purpose of picking up the liquid delivered by trained sprayer for providing the liquid distribution profile in height. Theoretically this should correspond to the ideal profile, which consists in a uniform distribution on the vegetation. If the profile is different from the ideal, a parameter setup is required on the sprayer. Nonetheless, some problems are hidden in the aforementioned statements: i) no wall measures exactly the distribution profile (i.e. the flow through the sections in the vertical plane, parallel to the direction of advancement of the sprayer). Compared to real profile, sensitive errors are introduced: the evaporation of the drops, the deviation of the air flows caused by the sensors panel themselves; by the possibility that the drops bounce on the wall panels, also due to the current of air that can push the liquid veil laterally or upwards, Moreover, everything varies depending on the geometry of the sensors, air velocity, air humidity; ii) no one knows what exactly is the optimal distribution profile. It is often considered as optimal a profile that reflects the amount of leaf area subtended by each section absorber: however, it is evident that the path of the droplets changes according to the sprayer typology (eg. radial-flow or horizontal flows). In this work a combined numerical-experimental approach is adopted, in order to assess some of the aforementioned issues: numerical data obtained by using computational fluid dynamics models are compared and validated with experimental data, in order to assess the reliability of numerical simulations in configurations which are difficult to analyze using an experimental setup

Application of Constrained Optimization Techniques in Optimal Shape Design of a Freezer to Dosing Line Splitter for Ice Cream Production

Design of multiple branches splitting of equal mass flow rate in complex rheological flows like ice cream near melting point temperature can be a challenging task. Pulsations in flow rate due to air pumping process and small fluctuations in temperature affecting flow rheology can determine a consistent difference in internal pipe velocity distribution, resulting in a significant difference in the distribution of ice cream dosage. Computational sciences and engineering techniques have allowed a major change in the way products and equipment can be engineered, as a computational model simulating physical processes can be more easily obtained, rather than making prototypes and performing multiple experiments. Among such techniques, optimal shape design (OSD) represents an interesting approach. In OSD, the essential element with respect to classical numerical simulations in fixed geometrical configurations relays on the introduction a certain amount of geometrical degrees of freedom as a part of the unknowns. This implies that the geometry is not completely defined, but part of it is allowed to move dynamically in order to minimize or maximize an objective function. From a mathematical point of view, OSD is a branch of differentiable optimization and more precisely the application of optimal control for distributed systems. OSD is still today numerically difficult to implement, because it relies on a computer intensive activity and moreover because the concept of “optimal” is a compromise between shapes that are good with respect to several criteria. In this work, the applications of a multivariate constrained optimization algorithm is proposed in the case of a mechanical ice cream 1 to 5 splitting system, required to distribute in an evenly way from one freezer into five dosing valves. Results allowed to design a retro-fitting system on an existing production plant reducing the dosing error down to 3% on the average

Drone and sensor technology for sustainable weed management: a review

Weeds are amongst the most impacting abiotic factors in agriculture, causing important yield loss worldwide. Integrated Weed Management coupled with the use of Unmanned Aerial Vehicles (drones), allows for Site-Specific Weed Management, which is a highly efficient methodology as well as beneficial to the environment. The identification of weed patches in a cultivated field can be achieved by combining image acquisition by drones and further processing by machine learning techniques. Specific algorithms can be trained to manage weeds removal by Autonomous Weeding Robot systems via herbicide spray or mechanical procedures. However, scientific and technical understanding of the specific goals and available technology is necessary to rapidly advance in this field. In this review, we provide an overview of precision weed control with a focus on the potential and practical use of the most advanced sensors available in the market. Much effort is needed to fully understand weed population dynamics and their competition with crops so as to implement this approach in real agricultural contexts

Numerical Modeling of Ohmic Heating of Heterogeneous Food

Ohmic heating is a promising thermal processing technique in the food industry, as it offers rapid, uniform heating by passing an electrical current directly through the food product. However, food materials often exhibit heterogeneous structures—composed of regions with different conductivities, varied moisture levels, and complex geometrical features—which can result in non-uniform temperature distributions and pose challenges for process control. This study aims to develop and validate a comprehensive numerical modelling approach to predict the heating behaviour in heterogeneous food systems subjected to ohmic heating. A coupled multi-physics framework is used to simulate both the electrical and thermal phenomena. The governing equations for electric field distribution (based on current continuity and Ohm’s law) and transient heat transfer (using the standard heat conduction equation) are solved simultaneously. Temperature-dependent electrical conductivity and heat capacity are incorporated to capture the dynamic variations of material properties during the heating process. Additionally, geometric complexities—including solid particles, convection effects and varying product matrices—are represented in the simulation to emulate real-world food products. The numerical results highlight the formation of localized hotspots in regions with higher electrical conductivity, as well as cooler zones in lower-conductivity regions. Numerical analyses reveal that factors such as voltage gradient, particle size and distribution, and flow behaviour (if any) can significantly influence the final temperature profiles. Validation against experimental data demonstrates good agreement, suggesting that the proposed modelling approach can serve as a reliable tool for process design and optimization. This work provides valuable insights into how heterogeneity affects thermal treatment outcomes and offers guidance for developing robust ohmic heating modelling to support efficient process scale-up

Scale-up analysis and critical issues of an experimental pilot plant for edible film production using agricultural waste processing

This study was developed to test a multifunctional experimental pilot plant with a reduced environmental impact that is able to process agricultural (fennel) and food production (liquid whey) waste. The pilot plant, using different thermal and filtration process parameters, is able to recover pectin and whey proteins in a single processing unit in order to produce edible films. An innovative feature of the proposed configuration is related to the possibility of coupling different types of waste treatment, obtaining a final product with a higher economical value, combining the two processing lines. Although an edible film production procedure based on pectin extracted from fennel matrix and whey proteins has already been published in literature, the scale-up process highlighted several critical issues, in particular related to the fennel matrix. Nonetheless, the pilot plant configuration allowed an edible film to be produced that is suitable for use as a direct coating to improve the shelf-life of food products

Microencapsulation of Phenolic Extracts from Cocoa Shells to Enrich Chocolate Bars

Cocoa bean shells were subjected to green extraction technologies, based on the absence of toxic organic solvents, to recover polyphenols; the extract was then encapsulated using a spray dryer and maltodextrin as coating agent. The best conditions observed in the spray drying tests (core-to-coating ratio 1:5; inlet temperature 150 °C; flow rate 6 ml min-1) were applied to produce the microcapsules used to enrich the same cocoa mass as the shells and processed for the preparation of the chocolate bars. Sensory analysis showed no significant differences between enriched chocolate bar and the unenriched reference one, except for the appearance. Both samples were then subjected to accelerated storage tests, at the end of which the polyphenols in the control chocolate bar (0.85 g 100 g-1) were reduced by about 50% (0.42 g 100 g-1), while in the enriched chocolate (1.17 g 100 g-1) by only 22% (0.97 g 100 g-1). The proposed process significantly enriched the chocolate bars with phenolic antioxidants recovered from cocoa waste without increasing the sensations of bitterness and astringency

Modelling Processes and Products in the Cereal Chain

ReviewIn recent years, modelling techniques have become more frequently adopted in the field of food processing, especially for cereal-based products, which are among the most consumed foods in the world. Predictive models and simulations make it possible to explore new approaches and optimize proceedings, potentially helping companies reduce costs and limit carbon emissions. Nevertheless, as the different phases of the food processing chain are highly specialized, advances in modelling are often unknown outside of a single domain, and models rarely take into account more than one step. This paper introduces the first high-level overview of modelling techniques employed in different parts of the cereal supply chain, from farming to storage, from drying to milling, from processing to consumption. This review, issued from a networking project including researchers from over 30 different countries, aims at presenting the current state of the art in each domain, showing common trends and synergies, to finally suggest promising future venues for researchinfo:eu-repo/semantics/publishedVersio