Enhancing Student Learning in Food Engineering Using Computational Fluid Dynamics Simulations

Introduction to the IFT 2011 Resource Guide for Approval and Re‐Approval of Undergraduate Food Science Programs

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A nanofluid containing water and nanoparticles made of copper (Cu) inside a cavity with square shape is simulated utilizing the computational fluid dynamics (CFD) approach. The nanoparticles made up 15% of the nanofluid. By performing the simulation, the CFD output is characterized by the coordinates in the x, y, nanofluid temperature, and velocity in the y-direction that these outputs are obtained for different physical time iterations. Moreover, the CFD outputs are examined by one of the artificial techniques, i.e. adaptive network-based fuzzy inference system (ANFIS). For this purpose, the data was clustered via grid partition clustering, and the type of membership functions (MFs) was chosen product of two sigmoidal membership functions (psigmf). After reaching 99.9% of intelligence in ANFIS, the nanofluid temperature is predicted for the entire data, which are included in the learning processes. The results showed that the method of ANFIS can predict the thermal properties in different physical times at different computing points without having a training background at those times. Additionally, this study shows that with three membership functions at each input, the model’s accuracy is higher than four functions.

This paper presents the predictions of deposition patterns using CFD simulations based on transient-flow behaviour of a 1.6 m high, 0.8 m diameter, pilot-scale spray dryer, following from previous studies assessing the use of Computational Fluid Dynamics (CFD) simulations to predict the deposition on a plate in a simple box configuration. The predicted deposition fluxes here have been compared with experimental data for the deposition fluxes of skim milk, maltodextrin and water. The CFD simulation results suggested that the effect of transient air flows on the vertical patterns of deposition fluxes with distance up the dryer wall for no inlet air swirl is small. The CFD simulations underpredicted the experimental values of the deposition fluxes by approximately 50%, but the simulations predicted the same experimental trends when changing the main air flow rate through the dryer. The experimentally-measured deposition fluxes were 38%, on average, higher at a main air flow rate of 113 kg/h compared with those at a flow rate of 88 kg/h. The CFD simulations predicted an average increase in deposition flux of 26% at 113 kg/h compared with 88 kg/h, so the trends with this change in operating conditions have been predicted well by the CFD simulations. One-way particle coupling has therefore shown correct trends in the deposition fluxes with respect to both positions in the dryer and different operating conditions, and such one-way coupling is several orders of magnitude faster than the more rigorous two-way coupling.

In infrastructure projects involving geothermal areas, it is imperative to prioritize human health and environmental protection by effectively removing toxic gases that may leak from underground geothermal sources. Computational Fluid Dynamics (CFD) simulations are an invaluable tool for accurately modeling and analyzing the complex physical processes associated with the transport and distribution of gases. To ensure safety, it is imperative to design an effective ventilation system that can remove toxic and/or combustible gases, such as hydrogen sulphide, carbon dioxide and methane. The system should be optimized by experts for the absorption, transport, and distribution of gases. Computational Fluid Dynamics (CFD) simulations can be used to confidently evaluate the system's design and performance. CFD is a powerful tool for modelling complex fluid dynamics related to the transport and distribution of gases. Utilising CFD simulations to model gases leaked from geothermal sources provides a better understanding of their movement throughout infrastructure projects and environmental impacts. Informed decisions can be made using CFD, resulting in positive impacts on the environment and infrastructure projects. CFD simulations are also essential in optimising factors such as ventilation system placement, duct design, air flow rates, and filtration efficiency. CFD simulations can effectively assess the safety and effectiveness of ventilation systems in removing toxic gases, minimizing environmental impacts. The study investigates the behavior of geothermal gases seeping from the ground in a sample tunnel structure, highlighting the importance of simulating ventilation systems during potential emergencies to evaluate risks. The fan installed inside the tunnel, along with the fan duct connected to it, effectively extracted the gases from the environment. The results were analyzed with confidence and objectivity.

1 - The Food Engineer

In this review, the application of computational fluid dynamics (CFD) simulations in analyzing thermal processes within food technology is explored. The focus is on understanding heat transfer, fluid flow, and temperature distribution during various food processing methods, such as baking, frying, pasteurization, and cooling. Detailed insights that are often challenging to obtain through experimental methods alone are provided by CFD simulations, allowing for the optimization of process parameters to enhance product quality and safety. It is demonstrated that CFD can effectively model complex thermal phenomena, providing valuable data on temperature gradients and flow patterns. These simulations assist in the designing of more efficient processing equipment, improving energy consumption, and ensuring uniform heat treatment, which is crucial for maintaining the nutritional and sensory attributes of food products. Furthermore, the integration of CFD in the food industry leads to significant advancements in product development, reducing the time and cost associated with experimental trials. Future research should focus on refining these models for greater accuracy and exploring their application in emerging food processing technologies.

This paper examines the use of Computational Fluid Dynamics (CFD) simulations to improve thermal comfort in enclosed spaces, such as theatres, halls, and offices. Thermal comfort is crucial for occupant well-being and productivity, and is influenced by various factors, including air temperature, humidity, and airflow. To create tailored models for specific spaces, we suggest using CFD simulations to predict airflow patterns and temperature distribution, thereby identifying discomfort zones. This research aims to optimize thermal comfort and improve building design and energy efficiency by iteratively adjusting ventilation strategies. The practical applications of this approach include improved design and construction, as well as retrofitting existing buildings. To achieve this, CFD simulations should be integrated into the design phase to proactively address thermal comfort issues and achieve energy-efficient layouts. Customising CFD models for existing buildings allows for the analysis of airflow patterns and optimisation of ventilation strategies to enhance thermal comfort. HVAC systems can be evaluated using CFD to identify areas for improvement and select energy-efficient solutions, leading to enhanced energy efficiency. The benefits of these improvements include enhanced well-being and productivity, as improved thermal comfort can lead to better occupant health and focus, ultimately boosting productivity. Additionally, the risks of Sick Building Syndrome (SBS) can be reduced. CFD analysis can help reduce the risks of Sick Building Syndrome by optimizing ventilation and improving indoor air quality. In the future, it is recommended to integrate CFD simulations with Building Information Modelling for comprehensive thermal comfort analysis. This study highlights the potential of CFD to optimize thermal comfort in enclosed spaces by promoting occupant well-being and energy efficiency through iterative ventilation adjustments. It is important to adhere to established standards when integrating CFD.

Validation of CFD simulations of the moored DeepCwind offshore wind semisubmersible in irregular waves

A program aimed at facilitating the use of computational fluid dynamics (CFD) simulations by the controls discipline is presented. The objective is to reduce the development time and cost for propulsion system controls by using CFD simulations to obtain high-fidelity system models for control design and as numerical test beds for control system testing and validation. An interdisciplinary team has been formed to develop analytical and computational tools in three discipline areas: controls, CFD, and computational technology. The controls effort has focused on specifying requirements for an interface between the controls specialist and CFD simulations and a new method for extracting linear, reduced-order control models from CFD simulations. Existing CFD codes are being modified to permit time accurate execution and provide realistic boundary conditions for controls studies. Parallel processing and distributed computing techniques, along with existing system integration software, are being used to reduce CFD execution times and to support the development of an integrated analysis/design system. This paper describes: the initial application for the technology being developed, the high speed civil transport (HSCT) inlet control problem; activities being pursued in each discipline area; and a prototype analysis/design system in place for interactive operation and visualization of a time-accurate HSCT-inlet simulation.

Computational Fluid Dynamics (CFD) simulations are able to produce complex and large outputs that accurately describe the physical properties of fluids and gases in various domains, such as air flow around a car, or the multi-phase flow inside an internal combustion engine. The simulation results, i.e. the flow fields, are often too complex to be analyzed directly. With the increasing number of simulations as well as their complexity, there is a need of automated processes that can analyze these complex outputs. In this paper, inspired by the success of convolutional neural networks (CNNs) in Computer Vision, we apply for the first time CNNs on CFD output. We show their capabilities in capturing and processing flow patterns. Furthermore, we design a novel CNN architecture tailored to the data produced by CFD simulations, as well as two conventional architectures and compare them. We propose and construct a new dataset of turbulent flow, within the application domain of steady flow around passenger cars. We use that dataset to evaluate and compare the proposed methods, on different tasks that depend on flow patterns. Finally, we compare our methods with a baseline k-nearest neighbor approach, tuned to be comparable to the state-ofthe-art.

South African societal stakeholders are in general not satisfied with the work preparedness of newly graduated food science and technology students. There is currently little local literature available that defines the graduate capabilities required of newly graduated food scientists and technologists in South Africa. Therefore, the outcomes of an empirical analysis conducted through stakeholder engagement to identify the required graduate capabilities of newly graduated students in food science and technology are reported in this article. A self‐developed questionnaire, administered as a web‐based survey, was used to conduct a needs analysis to identify the required graduate capabilities. The results of this study showed that the identified graduate capabilities composed of generic graduate attributes, including the related employability skills and characteristics of graduateness, the desirable personal attributes, and the foundational food science and technology knowledge, skills, and competencies required to be an effective food science and/or technology graduate that meets the expectations of stakeholders within the South African context. Comparison with the minimum educational requirements of international food science and technology organizations, including the Institute of Food Technologists 2018 “Standards and Essential Learning Outcomes,” showed considerable overlap with the required graduate capabilities identified in this study. However, it was also shown that within the South African context some additional skills and competencies in food science and technology are required from South African graduates and that existing curricula must be aligned to fully prepare students to be workplace ready.

We describe the use of computational fluid dynamics (CFD) simulations for calibrating a flush air data system (FADS). In particular, the HYFLEX hypersonic vehicle FADS is used as a case study. The HYFLEX FADS consists of nine pressure ports located flush with the vehicle nose surface, connected to onboard pressure transducers. After appropriate processing, surface pressure measurements can be converted into useful air data parameters. The FADS processing algorithm requires a pressure model which relates air data parameters to the measured pressures. In the past, such pressure models have been calibrated using combinations of flight data, experimental results, and numerical simulation. In this paper, we perform a calibration of the HYFLEX FADS using CFD simulations exclusively. The CFD simulations are used to build an empirical pressure model which accurately describes the HYFLEX nose pressure distribution over a range of flight conditions. We believe that CFD provides a quick and inexpensive way to calibrate the FADS, and is applicable to a broad range of flight conditions. When tested with HYFLEX flight data, the calibrated FADS is found to work well. The system predicts vehicle angle of attack and angle of sideslip to accuracy levels which generally satisfy flight control requirements. Dynamic pressure is predicted to within the resolution of the onboard inertial measurement unit. We find that wind-tunnel experiments and flight data are not necessary to accurately calibrate the HYFLEX FADS for hypersonic flight.

The accurate prediction of gas dispersion and the potential consequences of gas explosions hold a pivotal role in the assessment of explosion design loads for oil and gas processing facilities. This often involves the use of computational fluid dynamics (CFD) simulations, a widely adopted practice in the field. The extent of CFD simulations required depends on the specific characteristics and size of the facility. In many cases, a substantial number of simulations, often in the thousands, are needed to comprehensively assess the potential outcomes in the event of a hydrocarbon loss of containment. These simulations account for the complex three‐dimensional nature of the facility, the surrounding environmental conditions, and the properties of the leaking hydrocarbon fluids. Although unquestionably invaluable, CFD simulations impose significant temporal constraints upon their execution and necessitate the allocation of substantial efforts and Central Processing Unit (CPU) time. In this paper we develop a neural model tailored specifically for the analysis of CFD gas dispersion and gas explosion scenarios. This model leverages the capabilities of machine learning algorithms to expedite the execution of these complex studies. The proposed neural network model has the advantage of being able to handle a wide range of scenarios in a fraction of time it takes to perform the CFD simulations, making it particularly useful for large‐scale processes facilities. The accuracy of the predictions is remarkably high, providing a high level of confidence in the predictions of the flammable gas clouds sizes across various scenarios, as well as the resulting explosion overpressures.

This paper describes the use of computational fluid dynamics in teaching graduate students who were in a four‐year B. Tech program. Many of these students did not have a good background in mathematics, fluid dynamics, heat transfer, and programming; however, most of them were good at computer‐aided design in ProE and were very interested in learning computational fluid dynamics as a design tool in industries. Solidworks flow simulator was chosen as the computational fluid dynamics software to teach students the entire computational fluid dynamics process in a single integrated software environment. Based on projects, computational fluid dynamics numerical methods and fundamentals of heat transfer and fluid flow were introduced to help students understand the computational fluid dynamics process, interpret, and validate simulation results. The computational fluid dynamics simulation of an orifice meter is given as the basic example for the students. Orifice meters are the most common equipment used for measuring fluid flow because of their simple mechanical structure, versatility, and low cost. In this paper, computational fluid dynamics simulation has been used to predict the orifice flow with better accuracy. Computational fluid dynamics simulations have been performed using solidworks flow simulator and validated with the data available in published literature. A new system has been proposed to accurately measure the flow using orifice metering systems.