Recent advances in multiscale CFD modelling of cooling processes and systems for the agrifood industry

Ajani, Clement Kehinde; Zhu, Zhiwei; Sun, Da‐Wen

doi:10.1080/10408398.2020.1809992

Cited by 18 publications

(12 citation statements)

References 95 publications

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“…Commercial software such as ANSYS/CFX, FLUENT, and COMSOL Multiphysics or written codes such as lattice Boltzmann technology are used for numerical simulation of many food processes (Sun, 2002; Sun and Hu, 2002) including fluid mixing, whereas finite volume method and finite element method (FEM) have been used to solve the velocity field (Bayareh et al., 2020; Sattari et al., 2020; Ajania et al, 2021). Figure 2 shows the general simulation setup process, which is the common setting for all simulation software.…”

Section: Methodsmentioning

confidence: 99%

Computer simulation of submicron fluid flows in microfluidic chips and their applications in food analysis

Xie

Sun

2021

Comp Rev Food Sci Food Safe

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In recent years, countries around the world have maintained a zero‐tolerance attitude toward safety problems in the food industry. In order to ensure human health, a fast, sensitive, and high‐throughput analysis of food contaminants is necessary to ensure safe food products on the market. Microfluidics, as a high‐efficiency and sensitive detection technology, has many advantages in the detection of food contaminants, including foodborne pathogens, pesticides, heavy metal ions, toxic substances, and so forth, especially in conjunction with a variety of submicron fluid driving methods, making food detection and analysis more efficient and accurate. This review introduces the principle of submicron fluid driving modes and discusses the driving simulation of submicron fluid in microfluidic chips. In addition, the latest developments in the application of simulation in food analysis from 2006 to 2020 are discussed, and the computer simulation of submicron fluid flow in microfluidic chips and its application and development trend in food analysis are also highlighted. The review indicates that microfluidic technology, using numerical simulation as an auxiliary tool, combined with traditional methods has greatly improved the detection and analysis of food products. In addition, microfluidics combined with a variety of control methods embodies the ability of specific, multifunctional, and sensitive detection and analysis of food products. The development of high‐sensitivity, high‐throughput, portable, integrated microfluidic chips will enable the technology to be applied in practice.

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Section: Methodsmentioning

confidence: 99%

Computer simulation of submicron fluid flows in microfluidic chips and their applications in food analysis

Xie

Sun

2021

Comp Rev Food Sci Food Safe

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“…As the Reynolds number of the cold air in stacked packages ranging between 4.5 Â 10 3 and 3.15 Â 10 4 and Y + of this model was greater than 1, the k-ε equations were chosen to solve the turbulent air flow as (Ajani et al, 2021;Ambaw et al, 2013;D'Agaro, Cortella, & Croce, 2006)…”

Section: Governing Equationsmentioning

confidence: 99%

“…The governing equations of the continuity, Navier–Stokes, and energy in the airflow zone (Wang et al, 2021) were shown in Equations (1)–(3).

\frac{\partial ρ_{a}}{\partial t} goodbreak+ \frac{\partial ρ_{a} u_{i}}{\partial x_{i}} goodbreak= 0 .

\frac{\partial}{\partial t} ((), ρ_{a} u_{i}) goodbreak+ \frac{\partial}{\partial t} ((), ρ_{a} u_{i} u_{j}) goodbreak= goodbreak- \frac{\partial P}{\partial x_{i}} goodbreak+ \frac{\partial}{\partial x_{j}} ([], μ ((), \frac{\partial u_{i}}{\partial x_{j}} goodbreak+ \frac{\partial u_{j}}{\partial x_{i}})) goodbreak- \frac{\partial}{\partial x_{j}} ((), ρ_{a} \overset{u_{i}^{'} u_{j}^{'}}{true}) goodbreak- ρ_{a} g_{i} .

\frac{\partial}{\partial t} ((), ρ_{a} c_{p, a} T_{a}) goodbreak+ \frac{\partial}{\partial x_{j}} ((), ρ_{a} c_{p, a} T_{a}) goodbreak= goodbreak- \frac{\partial P}{\partial x_{i}} ((), λ_{a} \frac{\partial T_{a}}{\partial x_{j}}) goodbreak- \frac{\partial}{\partial x_{j}} ((), ρ_{a} c_{p, a} \overset{u_{j}^{'} T^{'}}{true}) .

As the Reynolds number of the cold air in stacked packages ranging between 4.5 × 10 3 and 3.15 × 10 4 and Y + of this model was greater than 1, the k – ε equations were chosen to solve the turbulent air flow as (Ajani et al, 2021; Ambaw et al, …”

Section: Numerical Model Formulationmentioning

confidence: 99%

“…Given the difficulties associated with the experimental research, simulated works played an important role in optimizing the profiles of the air velocity and temperature as well as the packages' structure (Ajani, Zhu, & Sun, 2021; Ambaw, Mukama, & Opara, 2017; Cao, Gong, & Zhang, 2020; Zhao, Han, Yang, Qian, & Fan, 2016). Most of the simulations were directed at a single package rather than the stacked packages as the pressure loss of each package was assumed to be constant.…”

Section: Introductionmentioning

confidence: 99%

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Energy consumption in relation to the number of stacked packages in forced air pre‐cooling of apples

Jia

Yang

Zhang

et al. 2022

J Food Process Engineering

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To better investigate the effect of the number of stacked packages on the seven‐eights cooling time and the specific fan energy consumption in forced air pre‐cooling (FAC), a three‐dimensional model of FAC in the stacked packages was developed. The predicted total pressure loss was less than the product of the number of stacked packages and the pressure loss of a single package. This was due to the pressure loss of the cold air flowing through the openings in the adjacent packages accounting for the dominant proportion of the stacked packages. The difference was used to prove the overestimation of the specific fan energy consumption calculated in terms of the product of the number of stacked packages and the pressure loss of a single package. In terms of the required parameters in the pre‐cooling industry, the optimal condition of three stacked packages at the air velocity of 1.0 m/s was suggested. Compared with the other complied conditions, the specific fan energy consumption of the optimal condition was reduced by more than 50% and 0.0289–0.975$ t−1, respectively. Practical applications Forced air pre‐cooling was an effective way to retain freshness and to improve the economic benefit of horticultural products. The quantitative relationships among the number of stacked packages, the pressure loss, and the fan energy consumption were obtained by computational fluid dynamics (CFD) simulation. Based on the results, the optimal condition in forced air pre‐cooling of apples was suggested.

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“…For the temperature monitoring of food packaging systems in the cold chain, the CFD model is widely recognized to have good consistency (Ajani et al., 2021; Söylemez et al., 2021). After sensitivity testing, CFD has met the measurement error requirements of most temperature measurement equipment used in the postharvest industry (Opara & Zou, 2007).…”

Section: Micro: Environmental Monitoring For Fcclmentioning

confidence: 99%

Sustainable food cold chain logistics: From microenvironmental monitoring to global impact

Chen

Qian

Yang

et al. 2022

Comp Rev Food Sci Food Safe

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Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low-temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade-off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon.Industry empowerment through next-generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real-time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low-carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development.

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Recent advances in multiscale CFD modelling of cooling processes and systems for the agrifood industry

Cited by 18 publications

References 95 publications

Computer simulation of submicron fluid flows in microfluidic chips and their applications in food analysis

Computer simulation of submicron fluid flows in microfluidic chips and their applications in food analysis

Energy consumption in relation to the number of stacked packages in forced air pre‐cooling of apples

Sustainable food cold chain logistics: From microenvironmental monitoring to global impact

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