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Precooling is of significant importance for postharvest fruits and vegetables to control the quality degradation and prolong the shelf-life. Current precooling methods include room cooling, forced- air cooling, hydrocooling, vacuum cooling, contact or package icing, and cryogenic cooling, all of which have their advantages and disadvantages. The first two methods with the cooling medium of air are extensively used because of the wide applicable range of fruits and vegetables. Numerous studies have been devoted to cope with the drawbacks of these two air-based precooling methods with various evaluation criteria and optimization methods. A systematic literature review on these studies is firstly conducted with respect to experimental and numerical investigations respectively for the two methods. The main contributions from the previous studies are also summarized respectively with the research objectives and performance metrics. The literature review indicates that the current performance evaluation is limited to apparent parameters and the optimal design is only proposed based on the performance evaluation and comparison. Furthermore, with inspiration from the research in other domains, a scheme of advanced evaluation and optimization for air-based precooling methods is proposed with thermodynamic evaluation metrics and constructal optimization methods from the interdisciplinary perspective. Key words: precooling; postharvest quality; evaluation; optimization; interdiscipline. on temperature management (Hsiao and Huang, 2016; Mercier et al., Introduction 2017). It was estimated that the cold chain market would be valued Shelf-life is the main concern to maintain high postharvest quality at USD 293.27 billion by 2023 driven by the growing international involved with attributes such as colour, flavour, moisture, hard- trade of perishable food products, while the segment of fruits and ness, visible decay, nutrient content, and microbiological enumer- vegetables was predicted to be at the fastest growth rate from 2018 ation. Temperature control is of great importance to achieve higher to 2023 (MarketsandMarkets, 2018). In particular, as firstly intro- postharvest quality and longer shelf-life. The food supply chain for duced in 1904, precooling is the first stage of cold chain logistics for long-distance transport from farmers to consumers without tem- postharvest fruits and vegetables (Ryall and Pentzer, 1982). perature control usually results in much food wastage (FAO, 2013). Precooling is defined as a postharvest procedure to rapidly remove Consequently, the cold chain logistics becomes more and more common for food security and quality preservation with the emphasis the field heat and reduce the product temperature with the ultimate © The Author(s) 2020. Published by Oxford University Press on behalf of Zhejiang University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by- nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact email@example.com Downloaded from https://academic.oup.com/fqs/article/4/2/59/5822988 by DeepDyve user on 27 August 2020 60 G. Wang and X. Zhang, 2020, Vol. 4, No. 2 motivation of prolonging the shelf-life (Brosnan and Sun, 2001). The forced-air cooling is developed to force air to flow through the con- postharvest horticultural products are still alive with diverse ongoing tainers and packages with a higher velocity at the product surface by physiological activities and biochemical reactions leading to quality differential pressure. However, the high-power fans used to generate deterioration (Prasanna et al., 2007; Vandendriessche et al., 2013). desirable pressure difference lead to high energy consumption in par- Precooling will retard such biological processes by controlling the ticular for the extremely rapid cooling (Baird et al., 1988; Thompson temperature at a lower level with the removal of field heat resulting and Chen, 1988). in promoted postharvest quality and longer shelf-life (Manganaris Numerous studies have been devoted to cope with the drawbacks et al., 2007; Pelletier et al., 2011; Souza et al., 2011; Rab et al., 2013; of the air-based precooling methods with various evaluation criteria Han et al., 2017b). In the past years, different precooling methods and optimization methods. Therefore, a review on these studies is ne- were developed using various cooling mediums such as water, ice, cessary to give insights into the optimal design of air-based precooling dry ice, liquid nitrogen, and cold air. processes with enhanced performances. Furthermore, novel methods Hydrocooling is a common precooling method to achieve heat for evaluation and optimization of air-based precooling processes are exchange between the products and chilled water by flood, spray, also proposed from the interdisciplinary perspective with inspiration or immersion operations. This method minimizes the moisture from the research in other domains. The new evaluation parameters loss (Wills and Golding, 2016) while achieving a moderate cooling in terms of thermodynamics give insight into the mechanisms behind rate (Sargent et al., 1988), but decay and microbial contamination the evolution of postharvest quality. The new constructal optimiza- may affect the product quality under wet conditions (Vigneault tion method will result in higher postharvest quality. et al., 2000; Macarisin et al., 2017). de Oliveira Alves Sena et al. (2019) investigated the effects of hydrocooling temperature on the Current Evaluation and Optimization Methods quality of cashew apples, while Zainal et al. (2019) studied the ef- of Air-Based Precooling fects of hydrocooling time on the physico-chemical attributes of the rockmelon. It was indicated that the products applicable to The research on air-based precooling methods including room hydrocooling must be water tolerant. cooling and forced-air cooling extends from the last century to re- Vacuum cooling is based on the removal of latent heat of water cent years with experimental and numerical studies. evaporation from the products under much lower pressure than the atmospheric pressure. Vacuum cooling becomes popular because of the rapid and uniform cooling (Sun and Zheng, 2006) as well as Room cooling high energy efficiency (Thompson and Chen, 1988). Whereas, the Early experimental observations for room cooling indicated a removal of water during vacuum cooling leads to the restrictions lower cooling rate than 0.5°C/h (Gibbon, 1972), which hardly met of applicability to different kinds of postharvest products (Brosnan the IIR recommendations for the precooling operation within 24 h and Sun, 2001). On the one hand, vacuum cooling is more suitable (IIR, 1967). Nonetheless, the higher cooling rate can be obtained for leafy vegetables with a much larger surface area for water evap- with well-ventilated packages or containers. For example, 8 h of oration when compared with non-leafy vegetables and fruits. On room cooling for carnations was performed with open boxes (Reid the other hand, vacuum cooling also results in certain weight loss et al., 1983). Furthermore, proper packaging was also investigated which was undesirable for postharvest products. Zhu et al. (2019) for table grapes that were only suitable for air-based precooling reviewed the potential of process design and parameters to promote (Nelson, 1978). the vacuum cooling performances and found out the feasibility gap In addition to the above experimental studies on optimal between the laboratory experiments and industrial applications. packaging design, Geyer et al. (2018) developed a new sensor for Contact or package icing is conducted with crushed ice or ice omnidirectional measurement of air velocity in the space between slurry inside or on top of the containers or packages. The melting of postharvest products and further measured the air velocity inside ice into water results in steady transport conditions with low tem- the bins and gaps between bins for room cooling of apples (Praeger perature and high relative humidity which reduce the weight loss et al., 2020). Duret et al. (2014) conducted a more comprehensive (Gillies and Toivonen, 1995), but the problems with wet conditions experimental research on the characteristics of airflow and heat still exist (Wills and Golding, 2016). Moreover, additional weight and mass transfer during room cooling by measuring the tempera- to the products as well as the expensive water-proof packages leads ture, velocity, humidity, and heat transfer coefficient. Based on such to inefficiency in terms of energy and cost (Thompson et al., 2008). experimental measurements, Laguerre et al. (2015) subsequently Alternatively, in spite of high operation cost, dry ice or liquid ni- proposed a simplified model to quickly predict the heat and mass trogen can be used for cryogenic cooling by conveying the products transfer involved with the room cooling process by solving the linear through the cooling tunnel (Senthilkumar et al., 2015). Due to the system of equations. Thanh et al. (2008) also developed a mechan- possible chilling damage and high operational cost, such precooling istic model based on the experimental data to achieve the real-time methods are mainly employed for the fish industry (Zhao et al., monitoring and control of the temperature distribution inside a cold 2019). room contained with boxes of potatoes. Air-based methods are old and traditional for the precooling In recent years, the numerical modelling approach was also used of fruits and vegetables in comparison with the aforementioned to evaluate and optimize the room cooling process. Chourasia and precooling methods. The products within packages and containers Goswami (2007) numerically analysed the effects of the product and for room cooling are simply placed inside a cold room where the operating parameters on the cooling time and moisture loss during circulated air from the fan coils cools down the products by flowing the cooling of stacks of bagged potatoes in the cold store. They con- around them. Room cooling enables consequent in-situ storage and cluded that the product temperature and moisture loss decreased thus lower loss and cost caused by less handling, but the cooling time with higher bulk medium porosity and product diameter while in- tends to be longer in comparison with other precooling methods creased with the respiration heat and higher air temperature. The (Kader and Rolle, 2004). In order to improve the cooling rate, humidification system with high pressure fogging of the cold room Downloaded from https://academic.oup.com/fqs/article/4/2/59/5822988 by DeepDyve user on 27 August 2020 Evaluation and optimization of air-based precooling for higher postharvest quality, 2020, Vol. 4, No. 2 61 was numerically studied by the modelling approach of combined velocity resulted in reduced energy consumption because of shorter discrete element and computational fluid dynamics (CFD) and the cooling time. effects of operating parameters were analysed to optimize the per- Optimal process design for forced-air cooling was still re- formances of the humidification system (Delele et al., 2009). Ambaw quired due to higher energy consumption in comparison with other et al. (2013) provided a comprehensive review on the CFD models precooling methods (Thompson et al., 2010). Mukama et al. (2017) used to investigate the postharvest handling of horticultural prod- studied the energy consumption of forced-air cooling by experi- ucts including room cooling processes. They suggested new strat- ments with pomegranate fruit and found out the significant effects egies using multiscale models to cope with complicated physical of airflow resistance inside the packages. In fact, the airflow charac- phenomena involved in product geometry, turbulence, and porous teristics in terms of the pressure drop within the packaging system media. In another review of modelling of postharvest storage, for forced-air cooling of table grapes were investigated in earlier re- Grubben and Keesman (2015) divided the modelling structures into search (Ngcobo et al., 2012). The cooling rate and weight loss in add- data-based models, lumped parameter models, and distributed par- ition to the airflow with different packaging designs for table grapes ameter models for postharvest storage of potatoes and respectively were further studied and the trade-off between the cooling rate and reviewed the specific models in the scope of different modelling struc- weight loss had to be reached when choosing the packages (Ngcobo tures. In order to achieve more accurate simulation with less compu- et al., 2013). Ferrua and Singh (2011) proposed a new multi-scale tation time, different turbulence and product models were compared packaging system ranging from clamshells and trays to overall air- for the numerical study on the airflow and heat transfer involved flow pattern across the pallets for forced-air cooling of strawberries with room cooling of apples (Hoang et al., 2015). It was indicated with higher uniformity and energy efficiency. Additionally, more from the comparison that shear stress transport (SST) k-ω turbu- performance parameters were evaluated on the scale of the entire lence model and solid bulk model of the products were respectively forced-air cooler. Olatunji et al. (2017) proposed the overall hetero- more appropriate considering the accuracy of Nusselt number and geneity index to quantify and visualize the non-uniform distribu- cooling time. For the room cooling and forced-air cooling of fresh tion of temperature during forced-air cooling of kiwifruit. Mukama cauliflowers, Le Bideau et al. (2018) combined the one-dimensional et al. (2019) investigated the dynamics of quality loss for forced- numerical model with the in-situ experiments to predict the tempera- air cooling of pomegranate fruit. They obtained the spatiotemporal ture history and mass loss. The heat transfer mechanisms combined profile of quality loss and analysed the effects of relative humidity, with convection, evaporation, and radiation were investigated. packages, and stack orientation. In addition to lab-scale research, ex- Numerical methods were also used to evaluate potential opti- periments in commercial forced-air coolers were recently conducted. mization strategies. The novel staggered placement of the product Defraeye et al. (2016) conducted ambient loading experiments with boxes for room cooling of oranges was compared with the trad- citrus using three airflow strategies within refrigerated containers. itional arrangement of in-line arrays in terms of different cooling The integral performance evaluation involved with the quantifica- performances through numerical simulation (Sajadiye and tion and spatial distribution of cooling rate, shelf-life, and other Zolfaghari, 2017). The reduction of cooling time and the promo- quality attributes. Wu et al. (2018b) investigated the influences of tion of the surface heat transfer coefficient were obtained with the packages for three kinds of citrus fruits on the cooling rate and het- staggered arrangement. Ghiloufi and Khir (2019) also proposed new erogeneity and identified the decreasing cooling rate and increasing air deflectors to optimize the distribution of airflow from the fan cooling heterogeneity caused by fruit wrapping. Mercier et al. (2019) outlets of the cooling unit. The use of these air deflectors resulted in studied the non-uniform distribution of temperature and cooling enhanced heat transfer between the cold air and products and conse- time for strawberries and achieved temperature prediction using quently reduced cooling time during room cooling of dates. In fact, limited sensors by the correlation between the cooling times on the air distribution systems such as supply/return diffusers and fabric outside and inside of the tunnel. Moreover, in the case of the difficul- ducts in the chilled food processing facilities were numerically and ties for practical forced-air cooling experiments, produce simulators experimentally proved to be able to achieve more uniform distribu- could be used to replace the real products with design guidelines in tions of air velocity and temperature, which consequently could be accordance with the geometry parameters, thermophysical proper- the optimization strategies of airflow organization for room cooling ties, and other constraints (Redding et al., 2016). (Parpas et al., 2017, 2018). Mathematical and CFD models for forced-air cooling processes In general, as given in Table 1, the research on room cooling was were also developed for performance evaluation and optimization initially focussed on experiments and turned to numerical simula- (Dehghannya et al., 2010; Zhao et al., 2016). The forced-air cooling tion in recent years. Most of these studies are mainly for evaluation of kiwifruit with palletized packages was numerically simulated with of apparent performances, while optimizations from experience are explicit geometry models of the packaging system in order to achieve achieved in some of them. an accurate prediction of the temperature history (O’Sullivan et al., 2016). Han et al. (2018) conducted similar numerical simulations Forced-air cooling for apple pallets and integrally evaluated the cooling rate and uni- In spite of 4–10 times higher cooling rate compared with room formity, moisture loss, and energy consumption. Wu and Defraeye cooling (Ryall and Pentzer, 1982), the effectiveness of forced-air (2018) numerically investigated the heterogeneities of airflow and cooling is also affected by a series of factors related to the products, cooling rate within an entire pallet of citrus fruit during forced-air packaging design, and operating conditions (Hass et al., 1976). The cooling as one of the scenarios in the whole cold chain and further effect of the ventilation rate was firstly investigated by experiments. predicted the quality evolution by combining with kinetic rate law It was reported that 30%–40% reduction of seven-eighths cooling models. In addition, Wu et al. (2019) further studied the influences time was achieved with double airflow rate (Emond et al., 1996), of the design and placement of the package on these heterogeneous while three to six times decrease of cooling time could be obtained performances. The packaging design was also the main concern for with the increase of air velocity from 0.2 to 3.65 m/s (Lambrinos other numerical research. Delele et al. (2013a) developed a three- et al., 1997). Albayati et al. (2007) further pointed out that higher air dimensional CFD model with explicit geometry of oranges within Downloaded from https://academic.oup.com/fqs/article/4/2/59/5822988 by DeepDyve user on 27 August 2020 62 G. Wang and X. Zhang, 2020, Vol. 4, No. 2 Table 1. Overview of research related to room cooling. Produce Reference Research method Research objectives Performance metrics Vegetables (Gibbon, 1972) Experiment Data collection Temperature history, cooling rate Grape (Nelson, 1978) Experiment Process optimization Temperature history, cooling time, quality loss Carnation (Reid et al., 1983) Experiment Process optimization Cooling time, vase-life Potato (Chourasia and CFD model Effects of product and operating Airflow pattern, temperature history, Goswami, 2007) parameters moisture loss, cooling time (Thanh et al., 2008) Experiment-based Monitoring and control of Temperature history, temperature field theoretical model temperature distribution Apple (Duret et al., 2014) Experiment Experimental characterization Air velocity, convective heat transfer coefficient, temperature history, cool- ing time, weight loss (Laguerre et al., 2015) Experiment-based Fast performance prediction Temperature history, weight loss theoretical model (Hoang et al., 2015) CFD model Comparison of numerical models Temperature history, cooling time, fields of air velocity, pressure, and temperature (Geyer et al., 2018; Experiment Experimental characterization Air velocity Praeger et al., 2020) (Wang and Zhang, Experiment Process evaluation and optimization Temperature history, cooling time, en- 2020) ergy flow, entropy generation, exergy destruction, entransy dissipation Orange (Sajadiye and CFD model Optimization of placement pattern Air velocity, surface heat transfer co- Zolfaghari, 2017) for pallets efficient, temperature history, cooling time, temperature heterogeneity Cauliflower (Le Bideau et al., 2018) One-dimensional Process evaluation and mechanism Temperature history, mass loss, air numerical model clarification velocity and experiment Dates (Ghiloufi and Khir, CFD model Optimization of airflow distribution Fields of air velocity and temperature 2019) Spherical (Delele et al., 2009) CFD model Effects and optimization of operating Histories of temperature and humid- produce parameters for the humidification ity, fields of air velocity, temperature, system and humidity, tracks of droplets ventilated cartons to simulate the airflow and heat transfer charac- mathematical model of the cross-section of a single package with teristics on the scale of the overall forced-air cooler. Getahun et al. spherical produce inside was solved by direct numerical simula- (2017a) developed a CFD model for cooling of palletized apple in- tion for forced-air cooling operation and the effects of vent con- side a fully loaded refrigerated shipping container with the simpli- figurations on the cooling rate and uniformity were analysed fication by porous medium approach and validated the numerical (Dehghannya et al., 2011, 2012). Delele et al. (2013b) numerically model by experimental measurements. The same authors further studied the effects of packaging design in terms of vent area, shape, compared the airflow and heat transfer characteristics affected by number, and position on the individual carton scale for forced-air different packages and airflow resistance (Getahun et al., 2017b). cooling of oranges using an explicit geometry model. Han et al. The cooling rate and uniformity of stacked pomegranates with two (2015) developed a CFD model of an individual slotted box with ex- different package designs were compared by numerical and experi- plicit geometry of apples inside to study the airflow and heat transfer mental studies and the increase of cooling time could be caused by therein. A similar modelling approach was also used to evaluate the the plastic liner (Ambaw et al., 2017). The cooling conditions and new packaging design for strawberries with improved airflow pat- packaging design were optimized to achieve reduced cooling time tern (Nalbandi et al., 2016). Defraeye et al. (2015) conducted a and improved cooling uniformity by rearrangement of airflow ac- comprehensive review on different evaluation metrics of packaging cording to the numerical simulation with a three-dimensional CFD performances and further proposed the trend of integral evaluation model of palletized kiwifruit (O’Sullivan et al., 2017). Similarly, the for future optimization. Han et al. (2017a) integrally evaluated the CFD models of stacked citrus fruit were developed to compare the packaging design for forced-air cooling of apples in terms of energy effects of different containers (Defraeye et al., 2013) and cooling consumption, chilling injury, and mass loss based on the simulation conditions (Defraeye et al., 2014) on the cooling performances in results obtained from their previous numerical model. Berry et al. terms of the cooling rate, convective heat transfer coefficient, and (2016, 2017) also conducted a multi-parameter evaluation of carton energy consumption. Gruyters et al. (2019) numerically compared design for apples considering both the cooling and mechanical per- the cooling rate, temperature uniformity, energy consumption, and formances. Wu et al. (2018a) evaluated the spatial distribution of quality loss for forced-air cooling of apples contained in corrugated cooling time and estimated the quality loss for forced-air cooling of boxes and reusable plastic crates and suggested the use of reusable a single package of orange fruit using the CFD model combined with plastic crates for energy-efficient processes. the kinetic quality model. Wang et al. (2019) established a numerical There was also other research focussed on the numerical model of forced-air cooling for strawberries in the packaging system simulation on the individual package scale. A two-dimensional of the clamshell and box to study the effects of the vent design on the Downloaded from https://academic.oup.com/fqs/article/4/2/59/5822988 by DeepDyve user on 27 August 2020 Evaluation and optimization of air-based precooling for higher postharvest quality, 2020, Vol. 4, No. 2 63 cooling time and cooling uniformity and further propose the optimal for air-based precooling processes, which will have a great influ- design guidelines. Similarly, Wang et al. (2020) recently numerically ence on the postharvest quality. Such interaction among different investigated the effects of opening ratio of the packaging system on evaluation parameters is shown in Figure 1. the cooling time, cooling uniformity, and energy consumption during With inspiration from the natural evolution laws, the constructal forced-air cooling of apples and further established a fitting function theory is widely used to predict the optimal design of various pro- of energy consumption in terms of opening ratio and air velocity for cesses and systems, a majority of which is involved with flow and fruits with different diameters. heat and mass transfer (Bejan and Lorente, 2008). The applicability Forced-air cooling seemed a more popular topic especially in of the constructal design to the problems of forced convective flow these years when compared with room cooling indicated by nu- within channels for cooling of volumes with heat generation results merous studies shown in Table 2. Both experimental and numerical in the possible use for optimization of airflow strategy for air-based investigations were extensively conducted with regard to different precooling (Feijó et al., 2018). The objectives of constructal opti- kinds of products. Moreover, the packaging design was the main mization can be case-sensitive, e.g. including minimization of the research focus because of the significant effects on the apparent temperature difference (Bejan, 1997), flow resistance (Bejan and performances. In addition, most of the optimization was mainly Errera, 2000), and entropy generation (Ghodoossi, 2004). Chen performed with regard to the packaging design, which was also a et al. (2018) recently combined the entransy dissipation extreme posteriori based on performance evaluation and comparison. principle with the constructal theory to deal with a series of op- timization problems and they achieved the improvement of global transfer performance with the optimized devices. Advanced Evaluation and Optimization With A similar problem of airflow maldistribution to the air-based Interdisciplinary Methods precooling method also exists for thermal management in data centre and different strategies have been proposed to promote the uniformity Air-based precooling involves complex mechanisms of airflow and of airflow and cooling (Chu and Wang, 2019). The aforementioned ad- heat transfer with respect to the products and facilities. The perform- vanced evaluation and optimization methods have been already used ances of such airflow and heat transfer directly affect the postharvest for the design of data centre cooling strategies. Qian et al. (2015) con- quality. As given in the previous section, current evaluation and op- ducted a thermodynamic analysis of airflow in data centre in terms of timization methods for air-based precooling are based on simple exergy and entransy based on the heat transfer network model and con- flow and heat transfer characteristics such as velocity, temperature, cluded that entransy analysis was more applicable for such research. heat transfer coefficient, and so on. However, recent development Xie et al. (2019) also proposed new exergetic evaluation parameters regarding evaluation and optimization of flow and heat transfer in- on different levels to identify the inefficient parts of the cooling system deed provides more advanced methods which are promising for fu- based on the experiment and simulation. Tian et al. (2019) developed ture applications for air-based precooling. a new mathematical model to implement the entransy theory to the Some of the thermodynamic parameters as the evaluation metrics multi-scale thermal management in data centre and provide optimiza- have been already used in the food industry. The system performance of tion measures for practical operations. Zhang et al. (2019) further vacuum cooling for mushroom was evaluated by exergy analysis of ex- employed the constructal design of the airflow strategies to improve perimental results and the system efficiency was respectively assessed in the cooling uniformity and extended the modular design to the whole terms of energy and exergy (Ozturk and Hepbasli, 2017). Zisopoulos large-scale data centre. et al. (2017) conducted a review to investigate the use of exergy in- As indicated from the above research, the scheme of advanced dicators in the food industry. The statistical results from their review evaluation and optimization for air-based precooling methods is indicated that exergy indicators were more popular for analyses of the shown in Figure 2. The original design can be evaluated by thermo- energy-intensive drying technologies and processes. Özilgen (2018) dynamic metrics according to experimentally measured data or nu- summarized the values of the specific cumulative energy and exergy merical simulation results. The constructal design is conducted based for various kinds of foods in order to facilitate such thermodynamic on the simplified geometry of the original design. Afterwards, the analysis of food processing processes. Wang and Zhang (2020) recently reconstruction of the geometry in consideration of the operating conducted a comprehensive thermodynamic analysis in terms of en- conditions results in the modified design. The thermodynamic per - ergy, entropy, exergy, and entransy for multi-scale precooling processes formance of the modified design is then assessed by CFD simulation for apples and proposed a new air-based precooling method based on and compared with that of the original design. Further adjustments the trade-off between room cooling and forced-air cooling. in consideration of the performance comparison and practical op- In addition to the overall evaluation of the processes and sys- eration are then performed and result in the optimal design. Such tems, the thermodynamic parameters can also be used as the ob- an optimal design can be in different configurations that have to be jective functions for the optimization of flow and heat transfer. compared in terms of thermodynamic performance by CFD simula- The minimization of entropy generation was first proposed as tion. Finally, the most appropriate optimization strategy is selected the optimization criterion for performance enhancement of heat based on the performance comparison and experimental verification. transfer processes and thermodynamic systems (Bejan, 2013). As derived from the entropy generation, minimal exergy destruction was also used for the enhancement of convective heat transfer Conclusions (Wang et al., 2015; Liu et al., 2018). The extremum principle of entransy dissipation was another widely used optimization method A systematic literature review on the studies related to air-based for conductive, convective, and radiative heat transfer processes precooling methods, i.e. room cooling and forced-air cooling, is firstly (Chen et al., 2013) and was further found out to be more appro- conducted with respect to experimental and numerical investigations, priate for convective heat transfer optimization (Chen et al., 2019). respectively. The performance evaluation parameters and optimization Therefore, thermodynamic parameters are suitable for evaluation methods to promote postharvest quality are specifically pointed out. and further optimization of flow and heat transfer characteristics Afterwards, from the interdisciplinary perspective, thermodynamic Downloaded from https://academic.oup.com/fqs/article/4/2/59/5822988 by DeepDyve user on 27 August 2020 64 G. Wang and X. Zhang, 2020, Vol. 4, No. 2 Table 2. Overview of research related to forced-air cooling. Produce Reference Research method Research objectives Performance metrics Straw- (Emond et al., 1996) Experiment Effects of airflow rate and packages Cooling time berry (Thompson et al., 2010) Experiment Data collection Energy consumption, energy coefficient (Ferrua and Singh, 2011) Experiment Package optimization Temperature history, cooling time, energy consumption (Nalbandi et al., 2016) CFD model Optimization of airflow system Fields of air velocity and temperature, temperature history (Mercier et al., 2019) Experiment Correlation for temperature prediction Temperature history, cooling time (Wang et al., 2019) CFD model Effects and optimization of packages Fields of air velocity and temperature, temperature history, cooling time, cooling uniformity coefficient Kiwifruit (Lambrinos et al., 1997) Experiment Effects of air velocity and packages Temperature history, cooling time (O’Sullivan et al., 2016) CFD model Process evaluation Temperature history, cooling time, fields of air velocity and temperature (O’Sullivan et al., 2017) CFD model Optimization of cooling conditions and Temperature history, cooling time, fields packages of air velocity and temperature, energy consumption (Olatunji et al., 2017) Experiment New heterogeneity index Overall heterogeneity index Apple (Albayati et al., 2007) Experiment Effects of air velocity Temperature history, convective heat transfer coefficient, Nusselt number (Han et al., 2015) CFD model Package optimization Fields of air velocity and temperature, temperature history, cooling time, cooling heterogeneity (Berry et al., 2016) CFD model Effects of packages Cooling time, convective heat transfer coefficient, energy consumption (Han et al., 2017a) CFD model Process evaluation Temperature history, cooling time, convective heat transfer coefficient, fields of air velocity and temperature, energy consumption, mass loss, quality loss (Berry et al., 2017) CFD model Effects of packages Compression strength, cooling time, con- vective heat transfer coefficient, energy consumption, (Getahun et al., 2017a) CFD model Process evaluation Temperature history, cooling time, fields of air velocity and temperature (Getahun et al., 2017b) CFD model Effects of packages Airflow resistance, air velocity, temperature history, temperature field (Han et al., 2018) CFD model Process evaluation Temperature history, cooling time, fields of air velocity and temperature, mass loss, quality loss, energy consumption (Gruyters et al., 2019) CFD model Effects of packages Cooling time, fields of air velocity and temperature, energy consumption, quality loss (Wang et al., 2020) CFD model Effects of air velocity and packages Cooling time, air velocity field, cooling inhomogeneity, energy consumption (Wang and Zhang, 2020) Experiment Process evaluation and optimization Temperature history, cooling time, energy flow, entropy generation, exergy destruction, entransy dissipation Papaya (Albayati et al., 2007) Experiment Effects of air velocity Temperature history, convective heat transfer coefficient, Nusselt number Grape (Albayati et al., 2007) Experiment Effects of air velocity Temperature history, convective heat transfer coefficient, Nusselt number (Thompson et al., 2010) Experiment Data collection Energy consumption, energy coefficient (Ngcobo et al., 2012) Experiment Effects of packages Airflow resistance (Ngcobo et al., 2013) Experiment Effects of packages Airflow resistance, temperature history, cooling time, quality loss Citrus (Delele et al., 2013a) CFD model Process evaluation Fields of air velocity, pressure, and temperature, temperature history (Delele et al., 2013b) CFD model Effects of packages Fields of air velocity, pressure, and temperature, temperature history (Defraeye et al., 2013) CFD model Effects of packages Temperature history, cooling time, convective heat transfer coefficient (Defraeye et al., 2014) CFD model Effects of packages and cooling Temperature history, cooling time, conditions convective heat transfer coefficient, energy consumption Downloaded from https://academic.oup.com/fqs/article/4/2/59/5822988 by DeepDyve user on 27 August 2020 Experimental Simulated verification geometry model Practical CFD consideration simulation Evaluation and optimization of air-based precooling for higher postharvest quality, 2020, Vol. 4, No. 2 65 Table 2. Continued Produce Reference Research method Research objectives Performance metrics (Defraeye et al., 2016) Experiment Effects of airflow strategies Temperature history, cooling time, mass loss, quality loss (Wu et al., 2018a) CFD model Process evaluation Temperature history, cooling time, con- vective heat transfer coefficient, quality loss (Wu et al., 2018b) Experiment Effects of packages and fruit sizes Temperature history, cooling time (Wu et al., 2019; Wu and CFD model Process evaluation and effects of pack- Air velocity, temperature history, cooling Defraeye, 2018) ages time, quality loss Pom- (Mukama et al., 2017) Experiment Effects of packages Airflow resistance, cooling time, energy egranate consumption, energy coefficient (Ambaw et al., 2017) CFD model Effects of packages Temperature history, cooling time, heat transfer coefficient, fields of air velocity, pressure, and temperature (Mukama et al., 2019) Experiment Dynamics of quality loss Weight loss, cooling time, quality loss Cauli- (Le Bideau et al., 2018) One-dimensional Process evaluation and mechanism clari- Temperature history, mass loss, air velocity flower numerical model fication and experiment Spherical (Dehghannya et al., 2011) CFD model Effects of packages Temperature field, cooling heterogeneity, produce temperature history (Dehghannya et al., 2012) CFD model Effects of packages Temperature field, cooling heterogeneity, temperature history, cooling time Simplified geometry model ORIGINAL CONSTRUCTAL THERMODYNAMIC Energy consumption, entropy generation, PARAMETERS exergy destruction, entransy dissipation ... DESIGN DESIGN Constructal theory Evaluating optimizing FLOW AND HEAT Velocity, temperature, pressure drop, TRANSFER PARAMETERS heat transfer coefficient ... affecting CFD simulation Thermodynamic MODIFIED evaluation DESIGN QUALITY Weight loss, quality loss, shelf-life ... PARAMETERS Figure 1. Interaction among different evaluation parameters. CFD simulation metrics and constructal theory are respectively introduced for advanced OPTIMAL Thermodynamic evaluation and optimization of air-based precooling methods for higher DESIGN evaluation postharvest quality. The main concluding remarks are as follows: (1) The research on room cooling was initially focussed on experiments and turned to numerical simulation in recent years. In comparison with room cooling, there was more research by either experimental or numerical methods on forced-air cooling with a wider range of OPTIMIZATION products, and the packaging design was the main research focus. STRATEGY (2) Current performance evaluation of air-based precooling methods is limited to apparent parameters such as air velocity, tempera- Figure 2. Scheme of advanced evaluation and optimization for air-based ture, cooling time, quality attributes, and their statistical charac- precooling methods. teristics and derivations. The optimization method is a posteriori based on performance evaluation and comparison. (3) Advanced evaluation and optimization methods should be considered from the interdisciplinary perspective. The thermo- Acknowledgements dynamic evaluation metrics and constructal optimization method The support of the National Key Research and Development Program are widely used in various domains including data centre cooling. (2016YFD0400106) and the support from Beijing Engineering Research This results in a possible application to solve the similar problem Center of City Heat are gratefully acknowledged. of airflow maldistribution for air-based precooling methods. 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