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When modelling a thermal process such as freezing, it is convenient to make use of thermal property models based on the food's composition; however, there does not appear to be any suitable models specifically for thermal diffusivity. In the absence of such models, thermal diffusivity may be determined from models for effective thermal conductivity, specific heat capacity and density as functions of composition, as well as an appropriate ice fraction model. However, the accuracy of the predictions is strongly dependent on the choice of effective thermal property models. In particular, the effective specific heat capacity model must incorporate the effect of latent heat release as a function of temperature. For meat during freezing, it is recommended that the Dul'Nev Novikov effective thermal conductivity model, Pham's ice fraction model and Chen's effective heat capacity model are used. [ABSTRACT FROM AUTHOR]

The twenty-first century is experiencing a wave of technologies and innovations making use of unique features of nanofluids, in applications such as industrial and process heating, air conditioning and refrigeration systems, heat pipes, solar energy, thermal storage systems, electronic cooling systems and others. Recent literature indicates that suspending solid nanoparticles in traditional working fluids can enhance heat transfer rates by increasing thermal conductivity and heat transfer coefficients. However, there is a wide variation in the extent of heat transfer enhancements reported in the literature. In this review, which mainly focuses on the research published within the last 5 years, experimental investigations from recent developments of nanofluids usage and performance in various heat transfer systems are summarised. In addition, heat transfer mechanisms in nanofluids, the challenges and future direction of nanofluids regarding heat transfer enhancement are discussed. Popular preparation methods of nanofluids and the models of thermophysical properties such as thermal conductivity and viscosity have been reviewed. [ABSTRACT FROM AUTHOR]

The thermal diffusivities of copper/linear-low-density polyethylene (Cu/LLDPE) composites were measured relative to the thermal diffusivity of pure LLDPE. The relative thermal diffusivities were similar to those obtained for copper/high-density polyethylene composites, but were noticeably different from estimated values derived from thermal conductivity, density and specific heat capacity data for Cu/LLDPE from the literature. The thermal diffusivity of the composite material initially decreased below that of the pure polymer with the addition of a small amount of copper, before increasing above it as more was added. There would appear to be marginal or no benefit from adding less than about 15 to 20% metal by volume to a polymer, since the relative increase in thermal diffusivity only becomes significant for greater volumes. [ABSTRACT FROM AUTHOR]

Thermal conductivity data are important for modelling thermal processes and designing refrigeration systems. The literature contains a large number of data for food products, but many are questionable, whether due to typographical errors during publication, indirect measurement methods or inherent variability in the properties of the food itself. This paper describes a method by which the reliability of thermal conductivity data can be checked using simple thermal conductivity models and basic heat transfer theory, provided composition data for the foods are available, or may be assumed based on foods with similar composition. The method is applied to a variety of foods to serve as worked examples. It is recommended that these models be used to check the credibility of measured data before they are published, and also to check published data before they are used in any calculations. [ABSTRACT FROM AUTHOR]

Thermal conductivity data are important for food process modelling and design. Where reliable thermal conductivity data are not available, they need to be predicted. The most accurate ‘first approximation’ methodology for predicting the isotropic thermal conductivity of foods based only on data for composition, initial freezing temperature and temperature dependent thermal conductivity of the major food components was sought. A key feature of the methodology was that no experimental measurements were to be required. A multi-step prediction procedure employing the Parallel, Levy and Effective Medium Theory models sequentially for the components other than ice and air, ice and then air respectively is recommended. It was found to provide the most accurate predictions over the range of foods considered (both frozen and unfrozen, porous and non-porous). The Co-Continuous model applied in a single step also provided prediction accuracy within ±20% (on average), except for the porous frozen foods considered. For greater accuracy more rigorous modelling approaches based on knowledge of the foods structure would be required. [ABSTRACT FROM AUTHOR]

There is reason to question some of the published thermal conductivity data for particulate foods. It is argued in this article, based on thermal conductivity bounds analysis, that the thermal conductivity of particulate foods should range between 0.03 W m–1K–1and 0.30 W m–1K–1(provided the porosity is greater than 25%), and that any data outside this range should be rejected. This argument was supported by data for five foods measured as part of this study (cocoa powder, corn starch, whole milk powder, sucrose, and wheat flour), and the majority of published data for particulate foods do indeed fall within this range. The geometric model was found to provide the most accurate thermal conductivity predictions of the models considered, and is recommended for use in first approximation predictions. [ABSTRACT FROM AUTHOR]

Abstract: Measurements of the thermal conductivity of a model system (squat aluminium cylinders suspended in an aqueous carbohydrate polymer gel) have been performed for a range of compositions up to the packing factor limit of the aluminium (approximately 0.6). Of a variety of models considered, the Cheng–Vachon model provided the most accurate predictions of thermal conductivity, and it was argued that this would also be the case for suspensions of most metals in polymer matrices. A modified form of the Cheng–Vachon model was used to obtain an even closer fit to the experimental data. [Copyright &y& Elsevier]

Abstract: It is often desirable to predict the thermal conductivity of a heterogeneous or composite material based on its composition, particularly where variations in composition are expected. In the case of particulate materials such as sand and soil the volume fraction of the solid phase is often known, while it''s thermal conductivity is not (and vice versa for the fluid phases). It is proposed that the thermal conductivity of the solid phase of granular materials may be determined simply by measuring the thermal conductivities of the material in both the bone-dry and water-saturated states and making use of a modified form of Maxwell''s conductivity model. The method was illustrated using beach sand. [Copyright &y& Elsevier]

Abstract: Effective thermal conductivity measurements were performed for two ice–air systems (ice chips produced by a commercial ice-maker and synthetic snow) using a transient comparative technique. These systems were chosen because the ice fraction could be controlled, thereby de-coupling the problems of ice fraction and thermal conductivity prediction. None of the commonly used simple effective thermal conductivity models (specifically the Series, Parallel, two forms of the Maxwell–Eucken and Effective Medium Theory models) provided adequate prediction accuracy over the full range of ice fractions in the experimental data. The best predictions were provided by a composite model that combined the Effective Medium Theory structure and the recently developed co-continuous structure. [Copyright &y& Elsevier]

Abstract: A new structural model for a heterogeneous material with multiple continuous phases is proposed. The corresponding equation for effective thermal conductivity was derived using three methods. The new model is substantially different from the conventional five fundamental structural models (Series, Parallel, two forms of Maxwell–Eucken, Effective Medium Theory). The model has two applications. First, as a new fundamental structural model to produce composite models using the combinatory rules previously proposed by J.F. Wang, J.K. Carson, M.F. North, D. J. Cleland, A new approach to modelling the effective thermal conductivity of heterogeneous materials, International Journal of Heat and Mass Transfer, 49 (17–18) (2006) 3075–3083. Second, to narrow the bounds of the effective thermal conductivity for heterogeneous materials where the physical structure can be characterised into general classes. [Copyright &y& Elsevier]

Abstract: The literature associated with modelling and predicting the thermal conductivities of food products has been reviewed. The uncertainty involved in thermal conductivity prediction increases as the differences between the food components'' thermal conductivities increase, which means that there is greater uncertainty involved with predicting the thermal conductivity of foods which are porous and/or frozen, than with unfrozen, non-porous foods. For unfrozen, non-porous foods, a number of simple effective thermal conductivity models that are functions only of the components'' thermal conductivities and volume fractions may be used to provide predictions to within ±10%. For frozen and/or porous foods, the prediction procedure is more complicated, and usually requires the prediction of porosity and/or ice fraction, which introduces another source of error. The effective thermal conductivity model for these foods may require an extra parameter (in addition to the components'' thermal conductivities and volume fractions) whose value must often be determined empirically. Recommendations for selecting models for different classes of foods are provided. There is scope for more research to be done in this area. [Copyright &y& Elsevier]

Abstract: In this study, it was shown that effective thermal conductivity models that are functions only of the components’ thermal conductivities and volume fractions could not be accurate for both granular-type porous foods (“external porosity”) and foam-type porous foods (“internal porosity”). An extra parameter is needed to make the model sufficiently flexible to allow it to be applied to porous foods with a range of different structures. A number of effective thermal conductivity models contain the required extra parameter, and of these, Krischer’s model appears to have received the greatest use in the food engineering literature; however, for isotropic materials it is recommended that a modified Maxwell model be used instead, because it assumes an isotropic physical structure, unlike Krischer’s model, and because the numerical value of the extra parameter may be estimated based on whether the food has internal or external porosity. A new procedure for predicting the effective thermal conductivity of non-frozen porous foods is presented as a flowchart. [Copyright &y& Elsevier]

Abstract: A unifying equation for five fundamental effective thermal conductivity structural models (Series, Parallel, two forms of Maxwell–Eucken, Effective Medium Theory) was derived. A procedure for modelling complex materials as composites of these five basic structural models using simple combinatory rules based on structure volume fractions was proposed. The combined models have advantages over other generic models such as the semi-empirical Krischer model, in that each has a distinct physical basis, and that they are not dependent on any empirical parameter. As a by-product, a physical description has been identified for Levy’s model, which was previously used with reservation by some researchers because it was derived solely by mathematical reasoning without any explicit physical basis. [Copyright &y& Elsevier]

Abstract: To avoid potential misapplication of effective thermal conductivity models, materials that may be described as ‘porous’ should be divided into two classes; ‘internal porosity’ materials which have bubbles/pores suspended within a continuous condensed phase (e.g. sponges, foams, honeycombs), and ‘external porosity’ materials which include granular/particulate materials. It is proposed that the effective thermal conductivity region bounded by the Hashin–Shtrikman bounds may be divided into internal porosity and external porosity regions by the Effective Medium Theory (EMT) equation. The use of the Hashin–Shtrikman and EMT equations as porosity bounds was supported by experimental data from the literature. [Copyright &y& Elsevier]

Measurements of the thermal conductivity of a pseudo-porous food analogue were performed over a range of porosities and mean pore sizes with the aim of contributing to the current understanding of the influence of porosity on the effective thermal conductivity of foods. Due to the size and non-homogeneous composition of the food-analogue samples, commonly used thermal conductivity measurement devices, such as the guarded hot-plate and thermal conductivity probe, were not suitable. Instead, a comparative method was used which produced results having estimated uncertainties of 2–5%. The porosity was varied between 0 and 0.65 and the mean pore size relative to the size of the sample container was varied between 10−6 and 4 × 10−3 (volume basis). The results are presented in a form that would be useful for evaluating effective thermal conductivity models. The mean pore size appeared to have a very small influence on the effective thermal conductivity. [Copyright &y& Elsevier]

The thermal conductivity probe is widely used to determine the thermal conductivity of foods; however, its accuracy is limited by a number of factors. In this paper the influence of particle size (relative to probe diameter) on measurement accuracy was examined by measuring the thermal conductivity of sucrose of varying grades, and coffee (both whole beans and ground) using two different methods; the thermal conductivity probe and a transient comparative method. Results showed that the measurement methods were in agreement for the small particulates; however, for the larger particulates the probe method appeared to under-predict thermal conductivity. A possible explanation for this observation is that the probe causes a local variation in the porosity of the sample due to the alteration of the packing density. A suggested rule-of-thumb is that thermal conductivity probes may not be suitable for particulate materials where the particle diameter is greater than the probe diameter. [ABSTRACT FROM AUTHOR]

Thermal properties are essential parameters for performing heat transfer calculations. The thermal properties of foods can have a strong dependence on temperature during thermal processing, particularly during freezing, which has posed a challenge to those attempting to develop generic models that can be applied to a wide range of foods. Ideally, thermal property models should not incorporate any parameters whose value would need to be determined by a physical measurement (since this may defeat the purpose of the model). Instead it is preferable to perform a prediction based only on composition data, in terms of the major food components. This study presents an evaluation of thermal property models requiring only composition data as inputs. It is recommended that the Additive model incorporating a new correlation equation of the specific heat capacity of water should be used for effective specific heat capacity predictions. The thermal conductivity predictions from a little-known model developed by Dul'nev and Novikov provided more accurate predictions on average than more widely known models that have been recommended in previous studies. • A comparison of different composition-based thermal property models is presented. • The Additive model is recommended for effective specific heat capacity predictions. • The Dul'nev and Novikov model shows the best thermal conductivity predictions. [ABSTRACT FROM AUTHOR]