Y.T. Gu’s research while affiliated with Queensland University of Technology and other places

This review examines the current state of research on microscale cellular changes during the drying of plant-based food materials (PBFM), with particular emphasis on computational modelling approaches. The review addresses the critical need for advanced computational methods in microscale investigations. We systematically analyse experimental studies in PBFM drying, highlighting their contributions and limitations in capturing cellular-level phenomena, including challenges in data acquisition and measurement accuracy under varying drying conditions. The evolution of computational models for microstructural investigations is thoroughly examined, from traditional numerical methods to contemporary state-of-the-art approaches, with specific focus on their ability to handle the complex, nonlinear properties of plant cellular materials. Special attention is given to the emergence of data-driven models and their limitations in predicting microscale cellular behaviour during PBFM drying, particularly addressing challenges in dataset acquisition and model generalization. The review provides an in-depth analysis of Physics-Informed Machine Learning (PIML) frameworks, examining their theoretical foundations, current applications in related fields, and unique advantages in combining physical principles with neural network architectures. Through this comprehensive assessment, we identify critical gaps in existing methodologies, evaluate the trade-offs between different modelling approaches, and provide insights into future research directions for improving our understanding of cellular-level transformations during PBFM drying processes. The review concludes with recommendations for integrating experimental and computational approaches to advance the field of food preservation technology.

Bulk level variations of plant foods during drying are mainly governed by microscale characteristic variations [1]. Investigating such microscale variations have been challenging with physics-based models due to heterogeneity of microstructures, largely unknown property data, and limitations of numerical simulations [2]. On the other hand, the development of data-driven machine learning (ML) models for predicting microscale variations has not yet been succeeded due to the inability of having a sufficient dataset for extracting an interpretable solution. Therefore, in this work, the Physics-Informed Neural Network (PINN) capabilities are explored to improve the prediction accuracy of moisture concentration variations of a single plant cell during drying with low dimensional input data. In particular, additional information using relevant physics conditions is provided into the feedforward neural network by altering the loss function. The performance of PINN is investigated and compared against pure data-driven ML model predictions for benchmark cases. It can be highlighted that PINN with additional physics information is significantly improved the prediction accuracy even if the training data is very low, indicating the possibilities of integrating PINN for accurately investigating microscale characteristic variations of plant foods during drying. REFERENCES [1] Z.

In this work, a numerical model that enables simulation of the deformation and flow behaviour of differently aged Red Blood Cells (RBCs) is developed. Such cells change shape and decrease in deformability as they age, thus impacting their ability to pass through the narrow capillaries in the body. While the body filters unviable cells from the blood naturally, cell aging poses key challenges for blood stored for transfusions. Therefore, understanding the influence RBC morphology and deformability have on their flow is vital. While several existing models represent young Discocyte RBC shapes well, a limited number of numerical models are developed to model aged RBC morphologies like Stomatocytes and Echinocytes. The existing models are also limited to shear and stretching simulations. Flow characteristics of these morphologies are yet to be investigated. This paper aims to develop a new membrane formulation for the numerical modelling of Stomatocyte, Discocytes and Echinocyte RBC morphologies to investigate their deformation and flow behaviour. The model used represents blood plasma using the Lattice Boltzmann Method (LBM) and the RBC membrane using the discrete element method (DEM). The membrane and the plasma are coupled by the Immersed Boundary Method (IBM). Previous LBM-IBM-DEM formulations represent RBC membrane response based on forces generated from changes in the local area, local length, local bending, and cell volume. In this new model, two new force terms are added: the local area difference force and the local curvature force, which are specially incorporated to model the flow and deformation behaviour of Stomatocytes and Echinocytes. To verify the developed model, the deformation behaviour of the three types of RBC morphologies are compared to well-characterised stretching and shear experiments. The flow modelling capabilities of the method are then demonstrated by modelling the flow of each cell through a narrow capillary. The developed model is found to be as accurate as benchmark Smoothed Particle Hydrodynamics (SPH) approaches while being significantly more computationally efficient.

This paper presents a Physics-Informed Neural Network-based (PINN-based) surrogate framework, which can couple time-based moisture concentration and moisture-content-based shrinkage of a plant cell during drying. For this, a set of differential equations are coupled to two distinct multilayer feedforward neural networks: (a) PINN-MC to predict Moisture Concentration (MC) with Fick's law of diffusion; and (b) PINN-S to predict Shrinkage (S) with ‘free shrinkage’ hypothesis. Results indicate that compared to a regular deep neural network (DNN), the PINN-MC with fundamental physics guidance produces 53% and 81% accuracy values when unknown data has the lowest five timesteps and the lowest 27 data points, respectively. Moreover, its accuracy is 80% better when predicting any unknown spatiotemporal domain variations. PINN-MC further demonstrates stable and accurate MC predictions irrespective of drying process parameters and microstructural variations. In addition, the PINN-S separately proves that utilising a derived relationship based on the ‘free shrinkage’ hypothesis can improve shrinkage predictions into a realistic behaviour. Also, the PINN-based surrogate framework combines multiple physics for predicting moisture concentration and shrinkage, reassuring its capability as a powerful tool for investigating complicated drying mechanisms. Accordingly, to the best of the authors' knowledge, this surrogate framework is the first of its kind in food engineering applications.

A liquid marble is a liquid droplet coated usually with hydrophobic particles that can hold a very small liquid volume without wetting the adjacent surface. This combination gives rise to a set of unique properties such as resistance to contamination, low-friction mobility and flexible manipulation, making them appealing for a myriad of engineering applications including miniature reactors, gas sensing and drug delivery. Despite numerous experimental studies, numerical modelling investigations of liquid marbles are currently underrepresented in the literature, although such investigations can lead to a better understanding of their overall behaviour while overcoming the use of cost- and time-intensive experimental-only procedures. This paper therefore evaluates the capabilities of three well-established and widely-used particle-based numerical frameworks, namely Smoothed Particle Hydrodynamics (SPH)-based approaches, Coarse-Grained (CG)-based approaches and Lattice Boltzmann Method (LBM)-based approaches, to investigate liquid-marble properties and their key applications. Through a comprehensive review of recent advancements, it reveals that these numerical approaches demonstrate promising capabilities of simulating complex multiphysical phenomena involved with liquid-marble systems such as their floatation, coalescence and surface-tension-surface-area relationship. The paper further elaborates on the perspective that benefiting from particle-based numerical and computational techniques, liquid marbles can become an even more effective and exciting platform for many cutting-edge large-scale engineering applications.

The red blood cell (RBC) deformability is a critical aspect, and assessing the cell deformation characteristics is essential for better diagnostics of healthy and deteriorating RBCs. There is a need to explore the connection between the cell deformation characteristics, cell morphology, disease states, storage lesion and cell shape-transformation conditions for better diagnostics and treatments. A numerical approach inspired from the previous research for RBC morphology predictions and for analysis of RBC deformations is proposed for the first time, to investigate the deformation characteristics of different RBC morphologies. The present study investigates the deformability characteristics of stomatocyte, discocyte and echinocyte morphologies during optical tweezers stretching and provides the opportunity to study the combined contribution of cytoskeletal spectrin network and the lipid-bilayer during RBC deformation. The proposed numerical approach predicts agreeable deformation characteristics of the healthy discocyte with the analogous experimental observations and is extended to further investigate the deformation characteristics of stomatocyte and echinocyte morphologies. In particular, the computer simulations are performed to investigate the influence of direct stretching forces on different equilibrium cell morphologies on cell spectrin link extensions and cell elongation index, along with a parametric analysis on membrane shear modulus, spectrin link extensibility, bending modulus and RBC membrane–bead contact diameter. The results agree with the experimentally observed stiffer nature of stomatocyte and echinocyte with respect to a healthy discocyte at experimentally determined membrane characteristics and suggest the preservation of relevant morphological characteristics, changes in spectrin link densities and the primary contribution of cytoskeletal spectrin network on deformation behaviour of stomatocyte, discocyte and echinocyte morphologies during optical tweezers stretching deformation. The numerical approach presented here forms the foundation for investigations into deformation characteristics and recoverability of RBCs undergoing storage lesion.

A better understanding of plant cell micromechanics would enhance the current opinion on “how things are happening” inside a plant cell, enabling more detailed insights into plant physiology as well as processing plant biomaterials. However, with the contemporary laboratory equipment, the experimental investigation of cell micromechanics has been a challenging task due to diminutive spatial and time scales involved. In this investigation, a three-dimensional (3-D) coupled Smoothed Particle Hydrodynamics (SPH) and Coarse-Grained (CG) computational approach has been employed to model micromechanics of single plant cells going through drying or dehydration. This meshfree-based computational model has conclusively demonstrated that it can effectively simulate the behaviour of stress and strain in a plant cell being compressed at different levels of dryness: ranging from a fresh state to an extremely dried state. In addition, different biological and physical circumstances have been approximated through the proposed novel computational framework in the form of different turgor pressures, strain rates, mechanical properties and cell sizes. The proposed computational framework has potential not only to study the micromechanical characteristics of plant cellular structure during drying, but also other equivalent, biological structures and processes with relevant modifications. There are no underlying difficulties in adopting the model to replicate other types of cells and more sophisticated micromechanical phenomena of the cells under different external loading conditions.

Curtailing the ever-increasing global energy demand remains an arduous challenge. The U.S. Energy Information Administration emphasized that the deployment of new heat exchanger technologies could revolutionize how industry uses energy. In this review, we highlight the significance of metal foam heat exchangers as an attractive alternative to traditional heat exchanger technologies. Research on metal foam heat exchangers is steadily gaining momentum. However, metal foam heat exchangers are highly susceptible to fouling which results in a myriad of issues such as low heat exchanger performance and high energy consumption. These issues are further compounded by the fact that the fundamental mechanisms governing particulate fouling in metal foam heat exchangers are poorly understood. As such, the overarching goal of reducing energy consumption and greenhouse gas emissions could be met by deploying energy-efficient heat exchanger technologies and also by gaining a solid comprehension of multiphase flows, heat transfer, and heat exchanger fouling mechanisms. The development of advanced numerical methods to unravel heat exchanger fouling mechanisms could pave the way for an optimized heat exchanger design with minimum energy consumption and greenhouse gas emissions. This paper provides researchers a review of the performance of metal foam heat exchangers for various industrial applications and the implications of particulate fouling on the thermal performance of metal foam heat exchangers.