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Recent advances in diagnostic neuroradiological imaging, allowed the detection of unruptured intracranial aneurysms (IAs). The shape - irregular or multilobular - of the aneurysmal dome, is considered as a possible rupture risk factor, independently of the size, the location and patient medical background. Disturbed blood flow fields in particular is thought to play a key role in IAs progression. However, there is an absence of widely-used hemodynamic indices to quantify the extent of a multi-directional disturbed flow. We simulated blood flow in twelve patient-specific anterior circulation unruptured intracranial aneurysms with daughter blebs utilizing the spectral/hp element framework Nektar++. We simulated three cardiac cycles using a volumetric flow rate waveform while we considered blood as a Newtonian fluid. To investigate the multidirectionality of the blood flow fields, besides the time-averaged wall shear stress (TAWSS), we calculated the oscillatory shear index (OSI), the relative residence time (RRT) and the time-averaged cross flow index (TACFI). Our CFD simulations suggest that in the majority of our vascular models there is a formation of complex intrasaccular flow patterns, resulting to low and highly oscillating WSS, especially in the area of the daughter blebs. The existence of disturbed multi-directional blood flow fields is also evident by the distributions of the RRT and the TACFI. These findings further support the theory that IAs with daughter blebs are linked to a potentially increased rupture risk.

In this paper, we present a meshless method belonging to the family of element-free Galerkin (EFG) methods. The presented meshless method allows accurate enforcement of essential boundary conditions. The method uses total Lagrangian formulation with explicit time integration to facilitate code simplicity and robust computations in applications that involve large deformations and non-linear materials. We use a regularized weight function, which closely approximates the Kronecker delta, to generate interpolating shape functions. The imposition of the prescribed displacements on the boundary becomes as straightforward as in the finite element method (FEM). The effectiveness and accuracy of the proposed method is demonstrated using 3D numerical examples that include cylinder indentation by 70% of its initial height, and indentation of the brain.

In the present contribution, we analyze the non-stationary, incompressible, laminar, natural convection flow in a rectangular enclosure filled with a micropolar-nanofluid (Al2O3/water) in the presence of a magnetic field, using Dynamic Mode Decomposition (DMD) and Proper Orthogonal Decomposition (POD) method. DMD method utilizes numerically or experimentally obtained data (often generated by nonlinear dynamics) to compute the eigenvalues and the eigenmodes of an approximated linear model, and the growth/decay (damp) rates, frequencies and spatial structures for each mode. POD provides the energy content measure for different modes. DMD and POD method have been applied in the study, optimization and control of large-scale dynamical systems. They can be used supplementary to each other or separately for cross comparison. We propose an efficient way to (i) snapshots sampling (ii) select the optimal number for analysis and (iii) define the grid resolution (grid density) for obtaining a grid independent solution for the DMD analysis. We numerically solve the flow equations using a meshless point collocation method (MPCM), using the Moving Least Square (MLS) method to approximate the unknown field functions and their spatial derivatives. We demonstrate stability of the system using the Ritz and dynamic spectrum along with the phase portrait of time coefficients of the most dominant DMD modes. We estimate the energy contents of the captured modes for various Hartman (Ha) and Rayleigh (Ra) numbers, nanoparticles volume fractions ( φ ), magnetic field ( ξ ) and square enclosure angles ( γ ). The energy content of each mode for Al2O3/water nanofluid is associated with the corresponding eigenvalue and discloses its contribution to the total energy. The results show that the above mentioned parameters significantly affect the energy contents of the captured modes.

SUMMARYA robotic manipulator’s classical mechanical capabilities are governed by the design parameters (mass, geometry, dimensions, etc.) of its kinematic pairs and its architecture (number of limbs, degrees of freedom, actuation ability, etc.). Using Clifford-Algebraic and network theoretic methods, this work presents a novel-theoretical framework which allows any two robot architectures and design parameters to be mathematically related to one another through combinations of discrete operators or ‘reassembling transformations’. Two theoretical case studies involving a 6R manipulator and Klann linkage are furnished in this work.

Abdominal aortic aneurysm (AAA) is a permanent and irreversible dilation of the lower region of the aorta. It is typically an asymptomatic condition that if left untreated can expand to the point of rupture. In simple mechanical terms, rupture of an artery occurs when the local wall stress exceeds the local wall strength. It is therefore understandable that numerous studies have attempted to estimate the AAA wall stress and investigate the relationship between the AAA wall stress and AAA symptoms.We conducted computational biomechanics analysis for 19 patients with AAA (a proportion of these patients were classified as symptomatic) to investigate whether the AAA wall stress fields (both the patterns and magnitude) correlate with the clinical definition of symptomatic and asymptomatic AAAs. For computation of AAA wall stress, we used a very efficient method recently presented by the Intelligent Systems for Medicine Laboratory. The Intelligent Systems for Medicine Laboratory's method uses geometry from computed tomography images and mean arterial pressure as the applied load. The method is embedded in the software platform BioPARR-Biomechanics based Prediction of Aneurysm Rupture Risk, freely available from http://bioparr.mech.uwa.edu.au/. The uniqueness of our stress computation approach is three-fold: i) the results are insensitive to unknown patient-specific mechanical properties of arterial wall tissue; ii) the residual stress is accounted for, according to Y.C. Fung's Uniform Stress Hypothesis; and iii) the analysis is automated and quick, making our approach compatible with clinical workflows.Symptomatic patients could not be identified from the plots (pattern) of AAA wall stress and stress magnitude. Although the largest stress was predicted for a patient who suffered from AAA symptoms, the three patients with the smallest stress were also symptomatic.The results demonstrate, contrary to the common view, that neither the wall stress magnitude nor the stress distribution appears to be associated with the presence of clinical symptoms.

In recent years, numerous strides have been taken towards to discovery of superconducting lattices, exhibiting the Meissner–Ochsenfeld effect, at increasingly higher temperatures. In this work, we present a novel and generalised mathematical formulation, which maps the atomic and structural characteristics of superconducting lattice structures to their critical temperature. This formulation models many-agent systems by representing them as spatially distributed networks in $${\mathbf{R}}^{4,1}$$ Conformal Geometric Algebraic space. Using these higher-dimensional mathematical representations, we present generalised relationships between the critical temperature and basic atomic information, including crystal unit cell data, for an arbitrary lattice structure. Case studies have been presented for Nd2Fe2Se2O3, LiFeAs, LaOFeAs, Sr2VO3FeAs, Sr2Mn2CuAs2O2 and Ba2YCu3O7.

This paper presents a novel and generalised mathematical formulation for mathematical and theoretical chemistry. The formulation presented employs network theoretic methods and standard Conformal Geometric Algebra (Conformal Clifford Algebra) with a unique algebraic extension that introduces special non-integer basis vectors, known as ‘shades’. The methodology developed lays preliminary foundations for both generic and more specialised characterisations and modelling of chemical systems by computing a multi-vector function, termed the ‘hyperfield’ (an algebra-geometric characterisation of a many-agent chemical system). In this paper, the formulation has been applied to inorganic compounds, in the context of their emergent properties (such as boiling and melting points) and intra-molecular characteristics. Case studies have been presented for the calculation of melting point, boiling point, maximum solubility in water and equilibrium bond length of 21 diatomic molecules. This work finds that a network theoretic model of many-agent systems modelled in $${\mathbf{R}}^{\mathrm{4,1}}$$ space, using an algebraic extension of non-integer basis vectors, presents a promising and unifying method for theoretical mathematical chemistry.

This work presents a novel paradigmatic revision of traditional neural networks, using network theoretic methods and Conformal Geometric Algebra. A unique theoretical framework called the ‘hyperfield cognition framework’ expands upon the mathematical foundations of neural networks in five-dimensional Conformal Geometric Algebraic space. This framework allows one to construct a novel theoretical computational engine, which is similar to a standard artificial neural network, but admits numerous added benefits: permits multiple training programs simultaneously, affords computational multiplicity in a single engine, reduces sensitivity to adversarial perturbations in training sets, affords broader capabilities and plasticity in the training of networks and produces robust and streamlined ‘neural networks’. We call this novel five-dimensional neural network a ‘hyperfield cognition network’ (HCN). This paper demonstrates the utility and merit of the proposed Hyperfield Cognition Network by presenting two case studies, the first of which investigates the fuel efficiency of various automobiles and the second of which models the residuary resistance per unit weight of displacement of ships.

In geochemistry, the elevated concentrations of certain elements in rock or mineral samples are used for the assessment of a mineralising system’s important geological characteristics. The relationship between the elements and system characteristics may take one of two forms, regression or classification. The collection and analysis of geochemical samples typically leads to the development of sparse and fuzzy multivariate datasets, which often have complex interrelationships. As a result, it is difficult to quantitatively and qualitatively interpret many of these geochemical datasets and relate the resulting data to relevant geological characteristics. There are no governing expressions in the current literature that can relate mineral-based trace element assemblages to geological characteristics. In this work, a generalised formulation for multivariate systems modelling is presented and it is applied to the discovery of the manifested system characteristics of arsenopyrite samples collected from Australian gold (Au) deposits, using fuzzy arsenopyrite trace element datasets. Arsenopyrite trace element assemblages, collected using laser ablation-inductively coupled plasma-mass spectrometry, are represented as spatially distributed networks embedded in $$ {\mathbf{R}}^{4,1} $$ conformal geometric algebraic space. The interactions of elements within these networks generate multivectors which are referred to as hyperfields. The functions and parameters used to generate these hyperfields are optimised to create multiple unique models, which are used to discover relationships between system characteristics and the multivariate dataset. The models generated allow a specialist to discover qualitative relations, using higher-dimensional representations of multivariate data. In this work, the generalised hyperfield formulation is applied to the problem of predicting the endowment, lithology and camp of Au deposits, using an arsenopyrite trace element dataset. The hyperfield formulation is compared against the state-of-the-art random forest decision tree algorithm ‘XGBoost’ and artificial neural networks (ANNs).

Computational biomechanics models play a key role in predicting/evaluating pedestrian head kinematics and injury risk in car-to-pedestrian collisions. The human multibody models most commonly used in car-to-pedestrian collision reconstruction, such as pedestrian model by The Netherlands Organisation for Applied Scientific Research TNO, are built using the anthropometry of Western European population as defined in TNO (2013) human multibody model database. In this study, we investigate the effects of the anthropometric differences between the Western European and Chinese populations on the pedestrian head kinematics and injury responses predicted using multibody models. The comparison was conducted through car-to-pedestrian collision simulations using pedestrian multibody models representing anthropometric characteristics of Western European and Chinese populations, three typical vehicle shapes (sedan, SUV and minivan), five initial vehicle impact speeds (30, 35, 40, 45, 50 km/h), and six pedestrian walking postures. The results indicate that the change of pedestrian model anthropometry (from Western European to Chinese) exerts appreciable effects on both the predicted initial boundary conditions of the head-to-windscreen impact (in particular the head-to-windscreen impact angle) and the head injury indices in the impact with the road surface (secondary impact).

We present comprehensive biomechanical analyses of abdominal aortic aneurysms (AAA) for 43 patients. We compare stress magnitudes and stress distributions within arterial walls of abdominal aortic aneurysms (AAA) obtained using two simulation and modelling methods: 1) Fully automated and computationally very efficient linear method embedded in the software platform BioPARR Biomechanics based Prediction of Aneurysm Rupture Risk, freely available from https://bioparr.mech.uwa.edu.au/; 2) More complex and much more computationally demanding Non-LISA Non-Linear Iterative Stress Analysis that uses a non-linear inverse iterative approach and strongly non-linear material model. Both methods predicted localised high stress zones with over 90% of AAA model volume fraction subjected to stress below 20% of the 99th percentile maximum principal stress. However, for the non-linear iterative method, the peak maximum principal stress (and 99th percentile maximum principal stress) was higher and the stress magnitude in the low stress area lower than for the automated linear method embedded in BioPARR. Differences between the stress distributions obtained using the two methods tended to be particularly pronounced in the areas where the AAA curvature was large. Performance of the selected characteristic features of the stress fields (we used 99th percentile maximum principal stress) obtained using BioPARR and Non-LISA in distinguishing between the AAAs that would rupture and remain intact was for practical purposes the same for both methods. This article is protected by copyright. All rights reserved.

Tumour resection requires precise planning and navigation to maximise tumour removal while simultaneously protecting nearby healthy tissues. Neurosurgeons need to know the location of the remaining tumour after partial tumour removal before continuing with the resection. Our approach to the problem uses biomechanical modelling and computer simulation to compute the brain deformations after the tumour is resected. In this study, we use meshless Total Lagrangian explicit dynamics as the solver. The problem geometry is extracted from the patient-specific magnetic resonance imaging (MRI) data and includes the parenchyma, tumour, cerebrospinal fluid and skull. The appropriate non-linear material formulation is used. Loading is performed by imposing intra-operative conditions of gravity and reaction forces between the tumour and surrounding healthy parenchyma tissues. A finite frictionless sliding contact is enforced between the skull (rigid) and parenchyma. The meshless simulation results are compared to intra-operative MRI sections. We also calculate Hausdorff distances between the computed deformed surfaces (ventricles and tumour cavities) and surfaces observed intra-operatively. Over 80% of points on the ventricle surface and 95% of points on the tumour cavity surface were successfully registered (results within the limits of two times the original in-plane resolution of the intra-operative image). Computed results demonstrate the potential for our method in estimating the tissue deformation and tumour boundary during the resection.

Our motivation is to enable non-biomechanical engineering specialists to use sophisticated biomechanical models in the clinic to predict tumour resection-induced brain shift, and subsequently know the location of the residual tumour and its boundary. To achieve this goal, we developed a framework for automatically generating and solving patient-specific biomechanical models of the brain. This framework automatically determines patient-specific brain geometry from MRI data, generates patient-specific computational grid, assigns material properties, defines boundary conditions, applies external loads to the anatomical structures, and solves differential equations of nonlinear elasticity using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithm. We demonstrated the effectiveness and appropriateness of our framework on real clinical cases of tumour resection-induced brain shift.

We use computational fluid dynamics (CFD) to simulate blood flow in intracranial aneurysms (IAs). Despite ongoing improvements in the accuracy and efficiency of body-fitted CFD solvers, generation of a high quality mesh appears as the bottleneck of the flow simulation and strongly affects the accuracy of the numerical solution. To overcome this drawback, we use an immersed boundary method. The proposed approach solves the incompressible Navier-Stokes equations on a rectangular (box) domain discretized using uniform Cartesian grid using the finite element method. The immersed object is represented by a set of points (Lagrangian points) located on the surface of the object. Grid local refinement is applied using an automated algorithm. We verify and validate the proposed method by comparing our numerical findings with published experimental results and analytical solutions. We demonstrate the applicability of the proposed scheme on patient-specific blood flow simulations in IAs.

In this paper, we develop a meshless collocation scheme for the numerical solution of magnetohydrodynamics (MHD) flow equations. We consider the transient laminar flow of an incompressible, viscous and electrically conducting fluid in a rectangular duct. The flow is driven by the current produced by electrodes placed on the walls of the duct. The method combines a meshless collocation scheme with the newly developed Discretization Corrected Particle Strength Exchange (DC PSE) interpolation method. To highlight the applicability of the method, we discretize the spatial domain by using uniformly (Cartesian) and irregularly distributed nodes. The proposed solution method can handle high Hartmann (Ha) numbers and captures the boundary layers formed in such cases, without the presence of unwanted oscillations, by employing a local mesh refinement procedure close to the boundaries. The use of local refinement reduces the computational cost. We apply an explicit time integration scheme and we compute the critical time step that ensures stability through the Gershgorin theorem. Finally, we present numerical results obtained using different orientation of the applied magnetic field.

A robotic manipulator is a networked assemblage of kinematic pairs, mobile platform(s) and/or tool(s) that generate motion and forces at its end-effector. A manipulator’s theoretical dynamic, kinematic and workspace capabilities are governed by the design parameters (mass, geometry, dimensions, etc.) of its kinematic pairs and its architecture (number of limbs, degrees of freedom, actuation ability, etc.). By adjusting or changing the manipulator’s parameters (both architectural and kinematic pair-related), either from a design standpoint or physically, the robot undergoes what the authors broadly refer to as a ‘reassembling transformation’. By effecting reassembling transformations on a robot, we may generate desired changes in the workspace, kinematic and dynamic capabilities for a specific task manoeuvre. This work presents a novel methodology that marries quaternion rotors, the Lagrangian formulation, geometric algebra and associated mathematical apparatus, to tackle the problem of analysing and modelling robotic manipulators or mechanisms that undergo such ‘reassembling transformations’. Upon presenting a quaternion-based methodology for position, kinematics and Lagrangian dynamics, reassembling transformation types are introduced and detailed. We apply reassembling transformations to a Delta manipulator, in two separate case studies, each with unique target changes to the dynamic capabilities of the post transformed robot in its chosen task manoeuvres. The framework presented in this paper is generalised and its methods can be used to analyse and transform any parallel manipulator and also extended to the study of non-linear systems of equations for modelling multi-body dynamics.

Neurosurgery for tumour resection requires precise planning and navigation to maximise tumour removal and at the same time protect healthy tissue. Biomechanical modelling and computer simulation provide a means for brain deformation prediction, facilitating millimetre-accurate neuronavigation. In this contribution, we modelled brain deformation due to tumour resection. We assumed that the brain deformations were caused by stresses on the resected tumour surface released when the tumour is removed. We computed deformations using nonlinear finite element analysis in Abaqus FEA software suite. We used the patient-specific geometry extracted from the pre-operative magnetic resonance image (MRI). We included the parenchyma tissue, cerebral tumour, cerebral ventricles, skull and cerebral falx in our computational biomechanics brain model. We created mixed (consisting of hexahedral and tetrahedral elements) patient-specific finite element mesh, and used realistic material properties together with appropriate contact conditions at boundaries. We compared our predicted intra-operative brain geometry (based on the calculated deformation field) contours with the intra-operative MRI to assess the accuracy of our results. The results indicate that our biomechanical models can complement medical image processing techniques when conducting non-rigid registration.

Our motivation is to enable non-specialists to use sophisticated biomechanical models in the clinic. To further this goal, in this study, we constructed a framework within 3D Slicer for automatically generating and solving patient-specific biomechanical models of the brain. This framework allows determining automatically patient-specific geometry from MRI data, generating patient-specific computational grid, defining boundary conditions and external loads, assigning material properties to intracranial constituents and solving the resulting set of differential equations. We used Meshless Total Lagrangian Explicit Dynamics Method (MTLED) to solve these equations. We demonstrated the effectiveness and appropriateness of our framework on a case study of craniotomy-induced brain shift.

Computational biomechanics analysis of neurologic injuries has so far been focused on the brain. This has resulted in the development of industrially applied computational biomechanics models of the brain and various criteria (such as cumulative strain damage measure, CSDM and maximum principal strain, MPS) to determine the likelihood of brain injuries. In contrast, spinal cord injury (SCI) has attracted relatively little attention from the computational biomechanics research community. This study, addresses this gap by applying computational biomechanics analysis to predict deformations of the cervical spinal cord and the risk of SCI. To do this, a three-dimensional finite element model of the cervical spinal cord was built using data from the Visible Human Project and incorporated into a well-established finite element model of the human body for injury analysis, created by the Global Human Body Models Consortium (GHMBC). The model was applied to simulate well-documented low-speed whiplash-type experiments previously conducted on volunteers by the Japan Automobile Research Institute and University of Tsukuba. The results suggest that direct application of CSDM and MPS criteria and the related injury risk curves leads to overestimation of SCI risk.

Cycling is a popular choice for urban transportation. Helmets are important and the most popular means of head protection for cyclists. However, a debate about the effectiveness of helmets in protecting a cyclist's head from injury continues.We employed computational biomechanics methods to analyze the head protection effectiveness of nine off-the-shelf-helmets for two typical impact scenarios that occur in cycling accidents: cyclist's head impacting a kerb (kerb-impact) and cyclist skidding (skidding impact) on the road surface. We conducted drop tests for all nine analyzed helmets, and used the test data for validation of the corresponding helmet finite element (FE) models created in this study. The validated helmet models were then used in the full-scale computer simulations (FE analysis for the skull, brain and helmet, and multibody dynamics for the remaining segments of the cyclist's body) of the cycling accidents for cyclists wearing a helmet and without a helmet.The results indicate that helmets can reduce both the peak linear acceleration of the cyclist head center of gravity (COG) and the risk of cyclist skull fracture. However, higher rotational acceleration of the head COG was predicted for cyclists wearing helmets. The results obtained using the injury criteria that rely on the brain deformations (maximum shear strain MPS and cumulative strain damage measure CSDM) suggest that helmets may offer protection in all the analyzed cyclist impact scenarios. However, the predicted level of protection varies for different helmets and impact scenarios with appreciable variations in the predictions obtained using different injury criteria. Reduction in the maximum principal strain (MPS

Magnetic drug targeting (MDT) is a noninvasive method for the medical treatment of various diseases of the cardiovascular system. Biocompatible magnetic nanoparticles loaded with medicinal drugs are carried to a tissue target in the human body (in vivo) under the applied magnetic field. The present study examines the MDT technique in various microchannels geometries by adopting the principles of biofluid dynamics (BFD). The blood flow is considered as laminar, pulsatile and the blood as an incompressible and non-Newtonian fluid. A two-phase model is adopted to resolve the blood flow and the motion of magnetic nanoparticles (MNPs). The numerical results are obtained by utilizing a meshless point collocation method (MPCM) alongside with the moving least squares (MLS) approximation. The numerical results are verified by comparing with published numerical results. We investigate the effect of crucial parameters of MDT, including (1) the volume fraction of nanoparticles, (2) the location of the magnetic field, (3) the strength of the magnetic field and its gradient, (4) the way that MNPs approach the targeted area, and (5) the bifurcation angle of the vessel.

In this work, a novel methodology for the modelling and forecasting of time series using higher-dimensional networks in $$\mathbf{R }^{4,1}$$ space is presented. Time series data is partitioned, transformed and mapped into five-dimensional conformal space as a network which we call the ‘Spatio-Temporal Ordinality Network’ (STON). These STONs are characterised using specific Clifford Algebraic multivector functions which are found to be highly effective as featurised variables that forecast future states of the time series. The proposed STONs present as unique mathematical constructions that capture the algebra-geometric inter-dependencies in the historical behaviour of a time series system, thereby presenting governing expressions for forecasting based on historical values. Case studies on the seasonally adjusted Australian unemployment rate and the NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis for Global Land-Ocean Temperature Index are furnished in this work and are compared against multilayer perceptrons (MLP), long short term memory (LSTM) neural networks, ARIMA and Holt-Winters methods. This formulation presents an alternative and effective modelling and forecasting paradigm for time series and multivariate systems with known or hidden variables.

These datasets contain Computed Tomography (CT) images of 19 patients with Abdominal Aortic Aneurysm (AAA) together with 19 patient-specific geometry data and computational grids (finite element meshes) created from these images applied in the research reported in Journal of Surgical Research article "Is There A Relationship Between Stress in Walls of Abdominal Aortic Aneurysm and Symptoms?"[1]. The images were randomly selected from the retrospective database of University Hospitals Leuven (Leuven, Belgium) and provided to The University of Western Australia's Intelligent Systems for Medicine Laboratory. The analysis was conducted using our freely-available open-source software BioPARR (Joldes et al., 2017) created at The University of Western Australia. The analysis steps include image segmentation to obtain the patient-specific AAA geometry, construction of computational grids (finite element meshes), and AAA stress computation. We use well-established and widely used data file formats (Nearly Raw Raster Data or NRRD for the images, Stereolitography or STL format for geometry, and Abaqus finite element code keyword format for the finite element meshes). This facilitates re-use of our datasets in practically unlimited range of studies that rely on medical image analysis and computational biomechanics to investigate and formulate indicators and predictors of AAA symptoms. ispartof: DATA IN BRIEF vol:30 ispartof: location:Netherlands status: published

The laminar, natural convective flow of a micropolar nanofluid in the presence of a magnetic field in a square porous enclosure was studied. The micropolar nanofluid is considered to be an electrically conductive fluid. The governing equations of the flow problem are the conservation of mass, energy, and linear momentum, as well as the angular momentum and the induction equations. In the proposed model, the Darcy&ndash, Brinkman momentum equations with buoyancy and advective inertia are used. Experimentally obtained forms of the dynamic viscosity, the thermal conductivity, and the electric conductivity are employed. A meshless point collocation method has been applied to numerically solve the flow and transport equations in their vorticity-stream function formulation. The effects of characteristic dimensionless parameters, such as the Rayleigh and Hartmann numbers, for a range of porosity and solid volume fraction of Al2O3 particles in a water-based micropolar nanofluid on the flow and heat transfer in the cavity are investigated. The results indicate that the intensity of the magnetic field significantly affects both the flow and the temperature distributions. Moreover, the addition of nanoparticles deteriorates the heat-transfer efficiency under specific conditions.