Anisotropic Material Properties Research Articles

Elasto-viscoplastic model for rayon yarns

AbstractIn this paper we develop a new model for the simulation of the mechanical behavior of rayon twisted yarns, at macroscopic level. A yarn with its continuous filaments is represented by an equivalent three-dimensional solid of cylindrical shape, discretized by finite elements, with properly defined local anisotropic material properties. The new constitutive model, inspired by experimental results on rayon untwisted yarns, is formulated in the framework of the thermodynamics of irreversible processes and includes visco-elastic and visco-plastic dissipation mechanisms. The effect of twist is taken into account by including the direction of the fibers in the free energy definition. The overall model is validated comparing numerical and experimental results on twisted rayon yarns.

Deep Learning-Based Acoustic Emission Signal Filtration Model in Reinforced Concrete

AbstractAcoustic emission is a nondestructive testing (NDT) technique, widely used to monitor the condition of structures for safety reasons especially in real time. The method utilizes the electrical signals generated by the elastic waves in a material under load to detect and locate damage in structures. However, identifying the sources of AE signals in concrete or composite materials can be challenging due to the anisotropic properties of materials and interpreting a large amount of AE data, leading to data misinterpretation and inaccurate detection of damage. Hence, the need for filtering out noise-induced signals from recorded data and emphasizing the actual AE source is crucial for monitoring and source localization of damage in real time. This study proposed a one-dimensional convolutional neural network (1D-CNN) deep learning approach to filter around 22,000 AE data in a reinforced concrete (RC) beam. The model utilizes significant AE parameters identified through neighborhood component analysis (NCA) to classify true AE signals from noise-induced signals. By using the optimized network parameters, a high classification accuracy of 97% and 96.29% was achieved during the training and testing phases, respectively. To check the reliability of the proposed AE filtering model in the real world, it was evaluated and verified using source location AE activities collected during a four-point bending test on a shear-deficient beam. The outcomes suggest that the proposed AE filtration model has the potential to accurately classify AE signals with an accuracy of 92.8% and proved that the filtration model provides accurate and valuable insight into source location determination.

Symmetry-Reduction Enhanced Polarization-Sensitive Photoresponse Based on One-Dimensional van der Waals Materials.

Designing high-performance polarization-sensitive photodetectors is essential for photonic device applications. Anisotropic one-dimensional (1D) van der Waals (vdW) materials have provided a promising platform to that end. Despite significant advances in 1D vdW photonic devices, their performance is still far from delivering practical potential. Herein, we propose the design of high-performance polarization-sensitive photodetectors using unique 1D vdW materials. By leveraging the chemical vapor transport technique, we successfully fabricate high-quality 1D vdW Nb2Pd1-xSe5 (x = 0.29) nanowires. The 1D vdW Nb2Pd1-xSe5 photodetector exhibits a high mobility of ∼56 cm2/(V s) and superior photoresponse performance, including a high responsivity of 1A/W and an ultrafast response time of ∼8 μs under 638 nm illumination. Moreover, the 1D vdW Nb2Pd1-xSe5 photodetector demonstrates excellent polarization-sensitive photoresponse with a degree of linear polarization (DOLP) up to 0.85 and can be modulated by adjusting the gate voltage, laser power density, and wavelength. Those exceptional performance are believed to be relevant to the symmetry-reduction induced by the partial occupation of Pd sites. This study offers feasible approaches to enhance the anisotropy of 1D vdW materials and the modulation of their polarization-sensitive photoresponse, which may provide deep insights into the physical origin of anisotropic properties of 1D vdW materials.

Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art.

With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced.

Study on the Thermal Sensitivity of Anisotropic Thin Sheets: Application to Hot Bulge Tests

This article presents a study on the thermal sensitivity of anisotropic thin sheets, focusing specifically on their behavior during hot bulge tests. Anisotropic materials exhibit different mechanical properties in different directions, and understanding their response to thermal loading is crucial for various engineering applications.
 The experimental investigation involves subjecting thin sheets of anisotropic materials to controlled thermal conditions and measuring their response. The hot bulge test, a well-established method, is employed to analyze the behavior of the sheets under elevated temperatures. This test involves applying a controlled internal pressure to a heated circular and elliptical specimen, causing it to deform and form a bulge.
 Through this study, the thermal sensitivity of anisotropic thin sheets is characterized by analyzing the bulge height, bulge profile, and strain distribution. The influence of various factors, such as temperature, material anisotropy, and loading rate, is examined to understand their effects on the sheet's response.
 Experimental results reveal significant variations in the thermal sensitivity of anisotropic thin sheets, depending on the material's orientation and temperature. The study demonstrates that certain orientations exhibit greater sensitivity to thermal loading, leading to distinct bulge profiles and strain distributions.
 Furthermore, numerical simulations are conducted using finite element analysis to validate and complement the experimental findings. The simulation models incorporate the anisotropic material properties and the thermal boundary conditions, enabling a comprehensive understanding of the thermal sensitivity behavior observed experimentally.
 The outcomes of this study provide valuable insights into the thermal behavior of anisotropic thin sheets, particularly in the context of hot bulge tests. The findings contribute to the knowledge base of material characterization and can aid in the design and optimization of structures and components subjected to thermal loading, where anisotropic materials are involved.

Experimental and Numerical Study on the Protection Efficiency of a Diamond-Shaped Thermal Jacket for Gun Barrels

Abstract Experimental and numerical investigations on the protection efficiency of a diamond-shaped thermal jacket made of the certain braided composites with anisotropic material properties for gun barrels were carried out to gain an insight into its thermal–structural behavior and performance. The experiments for the gun barrels with and without the thermal jacket subjected to unilateral thermal radiation were conducted on a thermal jacket protection efficiency experimental system. Three-dimensional finite element models were established to perform the thermal–structural analysis and approximate the temperature distribution and thermal deformation of the shrouded gun barrel and bare gun tube. The experimental results validated the numerical models for the study of solar radiation on the thermal–structural characteristics of the gun barrels with and without the thermal jacket. The temperature changes together with their gradients, and the thermal deformation of the gun tube shrouded with the thermal jacket were notably less than that of the bare gun tube. The nonuniform temperature distribution caused by unilateral radiation can be substantially ameliorated by the thermal jacket. The developed diamond-shaped thermal jacket owned satisfactory protection effectiveness. In addition, moderately increasing the thermal jacket's thickness or the air gap between the thermal jacket and gun tube can decrease the muzzle deflection of the shrouded gun barrels. The work is promising in providing both theoretical and practical contributions in design of shrouded tank gun barrels aiming at achieving high first shot accuracy.

Unraveling High Thermal Conductivity with In-Plane Anisotropy Observed in Suspended SiP2.

The anisotropic thermal transport properties of low-symmetry two-dimensional materials play an important role in understanding heat dissipation and optimizing thermal management in integrated devices. Examples of efficient energy dissipation and enhanced power sustainability have been demonstrated in nanodevices based on materials with anisotropic thermal transport properties. However, the exploration of materials with high thermal conductivity and strong in-plane anisotropy remains challenging. Herein, we demonstrate the observation of anisotropic in-plane thermal conductivities of few-layer SiP2 based on the micro-Raman thermometry method. For suspended SiP2 nanoflake, the thermal conductivity parallel to P-P chain direction (κ∥b) can reach 131 W m-1 K-1 and perpendicular to P-P chain direction (κ⊥b) is 89 W m-1 K-1 at room temperature, resulting in a significant anisotropic ratio (κ∥b/κ⊥b) of 1.47. Note that such a large anisotropic ratio mainly results from the higher phonon group velocity along the P-P chain direction. We also found that the thermal conductivity can be effectively modulated by increasing the SiP2 thickness, reaching a value as high as 202 W m-1 K-1 (120 W m-1 K-1) for κ∥b (κ⊥b) at 111 nm thickness, which is the highest among layered anisotropic phosphide materials. Notably, the anisotropic ratio always remains at a high level between 1.47 and 1.68, regardless of the variation of SiP2 thickness. Our observation provides a new platform to verify the fundamental theory of thermal transport and a crucial guidance for designing efficient thermal management schemes of anisotropic electronic devices.

Enhancing gust load alleviation performance in an optimized composite wing using passive wingtip devices: Folding and Twist approaches

This paper introduces an innovative numerical method for the design and optimization of high-aspect-ratio composite wings equipped with passive control systems, specifically, Folding WingTip (FWT) and Twist WingTip (TWT) devices. The aim is to enhance Gust Load Alleviation (GLA) performance in the baseline wing. Recent numerical studies have indicated that the inclusion of spring devices and wingtip modifications can offer additional benefits in alleviating gust loads during flight. The baseline wing is designed using a comprehensive multi-disciplinary optimization framework, taking into account aerostructural constraints and exploiting the anisotropic properties of composite materials. The proposed methodology integrates Finite Element (FE) software, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses, and Particle Swarm Optimization (PSO). This method, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, facilitated the streamlined design of composite wings with optimized aeroelastic and structural performance. The paper is divided into two main parts. Part 1 introduces a Multidisciplinary Design Optimization (MDO) approach for high-aspect-ratio composite wings, leading to the development of a baseline wing model. Part 2 evaluates the effectiveness of the FWT and TWT devices in alleviating gust loads on the baseline wing, with a focus on the Root Bending Moment (RBM) as a critical criterion for comparison. In wingtip modeling, geometrical nonlinearity is incorporated, and elastic trim is adjusted in each iteration to accommodate shape changes under load and aerodynamic panel movement is synchronized with structural adjustments.

Computational study on the effect of anisotropy in the transversely isotropic elasticity of elastoplastic solids on depth-sensing indentation with a point-sharp indenter

This study investigated depth-sensing indentation via finite element simulation using a sharp-pointed indenter on elastoplastic solids (EPSs) with transversely isotropic elasticity (TIE). It aims to quantify the effect of elastic anisotropy on the relationship between the indentation load, P, and the penetration depth of the indenter, h, that is, the P-h curve. The anisotropy of both Young’s modulus, E, and shear modulus, G, significantly affected the P-h curve. To quantify this effect, the revised Sneddon’s equation was modified as a function of the elastic anisotropy. The validity of an equation, which was developed elsewhere for isotropic elastoplastic solids, to extract the indentation elastic parameter from the P-h curve for EPSs with TIE was confirmed, where the parameter was given as a function of the relative residual depth and indentation unloading parameter. Using these equations, we obtained both the elastic and plastic deformation resistances in the indentation direction as functions of anisotropy in E and G for the EPSs with TIE. This study provides a method to quantify the influence of elastic anisotropy on the P-h curve, offering valuable insights for designing and predicting the mechanical properties of anisotropic materials in practical engineering applications.

Prediction of shape distortions in thermosetting composite parts using neural network interfaced visco-elastic constitutive model

The work aims to enhance the capabilities of a Finite Element tool, specifically related to a rheological thermo-chemo-viscoelastic constitutive model. This enhancement is intended to improve the tool’s ability to predict the distortions in composite parts caused by the polymerization of the thermoset composite matrix. These distortions occur due to internal residual stress generated by the inherent anisotropic properties of the thermoset composite material, including coefficients of thermal expansion and chemical shrinkage. The research work’s improvement is tied to the precise modelling of curing behaviour, which literature acknowledges as having a significant impact on manufacturing defects. In order to accommodate the influence of curing behaviour on various process variables—specifically, different thermal loading rates—a neural network model is implemented as an alternative to a standard diffusion cure-kinetics model. The neural network model is trained using Differential Scanning Calorimetry data and is integrated with the classical visco-elastic constitutive model to more accurately predict the progression of distinct thermoset resin states. This transition between cure states is assessed using two cure state variables: the degree of cure and the glass transition temperature. The enhanced predictions of state transitions lead to accurate assessments of internal residual stresses, especially when dealing with thick components subjected to thermal fluctuations. The anisotropic properties of thermoset composites, crucial for numerical analysis, are captured at various stages of cure. Ultimately, this methodology is employed to compare process-induced defects in the case study of the Z-shaped carbon/epoxy woven part, and the defects closely align with experimental measurements.

Topology Optimization and Efficiency Evaluation of Short-Fiber-Reinforced Composite Structures Considering Anisotropy

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers. Each iteration of topology optimization involves two consecutive steps: the first is a simulation of the injection molding process for obtaining the fiber orientation tensor, and the second is a structural analysis with anisotropic material properties. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids and then introduced as a variable in the stiffness matrix during the structural analysis. Structural analysis using a linear anisotropic material model was employed within the topology optimization. For verification, a non-linear elasto-plastic material model was used based on an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures, and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor is expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC), where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

Computational modeling of coupled mechanical damage and electrochemistry in ternary oxide composite electrodes

Performance degradation of ternary layered oxide cathodes largely originates from their loss of structural integrity in cyclic usage. Mechanical damage, such as intergranular fracture of the active particles, is not only a mechanical cleavage process but also interferes with electrochemical kinetics such as infiltration of liquid electrolyte, surface corrosion of the constituent primary particles, and may eventually isolate the primary grains from the electron conducting network. Here we develop a computational framework that integrates electrochemistry of a LiNixMnyCo1−x−yO2 (NMC) composite cathode with mechanical damage of the active particles. To fully examine the intricate chemomechanical behavior of the electrode, we evaluate the effects of the anisotropic material properties, the influence of mechanical potential on Li transport, and the concurrent intergranular fracture and electrolyte penetration along the grain boundaries upon multiple cycles. Electrolyte infiltration benefits capacity retention but aggravates further mechanical damage by corrosion. Structural failure mostly occurs in the first charging due to the anisotropic mechanical strain between the primary grains, while the resulting damage remains stable in the later few cycles. The results are consistent with experimental observations and the integration of electrochemistry and mechanical failure enables a step further understanding of the complex mechanism of battery degradation.

Fiber reinforced additive manufacturing: structurally motivated print orientation and sequential topology optimization of anisotropic material

PurposeFiber reinforced additive manufacturing (FRAM) is an emerging technology that combines additive manufacturing and composite materials. As a result, design freedom offered by the manufacturing process can be leveraged in design optimization. The purpose of the study is to propose a novel method that improves structural performance by optimizing 3D print orientation of FRAM components.Design/methodology/approachThis work proposes a two-part design optimization method that optimizes 3D global print orientation and topology of a component to improve a structural objective function. The method considers two classes of design variables: (1) print orientation design variables and (2) density-based topology design variables. Print orientation design variables determine a unique 3D print orientation to influence anisotropic material properties. Topology optimization determines an optimal distribution of material within the optimized print orientation.FindingsTwo academic examples are used to demonstrate basic behavior of the method in tension and shear. Print orientation and sequential topology optimization improve structural compliance by 90% and 58%, respectively. An industry-level example, an aerospace component, is optimized. The proposed method is used to achieve an 11% and 15% reduction of structural compliance compared to alternative FRAM designs. In addition, compliance is reduced by 43% compared to an equal-mass aluminum design.Originality/valueCurrent research surrounding FRAM focuses on the manufacturing process and neglects opportunities to leverage design freedom provided by FRAM. Previous FRAM optimization methods only optimize fiber orientation within a 2D plane and do not establish an optimized 3D print orientation, neglecting exploration of the entire orientation design space.

Multidisciplinary optimization of high aspect ratio composite wings with geometrical nonlinearity and aeroelastic tailoring

This study presents a systematic numerical approach for the design and optimization of high aspect ratio composite wings subjected to aerodynamic loads. The primary objective is to develop a multi-objective, multi-disciplinary optimization framework that considers aerostructural constraints, such as subsonic aeroelasticity and geometrical nonlinearity. The incorporation of anisotropic properties of composite materials is emphasized to construct lightweight aerospace structures. Aeroelastic tailoring, a technique leveraging these properties, is employed in the optimization process. The proposed methodology integrates three analysis tools, Finite Element software for structural behavior simulation, an in-house Reduced Order Model (ROM) framework for nonlinear aeroelastic analyses with tailoring capabilities, and Particle Swarm Optimization (PSO) as a population-based stochastic optimization method. This integration enables the development of a powerful numerical approach, implemented in the Nonlinear Aeroelastic Simulation Software (NAS2) package, for designing composite wings with optimized aeroelastic and structural performance. The proposed methodology has broad applicability in aerospace engineering, encompassing aircraft and unmanned aerial vehicles, offering significant potential to enhance their design and overall performance.

Mesostructured Materials with Controllable Long-Range Orientational Ordering and Anisotropic Properties.

Inorganic-organic mesophase materials provide a wide range of tunable properties, which are often highly dependent on their nano-, micro-, or meso-scale compositions and structures. Among these are macroscopic orientational order and corresponding anisotropic material properties, the adjustability of which are difficult to achieve. This is due to the complicated transient and coupled transport, chemical reaction, and surface processes that occur during material syntheses. By understanding such processes, general criteria are established and used to prepare diverse mesostructured materials with highly aligned channels with uniform nanometer dimensions and controllable directionalities over macroscopic dimensions and thicknesses. This is achieved by using a micropatterned semipermeable poly(dimethylsiloxane) stamp to manage the rates, directions, and surfaces at which self-assembling phases nucleate and the directions that they grow. This enables mesostructured surfactant-directed silica and titania composites, including with functional guest species, and mesoporous carbons to be prepared with high degrees of hexagonal order, as well as controllable orthogonal macroscopic orientational order. The resulting materials exhibit novel anisotropic properties, as demonstrated by the example of direction-dependent photocurrent generation, and are promising for enhancing the functionality of inorganic-organic nanocomposite materials in separations, catalysis, and energy conversion applications.

Small and large deformation rheology on pizza cheese as an example of application to study anisotropic properties of food soft materials

Various rheological techniques were applied to study pizza cheese, as a model anisotropic material. Small Amplitude Oscillatory Shear (SAOS) was combined with Large Amplitude Oscillatory Shear (LAOS) rheology, and the data compared to large deformation tests and confocal microscopy. Pizza cheese was analyzed at various stages of processing: before/during mixing, freshly stretched and after 1 and 2 weeks of storage. Directional sampling and testing were of critical importance for data interpretation. LAOS data were evaluated using a recently developed software that, with visual interactive analysis, including layover plots and similarity maps, allowed to provide a rheological fingerprint of the cheese's process and storage-induced structural changes. This work demonstrated that by using complementary rheological and microstructural techniques it is possible to improve our understanding of the anisotropy at different length scales, and to distinguish the differences in the networks among samples after various processing stages.

Single-test evaluation of directional elastic properties of anisotropic structured materials

When the elastic properties of structured materials become direction-dependent, the number of their descriptors increases. For example, in two-dimensions, the anisotropic behavior of materials is described by up to 6 independent elastic stiffness parameters, as opposed to only 2 needed for isotropic materials. Such high number of parameters expands the design space of structured materials and leads to unusual phenomena, such as materials that can shear under uniaxial compression. However, an increased number of properties descriptors and the coupling between shear and normal deformations render the experimental evaluation of material properties more challenging. In this paper, we propose a methodology based on the virtual fields method to identify six separate stiffness tensor parameters of two-dimensional anisotropic structured materials using just one tension test, thus eliminating the need for multiple experiments, as it is typical in traditional methods. The approach requires no stress data and uses full-field displacement data and global force data. We show the accuracy of our method using synthetic data generated from finite element simulations as well as experimental data from additively manufactured specimens.

Testing, analysis and design of wire and arc additively manufactured steel bolted connections

Capitalizing on Wire and Arc Additive Manufacturing (WAAM), 3D printed steel has shown significant potential in manufacturing large-scale steel structural elements in the construction industry. Due to the intrinsic difference in material properties, the mechanical performance of WAAM steel connections requires further investigation. In this paper, a total number of 24 WAAM steel coupon specimens with three different print layer orientations and 36 WAAM single-shear bolted connection specimens with one bolt of different design configurations were fabricated, dimensionally measured with 3D scanning technique, and tested under monotonic tension. The failure modes and ultimate capacities of the bolted connections were analysed, focusing on the print layer orientations of the WAAM steel plates. The current codified design provisions and design approaches proposed in the literature for steel structures were further evaluated by comparing the failure modes and ultimate capacities of the bolted connection specimens. This research shows that specimens with different print layer orientations present anisotropy phenomena in the coupon tests and bolted connection tests with differences of up to 10% and 20%, respectively. The relatively accurate predictions of the ultimate capacity of the WAAM steel bolted connection specimens following the current steel design standards are significantly compromised by predicting the incorrect failure modes, which could be attributed to the influence of anisotropic material properties of the WAAM steel plates and the failure modes of tilt-bearing and the end-splitting not being considered in current design provisions. This research conducted systematic experimental investigations focusing on the material properties of WAAM steel and the structural behaviours of WAAM single-shear bolted connections, which could potentially promote the application of WAAM technology in construction industry.

Inverse Design and Experimental Verification of Metamaterials for Thermal Illusion Using Genetic Algorithms

Thermal metamaterials offer a promising avenue for creating artificial materials with unconventional physical properties, such as thermal cloak, concentrator, rotator, and illusion. However, designs and fabrication of thermal metamaterials are of challenge due to the limitations of existing methods on anisotropic material properties. We propose an evolutionary framework for designing thermal metamaterials using genetic algorithm optimization. Our approach encodes unit cells with different thermal conductivities and performs global optimization using the evolution-inspired operators. We further fabricate the thermal functional cells using 3D printing and verify their thermal illusion functionality experimentally. Our study introduces a new design paradigm for advanced thermal metamaterials that can manipulate heat flows robustly and realize functional thermal metadevices without anisotropic thermal conductivity. Our approach can be easily applied to fabrications in various fields such as thermal management and thermal sensing.

Multi-scale neighborhood query graph convolutional network for multi-defect location in CFRP laminates

This paper presents a novel deep learning architecture named multi-scale neighborhood query graph convolutional network (MNQGN). In MNQGN, the spatial relationship between sensors is represented by constructing a vibration sensor distribution map. Furthermore, MNQGN enhances the feature representation by integrating a neighborhood query interaction mechanism to learn features from different scales. This approach better captures the common characteristics between multiple defects and reduces the impact of directional and anisotropic properties of composite materials. In addition, the spatio-temporal features in the collected signal data are extracted by the multi-scale spatio-temporal attention module in MNQGN, which focuses more on the time steps relevant to multi-defect localization tasks, thereby improving the accuracy and efficiency of the model. To evaluate the performance of MNQGN and demonstrate its effectiveness, experiments are conducted to test its ability to accurately identify and locate multiple defects within carbon fiber reinforced plastic laminates. The results show that MNQGN achieves a high accuracy rate of 97.90 % in multi-defect detection and localization, which is at least 1.67 % higher than the existing models.