Realistic Material Properties Research Articles

Seismic Fragility Analysis of Existing Old Newari Brick Masonry Building in Pokhara Valley

Most of the building stock in Nepal is based on masonry construction, which includes monumental, administrative, and residential structures. These structures are vulnerable during earthquakes, as evidenced by the massive structural damage, loss of human life, and property damage due to a lack of proper assessment and appropriate strengthening measures. An analysis of the seismic vulnerability of existing old Newari brick masonry buildings in the Pokhara Valley is presented. These buildings were built using indigenous knowledge and technology. The investigation is based on analytical studies, with some material properties obtained from field tests. Proper modeling of a masonry structure is crucial for reliable seismic resistance and structural design. However, modeling a real masonry structure is a challenging and computationally demanding task due to its complicated framework, requiring in-depth knowledge, realistic material properties, and relevant information. The aim of this research is to assess the seismic performance of old Newari masonry buildings using stress level and fragility curves. The research issues are addressed analytically through linear time history analysis using the finite element program-based software Sap 2000 v20. In dynamic analysis, numerical building models were subjected to three synthetic earthquakes. The performance status of the building based on various stress levels is evaluated, and weak regions are identified. The fragility curve of the structure is assessed, considering the ground motion parameters in the locality. The fragility function is plotted with the probability of failure at an interval of 0.10 g. The results of the analysis highlight that the studied structure is vulnerable compared to the codal provisions and standard recommendations.

Novel Computational Design of Polymer Micromachined Insect-Mimetic Wings for Flapping-Wing Nano Air Vehicles.

The flapping wings of insects undergo large deformations caused by aerodynamic forces, resulting in cambering. Insect-mimetic micro wings for flapping-wing nano air vehicles mimic these characteristic deformations. In this study, a 2.5-dimensional insect-mimetic micro wing model for flapping-wing nano air vehicles is proposed to realize this type of wing. The proposed model includes a wing membrane, a leading edge, a center vein, and a root vein, all of which are modeled as shell elements. The proposed wing is a 2.5-dimensional structure and can thus be fabricated using polymer micromachining. We conducted a design window search to demonstrate the capabilities of the wing. The design windows, which are areas of desirable design solutions in the design parameter space, are iteratively searched using nonlinear finite-element analysis under quasi-steady aerodynamic modeling. Here, thickness is selected as a design parameter. The properties of real insects, polymer materials, and fabrication conditions are used to determine the other parameters. A fabricable design solution that generates sufficient camber is found from the design windows.

Realistic material property prediction using domain adaptation based machine learning

Scientists aim to predict properties for a known subset of related out-of-distribution materials rather than universally distributed samples. Introducing target material information enhances machine learning models.

Used Isolation System to Reduce the Response of Offshore Structure Considering Soil Structure Interaction

Abstract Fixed offshore platforms are structures of steel or concrete located in the middle of the sea used to export oil, trade, or military. These are designed to withstand all types of weather and dynamic loads. The response was very high under applied these loads. This paper includes a proposal to apply a new isolation system to be placed in different places on the offshore platform to reduce vibrations from high water waves. Three cases that be used, fixed-based (column connect directly to the pile), isolation system in the pile head (isolation between column and pile), and isolation system in the pile head and the middle of columns. The dynamic movements in waves were simulated using ABAQUS programs. More accurate elements and real material properties were used to bring the results closer to reality. The Computational Fluid Dynamics (CFD) method was used to study the effect of water-structure interaction and soil-structure interaction. The results show that the use of the isolation system at the base of the offshore platform has a very significant effect on decreasing the response of the platform to the loads placed on it. Where the isolation system works as a hinge at the pile head and is not allowed to move and rotate for the pile, also the moments are zero. As for the shear force, it is the least that can be compared with the other cases.

Finite element solutions for bending analysis of simply supported two layered un-symmetric cross-ply (0/90) square plate under uniformly distributed transverse load

Laminated composite plates are extensively used in aerospace, military, civil infrastructure, and automotive industries due to their benefits, including superior fatigue resistance, high strength, and lightweight properties. Customizable fiber orientation, materials, and stacking patterns are additional advantages. Therefore, conducting rigorous analyses to evaluate deflections and stress levels in these plates is essential for maintaining structural integrity and optimal performance across various applications. The solutions presented in this study rely on the utilization of a higher-order shear deformation theory (HSDT), which offers a notably precise depiction of the plate's behavior. The theoretical model employed in this research accounts for deformations and the variation of stresses along the thickness direction. Finite element formulations are employed to derive the solutions. In order to ascertain the accuracy and reliability of the current formulations and solution methodologies, the obtained results are systematically compared with those documented in the existing literature. This rigorous comparison serves as a robust validation of the approach applied in this study. Once the accuracy of the solutions is established, numerical results for two layered cross-ply composites with real material properties are presented. These results are in good agreement with the findings reported in the literature. By incorporating Higher Order Shear Deformation Theory (HSDT), the accuracy of predictions is significantly enhanced. This improvement ensures a more dependable foundation for the design and optimization of composite structures.

Finite Element Simulation and Experimental Assessment of Laser Cutting Unidirectional CFRP at Cutting Angles of 45° and 90°.

Laser cutting of carbon fibre-reinforced plastics (CFRP) is a promising alternative to traditional manufacturing methods due to its non-contact nature and high automation potential. To establish the process for an industrial application, it is necessary to predict the temperature fields arising as a result of the laser energy input. Elevated temperatures during the cutting process can lead to damage in the composite's matrix material, resulting in local changes in the structural properties and reduced material strength. To address this, a three-dimensional finite element model is developed to predict the temporal and spatial temperature evolution during laser cutting. Experimental values are compared with simulated temperatures, and the cutting kerf geometry is examined. Experiments are conducted at 45° and 90° cutting angles relative to the main fibre orientation using a 1.1 mm thick epoxy-based laminate. The simulation accurately captures the overall temperature field expansion caused by multiple laser beam passes over the workpiece. The influence of fibre orientation is evident, with deviations in specific temperature data indicating differences between the estimated and real material properties. The model tends to overestimate the ablation rate in the kerf geometry, attributed to mesh resolution limitations. Within the parameters investigated, hardly any expansion of a heat affected zone (HAZ) is visible, which is confirmed by the simulation results.

Porous Melt Flow in Continental Crust—A Numerical Modeling Study

AbstractIn continental crust, rapid melt flow through macroscopic conduits is usually envisaged as the most efficient form of melt transport. In contrast, there is growing evidence that in hot continental crust, grain‐scale to meso‐scale porous melt flow may operate over long distances and over millions of years. Here, we investigate the dynamics of such porous melt flow by means of two‐dimensional thermo‐mechanical numerical models using the code ASPECT. Our models are crustal‐scale and describe the network of pores through which the melt flows by permeability that depends on the spacing of the pores. Our results suggest that assuming realistic material properties, melt can slowly migrate in the hot and thick continental crust through pores with a characteristic spacing of 1 mm or larger. Despite its low velocity (millimeters to centimeters per year), over millions of years, such flow can create large partially molten zones in the middle‐lower crust and significantly affect its thermal state, deformation, and composition. We examined the role of the permeability, melt and solid viscosities, the slope of the melting curve and temperature conditions. We obtained contrasting styles of melt distribution, melt flow, and solid deformation, which can be categorized as melt‐enhanced convection, growth of partially molten diapirs and melt percolation in porosity waves. Our numerical experiments further indicate that grain‐scale porous flow is more likely in rocks where the melt productivity increases slowly with temperature, such as in metaigneous rocks.

Design of a novel carbon/carbon composite microvascular solar receiver

Solar thermal power tower systems are the primary technology being proposed for solar electricity from thermal energy. Operational limits on these towers are often driven by mechanical properties under significant thermal loads, particularly at the receiver where incoming flux is converted to thermal energy. While the solar receiver’s efficiency is largely driven by its optical properties, thermomechanical stresses on the receiver limits the operational envelope. One pathway to higher efficiency is greater allowable solar fluxes on the receiver but novel materials are required. The present study uses computational fluid dynamics to describe a parametric design space for a microvascular carbon/carbon composite solar receiver as a new material option for high flux solar receivers. Simulations are conducted for different microvascular geometries considering the role of material properties and heat transfer fluids, for the impact on thermal efficiency, and allowable strain. Results show that microscale receiver modules made of the proposed carbon/carbon composite could achieve thermal efficiencies over 90% and full-scale receivers can achieve up to 85% thermal efficiency for the design explored considering realistic strain limits, flux levels, and material properties. These values are highly dependent on the heat transfer fluid pairing, the through plane thermal conductivity of the carbon/carbon composite, the path architecture of the microscale receiver, and the incident solar flux profiles.

A novel deep-learning-based objective function for inverse identification of material properties

Inverse methods are critical in exploring the constitutive models of materials. In the traditional finite element model updating method, an objective function is often established in the form of mean square error, which is simple and easy to use. However, this approach does not fully consider the prior knowledge of the material, leading to low computational stability and sensitivity to noise. To address these limitations, a novel objective function that considers deep semantic information and mechanical constraints was proposed based on deep learning techniques in this study. Compared with the traditional method, the proposed objective function can significantly reduce the effect of noise on inversion performance without any loss in computational efficiency. The method was validated through simulated experiments and successfully applied to the inverse identification of damage properties of real nuclear graphite materials under simple and complex stress states. The results demonstrated that the proposed objective function can significantly enhance the stability of the finite element model updating method, leading to a rapid convergence with high accuracy.

CRSS determination combining ab-initio framework and Surrogate Neural Networks

Critical Resolved Shear Stress (CRSS), fundamentally linked to the dislocation glide stress, is a crucial measure in dictating plastic deformation in metallic materials. A recent ab-initio predictive model for dislocation glide stress in Face-Centered Cubic (FCC) materials is developed which accurately predicts available experimental data, considering the anisotropic continuum energy, the atomistic misfit energy, and the minimum energy path for the intermittent motion of Shockley partials. The CRSS of a material is predominantly controlled by six parameters, namely, lattice constant, unstable/stable stacking-fault energies, and three anisotropic elastic constants for cubic materials, which are inputs to the predictive model. In this work, a large material dataset is produced incorporating properties of real materials and generating hypothetical combinations, subsequently calculating the CRSS for each combination using the predictive model. The hypothetical combinations of properties are employed to train a machine learning-based Surrogate Neural Network (SNN), and the ones of real materials are utilized to validate the SNN model yielding a 94% accuracy for 1,033 materials. The generated dataset is used to unravel the sensitivity of each material parameter to the predicted CRSS establishing a general trend for the FCC materials for the first time guiding the field in achieving superior mechanical properties.

Sub-nanometer-scale mapping of crystal orientation and depth-dependent structure of dislocation cores in SrTiO3

Defects in crystals play a fundamental role in modulating mechanical, electrical, luminescent, and magnetic behaviors of materials. However, accurate measurement of defect structures is hindered by symmetry breaking and the corresponding complex modifications in atomic configuration and/or crystal tilt at the defects. Here, we report the deep-sub-angstrom resolution imaging of dislocation cores via multislice electron ptychography with adaptive propagator, which allows sub-nanometer scale mapping of crystal tilt in the vicinity of dislocation cores and simultaneous recovery of depth-dependent atomic structure of dislocations. The realization of deep-sub-angstrom resolution and depth-dependent imaging of defects shows great potential in revealing microstructures and properties of real materials and devices.

The numerical study on performance evaluation of a thermal switch capacitor in a magnetocaloric cooling device.

Compact, solid thermal control devices offer a new way to control the intensity and direction of heat flow between the components of a system, which is crucial for both optimized performance and safety. In this work we study a thin, silicon thermal switch capacitor (TSC) used for heat transport in a magnetocaloric cooling system. A numerical model was developed to quantify the effects of various operating conditions and design parameters on the performance of a magnetocaloric device with an embedded TSC. Based on realistic material properties, a maximum cooling-power density of 4000 Wm-2 (2025 ) was obtained for a zero temperature span and an operating frequency of 20Hz. The use of the presented device was demonstrated on a battery system, motivating further experimental studies to develop a new, compact cooling device that can be directly attached to a heat reservoir, making it desirable for a variety of applications.

ERGOBOSS: Ergonomic Optimization of Body-Supporting Surfaces.

Humans routinely sit or lean against supporting surfaces and it is important to shape these surfaces to be comfortable and ergonomic. We give a method to design the geometric shape of rigid supporting surfaces to maximize the ergonomics of physically based contact between the surface and a deformable human. We model the soft deformable human using a layer of FEM deformable tissue surrounding a rigid core, with measured realistic elastic material properties, and large-deformation nonlinear analysis. We define a novel cost function to measure the ergonomics of contact between the human and the supporting surface. We give a stable and computationally efficient contact model that is differentiable with respect to the supporting surface shape. This makes it possible to optimize our ergonomic cost function using gradient-based optimizers. Our optimizer produces supporting surfaces superior to prior work on ergonomic shape design. Our examples include furniture, apparel and tools. We also validate our results by scanning a real human subject's foot and optimizing a shoe sole shape to maximize foot contact ergonomics. We 3D-print the optimized shoe sole, measure contact pressure using pressure sensors, and demonstrate that the real unoptimized and optimized pressure distributions qualitatively match those predicted by our simulation.

Simulation of Hand Anatomy Using Medical Imaging

Precision modeling of the hand internal musculoskeletal anatomy has been largely limited to individual poses, and has not been connected into continuous volumetric motion of the hand anatomy actuating across the hand's entire range of motion. This is for a good reason, as hand anatomy and its motion are extremely complex and cannot be predicted merely from the anatomy in a single pose. We give a method to simulate the volumetric shape of hand's musculoskeletal organs to any pose in the hand's range of motion, producing external hand shapes and internal organ shapes that match ground truth optical scans and medical images (MRI) in multiple scanned poses. We achieve this by combining MRI images in multiple hand poses with FEM multibody nonlinear elastoplastic simulation. Our system models bones, muscles, tendons, joint ligaments and fat as separate volumetric organs that mechanically interact through contact and attachments, and whose shape matches medical images (MRI) in the MRI-scanned hand poses. The match to MRI is achieved by incorporating pose-space deformation and plastic strains into the simulation. We show how to do this in a non-intrusive manner that still retains all the simulation benefits, namely the ability to prescribe realistic material properties, generalize to arbitrary poses, preserve volume and obey contacts and attachments. We use our method to produce volumetric renders of the internal anatomy of the human hand in motion, and to compute and render highly realistic hand surface shapes. We evaluate our method by comparing it to optical scans, and demonstrate that we qualitatively and quantitatively substantially decrease the error compared to previous work. We test our method on five complex hand sequences, generated either using keyframe animation or performance animation using modern hand tracking techniques.

Fueling the Simulation Machine: Considerations for High-Resolution 3D Microscopy to Support Computational Modeling in Battery Research

To meet the rapidly evolving energy needs of a growing population, energy storage and conversion devices such as batteries must rapidly evolve as well. Battery materials and other functional energy materials derive their properties not from their static state, but from how they interact with other materials in their service environment. Furthermore, the microstructure of these materials strongly dictates their performance and how they interact with other components (like electrolytes, binders, etc). In order to accelerate this development cycle, researchers are increasingly integrating computational modeling into their materials and device development efforts.Computational analysis is progressing to the point where simulations can incorporate real material microstructures, simulate statistically equivalent structures, and predict performance changes based on changes to these microstructures. At the same time experimental techniques have emerged that can deliver representative volumes of high-quality, high-resolution 3D microstructural images from real devices and materials. The interplay between advanced computational analysis and evolving characterization capabilities means we are rapidly approaching the realization of the “materials by design” paradigm where new materials are designed and tested digitally with input and validation provided by advanced materials characterization techniques. Within this paradigm, however, it is critical that the experimental techniques provide accurate, realistic, and representative input to the computational models. In the case of microstructural information, this means generating 3-dimensional images that accurately represents the materials in question.One key example of this is for materials, like battery materials and electrodes, which contain micro- and nano-scale porosity. Acquiring image data that can reproduce real material properties and feed accurate simulations is challenging and relies on satisfying several conditions. For example, the volume must be representative of the “bulk” material microstructure. The (2D) image resolution must capture the relevant scope of material features, and the 3D image must accurately represent the material in all 3 dimensions and at appropriate length scales. The focused-ion beam scanning electron microscope (FIB-SEM) is capable of imaging these micro- and nano-scale pore networks in 3D but care must be taken when considering these experiments, especially when the intent is to use the images to feed computational models.We present here a review of the factors researchers must consider when tackling these demanding tasks and highlight experimental recommendations for leveraging FIB-SEM tomography to produce the most relevant imaging data in this context. We use a lithium-ion battery cathode as a test case to demonstrate this approach, but the outcome and methods can be leveraged across the materials research space and beyond.

Modeling Adhesive Wear in Asperity and Rough Surface Contacts: A Review.

Wear is one of the most fundamental topics in tribology and adhesive wear is argued as the least avoidable wear type. Numerical techniques have allowed advances in more realistic simulations of adhesive wear mechanisms and promoted our understanding of it. This paper reviews the classic work on wear modeling by Archard and Rabinowicz, followed by a comprehensive summary of the adhesive wear numerical models and techniques based on physical parameters. The studies on wear mechanisms at the asperity level and rough surfaces are separately presented. Different models and their key findings are presented according to the method type. The advantages and deficiencies of these models are stated and future work, such as considering more realistic geometries and material properties for adhesive wear modeling, is suggested.

Bottom-up Coarse-Graining: Principles and Perspectives.

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

Validation of ASTEC2.1 using QUENCH-12 for VVER-Reactors

Russian VVER-type reactors are one of the most common used commercial reactors in the world. The validation of severe accident codes using experimental data is focused in core degradation both in-vessel and ex-vessel phenomena such as the QUENCH-12 tests performed at the Karlsruhe Institute of Technology (KIT). This step is needed before integral codes are applied to evaluate the behavior of VVER-plants under severe accident conditions. The main goal of the QUENCH-12 test was to evaluate the hydrogen generation resulting from the injection of cold quench water into an overheated and oxidized bundle of fuel rod simulators representing the VVER-fuel rods. Such phenomena are expected to occur during a severe accident sequence e.g. LOCA or as part of an accident management measure. This paper describes the investigations done to simulate the QUENCH-12 test using the ASTEC V2, which is developed by IRSN to simulate all physical and chemical phenomena during severe accident condition. A model of the QUENCH-12 test facility was developed for ASTEC first time taking into account the specific material data, geometry and boundary conditions. The evaluation of the results have shown, that ASTEC V2 is able to predict the main trends of key-parameters during the all test phases in good agreement with the measured data. The integral hydrogen generation is only over-predicted during the quench phase. As next steps, the model will be improved by taking into account the real material properties of the ZrNb-cladding typical of Russian reactors instead of the ones of Zr. Also the sensitivity studies regarding the electrical heater resistance will be carried out.

3D printed mechanically representative aortic model made of gelatin fiber reinforced silicone composite

Additive manufacturing (AM) is a useful technology to produce artificial aortic models for the training of transcatheter aortic valve replacement (TAVR) surgery. With AM, the models can be tailored towards the individualized aortic anatomy of patients. Most of these reported models so far are manufactured using single rubber-like materials. However, such materials do not replicate the mechanical properties of natural aortic tissue, especially the stress–strain response in higher strain (>0.1) regions. This could be problematic for surgeons training for surgeries using a model which does not exhibit properties of the real aorta. To overcome this limitation, we developed a 3D-printed, mechanically representative aortic model comprising gelatin fibers and silicone. The model is promising as a realistic analog of aortic sinus for mock TAVR surgery. Computerized tomography data was analyzed beforehand using medical imaging to identify the anatomy of a specific patient’s aortic sinus and the surrounding blood vessels. A novel silicone matrix composite reinforced with gelatin fibers designed in this work was tested and compared with the stress–strain response of aortic tissue. Such a model comprising both patient-specific geometries as well as realistic material properties of aortic tissue can be helpful for the development of next-generation medical phantoms.

Optimized Phonon Band Discretization Scheme for Efficiently Solving the Nongray Boltzmann Transport Equation

Abstract The phonon Boltzmann transport equation (BTE) is an important tool for studying the nanoscale thermal transport. Because phonons have a large spread in their properties, the nongray (i.e., considering different phonon bands) phonon BTE is needed to accurately capture the nanoscale transport phenomena. However, BTE solvers generally require large computational cost. Nongray modeling imposes significant additional complexity on the numerical simulations, which hinders the large-scale modeling of real nanoscale systems. In this work, we address this issue by a systematic investigation on the phonon band discretization scheme using real material properties of four representative materials, including silicon, gallium arsenide, diamond, and lead telluride. We find that the schemes used in previous studies require at least a few tens of bands to ensure the accuracy, which requires large computational costs. We then propose an improved band discretization scheme, in which we divide the mean free path domain into two subdomains, one on either side of the inflection point of the mean free path accumulated thermal conductivity, and adopt the Gauss–Legendre quadrature for each subdomain. With this scheme, the solution of the phonon BTE converges (error < 1%) with less than ten phonon bands for all these materials. The proposed scheme allows significantly reducing the time and memory consumption of the numerical BTE solver, which is an important step toward large-scale phonon BTE simulations for real materials.