Physics-based Finite Element Model Research Articles

Crack Identification in Solid Rocket Motors Through the Neyman–Pearson Detection Theory

Solid rocket motors (SRMs) are prone to bore cracking due to material degradation mechanisms and temperature changes that occur during storage and service life, and therefore early damage detection is of crucial importance. Structural health monitoring (SHM) strategies aim at measuring the load redistribution caused by a crack through embedded strain sensors. By acknowledging the existence of uncertainties, both in the material and measurement systems, this work employs a Neyman–Pearson detector that treats the crack identification problem as a binary statistical pattern recognition one. A typical SRM geometry, at its healthy state and with a bore crack of variable size, is considered in a probabilistic computational setting. A surrogate model is trained with synthetic data generated from a physics-based finite element model and then used for uncertainty propagation. Detection is first treated as a deterministic signal within noise, and next as an uncertain signal described by a probabilistic distribution. The system’s architecture is assessed through a procedure for arriving at the optimal number and location for sensor placement in conjunction with the SHM’s detection performance. The latter is described by receiver operating characteristic curves.

How cool is floating PV? A state-of-the-art review of floating PV's potential gain and computational fluid dynamics modeling to find its root cause

The noticeable rise in electricity demand, environmental concerns, and the intense land burden has led to installing PV systems on water bodies to create floating photovoltaic (FPV). Of all market niches, FPV is the one developing the fastest. Along with some of its well-documented merits comes a claim that FPV modules operate at a lower temperature than their ground-mounted counterparts (GPVs). This claim is essential due to the performance loss of PV modules at high operating temperatures. Some literature claims that FPVs are so well-cooled that they maintain around 10% higher efficiencies. However, this cooling is poorly quantified, and the root cause remains unclear in the industry. In this paper, an extensive review of all the latest published literature and white paper advertisements was analyzed. The gains in energy yield coming from different root causes range from 0.11% to 31.29%! This proves the point of lack of clarity of potential gain of FPV. The paper then analyses four possible explanations for this cooling effect and its root causes. The FPV performance parameters are isolated and systematically investigated through physics-based finite element modeling. The impacts of wind velocity, wind direction, water temperature, relative humidity, air temperature, proximity to water, tilt angle, and others are evaluated and explained. The outcomes dictate that FPV is cooled largely through wind convection. But the increase in efficiency is below the anticipated values, ranging from 0.5% to 3%.

Implementing intermittent current interruption into Li-ion cell modelling for improved battery diagnostics

A novel electroanalytical method, the intermittent current interruption (ICI) technique, has recently been promoted as a versatile tool for battery analysis and diagnostics. The technique enables frequent and continuous measurement of battery resistance, which then undergoes statistical analysis. Here, this method is implemented for commercial Li-ion cylindrical cells, and combined with a physics-based finite element model (FEM) of the battery to better interpret the measured resistances. Ageing phenomena such as solid electrolyte interphase (SEI) formation and metallic Li plating on the surface of the negative graphite particles are considered in the model. After validation, a long-term cycling simulation is conducted to mimic the ageing scenario of commercial cylindrical 21700 cells. The large number of internal resistance measurements obtained are subsequently visualized by creating a ‘resistance map’ as a function of both capacity and cycle numbers, providing a straight-forward image of their continuous evolution. By correlating the observed ageing scenarios with specific physical processes, the origins of ageing are investigated. The result shows that a decrease of the electrolyte volume fraction contributes significantly to the increase of internal resistance and affect the electrolyte diffusivity properties. Additionally, effects of porosity and particle radius of the different electrodes are investigated, providing valuable suggestions for battery design.

Physics-informed machine learning model for battery state of health prognostics using partial charging segments

The accurate and efficient estimation of battery state-of-health (SoH) is an ever-significant issue for applications of lithium-ion batteries (LIBs). Physics-of-failure (PoF) and machine-learning (ML) approaches have shown success in the field of prognostics and health management. However, accurate prediction of these models depends on clear understandings of complicated underlying physics, or full access to massive historical usage data of the system, which are not always obtainable in the engineering applications. Studies on physics-informed ML frameworks have gained attention because they embed physics knowledge into ML algorithms; recently, this method has demonstrated the potential to enhance performance over traditional model-based techniques, particularly when handling highly non-linear complex systems, such as LIBs. In this study, an advanced physics-informed ML (PIML) framework is proposed for the LIB SoH estimation, which includes three steps. The physics-based finite element (FE) model is firstly used to calculate the influences of the dominating aging mode, i.e., the solide electrolyte interface (SEI) growth on anode particle surfaces, and on capacity loss of LIB under various operating conditions. Then, the FE results are fused with experimental data from NASA Ames Prognostics Center of Excellence to construct a multi-fidelity model. Lastly, the Gaussian Process Regression (GPR) model is trained to create the mapping between voltage curves and the corresponding SEI thickness. A case study with the partial charging voltage segment as input is introduced to validate the PIML framework. Overall, the framework demonstrates a favorable performance for SoH estimation, as well as providing a basis for future online estimation frameworks.

Evaluation of brace root parameters and its effect on the stiffness of maize

Abstract Plant mechanical failure (lodging) causes significant yield loss for crops such as maize. Understanding this failure has relied on static measurements of plant biomechanics. In this study, digital image correlation techniques are used to capture dynamic motion to understand the plant mechanical behaviour of maize stalks in the presence and absence of brace roots, which are stem-borne aerial roots known to stabilize the maize stalks. The data show that brace roots function to secure the maize stalk, limiting both deflection and uplift. A finite element (FE) model is developed using ABAQUS software to validate the non-contact, video-based measured deflections captured by the dynamic motion and confirm the linear elastic behaviour of the stem, following fundamental principles of engineering mechanics. Good agreement is found between the field data captured using video-based measurements and the physics-based FE model when a rotational connector element is connected at the base to quantify the (i) relative contribution and moment resistance provided by the root system, (ii) displacement at any location along the stalk and (iii) flexural rigidity of the brace-stem system, where the rigidity can be associated with various phenotypes to design plant systems that are more resilient to lateral loading.

An analytical model to predict spalling and breaching of concrete plates under contact detonation

Under contact and close-in detonation, severe local failure is commonly observed on concrete elements and structures. So far prediction tools are available as empirically-based design diagrams, PC tools through replication scaling, or as physics based Finite Element Models (FEM). The last tool requires significant computational efforts and expertise from the modeler. As a fast and physics-based alternative, an analytical model is proposed in Tu et al.’s work [1], to quantitively predict compressive damage forming on the impacted face. In this paper, subsequent studies are conducted to (1) predict spalling damage size on the rear face and (2) predict whether concrete plates are breached by contact detonation. The analytical predictions are validated against a range of experimental data from published references. Good agreement shows the capability of the proposed model in effectively predicting local damage size and breaching of concrete plates subjected to contact detonation.

Impact responses of precast hollow reinforced concrete beams with prestress tendons using high-fidelity physics-based simulations

This study investigates the dynamic response, damage mechanisms, and residual capacity of precast hollow reinforced concrete beams with prestress tendons (HRCBPT) under impact loads by using High-Fidelity Physics-Based (HFPB) finite element (FE) models. The accuracy of the numerical model developed in this study has been fully calibrated against two different drop-weight impact tests available in open literature, including a scaled hollow reinforced concrete beam (HRCB) and a full-scale reinforced concrete (RC) beam. The results in this study show that under low to moderate impact loads, the flexural cracks and global deformation of the HRCBPT are similar to the hollow beam with normal reinforcement. Under high impact loads, although these beams exhibit similar diagonal shear cracks near the impact location, the HRCBPT experiences more local concrete spalling damage on the bottom flange, higher residual capacity index, and smaller reaction force at supports owing to the contribution of the initial prestress force. The results also suggest that the maximum compressive stress at the bottom flange of the section should be smaller than 17.6% of its compressive strength in order to maximise the beam’s impact-resistant capacity, while the prestress level in the tendon should be lower than 65% of the tendon yield strength to prevent premature failure under moderate impact conditions. Furthermore, different combinations of the impact velocity and impact mass would generate different impact force profiles on the beam resulting in dissimilar impact responses and residual capacity. In the design analysis, together with the critical impact location at the mid-span, the impact location close to the supports which may cause critical shear response of the beam, also needs be considered. In this study, the impact in the vicinity of the support yields the most severe damage to the beam resulting in the lowest residual capacity.

Metamodels’ Development for High Pressure Die Casting of Aluminum Alloy

Simulation is a very useful tool in the design of the part and process conditions of high-pressure die casting (HPDC), due to the intrinsic complexity of this manufacturing process. Usually, physics-based models solved by finite element or finite volume methods are used, but their main drawback is the long calculation time. In order to apply optimization strategies in the design process or to implement online predictive systems, faster models are required. One solution is the use of surrogate models, also called metamodels or grey-box models. The novelty of the work presented here lies in the development of several metamodels for the HPDC process. These metamodels are based on a gradient boosting regressor technique and derived from a physics-based finite element model. The results show that the developed metamodels are able to predict with high accuracy the secondary dendrite arm spacing (SDAS) of the cast parts and, with good accuracy, the misrun risk and the shrinkage level. Results obtained in the predictions of microporosity and macroporosity, eutectic percentage, and grain density were less accurate. The metamodels were very fast (less than 1 s); therefore, they can be used for optimization activities or be integrated into online prediction systems for the HPDC industry. The case study corresponds to several parts of aluminum cast alloys, used in the automotive industry, manufactured by high-pressure die casting in a multicavity mold.

Deep learning for nonlinear seismic responses prediction of subway station

A novel and computationally inexpensive method for predicting the nonlinear seismic response of subway stations using deep learning approaches is developed to reduce the computational cost in stochastic seismic responses analysis. The proposed method takes the deformation of the free field where the subway station is located as the input to predict seismic responses of the subway station according to the characteristic of seismic responses of underground structures. One-dimensional convolutional neural network (1D-CNN) and long-short term memory (LSTM) network are adopted for seismic responses modeling of a two-story and three-span subway station in a data-driven fashion as opposed to the computational expensive physics-based finite element model. The prediction performance and extrapolating ability of both models are evaluated and compared with a baseline multi-layer perceptron (MLP) model. With the same training samples, the 1D-CNN has better prediction performance and extrapolating ability than both LSTM and the baseline MLP model and the LSTM model has the worst performance among the three models. The good prediction performance of 1D-CNN makes it suitable to be applied in the stochastic seismic responses analysis using the probability density evolution method (PDEM) which is solved by the finite-difference method (FDM). The evolution characteristics of the probability density function of the layer drift and the distribution characteristics of the peak value of layer drift can be captured by a low computational cost with the proposed method.

4D Printing of Magnetoactive Soft Materials for On-Demand Magnetic Actuation Transformation.

Four-dimensional (4D) printed magnetoactive soft material (MASM) with a three-dimensional (3D) patterned magnetization profile possesses programmable shape transformation and controllable locomotion ability, showing promising applications in actuators and soft robotics. However, typical 4D printing strategies for MASM always introduced a printing magnetic field to orient the magneto-sensitive particles in polymers. Such strategies not only increase the cooperative control complexity of a 3D printer but may also induce local agglomeration of magneto-sensitive particles, which disturbs the magnetization of the already-printed structure. Herein, we proposed a novel 4D printing strategy that coupled the traditional 3D injection printing with the origami-based magnetization technique for easy fabrication of MASM objects with a 3D patterned magnetization profile. The 3D injection printing that can rapidly create complex 3D structures and the origami-based magnetization technique that can generate the spatial magnetization profile are combined for fabrication of 3D MASM objects to yield programmable transformation and controllable locomotion. A physics-based finite element model was also developed for the design guidance of origami-based magnetization and magnetic actuation transformation of MASM. We further demonstrated the diverse functions derived from the complex shape deformation of MASM-based robots, including a bionic human hand that played "rock-paper-scissors" game, a bionic butterfly that swung the wings on the flower, and a bionic turtle that crawled on the land and swam in the water.

Digital Twin: Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity

Digital Twin (DT) is an emerging technology that allows manufacturers to simulate and predict states of complex machine systems during operation. This requires that the physical machine state is integrated in a virtual entity, instantaneously. However, if the virtual entity uses computationally demanding models like physics-based finite element models or data driven prediction models, the virtual entity may become asynchronous with its physical entity. This creates an increasing lag between the twins, reducing the effectiveness of the virtual entity. Therefore, in this article, a model reduction method is described for a graph-based representation of multi-dimensional DT model based on spectral clustering and graph centrality metric. This method identifies and optimizes high-importance variables from computationally demanding models to minimize the total number of variables required for improving the performance of the DT.

Intramyocardial Injections to De-Stiffen the Heart: A Subject-Specific in Silico Approach.

We hypothesized that minimally invasive injections of a softening agent at strategic locations in stiff myocardium could de-stiffen the left ventricle (LV) globally. Physics-based finite element models of the LV were created from LV echocardiography images and pressures recorded during experiments in four swine. Results confirmed animal models of LV softening by systemic agents. Regional de-stiffening of myocardium led to global de-stiffening of LV. The mathematical set up was used to design LV global de-stiffening by regional softening of myocardium. At an end diastolic pressure of 23 mmHg, when 8 ml of the free wall was covered by intramyocardial injections, end diastolic volume (EDV) increased by 15.0%, whereas an increase up to 11 ml due to intramyocardial injections in the septum and free wall led to a 26.0% increase in EDV. Although the endocardial intramyocardial injections occupied a lower LV wall volume, they led to an EDV (44 ml) that was equal compared to intramyocardial injections in the mid-wall (44 ml) and larger compared to intramyocardial injections in the epicardium (41 ml). Using an in silico set up, sites of regional myocardium de-stiffening could be planned in order to globally soften overly stiff LV in heart failure with preserved ejection fraction. This novel treatment is built on subject-specific data. Hypothesis-testing of these simulation findings in animal models is warranted.

Numerical investigations on static and dynamic responses of reinforced concrete sub-assemblages under progressive collapse

To study the effect of blast pressure on structural resistance against progressive collapse under column removal scenario induced by contact detonation, and to investigate the development of catenary action within ultra-fast dynamic regime, a physics-based finite element model is developed in this paper. The model is first validated by a quasi-static test series on reinforced concrete sub-assemblages under middle column loss assumption and a blast test series using the same structural configurations. The sub-assemblage included a two-span beam, a middle column stub and two column stubs at both sides. Besides validations with sub-structure tests, some pull-out tests are also performed to verify the numerical models. After the verifications, parametric studies are conducted to investigate the influence of important dynamic and structural factors such as the boundary stiffness, damping ratio, and charge weight attached to the middle column. The study shows that under actual blast conditions, catenary action in sub-assemblages can be mobilised to prevent a structure from collapse even when the bottom longitudinal reinforcement in the bridging beam has already fractured. Moreover, stiffness of horizontal restraints plays an important role to mitigate disproportionate collapse in both static and blast conditions. A comparison is also made between nonlinear dynamic procedure and nonlinear static analysis incorporating simplified energy method for dynamic assessment. It is concluded that the simplified static approach in lieu of dynamic analysis can be considered as a conservative method for practical design purpose. Nonetheless, this method may over-estimate structural resistance if the localised damage is induced by a contact-detonation event.

An effective model for analysis of reinforced concrete members and structures under blast loading

The traditional fiber beam model has been widely used in the seismic analysis of reinforced concrete members and structures. However, the inability to capture shear failure restricts its application to blast loadings. In this article, a numerical model that considers both rate-dependent shear behavior and damage effect is proposed based on the traditional fiber beam element. This is achieved using the modified compression-field theory with a concrete damage model and bilinear steel model in the principal directions. Meanwhile, a condensed three-dimensional stress–strain relation from the isotropic hardening plasticity model is implemented to simulate longitudinal reinforcement bars, as large shear strain would be produced under severe blast loads. The proposed model is validated by comparing the numerical and test results. The high-fidelity physics-based finite element model, validated by the same experiment, is also used in the study to prove the efficiency of the proposed model. Case studies of a reinforced concrete beam and a six-story reinforced concrete frame structure subjected to blast loads are then carried out. The results indicate that the proposed model is reliable compared with the high-fidelity physics-based model. In addition to the accuracy, comparisons of the computational time show an excellent performance with respect to the efficiency of the proposed model.

Detecting Loosening of Bolted Connections in a Pipeline Using Changes in Natural Frequencies

Loosening of bolted connections in a structure can significantly reduce its load-bearing capacity. Detecting loosening of bolted connections at an early stage can prevent failure of the structure. Due to the complex geometry of a bolted connection and material discontinuity between clamped components, it is difficult to detect loosening of a bolted connection using conventional nondestructive test methods. A vibration-based method that uses changes in natural frequencies of a structure to detect locations and extent of damage can be used to detect loosening of bolted connections since the method focuses on detecting a stiffness reduction, which can result from loosening of bolted connections. Experimental and numerical damage detection was conducted to detect loosening of bolted connections in a full-size steel pipeline with bolted flanges using the vibration-based method. With the recent development of a modeling technique for bolted connections in thin-walled structures, an accurate physics-based finite element model of the pipeline that is required by the vibration-based damage detection method is developed. A trust-region search strategy is employed to improve the damage detection method so that global convergence of the damage detection algorithm can be ensured for underdetermined systems, and robustness of the algorithm can be enhanced when relatively large modeling error and measurement noise are present. The location and extent of loosened bolted connections were successfully detected in experimental damage detection using changes in natural frequencies of the first several elastic modes of the pipeline; the exact location and extent of the loosened bolted connections can be detected in numerical simulation where there are no modeling error and measurement noise.

Evaluating the effects of loading parameters on single-crystal slip in tantalum using molecular mechanics

This study is aimed at developing a physics-based crystal plasticity finite element model for body-centred cubic (BCC) metals, through the introduction of atomic-level deformation information from molecular dynamics (MD) investigations of dislocation motion at the onset of plastic flow. In this study, three critical variables governing crystal plasticity mediated by dislocation motion are considered. MD simulations are first performed across a range of finite temperatures up to 600K to quantify the temperature dependence of critical stress required for slip initiation. An important feature of slip in BCC metals is that it is not solely dependent on the Schmid law measure of resolved shear stress, commonly employed in crystal plasticity models. The configuration of a screw dislocation and its subsequent motion is studied under different load orientations to quantify these non-Schmid effects. Finally, the influence of strain rates on thermal activation is studied by inducing higher stresses during activation at higher applied strain rates. Functional dependence of the critical resolved shear stress on temperature, loading orientation and strain rate is determined from the MD simulation results. The functional forms are derived from the thermal activation mechanisms that govern the plastic behaviour and quantification of relevant deformation variables. The resulting physics-based rate-dependent crystal plasticity model is implemented in a crystal plasticity finite element code. Uniaxial simulations reveal orientation-dependent tension–compression asymmetry of yield that more accurately represents single-crystal experimental results than standard models.

An investigation of the effects of traffic induced local dynamics on global damping estimates using operational modal analysis

Simulations of estimating the modal damping on a bridge from hour-long records of traffic loading were conducted by combining physics-based finite element modeling and signal processing. The finite element method was used to model a bridge consisting of a series of stringer beams resting atop a larger girder. The traffic loads were separated into trains and cars, with the trains modeled as partially distributed moving masses traveling along the girder and the cars modeled as point loads moving along the stringers. From the acceleration time histories, different operational modal analysis (OMA) techniques were used to find estimates for the modal coefficients of damping. The results demonstrated that a quasi-periodic component in the traffic loading introduces significant error to the damping estimates. This error could be observed in the distortion of the peaks for the power spectral densities (PSD) generated from the responses to the traffic simulations. The main OMA technique explored for the damping estimates was Enhanced Frequency Domain Decomposition (EFDD), but it could not compensate or correct for any alterations to the PSD. Other techniques such as the Stochastic Subspace Identification (SSID) method and curve-fitting frequency domain analysis were evaluated, but they produced comparable damping ratio estimates to EFDD and similarly resulted in large errors for the distorted modes. The influence of quasi-periodic loads was perceptible, which means that for certain cases, the nature of traffic loads may result in damping estimates that are considerably inaccurate no matter what OMA technique is chosen.

A vibration-based structural damage detection method and its applications to engineering structures

Two major challenges associated with avibration-based damage detection method using changes in natural frequencies are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistic function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using the Levenberg–Marquardt method is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. It is applied to various engineering structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation, and the locations and extent of damage can be successfully detected in experimental damage detection.

Structural Damage Detection Using Changes in Natural Frequencies: Theory and Applications

A vibration-based method that uses changes in natural frequencies of a structure to detect damage has advantages over conventional nondestructive tests in detecting various types of damage, including loosening of bolted joints, using minimum measurement data. Two major challenges associated with applications of the vibration-based damage detection method to engineering structures are addressed: accurate modeling of structures and the development of a robust inverse algorithm to detect damage, which are defined as the forward and inverse problems, respectively. To resolve the forward problem, new physics-based finite element modeling techniques are developed for fillets in thin-walled beams and for bolted joints, so that complex structures can be accurately modeled with a reasonable model size. To resolve the inverse problem, a logistical function transformation is introduced to convert the constrained optimization problem to an unconstrained one, and a robust iterative algorithm using a trust-region method, called the Levenberg-Marquardt method, is developed to accurately detect the locations and extent of damage. The new methodology can ensure global convergence of the iterative algorithm in solving under-determined system equations and deal with damage detection problems with relatively large modeling error and measurement noise. The vibration-based damage detection method is applied to various structures including lightning masts, a space frame structure and one of its components, and a pipeline. The exact locations and extent of damage can be detected in the numerical simulation where there is no modeling error and measurement noise. The locations and extent of damage can be successfully detected in experimental damage detection.

Numerical Assessment of Electric Roadway Deicing System Utilizing Emerging Carbon Nanofiber Paper

Traditional deicing approaches such as salting pollute the environment and corrode reinforcing steel bars in concrete. Meanwhile, recently emerging carbon nanofiber material has improved electrical conductivity, a large heating capacity at low voltage, high strength, and, in particular, natural immunity to corrosion. This material offers an excellent alternative as the heating element in electrical resistive deicing systems. This paper aims to assess the effectiveness of a deicing system that has embedded carbon nanofiber paper heating elements and to evaluate the effects of several key parameters on the performance of such a deicing system. A physics-based finite-element model was built, validated by test results, and used to assess the performance of such a deicing system when applied to roadway conditions. Factors considered include air temperature, wind speed, pavement and insulation layer thicknesses, and heating input. Results show that the proposed system can be very effective for roadway deicing. Future research is needed to validate its feasibility and effectiveness in a field experiment.