Mechanics-based Model Research Articles

State-of-the-Art Review of Continuum Mechanics-Based Modelling of Soil Surface Erosion

AbstractSoil surface erosion can shape the morphography of rivers and estuaries in the natural environment and induce high potential risks to structures in engineering. Numerical simulations based on continuum mechanics theory can provide reliable assessments of the evolution of surface erosion from the perspective of a large-scale view. However, current studies on continuum mechanics-based modelling are still limited. This paper comprehensively reviews such numerical simulations of soil surface erosion. This review begins by discussing the fundamental physical mechanisms of surface erosion. Subsequently, it explores the basic physics-based conservation equations controlling soils and fluids in surface erosion. Then, the empirical formulae depicting the different stages of surface erosion are presented. Building on these mathematical foundations, this paper reviews various numerical methods for surface erosion modelling from a continuum mechanics perspective. Finally, this paper discusses the advantages and limitations of the numerical methods. This work can provide researchers convenience for using numerical models on surface erosion simulations.

Ab Initio Molecular Dynamics Investigation of Water and Butanone Adsorption on UiO-66 with Defects.

Volatile organic compounds (VOCs) are harmful chemicals that are found in minute quantities in the atmosphere and are emitted from a variety of industrial and biological processes. They can be harmful to breathe or serve as biomarkers for disease detection. Therefore, capture and detection of VOCs is important. Here, we have examined if the Zr-based UiO-66 metal-organic framework (MOF) can be used to capture butanone─a well-known VOC. Toward that end, we have performed Born-Oppenheimer ab initio molecular dynamics (AIMD) at 300 and 500 K to probe the energetics and molecular interactions between butanone [CH3C(O)CH2CH3] and open-cage Zr-UiO-66. Such interactions were systematically interrogated using three MOF structures: defective MOF with a missing 1,4-benzene-dicarboxylate linker and two H2O; pristine MOF with two H2O; and pristine dry MOF. These structures were loaded with one and four molecules of butanone to examine the effect of concentration as well. One-molecule loading interacted favorably with the defective structure at 300 K, only. In comparison, interactions with four-molecule loading were energetically favorable for all conditions. Persistent hydrogen bonds between the O atom of butanone, H2O, and μ3-OH hydroxyl attachments at Zr nodes substantially contributed to the intermolecular interactions. At higher loadings, butanone also showed a pronounced tendency to diffuse into the adjoining cages of Zr-UiO-66. The effect of such movement on interaction energies was rationalized using simple statistical mechanics-based models of interacting and noninteracting gases. Broadly, we learn that the presence of prior moisture within the interstitial cages of Zr-UiO-66 significantly impacts the adsorption behavior of butanone.

Development of an Angular Stiffness Sensor to Measure Dental Implant Stability In Vitro.

This investigation aims to develop an angular stiffness sensor intended for measuring dental implant stability in bone. The sensor hardware included a tiny eccentric motor and an accelerometer to measure a flex constant of an implant with its abutment. The sensor software included a mechanics-based model to convert the flex constant to angular stiffness at the implant/abutment junction to indicate the stability. The sensor's accuracy and effectiveness are demonstrated through use of Sawbones slab models that mimic a mandibular premolar section. The models include a Branemark Mk III implant inserted into Sawbones slabs of 5 different densities with a locator abutment. An incremental insertion torque was first recorded while the implant was placed in the Sawbones models. Then benchtop experiments were conducted to measure resonance frequencies and angular stiffness. Results indicated that angular stiffness increased with Sawbones density, showing high correlation with the measured resonance frequency (R=0.977) and the incremental insertion torque (R=0.959). Finally, accuracy of the angular stiffness sensor is calibrated in light of the resonance frequency. Angular stiffness scores 99% and 95% accuracy for Sawbones models mimicking medium cancellous bones with and without a cortical layer, respectively.

Adaptive control scheme for cooperative transport systems navigation under uncertainty

An adaptive robust control scheme for a cooperative transport system is proposed to tackle the challenges arising from parameter uncertainty, external interference, measurement errors, and other factors. The cooperative transport system consists of a leader automated ground vehicle, a baggage carrier, and a follower automated ground vehicle. Firstly, a mechanics-based independent dynamic model without constraints is established for each component within the system, and the coupling relationship between components is analyzed to design the constraint function. Secondly, to address inaccuracies in initial conditions, the trajectory errors in both their zero- and first-order forms are introduced. A closed-form dynamic model that is subject to constraints is then developed for the entire cooperative system, incorporating both structural and performance constraints. Thirdly, an innovative adaptive robust control scheme is introduced for mechanical systems facing uncertainty. An adaptive law is devised to estimate the bounds of uncertainty. The control is deterministic and can be mathematically expressed in a closed-form. Fourthly, a constrained optimization problem is formulated using the fuzzy information of uncertainty to choose an appropriate optimal gain kopt of the adaptive law. This is achieved by minimizing the combination of average fuzzy system performance and control effort. Finally, numerical simulations are conducted to verify the effectiveness and adaptability of the proposed control method. The performance of controlled cooperative transport system is both deterministically guaranteed and fuzzily optimized.

Mechanical properties of aramid and UHMWPE thermoplastic composites: numerical and experimental trials

The out-of-plane behaviour of 8 different composites exposed to very high deformation rates and non-standard conditions was investigated in our previous studies. The mechanical properties of these composites have been evaluated through tests conducted at deformation rates comparable to those experienced during explosions. Based on the relevant test results, this study conducted a numerical analysis of 3 best-performing Aramid UD GS3000, Artec Aramid/Woven Aramid CT736, and H62 UD-UHMWPE-reinforced composites. In this regard, LS-DYNA's MAT54 model was compared with other failure mechanics-based models and preferred because it requires less experimental data and can simulate damage progression in dynamic failures. Since the samples used in the study were non-standard, quasi-static tensile and shear tests were performed based on the loads to which the composites would be exposed to create an accurate numerical analysis. Both tensile and shear strength values of the Artec Aramid/Woven Aramid CT736 composite are higher than those of the Aramid UD GS3000 and H62 UD-UHMWPE composites. When the numerical analysis results were compared with the tensile test data, compliance rates of 99.84% for Aramid UD GS3000 reinforced composite, 99.34% for Artec Aramid/Woven Aramid CT736 reinforced composite, and 96.42% for H62 UD-UHMWPE reinforced composite were determined. The analysis showed that the shear stress-strain curves provided 100% agreement at the maximum stress value.

Shear capacity prediction of carbon-fibre-reinforced polymer mesh fabric (CFRP-MF) stirrups reinforced concrete beams

Fibre-reinforced polymer mesh fabric (FRP-MF) is an effective substitute for normal steel stirrups in concrete to prevent steel corrosion in reinforced concrete (RC) structures. Nevertheless, methods for calculating the shear resistance of RC members rehabilitated in shear with FRP-MF configurations are lacking. Moreover, the methods for quantitative analysis of the shear contribution caused by the cracking control effect produced by CFRP-MF longitudinal FRP strips are unclear. In this paper, a mechanics-based segmental model is presented based on partial interaction analysis considering both transverse FRP strips and longitudinal reinforcements. By incorporating the carbon-fibre-reinforced polymer mesh fabric (CFRP-MF) stirrups into this model, a shear strength model was proposed and extended for design purposes in a closed form. In addition, the modified compression field theory (MCFT) model considering the shear strength of concrete in compression and the shear friction model (SFM) in the literature were appropriately modified and used for comparison with the segmental model. It should be noted that if the tensile strength of the longitudinal strips was added into the corresponding calculation formulas, it would cause the difference between the proposed novel models with the previous models. Finally, the shear strength of 14 CFRP-MF reinforced concrete beams were evaluated using these three models. The average ratios of the experimental values of the CFRP-MF RC beams to the shear capacity predicted by the modified segmental model, MCFT with the shear contribution of compressed concrete and SFM were 1.30, 1.19 and 1.19, respectively. It turns out that the modified mechanics-based segmental model yields the minimum discreteness based on the experiment results. This study can provide shear capacity calculation and design reference of RC beams with longitudinal FRP strip reinforcements for researchers and engineers.

Design, Synthesis, Anti-Cancer, Anti-Inflammatory and In Silico Studies of 3-Substituted-2-Oxindole Derivatives.

This study focuses on the design and synthesis of 3-substituted-2-oxindole derivatives aimed at developing dual-active molecules with anti-cancer and anti-inflammatory properties. The molecules were designed with diverse structural and functional features while adhering to Lipinski, Veber, and Leeson criteria. Physicochemical properties were assessed using SWISSADME to ensure drug-likeness and favourable pharmacokinetics. Multistep synthetic procedures were employed for molecule synthesis. In vitro evaluations confirmed the dual activity of the derivatives, with specific emphasis on the significance of dialkyl aminomethyl substitutions for potency against various cell lines. 4 a exhibited GI50 value 3.00E-05 against MDA-MB-231, 4 b has shown GI50 value 2E-05 against MDA-MB-231, 4 c has shown GI50 value 6E-05 against VERO, 4 d has shown GI50 value 8E-05 each against both the MDA-MB-231 and MCF-7 and 4 e has shown GI50 values 2E-05 and 5E-05 each against both the MCF-7 and VERO. The analysis indicates that compounds 3 c (71.19 %), 3 e (66.84 %), and 3 g (63.04 %) exhibited significant anti-inflammatory activity. Additionally, in silico binding free energy analysis and interaction studies revealed significant correlations between in vitro and computational data, identifying compounds 4 d, 4 e, 3 b, 3 i, and 3 e as promising candidates. Key residues such as Glu917, Cys919, Lys920, Glu850, Lys838, and Asp1046 were found to play critical roles in ligand binding and kinase inhibition, providing valuable insights for designing potent VEGFR2 inhibitors. The Quantum Mechanics-based Independent Gradient Model analysis further highlighted the electronic interaction landscape, showing larger attractive peaks and higher electron density gradients for compounds 4 d and 4 e compared to Sunitinib, suggesting stronger and more diverse attractive forces. These findings support the potential of these compounds for further development and optimization in anticancer drug design.

Deformation level and specimen geometry in compression perpendicular to the grain of solid timber, GLT and CLT timber products

Compression Perpendicular to Grain (CPG) is a crucial aspect of timber design, particularly with the advent of engineering wood products like Cross Laminated Timber (CLT), often used in point-supported structures. After many years of discussion, the mechanics-based model by Van der Put has finally been included in the new Eurocode draft. This model offers a more systematic approach to design with a solid theoretical foundation for stress dispersion. However, there is still a need for improvement, especially in two areas: accounting for the capacity increment associated with increasing levels of deformation and determining the maximum effective height to be assumed in calculating the load spreading in deep timber elements. In this paper, the authors focus on these two areas of improvement for GLT, solid timber and CLT timber products. They analyse the data of two extensive experimental campaigns conducted at Norsk Treteknisk Institutt (Norwegian Institute of Wood Technology) and at the Norwegian University of Science and Technology (NTNU) on Glued Laminated Timber (GLT), Solid Timber (ST) and CLT. Additionally, they validated a parametric finite element (FE) model in Abaqus to assess the effect of the height of the timber element on stress distribution. A parametric approach developed in Abaqus aims to find the optimal height threshold to be assumed in the new code formulation.

Regression prediction model for shear strength of cold joint in concrete

The shear resistance of cold joint in concrete is influenced by various design parameters. Traditional mechanical models typically consider only a limited number of parameters to predict the shear strength of cold joints. This research aims to explore the complex nonlinear relationship between design parameters and cold joint shear strength using statistical method and machine learning technology. The goal is to develop a more accurate, reliable, and engineering applicable regression model for predicting shear strength. A dataset of 546 Z-shaped shear specimens characterizing cold joint in concrete was constructed, involving a total of 16 variables that may affect shear performance. Correlation analysis and recursive elimination were adopted to eliminate correlated and insignificant variables based on their importance. Multiple linear regression (MLR), random forest regression (RFR), and support vector machine regression (SVR) prediction models for cold joint shear strength were established based on rigorously screened variables and comprehensively evaluated using multiple methods. It was found that the most significant factors influencing the shear strength of cold joints are concrete strength, interface shear key, product of interface reinforcement strength and its reinforcement ratio, normal stress, fiber length of new concrete, casting method of the new concrete, and product of the fiber length and its tensile strength of old concrete. The MLR, SVR, and RFR models all exhibited superior performance relative to traditional mechanics-based models with regard to shear strength prediction of cold joints. The RFR model is recommended for predicting the shear strength of cold joints due to its superior evaluation indexes in comparison to the MLR and SVR models, and variable sensitivity analysis shows that it does not yield common-sense errors.

Enhancing FRP-concrete interface bearing capacity prediction with explainable machine learning: A feature engineering approach and SHAP analysis

This study introduces a novel approach to predict the shear bearing capacity of FRP-concrete interfaces using explainable machine learning. Eight algorithms are employed: three standalone models (Artificial Neural Network, Support Vector Regression, and Decision Tree) and five ensemble learning models (Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting). Four scenarios with varying input features, including engineered features inspired by mechanics-based bearing capacity equations, are examined. Notably, the inclusion of engineered features such as the stiffness of the FRP strip (Kf) significantly enhanced prediction accuracy and efficiency, although the width correction coefficient (bf/bc) did not yield significant benefits, contrary to findings in mechanics-based models. The Extreme Gradient Boosting (XGBoost) algorithm emerged as the top-performing model, achieving an impressive R-square value of 0.949. In terms of explainability, the study utilized the Shapley Additive Explanation (SHAP) technique to comprehensively elucidate the significance, dependency, and interaction effects of features within the best-performing model. Remarkable, approximately 75 % of the total mean absolute SHAP values were attributed to Kf and bf of the FRP, highlighting their pivotal role in the prediction process. This detailed explanation of the model using SHAP instills trust in the predictions and facilitates its practical implementation in real-world scenarios.

An improved method for statistics estimation of out-of-plane load resistance of masonry walls using design-code models and mechanics-based finite element models

The susceptibility of masonry walls to out-of-plane failure under seismic loading presents a significant engineering concern. In adherence to contemporary limit state design philosophy and as mandated by performance-based design guidelines, it is crucial to accurately estimate statistics parameters such as the mean and variance of out-of-plane resistance. To achieve this, two deterministic models are often considered within the Monte Carlo simulation framework: design-code models, known for their computational efficiency but potential for significant model error, and mechanics-based finite element models, noted for their accuracy but computational intensity. Relying solely on either model for statistics estimation presents challenges due to their respective inherent limitations. This study introduces an improved strategy that synergizes the strengths of both models, resulting in enhanced statistics estimators for the out-of-plane resistance of masonry walls. The proposed methodology involves the use of the control variate method by integrating numerous design-code model evaluations to boost computational efficiency while incorporating a limited number of finite element model evaluations to ensure accuracy. The constructions of the proposed estimators follow rigorous optimization procedures to minimize associated errors and variances. Theoretical derivations are provided for essential elements in the estimator constructions, such as model evaluation numbers for finite element and design-code models and control variate coefficients. Moreover, the accuracy of the proposed estimators is compared with that of the crude Monte Carlo estimator. A key benefit of the proposed estimators is their unbiased nature, and their accuracy is not compromised by the discrepancies between the finite element model and the design-code model. To demonstrate the practicality of these estimators, two case studies are illustrated: one focusing on unreinforced masonry walls and the other on reinforced masonry walls. The results indicate that design-code model-based estimators exhibit large bias, and compared to the estimators that solely rely on the finite element model, the proposed estimators achieve higher accuracy given the same computational budget.

Data-driven shear strength predictions of prestressed concrete hollow-core slabs

Although providing sufficient shear strength is crucial in the design of hollow-core slabs (HCS) to prevent shear failure, conventional shear equations for HCS could not always represent the non-linear relationship between the shear strength and influencing parameters, especially for deep HCS. Back-propagation-neural-network- (BPNN)-based models could be a possible solution since they have been widely used in predicting structural behaviour of other concrete structures. However, the feasibility of such an application has not been examined previously. Therefore, a set of BPNN-based models with and without optimisation algorithms were proposed for predicting the shear strength of prestressed HCS. The proposed BPNN-based models were trained and verified against a database of 227 HCS tested in shear. Comparisons between BPNN-based and mechanics-based models were also performed. The corresponding performances of different models were evaluated by the mean Vut/Vpre, R2, RMSE, MAE and MAPE criteria. To the author's best knowledge, this is the first study to apply hybrid-BPNN-based models for shear strength prediction of HCS. The study found that all proposed BPNN models were effective in predicting the shear strength, while the ACO-BPNN model yielded the best predictions among all the BPNNs. It was also shown that the ACO-BPNN model provided better predictions than the ACI 318 code provisions with/without the size effect factor. Besides, the well-trained ACO-BPNN model could present the size effect of slab thickness on shear strength, which was not explicitly considered in ACI 318. In summary, the ACO-BPNN-based model could a rational tool for predicting shear strength for HCS.

Dynamic response and sound radiation of cracked asphalt pavements under moving loads and variable thermal profiles

ABSTRACT This study investigates the acoustic performance and dynamic response of cracked, viscoelastic porous asphalt pavements under moving loads and variable thermal conditions. Realistic pavement cracks are modelled using an advanced line spring model with fracture compliance coefficients. The novelty lies in the synergistic integration of a heterogeneous triple-porosity media model, a non-uniform depth-dependent temperature profile, and an advanced fracture mechanics-based line spring model with arbitrary crack orientation. Variations in temperature that are uniform, linear, and nonlinear with respect to the thickness layer axis are taken into account. The governing equations are derived by Hamilton’s principle and solved analytically via Fourier-Laplace transforms. The acoustic pressure is first-time predicted through Rayleigh integral analysis. Durbin’s numerical inversion validates the model’s accuracy versus sophisticated finite element simulations. Parametric analysis offers new insights into the effects of crack length, pore size distribution, loading frequency, and thermal gradients on noise pollution and fatigue cracking. The integrated chemo-thermo-mechanical modelling enables optimal structural design and accelerated pavement testing to limit acoustic radiation and premature failure in porous asphalt pavements. It allows for the development of noise-reducing porous asphalt mixtures by modifying aggregate gradation, binder content, and air void distribution.

Comparative assessment of a continuum damage mechanics-based creep damage models for India-specific RAFM steel

ABSTRACT In the present investigation, a comparative assessment of Kachanov-Rabotnov (KR) and Liu-Murakami (LM) Creep Damage Models is presented for India-Specific Reduced Activation Ferritic Martensitic (IN-RAFM) steel, in the framework of Continuum Damage Mechanics (CDM) approach using ABAQUS finite element analysis software. The assessment is made based on the prediction of entire creep curve and creep rupture life of IN-RAFM steel at 823 K under uniaxial tensile loading at the stress levels of 200–260 MPa. Results disclosed that LM model accurately predicts the rupture life and creep deformation in comparison to KR model and the corresponding R-square values are 0.999 (LM model) and 0.993 (KR model), respectively. The material constant q of LM model controlled the damage evolution in tertiary creep stage. In contrast, the high value of constant ∅ in damage rate equation of KR model, led to over prediction of creep strain owing to high damage rate evolution.

Statistical mechanics of active vesicles and the size distribution paradox

Vesicles are the primary modes of communication and transport in cell biology. Conventional wisdom based on thermodynamic equilibrium says that vesicles should have a certain minimum size and size distribution dictated by their thermal fluctuations. However, there is compelling experimental evidence that vesicles exhibit a vast variety of size distributions depending on their formation process and function which cannot be explained by equilibrium statistical mechanics alone. We investigate a non-equilibrium statistical mechanics-based model to understand the role of active membranes on the size distribution of vesicles. Active membranes contain proteins that use external energy sources, such as adenosine triphosphate hydrolysis, and are known to exert forces on the membrane during their activity to carry out different biological functions. The central idea behind our model is that activity, attributed to different sources, impacts vesicle fluctuations in two opposing ways — by active noise which enhances fluctuations, and membrane tension which decreases fluctuations. The interplay of active fluctuations and active tension endows the vesicles with the ability to achieve size distributions that are deemed improbable by equilibrium statistical mechanics. We show that our model for active vesicles, based on linearized curvature elasticity, can reproduce different experimental data for vesicle size distributions available in the literature by varying the activity. Elucidating how these vesicles achieve such diverse size distributions can open avenues for a deeper understanding of physiological and pathological processes and help design vesicles for diagnostics and drug delivery applications.

Fracture Mechanics-Based Modelling of Tool Wear in Machining Ti6Al4V Considering the Microstructure of Cemented Carbide Tools

Fracture Mechanics-Based Modelling of Tool Wear in Machining Ti6Al4V Considering the Microstructure of Cemented Carbide Tools

Mechanics based lateral stability design for a railway Electric Multiple Unit

This paper presented a mechanics-based method to address the typical lateral stability issue of railway trains. A Base Electric Multiple Unit (Base EMU) and an upgraded EMU were used as the research objects. Mechanics-based models were developed for both systems; the Base EMU model was validated using actual test data. Different wheel-rail equivalent conicity values and suspension parameters were investigated. The results showed that inadequate wheel-rail guiding capacity under low conicity can led to the occurrence of lateral stability issues. When the train is pushed by motor cars, coupler lateral forces aggravate stability issues. Altering the yaw damper’s angle and damping properties can significantly reduce lateral swaying and improve the ride index. This represents a low-cost and favored solution. Decreasing the stiffness of the axle box pull rod is also effective in mitigating low-frequency lateral swaying.

Modeling of spatiotemporal dynamics of ligand-coated particle flow in targeted drug delivery processes

Nanoparticles tethered with vasculature-binding epitopes have been used to deliver the drug into injured or diseased tissues via the bloodstream. However, the extent that blood flow dynamics affects nanoparticle retention at the target site after adhesion needs to be better understood. This knowledge gap potentially underlies significantly different therapeutic efficacies between animal models and humans. An experimentally validated mathematical model that accurately simulates the effects of blood flow on nanoparticle adhesion and retention, thus circumventing the limitations of conventional trial-and-error-based drug design in animal models, is lacking. This paper addresses this technical bottleneck and presents an integrated mathematical method that derives heavily from a unique combination of a mechanics-based dispersion model for nanoparticle transport and diffusion in the boundary layers, an asperity model to account for surface roughness of endothelium, and an experimentally calibrated stochastic nanoparticle-cell adhesion model to describe nanoparticle adhesion and subsequent retention at the target site under external flow. PLGA-b-HA nanoparticles tethered with VHSPNKK peptides that specifically bind to vascular cell adhesion molecules on the inflamed vascular wall were investigated. The computational model revealed that larger particles perform better in adhesion and retention at the endothelium for the particle sizes suitable for drug delivery applications and within physiologically relevant shear rates. The computational model corresponded closely to the in vitro experiments which demonstrates the impact that model-based simulations can have on optimizing nanocarriers in vascular microenvironments, thereby substantially reducing in vivo experimentation as well as the development costs.

Elastic properties prediction of two- and three-dimensional multi-material lattices

Advances in multi-material additive manufacturing have opened unprecedented new opportunities for the design and manufacture of lightweight multifunctional structures. The ability to create complex topologies, at a relatively fine resolution, in addition to controlling the material composition on a voxel basis have significantly expanded the design space. To explore this large design space efficiently, accurate and cost-effective modeling tools are essential. In this paper, mechanics-based models for predicting the elastic properties of multi-material 2D and 3D lattice structures are developed or extended. The outcomes are compared with the predictions obtained from finite element models and experimental data. The results reveal that the adapted analytical models demonstrate good accuracy in predicting the elastic modulus of multi-material lattices for relative densities up to approximately 25 % while have considerably less computational cost compared to finite element using solid elements (providing the most accurate results in comparison with experiment). Careful consideration of the accuracy of the predictions is necessary for the use of these models for lattices with high relative density values. Besides, several homogenization-based models were studied to investigate their applicability to multi-material lattice structures when the assumption of scale-separation is considered valid. The capability of these models in predicting the whole elasticity tensor and the potential of multi-material lattices in manipulating the anisotropy are demonstrated. Finally, the introduced prediction frameworks are compared in order to provide an overview of their respective advantages and disadvantages in the case of multi-material lattice structures.

On the conditions for self-catapulting of freezing water droplets

Despite the remarkable progress of anti-icing and deicing technologies in the past decades, it remains a grand challenge to dislodge freezing water from a solid surface without consuming external energy. Herein, we propose a strategy to dislodge freezing water from solid surfaces just by leveraging its volume expansion resulting from the phase change from water to ice. The implementation of this energy-saving strategy relies on a simple micropillar-based gadget on the surface, termed freezing-empowered droplet catapult (FEDC), whereby the work done by the volume expansion of a freezing droplet accreted on it can be harvested and stored as elastic energy, and subsequently released in due course to catapult the freezing droplet away from the surface. Mechanics-based modelling is carried out to reveal the necessary conditions of occurrence for the self-catapulting of freezing water droplets on an FEDC, yielding a phase map that manifests the necessary synergy among the characteristic dimensions and stiffness of the FEDC as well as the volume of the droplet in triggering its self-catapulting. Our study not only demonstrates the feasibility of an energy-saving strategy for shedding off freezing droplets from solid surfaces but also provides a quantitive guideline for the design of the gadget for implementation, paving the way to a new technology for controlling ice accretion on solid surfaces with zero consumption of external energy.