Development of a fast-solving numerical model for the structural analysis of cricket balls

In cricket, high speed impacts occur between the cricket ball, the bat, players and their protective equipment. Improved understanding of impact dynamics has the potential to significantly improve the development of cricket equipment and also contribute to improving the player safety and performance. In particular, the development of high performance cricket balls with enhanced structural properties (e.g. improved durability) would benefit greatly from such insight. This article presents the development of two fast-solving numerical models as well as a universal FE model for the structural analysis of cricket balls. The models were developed using experimental data obtained from drop tests and high speed impact tests. These models predict impact characteristics with very little computing cost. A universal Finite Element (FE) ball model has also been developed using ABAQUS, which combines an FE model template and a material parameter selection tool based on an Artificial Neural Network (ANN) model. This approach allows for rapid model development while producing accurate results at different impact speeds. Comparison of results revealed good agreement between simulation and experimental results. The developed FE-ANN model can be used to predict the impact behaviour of different types of cricket balls under various dynamic conditions. This flexibility represents an advantage that can be utilized by sports equipment developers to rapidly develop different cricket ball models needed for inclusion in larger simulations involving impact of a cricket ball with other objects. This represents an invaluable tool for facilitating design, analysis and structural optimisation of cricket-related sport equipment

Development of an FE model of a cricket ball

Studies of impact dynamics of cricket balls have the potential of significantly improving the development of cricket equipment and also contribute to improving the player's safety and performance. This work presents the development of a detailed multi-layer FE model for the structural analysis of cricket balls. The model was derived using experimental data obtained from tests developed for this purpose, including drop tests and high speed impact tests. The multi-layer, multi-material FE model was constructed using ABAQUS. Calibration of the model involves a multidisciplinary optimization technique. Comparison shows good agreement between experimental results and predictions from the refined model

Performance of three PAC approaches in simulation type III.

The interferential oscillations were sinusoidal oscillations. (a) Simulation data were constructed without noise. (b) Noise level σ = 0.1. (c) Noise level σ = 0.2.</p

Performance of three PAC approaches in simulation type II.

The interferential oscillations were sinusoidal oscillations. (a) Simulation data were constructed without noise. (b) Noise level σ = 0.1. (c) Noise level σ = 0.2.</p

A Precise Annotation of Phase-Amplitude Coupling Intensity

<div><p>Neuronal information can be coded in different temporal and spatial scales. Cross-frequency coupling of neuronal oscillations, especially phase-amplitude coupling (PAC), plays a critical functional role in neuronal communication and large scale neuronal encoding. Several approaches have been developed to assess PAC intensity. It is generally agreed that the PAC intensity relates to the uneven distribution of the fast oscillation amplitude conditioned on the slow oscillation phase. However, it is still not clear what the PAC intensity exactly means. In the present study, it was found that there were three types of interferential signals taking part in PAC phenomenon. Based on the classification of interferential signals, the conception of PAC intensity is theoretically annotated as the proportion of slow or fast oscillation that is involved in a related PAC phenomenon. In order to make sure that the annotation is proper to some content, simulation data are constructed and then analyzed by three PAC approaches. These approaches are the mean vector length (MVL), the modulation index (MI), and a new permutation mutual information (PMI) method in which the permutation entropy and the information theory are applied. Results show positive correlations between PAC values derived from all three methods and the suggested intensity. Finally, the amplitude distributions, i.e. the phase-amplitude plots, obtained from different PAC intensities show that the annotation proposed in the study is in line with the previous understandings.</p></div

Saffranin O staining of bone formation (black star) and scaffolds remnant (yellow star) within defects in the drill control, silk, BG/silk and MBG/silk groups at 2 and 4 weeks.

<p>Traces of cartilage matrix (red arrow head) can be observed in MBG/silk groups. Lower magnification (x100; A-C, I-L; bar=200 µm); higher magnification (x400; E-H, M-P; Bar=50µm). The red dotted line indicated defect margin.</p

Performance of three PAC approaches in simulation type II.

<p><b>The interferential oscillations were filtered from experimental LFP.</b> (a) The interferential oscillations were filtered from the CA1 LFP of the puberty rat. (b) The interferential oscillations were filtered from the CA3 LFP of the adult rat.</p

Cell proliferation and osteogenic differentiation.

<p>S-MSCs (A) and O-MSCs (B) proliferation in silk, BG/silk and MBG/silk scaffolds by CCK-8 assay at 1,3,7,11 and 15 d; (n=4 per group; #: P<0.05 BG/silk vs silk group, *:P<0.05 MBG/silk vs silk group) Quantitative Alp activity of S-MSCs (C) and O-MSCs (D) on silk, BG/silk and MBG/silk scaffolds at 7 and 14 d in osteogenic culture. (n=3 per group; *P<0.05).</p

Bone regeneration and scaffold remnant within defect.

<p>(A) Semi-quantitative scores of bone regeneration in the femur defects are presented as box plots, where the boxes represent the first and third quartiles. (B) Quantitative data of scaffold remnant fraction in BG/silk and MBG/silk group at 2 and 4 weeks. Four sections per sample in each group were used at each time point per analysis. Scaffold remnant=area of silk scaffolds/total area; * P<0.05, **P<0.01, ***P<0.001.</p

Immunohistochemical markers of COL I, OPN, BSP, OCN and primary Ab(-) control in silk, BG/silk and MBG/silk groups at 2 weeks.

<p>Black arrow indicated the positive staining in bone forming tissue, and white arrow indicated silk scaffold. (Bar=200µm).</p