Table 2 Key research gaps and... | Emerald Publishing

Table 2

Key research gaps and development directions

Challenges	Current impact	Future solution directions
Lack of large-scale publicly available experimental datasets	- Difficulty in performing comprehensive model calibration and validation - Limited ability to compare results across different studies - Reduced generalisability and reliability when applied in real-world conditions	- Establish standardised open databases - Strengthen collaboration among research institutes, operators, and manufacturers - Develop data-sharing platforms
Trade-off between model accuracy and computational cost	- Multi-physics and highly nonlinear models provide high accuracy but are difficult to implement in real-time applications - Simplified models may neglect important instability mechanisms - Limited applicability in optimisation and online monitoring systems	- Develop multi-fidelity modeling frameworks - Integrate physics-based models with AI approaches - Optimise algorithms and exploit parallel computing resources
Lack of standardisation of safety indices for HSR	- Difficulty in comparing results across different studies - Inconsistent evaluation thresholds for the L/V ratio, wheel unloading ratio, and critical speed - Limited integration into early warning and automated monitoring systems	- Develop comprehensive multi-parameter safety index frameworks - Establish threshold values based on experimental data and probabilistic analysis - Harmonise evaluation criteria with international standards
Strong nonlinearity and multi-physics coupling	- High complexity in modeling and analysis due to strong nonlinear interactions - Difficulties in accurately coupling mechanical, thermal, wear, and fatigue effects - Challenges in achieving stable and efficient numerical solutions for large-scale simulations	- Develop integrated multiphysics modelling frameworks - Combine long-term simulations with real operational data - Apply hybrid physics-data-driven methods
Lack of Monitoring and Predictive Maintenance Systems	- Maintenance actions remain largely reactive - Difficulties in early detection of instability or degradation of contact geometry	- Integrate intelligent sensors and Internet of Things (IoT) technologies - Apply AI for anomaly detection and predictive analysis - Develop comprehensive digital dynamic monitoring systems

Challenges	Current impact	Future solution directions
Lack of large-scale publicly available experimental datasets	- Difficulty in performing comprehensive model calibration and validation - Limited ability to compare results across different studies - Reduced generalisability and reliability when applied in real-world conditions	- Establish standardised open databases - Strengthen collaboration among research institutes, operators, and manufacturers - Develop data-sharing platforms
Trade-off between model accuracy and computational cost	- Multi-physics and highly nonlinear models provide high accuracy but are difficult to implement in real-time applications - Simplified models may neglect important instability mechanisms - Limited applicability in optimisation and online monitoring systems	- Develop multi-fidelity modeling frameworks - Integrate physics-based models with AI approaches - Optimise algorithms and exploit parallel computing resources
Lack of standardisation of safety indices for HSR	- Difficulty in comparing results across different studies - Inconsistent evaluation thresholds for the L/V ratio, wheel unloading ratio, and critical speed - Limited integration into early warning and automated monitoring systems	- Develop comprehensive multi-parameter safety index frameworks - Establish threshold values based on experimental data and probabilistic analysis - Harmonise evaluation criteria with international standards
Strong nonlinearity and multi-physics coupling	- High complexity in modeling and analysis due to strong nonlinear interactions - Difficulties in accurately coupling mechanical, thermal, wear, and fatigue effects - Challenges in achieving stable and efficient numerical solutions for large-scale simulations	- Develop integrated multiphysics modelling frameworks - Combine long-term simulations with real operational data - Apply hybrid physics-data-driven methods
Lack of Monitoring and Predictive Maintenance Systems	- Maintenance actions remain largely reactive - Difficulties in early detection of instability or degradation of contact geometry	- Integrate intelligent sensors and Internet of Things (IoT) technologies - Apply AI for anomaly detection and predictive analysis - Develop comprehensive digital dynamic monitoring systems

Source(s): Authors' own work