This paper is concerned with liquid sloshing in a partially filled container due to 3‐dimensional vehicle motion. The liquid sloshing is described by a set of linear modal equations derived from the potential flow theory, which can be applied to liquid sloshing induced by arbitrary combination of lateral, longitudinal, and rotational excitations. The sloshing force and moment are expressed with a set of hydrodynamic coefficients that are determined by the linear velocity potential. These coefficients can be precalculated and incorporated into the motion equations of the vehicle system so that a fully coupled vehicle‐sloshing model is available. In addition, we propose an approach to calculate the hydrodynamic coefficients using the outputs of commercial frequency‐domain boundary element software in order to maximize the efficiency of modelling and computation. The accuracy of the proposed model is examined by comparison with available CFD and model test data in the literature. The case of a road tanker encountering a road bump during acceleration/braking is investigated. Results show that the tank rotational motion will affect the amplitude and the sloshing force, and neglecting tank rotation may lead to underestimation of the sloshing force magnitude.

Site-specific weather forecasts are essential for accurate prediction of power demand and are consequently of great interest to energy operators. However, weather forecasts from current numerical weather prediction (NWP) models lack the fine-scale detail to capture all important characteristics of localised real-world sites. Instead, they provide weather information representing a rectangular gridbox (usually kilometres in size). Even after post-processing and bias correction, area-averaged information is usually not optimal for specific sites. Prior work on site-optimised forecasts has focused on linear methods, weighted consensus averaging, and time-series methods, among others. Recent developments in machine learning (ML) have prompted increasing interest in applying ML as a novel approach towards this problem. In this study, we investigate the feasibility of optimising forecasts at sites by adopting the popular machine learning model “gradient boosted decision tree”, supported by the XGBoost package (v.1.7.3) in the Python language. Regression trees have been trained with historical NWP and site observations as training data, aimed at predicting temperature and dew point at multiple site locations across Australia. We developed a working ML framework, named “Multi-SiteBoost”, and initial test results show a significant improvement compared with gridded values from bias-corrected NWP models. The improvement from XGBoost (0.1–0.6 °C, 4–27% improvement in temperature) is found to be comparable with non-ML methods reported in the literature. With the insights provided by SHapley Additive exPlanations (SHAP), this study also tests various approaches to understand the ML predictions and increase the reliability of the forecasts generated by ML.