Identification and Interpretation of Earth’s Atmosphere Dynamics’ and Thermodynamics’ Similarities between Rogue Waves and Oceans’ Surface Geostrophic Wind

In their daily practices, meteorologists make extensive use of the geostrophic wind properties to explain many weather phenomena such as the meaning and direction of the horizontal winds that take place around the low atmospheric pressures. The biggest challenge that faces the public who is interested in information disseminated by meteorologists is to know exactly what means the geostrophic wind. Besides the literal definitions scattered in very little scientific work, there is unfortunately no book which gives importance to the algebraic definition of the geostrophic wind. Our work shows that to better understand the behavior of natural phenomena, it is essential to combine the theories with based observations. Obviously, observations cannot be relevant without a theory that guides the observers. Conversely, no theory can be validated without experimental verification. Synoptic observations show that in the “free atmosphere!” the wind vectors are very nearly parallel to isobars, and the flow is perpendicular to the horizontal pressure gradient force, at least at any given instant. This kind of information recommends great caution when making geostrophic approximations. Our work also shows that for tornadoes, there is no need to move away from the surface of the oceans to observe the geostrophic balance. Undoubtedly, identification and interpretation of earth’s atmosphere dynamics’ and thermodynamics’ similarities between rogue waves and oceans’ surface geostrophic wind will be an easy exercise to researchers who will give importance to result provided by this paper.

Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features

To fully exploit the rich characteristic variation laws of an integrated energy system(IES)and further improve the short-term load-forecasting accuracy,a load-forecasting method is proposed for an IES based on LSTM and dynamic similar days with multi-features.Feature expansion was performed to construct a comprehensive load day covering the load and meteorological information with coarse and fine time granularity,far and near time periods.The Gaussian mixture model(GMM)was used to divide the scene of the comprehensive load day,and gray correlation analysis was used to match the scene with the coarse time granularity characteristics of the day to be forecasted.Five typical days with the highest correlation with the day to be predicted in the scene were selected to construct a“dynamic similar day”by weighting.The key features of adjacent days and dynamic similar days were used to forecast multi-loads with fine time granularity using LSTM.Comparing the static features as input and the selection method of similar days based on non-extended single features,the effectiveness of the proposed prediction method was verified.

Calibration of discrete element modeling: Scaling laws and dimensionless analysis

In this paper,dynamic similarity conditions are derived for discrete element simulations by non-dimensionalising the governing equations.These conditions must be satisfied so that the numerical model is a good representation of the physical phenomenon.For a pure mechanical system,if three independent ratios of the corresponding quantities between the two models are set,then the ratios of other quantities must be chosen according to the similarity principles.The scalability of linear and non-linear contact laws is also investigated.Numerical tests of 3D uni-axial compression are carried out to verify the theoretical results.Another example is presented to show how to calibrate the model according to laboratory data and similarity conditions.However,it is impossible to reduce computer time by scaling up or down certain parameters and continue to uphold the similarity conditions.The results in this paper provide guidelines to assist discrete element modelers in setting up the model parameters in a physically meaningful way.