Codimension-two bifurcation of axial loaded beam bridge subjected to an infinite series of moving loads

A novel model is proposed which comprises of a beam bridge subjected to an axial load and an infinite series of moving loads. The moving loads, whose distance between the neighbouring ones is the length of the beam bridge, coupled with the axial force can lead the vibration of the beam bridge to codimension-two bifurcation. Of particular concern is a parameter regime where non-persistence set regions undergo a transition to persistence regions. The boundary of each stripe represents a bifurcation which can drive the system off a kind of dynamics and jump to another one, causing damage due to the resulting amplitude jumps. The Galerkin method, averaging method, invertible linear transformation, and near identity nonlinear transformations are used to obtain the universal unfolding for the codimension-two bifurcation of the mid-span deflection. The efficiency of the theoretical analysis obtained in this paper is verified via numerical simulations.

Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

Analysis of seasonal position variation for selected GNSS sites in Poland using loading modelling and GRACE data

In this study we compared weekly GNSS position time series with modelled values of crustal deformations on the basis of Gravity Recovery and Climate Experiment （GRACE） data. The Global Navigation Satellite Systems （GNSS） time series were taken from homogeneously reprocessed global network solutions within the International GNSS Service （IGS） Reprucessing 1 project and from regional solutions performed by Warsaw University of Technology （WUT） European Permanent Network （EPN） Local Analysis Center （LAC） within the EPN reprocessing project. Eight GNSS sites from the territory of Poland with observation timespans between 2.5 and 13 years were selected for this study. The Total Water Equivalent （TWE） estimation from GRACE data was used to compute deformations using the Green＇s function formalism. High frequency components were removed from GRACE data to avoid aliasing problems. Since GRACE observes mainly the mass transport in continental storage of water, we also compared GRACE deformations and the GNSS position time series, with the deformations computed on the basis of a hydrosphere model. We used the output of Water GAP Hydrology Model （WGHM） to compute deformations in the same manner as for the GRACE data. The WGHM gave slightly larger amplitudes than GNSS and GRACE. The atmospheric non-tidal loading effect was removed from GNSS position time series before comparing them with modelled deformations. The results confirmed that the major part of observed seasonal variations for GNSS vertical components can be attributed to the hy- drosphere loading. The results for these components agree very well both in the amplitude and phase. The decrease in standard deviation of the residual GNSS position time series for vertical components corrected for the hydrosphere loading reached maximally 36% and occurred for all but one stations for both global and regional solutions. For horizontal components the amplitudes are about three times smaller than for vertical components therefore the comparison is much more complicated and the conclusions are ambiguous.