Understanding the complex dynamics of climate patterns under different anthropogenic emissions scenarios is crucial for predicting future environmental conditions and formulating sustainable policies. Using Dynamic Mode Decomposition with control (DMDc), we analyze surface air temperature patterns from climate simulations to elucidate the effects of various climate-forcing agents. This improves upon previous DMD-based methods by including forcing information as a control variable. Our study identifies both common climate patterns, like the North Atlantic Oscillation and El Niño Southern Oscillation, and distinct impacts of aerosol and carbon emissions. We show that these emissions’ effects vary with climate scenarios, particularly under conditions of higher radiative forcing. Our findings confirm DMDc’s utility in climate analysis, highlighting its role in extracting modes of variability from surface air temperature while controlling for emissions contributions and exposing trends in these spatial patterns as forcing scenarios change.

A compressible large eddy simulation (LES) is performed to study a pulsed jet actuator that is used to control a turbulent axisymmetric bluff body wake. The actuator is driven at low-frequency (f = 200$f = 200$Hz, S{t_\theta } = 0.029$S{t_\theta } = 0.029$) and high amplitude ({C_\mu } = 0.034${C_\mu } = 0.034$). The numerical scheme and a suitable boundary condition for the pulsed jet are validated, showing good agreement with experimental results. A comparison of the velocity boundary condition and the moving boundary condition shows that, in the vicinity of the orifice/slot and in the downstream region, the results from these two methods are identical, while the fluid behaviour inside the cavity shows difference. An analysis of the pulsed jet actuator shows that the phase lag of the cavity pressure is determined by the integration of the diaphragm motion and the pulsed jet. The mean total pressure distribution shows that the total pressure loss is concentrated in the vicinity of the slot. Dynamic mode decomposition (DMD) on the pressure field is used to extract coherent structures which oscillate with the same frequency as that of the diaphragm motion. Some small-scale high-frequency structures are also apparent.

We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD). The application focus is the condition monitoring of wind turbine gearboxes under varying load conditions, in particular irregular and stochastic wind fluctuations. We analyze data stemming from a simulated vibration response of a simple nonlinear gearbox model in a healthy and damaged scenario and under different wind conditions. With mrDMD applied on time-delay snapshots of the sensor data, we can extract components in these vibration signals that highlight features related to damage and enable its identification. A comparison with Fourier analysis, time synchronous averaging, and empirical mode decomposition shows the advantages of the proposed mrDMD-based data analysis approach for damage detection.

The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability of powerful machine learning techniques. This twin concept combines physics-based models within a model order reduction framework—to obtain real-time feedback rates—and data science. Thus, the main idea of the HT is to develop on-the-fly data-driven models to correct possible deviations between measurements and physics-based model predictions. This paper is focused on the computation of stable, fast, and accurate corrections in the HT framework. Furthermore, regarding the delicate and important problem of stability, a new approach is proposed, introducing several subvariants and guaranteeing a low computational cost as well as the achievement of a stable time-integration.