Modeling and Optimal Operation of Hydraulic, Wind and Photovoltaic Power Generation Systems

The transition to 100% renewable energy in the future is one of the most important ways of achieving "carbon peaking and carbon neutrality" and of reducing the adverse effects of climate change. In this process, the safe, stable and economical operation of renewable energy generation systems, represented by hydro-, wind and solar power, is particularly important, and has naturally become a key concern for researchers and engineers. Therefore, this book focuses on the fundamental and applied research on the modeling, control, monitoring and diagnosis of renewable energy generation systems, especially hydropower energy systems, and aims to provide some theoretical reference for researchers, power generation departments or government agencies

Some remarks on load modeling in nonlinear structural analysis–Statics with large deformations–Consistent treatment of follower load effects and load control

Load modeling in nonlinear statics, particularly incorporating large deformations differs significantly from the treatment in linear analysis. As in structural dynamics masses in a gravity field generate the loading, their location, and their modifications within the deformation process must be considered in a nonlinear simulation. A specific view besides loading by masses is on gas and fluid interaction with structures. In addition, load control using specifically designed algorithms is evaluated with respect to realistic applications. Within the load modeling an unavoidable, however side aspect, is the general discussion about the so-called follower forces and non-conservative loading. As an example of real-world applications, the specifics of inflated rubber dams are presented

Flow Reorganization in an Anthropogenically Modified Tidal Channel Network: An Example From The Southwestern Ganges-Brahmaputra-Meghna Delta

We examine variations in discharge exchange between two parallel, 1‐ to 2‐km‐wide tidal channels (the Shibsa and the Pussur) in southwestern Bangladesh over spring‐neap, and historical timescales. Our objective is to evaluate how large‐scale, interconnected tidal channel networks respond to anthropogenic perturbation. The study area spans the boundary between the pristine Sundarbans Reserved Forest, where regular inundation of the intertidal platform maintains the fluvially abandoned delta plain, and the anthropogenically modified region to the north, where earthen embankments sequester large areas of formerly intertidal landscape. Estimates of tidal response to the embankment‐driven reduction in basin volume, and hence tidal prism, predict a corresponding decrease in size of the mainstem Shibsa channel, yet the Shibsa is widening and locally scouring even as the interconnected Pussur channel faces rapid shoaling. Rather, the Shibsa has maintained or even increased its pre‐polder tidal prism by capturing a large portion of the Pussur\u27s basin via several transverse channels that are themselves widening and deepening. We propose that an enhanced tidal setup in the Pussur and the elimination of an effective Shibsa‐Pussur flow barrier are driving this basin capture event. These results illustrate previously unrecognized channel interactions and emphasize the importance of flow reorganization in response to perturbations of interconnected, multichannel tidal networks that characterize several large tidal delta plains worldwide

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage

Rotating potential of a stochastic parametric pendulum

The parametric pendulum is a fruitful dynamical system manifesting some of the most interesting phenomena of nonlinear dynamics, well-known to exhibit rather complex motion including period doubling, fold and pitchfork bifurcations, let alone the global bifurcations leading to chaotic or rotational motion. In this thesis, the potential of establishing rotational motion is studied considering the bobbing motion of ocean waves as the source of excitation of a oating pendulum. The challenge within this investigation lies on the fact that waves are random, as well as their observed low frequency, characteristics which pose a broader signi cance within the study of vibrating systems. Thus, a generic study is conducted with the parametric pendulum being excited by a narrow-band stochastic process and particularly, the random phase modulation is utilized. In order to explore the dynamics of the stochastic system, Markov-chain Monte-Calro simulations are performed to acquire a view on the in uence of randomness onto the parameter regions leading to rotational response. Furthermore, the Probability Density Function of the response is calculated, applying a numerical iterative scheme to solve the total probability law, exploiting the Chapman-Kolmogorov equation inherent to Markov processes. A special case of the studied structure undergoing impacts is considered to account for extreme weather conditions and nally, a novel design is investigated experimentally, aiming to set the ground for future development