A New Turbomachine for Clean and Sustainable Hydrocarbon Cracking

Decarbonising highly energy-intensive industrial processes is imperative if nations are to comply with 2050 greenhouse gas emissions. This is a significant challenge for high-temperature industrial processes, such as hydrocarbon cracking, and there have been limited developments thus far. The novel concept presented in this study aims to replace the radiant section of a hydrocarbon cracking plant with a novel turbo-reactor. Rather than using heat from the combustion of natural gas, the novel turbo-reactor can be driven by an electric motor powered by renewable electricity. Switching the fundamental energy transfer mechanism from surface heat exchange to mechanical energy transfer significantly increases the exergy efficiency of the process. Theoretical analysis and numerical simulations show that the ultra-high aerodynamic loading rotor is able to impart substantial mechanical energy into the feedstock without excess temperature difference and metal temperature magnitude. The required enthalpy rise can be supplied within a reactor volume 500 times smaller than that for a conventional furnace. A significantly lower wall surface temperature, supersonic gas velocities and a shorter primary gas path enable a controlled reduction in the residence time for chemical reactions, which optimises the yield. For the same reasons the conditions for coke deposition on the turbo-reactor surfaces are unfavourable, leading to an increase in plant availability. This study demonstrates that the mechanical work input into the feedstock can be dissipated through an intense turbulent mixing process which maintains an ideal and controlled pressure level for cracking.

Convective self–aggregation in a mean flow

Convective self-aggregation is an atmospheric phenomenon seen in numerical simulations in a radiative convective equilibrium framework thought to be informative of some aspects of the behavior of real-world convection in the deep tropics. We impose a background mean wind flow on convection-permitting simulations through the surface flux calculation in an effort to understand how the asymmetry imposed by a mean wind influences the propagation of aggregated structures in convection. The simulations show that, with imposing mean flow, the organized convective system propagates in the direction of the flow but slows down compared to what pure advection would suggest, and it eventually becomes stationary relative to the surface after 15 simulation days. The termination of the propagation arises from momentum flux, which acts as a drag on the near-surface horizontal wind. In contrast, the thermodynamic response through the wind-induced surface heat exchange feedback is a relatively small effect, which slightly retards the propagation of the convection relative to the mean wind.

A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed to support the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of researchers using sensors to understand lake functioning and address questions about how lakes around the world respond to climate and land use change. The scale and diversity of lake types, locations, and sizes, and the expanding observational datasets created the need for a robust community model of lake dynamics with sufficient flexibility to accommodate a range of scientific and management questions relevant to the GLEON community. This paper summarizes the scientific basis and numerical implementation of the model algorithms, including details of sub-models that simulate surface heat exchange and ice cover dynamics, vertical mixing, and inflow–outflow dynamics. We demonstrate the suitability of the model for different lake types that vary substantially in their morphology, hydrology, and climatic conditions. GLM supports a dynamic coupling with biogeochemical and ecological modelling libraries for integrated simulations of water quality and ecosystem health, and options for integration with other environmental models are outlined. Finally, we discuss utilities for the analysis of model outputs and uncertainty assessments, model operation within a distributed cloud-computing environment, and as a tool to support the learning of network participants.

Intellectual modeling of surface heat-exchange enhancer based on artificial neural networks

The results of neural network modeling of average heat transfer in the channels of exchangers with surface enhancer of different shapes are presented. Artificial neural networks are trained using experimental data, which covers more than ten sources. The possibility and prospects of building artificial neural networks for modeling the characteristics of heat exchange surfaces are shown.