Development and Demonstration of Critical Components of Aluminum Based Energy Storage Devices Using the Chloroaluminate Ionic Liquids

This dissertation considers the development of porous carbon materials as the substrates for Al deposition/dissolution in an Al based ionic liquid flow battery (ILFB) and demonstration of an Al based hybrid supercapacitor. The Aluminum chloride/ 1-ethyl-3-methylimidazolium chloride chloroaluminate ionic liquid is utilized as the electrolyte for these Al based energy storage devices. The ILFB has less capital cost than the all-vanadium redox flow battery because of the inexpensive AlCl3. The feasibility to equip a tank of solid aluminum chloride in an ILFB system aiming to improve energy density is investigated. A critical range of temperature data (50-130 celsius degree) for aluminum chloride dissolution and precipitation from saturated chloroaluminate ionic liquids is measured by differential scanning calorimetry. The process of Al deposition on porous carbon materials is investigated in the static electrolyte and a flow-through cell aiming to improve the current density, the amount of Al deposits stored in substrates and limit the dendrite growth. Fourier transform infrared spectroscopy and scanning electron microscope are applied to characterize the Al deposits on the porous carbon materials. By the flow-through method providing enhanced diffusion to porous carbon materials, the current density of Al deposition on carbon paper is remarkably higher than that on Al disk. However, dendrites prefer to grow on the Al disk substrate. The electrolyte flow rate and the flow direction also play important roles in determining current densities and dendrite formation for Al deposition on porous carbon materials. We successfully demonstrate an Al based hybrid supercapacitor using high surface area carbon materials such as graphene and activated carbon. The activated carbon is preferred because of less catalytic ability to evolve chlorine. The mismatch between the small pore size of activated carbon and the large ion size of complex ions results in the high charge-transfer resistance measured by the electrochemical impedance measurements. The wettability of electrodes determined by different polymer binders, Polytetrafluoroethylene and the aqueous base modified styrene butadiene rubber, has a significant effect on specific capacitance of activated carbon. The hydrophilic property of SBR may promote the entrance of ions to the micropores of activated carbon

Exploring a better turbine layout in vertically staggered wind farms

Vertical staggering of wind turbines can lead to an increased power production in the entrance region of a wind farm because downstream turbines are consequently outside the wakes of preceding turbines. We perform large eddy simulations of different vertically staggered wind farm configurations for which we keep the average turbine hub height the same. We find that the turbine power output in the entrance region of the wind farm is significantly higher when the first turbine row is elevated than when the first turbine row is lowered. The reason is that this allows the first high turbine row to fully benefit from the strong winds at a high elevation. In the fully developed region of the wind farm the power production of the vertically staggered wind farms is similar to the power production of the corresponding reference aligned wind farm, while the normalized power fluctuations can be significantly higher than in the reference wind farm

Charge injection enhanced natural convection heat transfer in horizontal concentric annuli filled with a dielectric liquid

The natural convection heat transfer in a highly insulating liquid contained between two horizontal concentric cylinders is shown by two-dimensional numerical simulations to be noticeably enhanced by imposing a direct current electric field. This augmentation of heat transfer is due to the radial flow motion induced by unipolar injection of ions. It is found that there exists a threshold of the electric driving parameter T, above which the heat transfer enhancement due to the electric effect becomes significant. For relatively small T values, the mean Nusselt numbers are closely related to the flow pattern and Rayleigh number Ra. In addition, for sufficiently high T values, the flow is fully dominated by the Coulomb force, and thus the heat transfer rate no long depends on Ra.Ministerio de Ciencia y Tecnología FIS2011-25161Junta de Andalucía P10-FQM-5735Junta de Andalucía P09-FQM-458

Numerical analyses of wire-plate electrohydrodynamic flows

We present numerical analyses of 2-D electrohydrodynamic (EHD) flows of a dielectric liquid between a wire electrode and two plate electrodes with a Poiseuille flow, using direct numerical simulation and global stability analysis. Both conduction and injection mechanisms for charge generation are considered. In this work, we focused on the intensity of the cross-flow and studied the EHD flows without a cross-flow, with a weak cross-flow and with a strong cross-flow. (1)In the case without a cross-flow, we investigated its nonlinear flow structures and linear dynamics. We found that the flow in the conduction regime is steady, whereas the flow in the injection regime is oscillatory, which can be explained by a global stability analysis. (2)The EHD flow with a weak cross-flow is closely related to the flow phenomena in electrostatic precipitator (ESP). Our analyses indicate that increasing the cross-flow intensity or the electric Reynolds number leads to a less stable flow. Based on these results, we infer that one should adopt a relatively low voltage and weak cross-flow in the wire-plate EHD flow to avoid flow instability, which may hold practical implications for ESP. (3)The case of strong cross-flow is examined to study the EHD effect on the wake flow. By comparing the conventional cylindrical wake with the EHD wake in linear and nonlinear regimes, we found that the EHD effect brings forward the vortex shedding in wake flows. Besides, the EHD effect reduces the drag coefficient when the cross-flow is weak, but increases it when it is strong

Patched Denoising Diffusion Models For High-Resolution Image Synthesis

We propose an effective denoising diffusion model for generating high-resolution images (e.g., 1024$\times$512), trained on small-size image patches (e.g., 64$\times$64). We name our algorithm Patch-DM, in which a new feature collage strategy is designed to avoid the boundary artifact when synthesizing large-size images. Feature collage systematically crops and combines partial features of the neighboring patches to predict the features of a shifted image patch, allowing the seamless generation of the entire image due to the overlap in the patch feature space. Patch-DM produces high-quality image synthesis results on our newly collected dataset of nature images (1024$\times$512), as well as on standard benchmarks of smaller sizes (256$\times$256), including LSUN-Bedroom, LSUN-Church, and FFHQ. We compare our method with previous patch-based generation methods and achieve state-of-the-art FID scores on all four datasets. Further, Patch-DM also reduces memory complexity compared to the classic diffusion models

Maximum power point tracking control of the permanent magnet synchronous generator based wind turbine

Wind power generation is a promising renewable energy source. The reduced cost of electricity supplied from wind power plants may be attributed to good control strategies such as maximum power point tracking. The control algorithm for maximum power generation is analysed in this thesis. The control algorithm is proposed by regulating the d-q axis voltages of electrical machines in order to control machine torque and rotational speed that allows wind turbines to always extract maximum power from the wind energy source. A conventional way to control the electrical machine is by using vector control together with PI controllers to regulate voltages. This control method is mature and robust enough for electrical machine control. However, vector control may have difficulties in handling system interconnected nonlinearity and time varying wind power input variables. To improve the control strategy and provide controllers with a wider range of applicability, feedback linearization and nonlinear adaptive control algorithms are investigated. Feedback linearization control cancels out all the nonlinearities of q-axis items to expand operational range and develop interaction between the d-axis and q-axis dynamics for machine torque. For nonlinear adaptive control, the original nonlinear multi-input multi-output system is divided into inter-related subsystems and the system nonlinear items and uncertainties are estimated in order to cancel out the existing nonlinearities. Wind power generation maximum power point tracking is accomplished by using conventional vector control, feedback linearization control and nonlinear adaptive control. Practically, due to the small range of control capability, the gain-scheduled conventional control strategy requires a set of control parameters in order to match the different input wind speed. And a mapping technique which relies on the wind speed and current sensors is essential for this control strategy. The feedback linearization control strategy proposed in the report gives global trajectory tracking, so only one set of controller parameter is able to handle all the different wind speed inputs. However, the feedback linearization control still requires some of the machine operational parameters such as rotor speed, stator winding current, etc. Therefore, the nonlinear adaptive control strategy is proposed which uses the estimated machine operational parameters instead of actual parameters. This would further improve the controller capability and robustness. The simulation in this thesis have shown that the proposed nonlinear control strategies are also able to conduct wind turbine maximum power point tracking compare to conventional gain-scheduled control strategy. In the real case, if the proposed nonlinear control strategies can be successfully implemented for wind turbine, it will reduce the number of sensors and the corresponding devices used and thus reduce the cost and enhance the wind turbine robustness. A magnetic equivalent circuit model of the permanent magnet synchronous machine is developed to analyse the electrical machine performance consider magnetic saturation. This model is usually used for electrical machine design and optimization purpose. It has a significant advantage in computational speed compared to another popular tool, finite element method. The magnetic equivalent circuit model may be used to calculate electrical machine properties such as electromotive force and flux linkages for machine control. The flux worked out by using this model is compared with finite element method analysis and the result shows that this model is five times faster in calculations and gives the percentage error less than ten. Currently, due to the uncertainties of magnetic saturated machines, the electrical machine controller only handles the linear region of machine power speed curve. If the proposed model has the calculation speed fast enough to give real time machine operational parameters, the uncertain parameters can be obtained even when the machine encounters magnetic saturation. It has to be emphasized that the nonlinearities in the magnetic equivalent circuit model is due to the magnetic material, while the nonlinearities in machine controller are due to the summation or product of multiple state variables, they are essentially different

Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https://github.com/CrossmodalGroup/MaskedVectorQuantization.Comment: accepted by CVPR 202