Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures

Reconfigurable architectures are becoming mainstream: Amazon, Microsoft and IBM are supporting such architectures in their data centres. The computationally intensive nature of atmospheric modelling is an attractive target for hardware acceleration using reconfigurable computing. Performance of hardware designs can be improved through the use of reduced-precision arithmetic, but maintaining appropriate accuracy is essential. We explore reduced-precision optimisation for simulating chaotic systems, targeting atmospheric modelling, in which even minor changes in arithmetic behaviour will cause simulations to diverge quickly. The possibility of equally valid simulations having differing outcomes means that standard techniques for comparing numerical accuracy are inappropriate. We use the Hellinger distance to compare statistical behaviour between reduced-precision CPU implementations to guide reconfigurable designs of a chaotic system, then analyse accuracy, performance and power efficiency of the resulting implementations. Our results show that with only a limited loss in accuracy corresponding to less than 10% uncertainty in input parameters, the throughput and energy efficiency of a single-precision chaotic system implemented on a Xilinx Virtex-6 SX475T Field Programmable Gate Array (FPGA) can be more than doubled

Adaptive Hybrid Projective Synchronization Of Hyper-chaotic Systems

In this paper, we design a procedure to investigate the hybrid projective synchronization (HPS) technique among two identical hyper-chaotic systems. An adaptive control method (ACM) is pro- posed which is based on Lyapunov stability theory (LST). The considered technique globally determines the asymptotical stability and establishes identification of parameter simultaneously via HPS approach. Additionally, numerical simulations are carried out for visualizing the effectiveness and feasibility of discussed scheme by using MATLAB

Quantifying and reducing uncertainty in tidal energy yield assessments

Tidal stream energy has the potential to contribute to a diverse future energy mix. As the industry moves towards commercialisation and array scale deployment, there is an opportunity to better understand the uncertainties around energy yield assessments. Energy yield assessments are used widely in the wind industry to evaluate the potential energy production from a prospective project. One of the key challenges is to quantify and reduce uncertainty in energy yield assessment. This thesis investigates ways to achieve this through utilising lessons learnt from the established wind industry. An evaluation of both the wind and tidal energy yield assessment process is conducted, highlighting where synergies can be used to increase understanding of uncertainty for the nascent tidal industry. The processes are comparable starting with a campaign to collect site data to characterise the resource at the measurement location. The next stage is to evaluate the long term variations, however this is where the two methods differ. Analysis of long term wind effects requires correlations to be made between short term site data and long term reference data from alternative sources. An assessment of tidal variations over longer periods utilises harmonic analysis, which is capable of deconstructing the individual astronomical variations of the tide and reconstructing them to predict future variations. Despite harmonic analysis being able to determine the astronomical effects of the tide, there are uncertainties in the measurements of tidal flow which are associated with non-astronomical effects. Effects such as turbulence introduce uncertainty when evaluating measured tidal data. This is one area which is investigated further in the thesis. Methods to evaluate the turbulence intensity from real ADCP data are investigated. The next stages require creating a numerical model of the site to extrapolate the data spatially to other areas of interest (such as a turbine location). Energy yield predictions for both wind and tidal are made by combining a power curve with the long term resource. The energy yield outputs are then adjusted to account for energy losses and uncertainties are applied to produce final energy yield values with the attributed probability values associated. Statistical methods are applied to harmonic analysis to assess the level of uncertainty in long term predictions of tidal variations. A method using spectral analysis is applied to evaluate the residuals between measured and modelled data and proves to be accurate at determining missing tidal constituents from the analysis. A method for evaluating the turbulence intensity of the flow is shown, to better understand the stochastic nature of the tidal signal. An investigation is conducted to assess the propagation of bed friction uncertainty, in hydrodynamic modelling, and the resulting impact on the predicted power output from a theoretical fence of tidal turbines spanning a tidal channel. The methodology is based on first conducting sensitivity studies by varying a parameter in the model and calculating the power. Then using a mean and standard deviation for the input parameter, the impact of the uncertainty can be transferred to the estimate of power. The results show that a larger uncertainty associated with the bed roughness tends to over predict the estimation of power. This work aims to inform the standardisation of practices and guidelines in tidal resource assessment and to support developers, consultants and financiers in future tidal energy yield assessments. The final chapter includes procedural recommendations for future tidal energy projects, summarising methods to calculate uncertainty and recommendations to reduce them

Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures

Reconfigurable architectures are becoming mainstream: Amazon, Microsoft and IBM are supporting such architectures in their data centres. The computationally intensive nature of atmospheric modelling is an attractive target for hardware acceleration using reconfigurable computing. Performance of hardware designs can be improved through the use of reduced-precision arithmetic, but maintaining appropriate accuracy is essential. We explore reduced-precision optimisation for simulating chaotic systems, targeting atmospheric modelling, in which even minor changes in arithmetic behaviour will cause simulations to diverge quickly. The possibility of equally valid simulations having differing outcomes means that standard techniques for comparing numerical accuracy are inappropriate. We use the Hellinger distance to compare statistical behaviour between reduced-precision CPU implementations to guide reconfigurable designs of a chaotic system, then analyse accuracy, performance and power efficiency of the resulting implementations. Our results show that with only a limited loss in accuracy corresponding to less than 10% uncertainty in input parameters, the throughput and energy efficiency of a single-precision chaotic system implemented on a Xilinx Virtex-6 SX475T Field Programmable Gate Array (FPGA) can be more than doubled

Quantifying and reducing uncertainty in tidal energy yield assessments

Tidal stream energy has the potential to contribute to a diverse future energy mix. As the industry moves towards commercialisation and array scale deployment, there is an opportunity to better understand the uncertainties around energy yield assessments. Energy yield assessments are used widely in the wind industry to evaluate the potential energy production from a prospective project. One of the key challenges is to quantify and reduce uncertainty in energy yield assessment. This thesis investigates ways to achieve this through utilising lessons learnt from the established wind industry. An evaluation of both the wind and tidal energy yield assessment process is conducted, highlighting where synergies can be used to increase understanding of uncertainty for the nascent tidal industry. The processes are comparable starting with a campaign to collect site data to characterise the resource at the measurement location. The next stage is to evaluate the long term variations, however this is where the two methods differ. Analysis of long term wind effects requires correlations to be made between short term site data and long term reference data from alternative sources. An assessment of tidal variations over longer periods utilises harmonic analysis, which is capable of deconstructing the individual astronomical variations of the tide and reconstructing them to predict future variations. Despite harmonic analysis being able to determine the astronomical effects of the tide, there are uncertainties in the measurements of tidal flow which are associated with non-astronomical effects. Effects such as turbulence introduce uncertainty when evaluating measured tidal data. This is one area which is investigated further in the thesis. Methods to evaluate the turbulence intensity from real ADCP data are investigated. The next stages require creating a numerical model of the site to extrapolate the data spatially to other areas of interest (such as a turbine location). Energy yield predictions for both wind and tidal are made by combining a power curve with the long term resource. The energy yield outputs are then adjusted to account for energy losses and uncertainties are applied to produce final energy yield values with the attributed probability values associated. Statistical methods are applied to harmonic analysis to assess the level of uncertainty in long term predictions of tidal variations. A method using spectral analysis is applied to evaluate the residuals between measured and modelled data and proves to be accurate at determining missing tidal constituents from the analysis. A method for evaluating the turbulence intensity of the flow is shown, to better understand the stochastic nature of the tidal signal. An investigation is conducted to assess the propagation of bed friction uncertainty, in hydrodynamic modelling, and the resulting impact on the predicted power output from a theoretical fence of tidal turbines spanning a tidal channel. The methodology is based on first conducting sensitivity studies by varying a parameter in the model and calculating the power. Then using a mean and standard deviation for the input parameter, the impact of the uncertainty can be transferred to the estimate of power. The results show that a larger uncertainty associated with the bed roughness tends to over predict the estimation of power. This work aims to inform the standardisation of practices and guidelines in tidal resource assessment and to support developers, consultants and financiers in future tidal energy yield assessments. The final chapter includes procedural recommendations for future tidal energy projects, summarising methods to calculate uncertainty and recommendations to reduce them