Investigation of ventricular cerebrospinal fluid flow phase differences between the foramina of Monro and the aqueduct of Sylvius

In this paper, phase contrast magnetic resonance flow measurements of the foramina of Monro and the aqueduct of Sylvius of seven healthy volunteers are presented. Peak volume flow rates are of the order of 150 mm3/s for the aqueduct of Sylvius and for the foramina of Monro. The temporal shift between these volume flows is analyzed with a high-resolution cross-correlation scheme which reveals high subject-specific phase differences. Repeated measurements show the invariability of the phase differences over time for each volunteer. The phase differences as a fraction of one period range from -0.0537 to 0.0820. A mathematical model of the pressure dynamics is presented. The model features one lumped compartment per ventricle. The driving force of the cerebrospinal fluid is modeled through pulsating choroid plexus. The model includes variations of the distribution of the choroid plexus between the ventricles. The proposed model is able to reproduce the measured phase differences with a very small (5%) variation of the distribution of the choroid plexus between the ventricles and, therefore, supports the theory that the choroid plexus drives the cerebrospinal fluid motio

Evolutionary Optimization of Feedback Controllers for Thermoacoustic Instabilities

International audienceWe present the system identifcation and the online optimization of feedback controllers applied to combustion systems using evolutionary algorithms. The algorithm is applied to gas turbine combustors that are susceptible to thermoacoustic instabilities resulting in imperfect combustion and decreased lifetime. In order to mitigate these pressure oscillations, feedback controllers sense the pressure and command secondary fuel injectors. The controllers are optimized online with an extension of the CMA evolution strategy capable of handling noise associated with the uncertainties in the pressure measurements. The presented method is independent of the specifc noise distribution and prevents premature convergence of the evolution strategy. The proposed algorithm needs only two additional function evaluations per generation and is therefore particularly suitable for online optimization. The algorithm is experimentally verifed on a gas turbine combustor test rig. The results show that the algorithm can improve the performance of controllers online and is able to cope with a variety of time dependent operating conditions

Partitioned Quasi-Newton Approximation for Direct Collocation Methods and Its Application to the Fuel-Optimal Control of a Diesel Engine

The numerical solution of optimal control problems by direct collocation is a widely used approach. Quasi-Newton approximations of the Hessian of the Lagrangian of the resulting nonlinear program are also common practice. We illustrate that the transcribed problem is separable with respect to the primal variables and propose the application of dense quasi-Newton updates to the small diagonal blocks of the Hessian. This approach resolves memory limitations, preserves the correct sparsity pattern, and generates more accurate curvature information. The effectiveness of this improvement when applied to engineering problems is demonstrated. As an example, the fuel-optimal and emission-constrained control of a turbocharged diesel engine is considered. First results indicate a significantly faster convergence of the nonlinear program solver when the method proposed is used instead of the standard quasi-Newton approximation

Age-Specific Characteristics and Coupling of Cerebral Arterial Inflow and Cerebrospinal Fluid Dynamics

The objective of this work is to quantify age-related differences in the characteristics and coupling of cerebral arterial inflow and cerebrospinal fluid (CSF) dynamics. To this end, 3T phase-contrast magnetic resonance imaging blood and CSF flow data of eleven young ( years) and eleven elderly subjects ( years) with a comparable sex-ratio were acquired. Flow waveforms and their frequency composition, transfer functions from blood to CSF flows and cross-correlations were analyzed. The magnitudes of the frequency components of CSF flow in the aqueduct differ significantly between the two age groups, as do the frequency components of the cervical spinal CSF and the arterial flows. The males' aqueductal CSF stroke volumes and average flow rates are significantly higher than those of the females. Transfer functions and cross-correlations between arterial blood and CSF flow reveal significant age-dependence of phase-shift between these, as do the waveforms of arterial blood, as well as cervical-spinal and aqueductal CSF flows. These findings accentuate the need for age- and sex-matched control groups for the evaluation of cerebral pathologies such as hydrocephalus

Short-term thermal and electric load forecasting in buildings

Increasing environmental awareness and energy costs encourage the increase of the contribution of renewable energy sources (RES) to the energy supply of buildings. However, the integration of RES and energy storage systems introduces significant challenges for the energy management system (EMS) of complex building energy systems. An energy management strategy based on fixed control rules may fail to efficiently operate such systems. These circumstances raise the need to apply advanced control strategies. A promising approach is model predictive control (MPC), which allows the consideration of the expected dynamic system behavior as well as of forecasts of the loads and of the renewable energy generated. Obviously, the performance of an MPC-based EMS crucially depends on the accuracy of the load forecasts. The goal of this paper is to compare the capabilities of neural networks (NNs) and of the least squares support vector machine (LS-SVM) in forecasting the hourly thermal and electric load of buildings. Two short-term load forecasting algorithms are evaluated which treat every hour of the day separately by an individual forecasting model. Additionally, the algorithms also distinguish between working days, weekends and holidays. In order to adapt to changing load patterns, the algorithms use the sliding window training approach. Both algorithms are tested using the measured thermal and electric load data of a large office building and of a small building which houses a kindergarten. In the tests conducted, in general, the forecasting algorithm based on the LS-SVM shows a better performance than the forecasting algorithm based on NNs. In addition, the LS-SVM involves fewer free parameters to be determined than a NN, which makes the former easier to apply. The results reported further indicate that the accurate forecasting of the load of a small building is the more challenging task compared to the load forecasting of a large office building. Furthermore, using a training window size of more than 20 days does not significantly improve the performance of the algorithms examined

Feedback linearization idle-speed control: design and experiments

summary:This paper proposes a novel nonlinear control algorithm for idle-speed control of a gasoline engine. This controller is based on the feedback linearization approach and extends this technique to the special structure and specifications of the idle-speed problem. Special static precompensations and cascaded loops are used to achieve the desired bandwidth separation between the fast spark and slow air-bypass action. A key element is the inclusion of the (engine-speed dependent) induction to power stroke delay in the engine model and in the subsequent controller design. The proposed method is partially validated on an engine test bench using the air paths, only. For the analyzed five cylinder engine, the results show no superior behaviour of the nonlinear approach compared to classical idle-speed controllers. For engines with fewer cylinders, however, the nonlinear approach is expected to perform substantially better

Vehicle Propulsion Systems: Introduction to Modeling and Optimization

This text provides an introduction to the mathematical modeling and subsequent optimization of vehicle propulsion systems and their supervisory control algorithms. Automobiles are responsible for a substantial part of the world's consumption of primary energy, mostly fossil liquid hydrocarbons and the reduction of the fuel consumption of these vehicles has become a top priority. Increasing concerns over fossil fuel consumption and the associated environmental impacts have motivated many groups in industry and academia to propose new propulsion systems and to explore new optimization methodologies. This third edition has been prepared to include many of these developments. In the third edition, exercises are included at the end of each chapter and the solutions are available on the web