Optimizing Nuclear Reactor Operation Using Soft Computing Techniques

The strict safety regulations for nuclear reactor control make it di±cult to implement new control techniques such as fuzzy logic control (FLC). FLC however, can provide very desirable advantages over classical control, like robustness, adaptation and the capability to include human experience into the controller. Simple fuzzy logic controllers have been implemented for a few nuclear research reactors, among which the Massachusetts Institute of Technology (MIT) research reactor [1] in 1988 and the first Belgian reactor (BR1) [2] in 1998, though only on a temporal basis. The work presented here is a continuation of earlier research on adaptive fuzzy logic controllers for nuclear reactors at the SCK²CEN [2, 3, 4] and [5] (pp 65{82). A series of simulated experiments has been carried out using adaptive FLC, genetic algorithms (GAs) and neural networks (NNs) to find out which strategies are most promising for further research and future application in nuclear reactor control. Hopefully this contribution will lead to more research on advanced FLC in this domain and finally to an optimised and intrinsically safe control strategy

Optimizing Nuclear Reactor Operation Using Soft Computing Techniques

The strict safety regulations for nuclear reactor control make it di±cult to implement new control techniques such as fuzzy logic control (FLC). FLC however, can provide very desirable advantages over classical control, like robustness, adaptation and the capability to include human experience into the controller. Simple fuzzy logic controllers have been implemented for a few nuclear research reactors, among which the Massachusetts Institute of Technology (MIT) research reactor [1] in 1988 and the first Belgian reactor (BR1) [2] in 1998, though only on a temporal basis. The work presented here is a continuation of earlier research on adaptive fuzzy logic controllers for nuclear reactors at the SCK²CEN [2, 3, 4] and [5] (pp 65{82). A series of simulated experiments has been carried out using adaptive FLC, genetic algorithms (GAs) and neural networks (NNs) to find out which strategies are most promising for further research and future application in nuclear reactor control. Hopefully this contribution will lead to more research on advanced FLC in this domain and finally to an optimised and intrinsically safe control strategy

Multilevel distributed structure optimization

An iterative optimisation routine for aircraft structures using Genetic Algorithms (GAs) and Neural Networks (NNs) is presented. In this setup the NNs form a response surface, approximating the key mechanical properties of substructures. NNs are updated every iteration. The GA uses these NNs in the optimisation to quickly determine the feasibility of different variants. All found optimal substructures are checked using a Finite Element (FE) calculation. When the FE outputs differ too much from the NN approximations the solution is added to the NN training set, thus improving the NN’s performance. Main advantages of the algorithm are firstly the possibility to take into account many topologically distinct designs and secondly the flexibility to quickly evaluate the influence of updated loads or different design restrictions (e.g. materials, access holes) on the optimum. The benefit of the feedback of inaccurately estimated panel properties (according to the FE verification) is the improvement of accuracy and convergence. Also this principle drastically reduces the number of datasets (i.e. FE calculations) needed to train the NNs initially. Two levels are implemented: a global level containing the structure as a whole, and a local level to describe the composite panels the structure is made of more accurately. On the global level a coarse mesh can be used, for it is only needed to derive the loading of the panels. On the local level more detail is needed, for linear static buckling and local strains must be analysed accurately

Assessment of human pilot mental workload in curved approaches

Which cognitive challenges do human pilots face during the execution of curved (RNP-AR) approaches? We hypothesize that the mental model required for a curved approach will be more complex than for a straight one. To investigate this, we compare risk and mental effort through physiological factors, control-input, and performance. Our current experiments focus on straight landing approaches under different visibility conditions and we compare the long and short final to establish which methods can be used to analyze mental effort and safety. Both student and professional pilots took part in our fixed base B747-400 simulator experiments. The control-input, performance, and ecg analyses appear to be particularly useful, whereas blink and especially pupil diameter data obtained from an eye camera is more difficult to use in analysis. To safely implement RNP-AR, we need to further investigate the necessities of cognitive skill training.20-22 November 2013, Takamatsu, Japa

Improving coaxial measurements in laser welding by correcting distortions of a laser focus lens with a wide field of view

A compact, lightweight, and multifunctional head for robotic laser welding applications has been equipped with a camera to provide a real time image stream of the work piece for seam teaching, tracking, and inspection purposes [D. Iakovou, R. G. K. M. Aarts, and J. Meijer, “Integrated sensors for robotic laser welding,” in Proceedings of the Third International WLT Conference on Lasers in Manufacturing, Munich, Germany (AT-Fachverlag GmBH, Stuttgart, 2005), pp. 121–126; D. Iakovou, R. G. K. M. Aarts, and J. Meijer “Sensor integration for robotic laser welding processes,” (paper No. 2301), in Proceedings of the International Congress on Applications of Lasers and Electro-Optics (ICALEO), Miami, 2005 (unpublished)]. The camera uses part of the laser focusing optics. Research has been done to identify and correct for positioning errors introduced by the optical system. A robust camera and lens calibration method has been developed. Calibration and seam detection experiments have been performed and the results were used for seam trackin