摘要:

AbstractAt the Delft University of Technology Interfaculty Reactor Institute, a facility has been installed to irradiate cylindrical samples with diameters up to 15 cm and weights up to 50 kg for instrumental neutron activation analysis (INAA) purposes. To be able to do quantitative INAA on voluminous samples, it is necessary to correct for gamma-ray absorption, gamma-ray scattering, neutron absorption, and neutron scattering in the sample. The neutron absorption and the neutron scattering are discussed. An analytical solution is obtained for the diffusion equation in the geometry of the irradiation facility. For samples with known composition, the neutron flux—as a function of position in the sample—can be calculated directly. Those of unknown composition require additional flux measurements on which least-squares fitting must be done to obtain both the thermal neutron diffusion coefficient Dsand the diffusion length Lsof the sample. Experiments are performed to test the theory.

ISSN:0029-5639    

DOI:10.13182/NSE94-A28530

出版商:Taylor&Francis    

年代:1994

数据来源: Taylor

摘要:

AbstractThe KENO-NR Monte Carlo code was developed to simulate the measurement of R(ω) = G*12(ω)G13(ω)/G11(ω)G23(ω), a ratio of spectral densities measured by the252Cf source-driven noise analysis (CSDNA) method for determining subcriticality. From a direct comparison of simulated and measured R(ω), cross sections and the physical system model can be benchmarked and then used in standard criticality codes for determining kefffor a multiplying system. This procedure eliminates the dependence of the CSDNA method on the point-kinetics model and allows cross-section and geometry models to be validated for noncritical configurations. For a set of uranium cylinders (93.2 wt%235sU and 17.7-cm outer diameter) of varying height, the simulated and the measured R(ω) values in the low-frequency limit and the prompt neutron decay constant a agreed to within 10%. These results indicate that the approach of validating a simulation of the direct experimental data should lead to improved neutronic parameters for fissile systems.

ISSN:0029-5639    

DOI:10.13182/NSE94-A28531

出版商:Taylor&Francis    

年代:1994

数据来源: Taylor

摘要:

AbstractA robust feedforward-feedback controller is proposed for wide-range operations of nuclear reactors. This control structure provides (a) optimized performance over a wide operating range resulting from the feedforward element and (b) guaranteed robust stability and performance resulting from the feedback element. The feedforward control law is synthesized via nonlinear programming, which generates an optimal control sequence over a finite-time horizon under specified constraints. The feedback control is synthesized via the structured singular valueµapproach to guarantee robustness in the presence of disturbances and modeling uncertainties. The results of simulation experiments are presented to demonstrate efficacy of the proposed control structure for a large rapid power reduction to avoid unnecessary plant trips.

ISSN:0029-5639    

DOI:10.13182/NSE94-A28532

出版商:Taylor&Francis    

年代:1994

数据来源: Taylor

摘要:

AbstractLayered neural networks, which are a class of models based on neuronal computation in biological systems, are applied to the task of reactivity monitoring in a nuclear reactor to improve the safety and the reliability of the operating plant. Training is done with a maximum likelihood method, which is suitable for on-line training. Operational data from the Fast Breeder Test Reactor are used to study its performance. The adaptability of the network to slow variations in the system parameters and its ability to learn in a noisy environment are studied.

ISSN:0029-5639    

DOI:10.13182/NSE94-A28533

出版商:Taylor&Francis    

年代:1994

数据来源: Taylor

摘要:

AbstractThe Boltzmann machine is a general-purpose artificial neural network that can be used as an associative memory as well as a mapping tool. The usual information entropy is introduced, and a network energy function is suitably defined. The network’s training procedure is based on the simulated annealing during which a combination of energy minimization and entropy maximization is achieved.,An application in the nuclear reactor field is presented in which the Boltzmann input-output machine is used to detect and diagnose a pipe break in a simulated auxiliary feedwater system feeding two coupled steam generators. The break may occur on either the hot or the cold leg of any of the two steam generators. The binary input data to the network encode only the trends of the thermohydraulic signals so that the network is actually a polarity device. The results indicate that the trained neural network is actually capable of performing its task. The method appears to be robust enough so that it may also be applied with success in the presence of substantial amounts of noise that cause the network to be fed with wrong signals.

ISSN:0029-5639    

DOI:10.13182/NSE94-A28534

出版商:Taylor&Francis    

年代:1994

数据来源: Taylor