• Nuclear Engineering and Technology
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• 제56권7호
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• pp.2748-2755
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• 2024

This paper presents two new methods for variance reduction for shielding calculation in Monte Carlo radiation transport. One method is CADIS-NEE, which combines Consistent Adjoint Driven Importance Sampling (CADIS) and next-event estimator (NEE) methods to increase the calculation efficiency of tallies at points. The other is CADIS-deterministic transport (DXTRAN), which combines CADIS and DXTRAN to obtain higher performance than using CADIS and DXTRAN separately. The combination processes are derived and implemented in the hybrid Monte-Carlo-Deterministic particle-transport code NECP-MCX. Various problems are tested to demonstrate the effectiveness of the two methods. According to the results, the two combination methods have higher efficiency than using CADIS, NEE or DXTRAN separately. In a long-distance photon-transport problem, CADIS-NEE converges faster than NEE and the figure of merit (FOM) of CADIS-NEE is 75.6 times of NEE. In a labyrinthine problem, CADIS-DXTRAN's FOM surpasses that of DXTRAN and CADIS by a factor of 45.3 and 17.7, respectively. Therefore, it is advisable to employ these two novel methods selectively in appropriate scenarios to reduce variance.

• Nuclear Engineering and Technology
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• 제47권7호
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• pp.838-848
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• 2015

One of the most important operations in nuclear power plants is load following, in which an imbalance of axial power distribution induces xenon oscillations. These oscillations must be maintained within acceptable limits otherwise the nuclear power plant could become unstable. Therefore, bounded xenon oscillation is considered to be a constraint for the load following operation. In this paper, the design of a sliding mode control (SMC), which is a robust nonlinear controller, is presented.SMCis ameansto control pressurized water nuclear reactor (PWR) power for the load following operation problem in a way that ensures xenon oscillations are kept bounded within acceptable limits. The proposed controller uses constant axial offset (AO) strategy to ensure xenon oscillations remain bounded. The constant AO is a robust state constraint for the load following problem. The reactor core is simulated based on the two-point nuclear reactor model with a three delayed neutron groups. The stability analysis is given by means of the Lyapunov approach, thus the control system is guaranteed to be stable within a large range. The employed method is easy to implement in practical applications and moreover, the SMC exhibits the desired dynamic properties during the entire output-tracking process independent of perturbations. Simulation results are presented to demonstrate the effectiveness of the proposed controller in terms of performance, robustness, and stability. Results show that the proposed controller for the load following operation is so effective that the xenon oscillations are kept bounded in the given region.

• Nuclear Engineering and Technology
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• 제52권4호
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• pp.700-707
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• 2020

This study is concerned with the investigation of the half-space albedo problem for "İnönü-linear-quadratic anisotropic scattering" by the usage of Modified FN method. The method is based on Case's method. Therefore, Case's eigenfunctions and its orthogonality properties are derived for anisotropic scattering of interest. Albedo values are calculated for various linear, quadratic and İnönü anisotropic scattering coefficients and tabulated in Tables.

• 전기의세계
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• 제26권6호
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• pp.66-71
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• 1977

In this paper a method is presented for the optimal control of a nuclear reactor at equilibrium state by use of a digital computer. Using the optimal control theory, we formulate the control problem of the reactor as a discrete-time linear regulator problem. A quadratic performance index is defined. The effects of choosing different performance index weighting matrices to the feedback gain matrix and reactor transient responses are studied for the deterministic optimal control with all state variables accessible to measurement.

• Nuclear Engineering and Technology
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• 제34권3호
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• pp.250-258
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• 2002

This Paper is to apply the well-established coarse mesh finite difference(CMFD) method to the method of characteristics(MOC) transport calculation as an acceleration scheme. The CMFD problem is first formulated at the pin-cell level with the multi-group structure To solve the cell- based multi-group CMFD problem efficiently, a two-group CMFD formulation is also derived from the multi-group CMFD formulation. The performance of the CMFD acceleration is examined for three test problems with different sizes including a realistic quarter core PWR problem. The CMFD formulation provides a significant reduction in the number of ray tracings and thus only about 9 ray tracing iterations are enough for the realistic problem. In computing time, the CMFD accelerated case is about two or three times faster than the coarse-mesh rebalancing(CMR) accelerated case.

• Nuclear Engineering and Technology
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• 제49권6호
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• pp.1326-1338
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• 2017

The continuous energy Monte Carlo neutron transport code, MC21, was coupled to the CTF subchannel thermal-hydraulics code using a combination of Consortium for Advanced Simulation of Light Water Reactors (CASL) tools and in-house Python scripts. An MC21/CTF solution for VERA Core Physics Benchmark Progression Problem 6 demonstrated good agreement with MC21/COBRA-IE and VERA solutions. The MC21/CTF solution for VERA Core Physics Benchmark Progression Problem 7, Watts Bar Unit 1 at beginning of cycle hot full power equilibrium xenon conditions, is the first published coupled Monte Carlo neutronics/subchannel T-H solution for this problem. MC21/CTF predicted a critical boron concentration of 854.5 ppm, yielding a critical eigenvalue of $0.99994{\pm}6.8E-6$ (95% confidence interval). Excellent agreement with a VERA solution of Problem 7 was also demonstrated for integral and local power and temperature parameters.

• Nuclear Engineering and Technology
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• 제37권2호
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• pp.127-138
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• 2005

Approaches to several recent issues in the operation of nuclear power plants using computational intelligence are discussed. These issues include 1) noise analysis techniques, 2) on-line monitoring and sensor validation, 3) regularization of ill-posed surveillance and diagnostic measurements, 4) transient identification, 5) artificial intelligence-based core monitoring and diagnostic system, 6) continuous efficiency improvement of nuclear power plants, and 7) autonomous anticipatory control and intelligent-agents. Several changes to the focus of Computational Intelligence in Nuclear Engineering have occurred in the past few years. With earlier activities focusing on the development of condition monitoring and diagnostic techniques for current nuclear power plants, recent activities have focused on the implementation of those methods and the development of methods for next generation plants and space reactors. These advanced techniques are expected to become increasingly important as current generation nuclear power plants have their licenses extended to 60 years and next generation reactors are being designed to operate for extended fuel cycles (up to 25 years), with less operator oversight, and especially for nuclear plants operating in severe environments such as space or ice-bound locations.

• Nuclear Engineering and Technology
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• 제44권4호
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• pp.369-378
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• 2012

A load-following operation in APR+ nuclear plants is necessary to reduce the need to adjust the boric acid concentration and to efficiently control the control rods for flexible operation. In particular, a disproportion in the axial flux distribution, which is normally caused by a load-following operation in a reactor core, causes xenon oscillation because the absorption cross-section of xenon is extremely large and its effects in a reactor are delayed by the iodine precursor. A model predictive control (MPC) method was used to design an automatic load-following controller for the integrated thermal power level and axial shape index (ASI) control for APR+ nuclear plants. Some tracking controllers employ the current tracking command only. On the other hand, the MPC can achieve better tracking performance because it considers future commands in addition to the current tracking command. The basic concept of the MPC is to solve an optimization problem for generating finite future control inputs at the current time and to implement as the current control input only the first control input among the solutions of the finite time steps. At the next time step, the procedure to solve the optimization problem is then repeated. The support vector regression (SVR) model that is used widely for function approximation problems is used to predict the future outputs based on previous inputs and outputs. In addition, a genetic algorithm is employed to minimize the objective function of a MPC control algorithm with multiple constraints. The power level and ASI are controlled by regulating the control banks and part-strength control banks together with an automatic adjustment of the boric acid concentration. The 3-dimensional MASTER code, which models APR+ nuclear plants, is interfaced to the proposed controller to confirm the performance of the controlling reactor power level and ASI. Numerical simulations showed that the proposed controller exhibits very fast tracking responses.

• Nuclear Engineering and Technology
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• 제55권6호
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Nuclear Engineering and Technology
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• 제48권3호
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• pp.719-726
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• 2016

The problem of separation of isotopes in a concurrent gas centrifuge is solved analytically for an arbitrary binary mixture of isotopes. The separative power of the optimised concurrent gas centrifuges for the uranium isotopes equals to ${\delta}U=12.7(V/700m/s)^2(300K/T)(L/1m)kg{\cdot}SWU/yr$, where L and V are the length and linear velocity of the rotor of the gas centrifuge and T is the temperature. This equation agrees well with the empirically determined separative power of optimised counter-current gas centrifuges.