Nodal models of Pressurized Water Reactor core for control purposes – A comparison study

Puchalski, Bartosz; Duzinkiewicz, Kazimierz

doi:10.1016/j.nucengdes.2017.07.005

Cited by 33 publications

(7 citation statements)

References 16 publications

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“…Consider the first test case of six precursor groups [16] with zero reactivity and clean startup conditions. The reactor parameters are λ = {0.0124, 0.0305, 0.1110, 0.3010, 1.1400, 3.0100} (s −1 ), ν = 2.5, Λ = 0.0000179 (s), β = {0.000215, 0.001424, 0.001274, 0.002568, 0.000748, 0.000273}.…”

Section: Resultsmentioning

confidence: 99%

“…where m f is the fuel mass, c p f is the specific heat of the fuel, f f is the generated power fraction, h is the coefficient of heat transfer, A is the effective area of heat transfer, m c is the mass of coolant, c pc is the coolant specific heat, and W c is the coolant mass flow rate. Other thermal models can be also considered; see (e.g., [16,17]). The total reactivity ρ includes reactivity due to fuel temperature deviations ∆T f , coolant temperature deviation ∆T c , and the external, induced by control rods, reactivity ρ ext .…”

Section: The Point-reactor Model With Stochastic Variationsmentioning

confidence: 99%

“…Table 4. Parameters used in thermal hydraulics for first case [16]. The thermal hydraulics were estimated for the first case using the parameters shown in Table 4.…”

Section:  mentioning

confidence: 99%

“…The inlet coolant temperature is T in = 282 • C. Table 4. Parameters used in thermal hydraulics for first case [16]. Figure 6 shows the deviations in the fuel temperature for α = 25%.…”

Section: Nominal Powermentioning

confidence: 99%

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Uncertainty Quantification Spectral Technique for the Stochastic Point Reactor with Random Parameters

Alaskary¹,

El-Beltagy

2020

Energies

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The stochastic point reactor with random parameters is considered in this work. The hybrid uncertain variations—noise and random parameters—are analyzed with the spectral techniques for the efficiency and high rates of convergence. The proposed hybrid technique enables one to derive an equivalent deterministic system that can be solved to get the mean solution and deviations due to each uncertainty. The contributions of different sources uncertainties can be decomposed and quantified. The deviations in the thermal hydraulics are also computed in the current work. Two model reactors are tested with the proposed technique and the comparisons show the advantages and efficiency compared with the other techniques.

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Section: Resultsmentioning

confidence: 99%

Section: The Point-reactor Model With Stochastic Variationsmentioning

confidence: 99%

“…Table 4. Parameters used in thermal hydraulics for first case [16]. The thermal hydraulics were estimated for the first case using the parameters shown in Table 4.…”

Section:  mentioning

confidence: 99%

“…The inlet coolant temperature is T in = 282 • C. Table 4. Parameters used in thermal hydraulics for first case [16]. Figure 6 shows the deviations in the fuel temperature for α = 25%.…”

Section: Nominal Powermentioning

confidence: 99%

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Uncertainty Quantification Spectral Technique for the Stochastic Point Reactor with Random Parameters

Alaskary¹,

El-Beltagy

2020

Energies

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“…T Reactor neutron dynamic is modeled by using the point kinetic equation with six groups of delayed neutrons and the reactivity feedback from fuel and coolant temperatures are considered in total reactivity. It is assumed that the spatial distribution shape of the neutron flux density do not change during the transients [9][10][11].…”

Section: Psap Power System Analysis Software Packagementioning

confidence: 99%

Linearized Mathematical Model for PWR Dynamics Simulation

Osman

2020

Arab Journal of Nuclear Sciences and Applications

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A linearized mathematical model for the pressurized water reactor (PWR) nuclear power plant dynamics simulation is presented based on conservation of energy and mass balance. The core and coolant system are treated as a lumped parameter. The delayed neutrons effect is considered. The model state variables are linearized in first order differential equations with steady state initial values. The reactor core dynamic response is investigated through transients represented in external reactivity insertion e.g.(0.001 Δk/k),10% step decrease in core flow rate and 10 o F step decrease in core inlet coolant temperature. The simulation results demonstrated the role of feedback reactivity form coolant temperature and Doppler Effect in stabilizing the core power and affecting the steady state values of core variables.

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Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks

Puchalski

2020

Artificial Intelligence and Soft Computing

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Nodal models of Pressurized Water Reactor core for control purposes – A comparison study

Cited by 33 publications

References 16 publications

Uncertainty Quantification Spectral Technique for the Stochastic Point Reactor with Random Parameters

Uncertainty Quantification Spectral Technique for the Stochastic Point Reactor with Random Parameters

Linearized Mathematical Model for PWR Dynamics Simulation

Approximation of Fractional Order Dynamic Systems Using Elman, GRU and LSTM Neural Networks

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