Adaptive Nuclear Reactor Control Based on Optimal Low-Order Linear Models

Bereznai, George T.

doi:10.1109/tns.1973.4327020

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Core power control systems are usually required to achieve two key functions, core power control and reactivity disturbance suppression 3 . In recent years, with the popularity of fully digital instrumentation and control systems in large commercial reactors, many advanced control methods based on digital instrumentation and control systems have been proposed, such as model-referenced adaptive control 4 , 5 , model predictive control 6 – 8 , fuzzy logic control 9 , linear quadratic Gaussian with loop transfer recovery 10 , 11 , sliding mode control 12 – 14 , and fractional-order control 15 , 16 , etc. These advanced control methods have been gradually tested and demonstrated in some Nuclear Power Plants (NPPs) with relatively high technology maturity, as the technology maturity has increased to ensure reactor safety.…”

Section: Introductionmentioning

confidence: 99%

Reactivity disturbance suppression method for small modular reactors based on core coolant flow control

Xie

Duan²,

Ping³

et al. 2022

Sci Rep

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Small modular reactors (SMR) have an exceptionally wide range of applications due to their flexibility. But the reactivity of SMR is more susceptible to disturbance than that of large commercial reactors, which may cause the core power to deviate from the set value, and the limited internal space makes it difficult for SMR to compensate or adjust for reactivity disturbance by setting a sufficient number of control rods as in large commercial reactors. Therefore, in order to improve the operational stability of SMR, a method is proposed to indirectly change the nuclear fuel temperature by adjusting the coolant flow rate and thus compensate the reactivity disturbance by the Doppler effect of nuclear fuel resonance absorption. Simulation experiments show that the method can effectively eliminate reactive disturbances that cannot be completely eliminated by control rods under the conditions of restricted SMR space and limited number of control rod sets, thus providing operational stability of SMR.

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Section: Introductionmentioning

confidence: 99%

Reactivity disturbance suppression method for small modular reactors based on core coolant flow control

Xie

Duan²,

Ping³

et al. 2022

Sci Rep

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“…For the core power level control, there are some advanced methods are developed gradually. Including Model Reference adaptive control [4][5], model predictive control [6][7][8], fuzzy logical control [9], linear quadratic gaussian with loop transfer recovery [10][11], sliding mode control [12][13][14], and fractional order control [15,16]. Some of them have gradually become mature methods for power level control.…”

Section: Introductionmentioning

confidence: 99%

Small Modular Reactor Reactivity Disturbance Suppression Method Based on Core Coolant Flow Control

Xie

Duan

Ping

et al. 2021

Preprint

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Nuclear reactors may suffer from various disturbances during operation. These disturbances can cause core power deviates from the set parameters, and affect the power level of reactors. Due to the limited internal space of the reactor, the number of control rods is small. It is difficult to set up control rod groups dedicated to reactive compensation for modular reactor of medium or small size. Therefore, it is necessary to study a set of reactivity compensation measures that do not rely on control rods according to the actual needs of modular reactors to compensate for the power deviation caused by reactivity disturbances. A disturbance suppression method based on coolant flow control is proposed in the study. This method takes advantage of the Doppler effect of the coolant temperature, and changes the coolant flow rate to affect its temperature when reactive disturbances occur, thereby compensating for fluctuations of reactivity. Numerical experiments show that this method can effectively suppress the power deviation caused by reactive disturbances, and has engineering application value.

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“…From 1942 to 1960, much of the analysis and design of control systems for nuclear reactors was based on classical methods [Lipinsky and Vacroux 1970]. Different approaches of adaptive control theory, such as model reference, self-tuning, and adaptive linearization, among others, have been proposed for nuclear power control [Bereznai 1973;Benitez and Jamshidi 1992;Jungin et al 1993;Nah 1995]. The concepts of optimal control were frrst used in the 1970s, developing optimal regulators and time-optimal controllers for nuclear power plants [ Raju and Fadra 1973;Tepper 1975;Saif 1989;Benitez et al 1992).…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Systems and Soft Computing in Nuclear Engineering

Ruan¹

2000

Studies in Fuzziness and Soft Computing

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Fuzzy systems and soft computing in nuclear engineering 1 Da Ruan (ed.). (Studies in fuzziness and soft computing; Vol. 38) This work © Springer-Verlag Berlin Heidelberg 20()() Originally published by Physica-Verlag Heidelberg New York in 20()() Softcover reprint ofthe hardcover 1st edition 2000The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Hardcover Design: Erich Kirchner, Heidelberg SPIN 10745440 88/2202-5 4 3 2 I 0 -Printed on acid-free paper DaRuan Mol, May 1999 References 21 Diagnosis of Unanticipated Plant Component Faults in

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Adaptive Nuclear Reactor Control Based on Optimal Low-Order Linear Models

Cited by 8 publications

References 25 publications

Reactivity disturbance suppression method for small modular reactors based on core coolant flow control

Reactivity disturbance suppression method for small modular reactors based on core coolant flow control

Small Modular Reactor Reactivity Disturbance Suppression Method Based on Core Coolant Flow Control

Fuzzy Systems and Soft Computing in Nuclear Engineering

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