FOR TWO-PHASE ANNULAR FLOW IN NUCLEAR POWER ... overseeing of a pipe and diagnosis of flooding or flow reversal phenomena in vertical pipes. .... The gas in the center has an upward direction. .... of flight (ToF) is computed and the diameter of the pipe ... water, gas flows up in the center of pipe while the water.
Proceedings of the International Conference on Optimization using Exergy-Based Methods and Computational Fluid Dynamics Berlin, Germany, October 20-23, 2009
PSEUDO NEURAL NETWORK-BASED DIAGNOSTIC SYSTEM FOR TWO-PHASE ANNULAR FLOW IN NUCLEAR POWER PLANTS
M. Alamaniotis1*, M. Youtsos2, R. Gao1, L.H. Tsoukalas1 1
School of Nuclear Engineering, Purdue University, USA {malamani, gao, tsoukala}@ecn.purdue.edu 2 Department of Engineering, University of Cambridge, UK {msy21}@cam.ac.uk
Abstract Intelligent diagnostic methods for nuclear power plants contribute significantly to overall reactor and plant safety. Sensor networks, with a smart diagnostic layer, have been considered for monitoring a plant’s condition and report any non anticipated situation. Thermo-hydraulic components, which are the basis for heat removal and energy distribution in a nuclear power plant, require reliable and fast surveillance instruments. In this paper a new methodology for automated and non invasive monitoring of two phase flow in pipes is proposed. The synergy of acoustic sensors with a neural network allows continuous overseeing of a pipe and diagnosis of flooding or flow reversal phenomena in vertical pipes.
Keywords: neural network, annular flow, diagnostic, flow reversal, flooding.
1. Introduction Nuclear power plants are engineering systems resulting from the integration of a variety of subsystems. The implied heterogeneity of a plant leads to a relatively higher cost for maintenance and surveillance. Moreover, the use of nuclear fuel for energy production and the destructive impact from a possible accident enhance the need for reliable prognostic and diagnostic instruments. Two phase flow [1] is the basic mechanism used in nuclear industry for heat transport. The main property of this class of flow is the existence of two phases; the most common case is a gas and a liquid flowing [2] in the same mean. Between the two phases, there are distinct interfaces which comprise the basic criterion for classification of the different regimes into bubbly, slug, stratified and annular. So, it is of great significance to monitor continuously all systems related to two-phase flow. Immediate and accurate diagnosis of instabilities is required to prevent possible accidents. In that direction, several researchers focused on novel methods for identifying flow regimes [3] as a tool in predicting or identifying non desirable phenomena. In those cases, parameters to be optimized are the accuracy and speed of diagnostics. At the same time the ____________________________________________ * Corresponding author
effect of the method in the flow should be minimal (non-invasive). ` In this paper a new diagnostic method for nuclear power plants is proposed. The methodology focuses on two phase annular flow [4] in pipes. Specifically it aims to detect flooding or flow reversal phenomena. Diagnosis is based on the well defined ultrasonic interrogation [5][6], performed at multiple points by the respective acoustic sensors. The sensors are deployed along the pipe so as to form a distributed system. Additionally, the sensor network is equipped with a data processing unit which is implemented with a neural network [7]. The neural network collects the information of the sensors and indicates whether the flow is as expected along the pipe. If it is not, then the system outputs a signal pointing the part of the pipe in which an undesired phenomenon occurs. In the next sections the system is described and discussed. Before that a brief introduction to nuclear power plant safety and annular flow is presented. Next the system’s operation is demonstrated for the case of flooding, which easily can be adapted for flow reversal. The paper is concluded with a comprehensive summary of the methodology and some future extensions of it. In that direction, there exists a brief reference to other non-
invasive methods that can be adopted by the proposed system.
plants’ structure and reactor scram failure. Since scram is not possible then explosion of reactor is unavoidable.
2. Nuclear Power Plant Safety
2.1.2. Cooling-failure accidents
2.1. Nuclear Accidents
Cooling-failures are considered a major type of accident in power plants. They refer to failures that occur in components of the reactor’s cooling systems. Of great significance is the loss of flow accidents which happen mainly due to a pump failure. If parameters of coolant flow are altered and flow decreases or vanishes then proper heat removal from the reactor leads to overheating and total failure of the plant. Another member in this group of accidents is the loss of flow in secondary loop cooling. It should be mentioned that this is possible only in reactors that are comprised of two cooling loops such as PWR. Loss of coolant accidents are a primary set of accidents and critical for function of power plants. Any mass and energy loss of the reactor coolant due to leakage or failure of a component reduces heat transfer efficiency and leads to overheating.
Safety of nuclear power plants is crucial for the energy production and for health of people. In the past, we have experienced big catastrophes from accidents that occurred in nuclear plants. Consequences were lethal for humans and extremely bad for the environment. Release of huge amounts of radiation polluted the surface of the earth and caused big changes to many areas. Nuclear plants are vulnerable to accidents due to their complexity. Such a complexity determines many weak points that should always be under surveillance. An improper function of a component might lead to a propagation of the malfunction and result in an unstable state and consequent severe accident. Accidents in nuclear plants are grouped into four major categories:
Reactivity accidents, Cooling-failure accidents, Fuel handling accidents, Site-induced accidents.
The main factor for classifying each accident is the initial source of malfunction that caused the accident. However, severity of the accident is usually determined by the depth of the malfunction propagation and the number of implicated subsystems. In order to make things clearer, a brief description of each class of accidents is presented below. 2.1.1. Reactivity Accidents Initialization of reactivity accidents takes place in the reactor. This type of accident refers mainly to the neutronic aspect of the power plant. Specifically, if the fission chain goes out of operator’s control, then the reactor is in a supercritical state. In a supercritical state the number of neutrons produced is larger than that consumed (absorption, fission, capture). In this case, we can have either overpowered production or nuclear fission excursion. In both cases, there is excess heat production, which leads to fuel melting and to its vaporization. Furthermore, there is the possibility that the fuel might interact with the coolant, which will result in vapor explosion. However, in all cases we are going to have huge amounts of excess heat that cause the reactor’s water fragmentation. Specifically, the vapor film collapses and there is direct contact of the fuel wall with the coolant which transfers more heat than needed. As a result the overproduction of energy results in severe damages in
2.1.3. Fuel handling accidents These types of accidents are rare and include all cases that initiate an accident due to a fault in fuel handling. Such cases can be accidents during renewal of reactor fuel or during replacement of a limited fraction of the core. Furthermore, all cases in which fuel melting occurs due to failure belong to this group. Overall, the improper fuel handling can cause significant raise in reactivity and subsequent explosion of the reactor. 2.1.4. Site-induced accidents External phenomena that are local and characteristic of the nuclear plant’s location can cause the so called site-induced accidents. To be more specific such phenomena include natural and weather phenomena such as: wind, earthquake, tornado, hurricane and other. Additionally, rare but possible accidents can be caused by aircraft crashes or terrorist attacks. In addition to that, fires are a source of accidents. At the end accidents initiated by non-desired situations such as electric station blackout (AC power loss) are classified as site accidents. 2.2. Safety Systems Presentation of categories of nuclear plant’s accidents demonstrated the necessity for accurate and reliable safety mechanisms. Towards that there are two ways of designing safety systems. The first refers to minimizing the probability of accidents and the second to the methods for protecting the plant before and after an accident. Reduction of accident probability requires specific protocols to be followed while building a plant.
Moreover, certain operational proceedings should be taken and maintenance procedures should be strict and efficient. On the other hand, plant protection includes prognostic and diagnostic systems. Additionally, automatic actions (scram, relief valves) and fission product barriers are crucial for reactor protection. Of great importance is the reactor’s shut down system, which is comprised of several control rods, and the decay heat removal system for protection of core after the scram. At this point it should be emphasized the basic role that engineered safety featured systems play in power plant safety. Such systems include: the emergency core cooling system (ECCS), the containment cooling isolation system, the fission’s product filtering and containment depressurization. Other engineered systems are flow restriction nozzles, isolation valves, pressure relief valves, core spray systems and suppression pools. In the last decades there is a tendency among researchers of applying artificial intelligence for developing smart systems for nuclear plant safety. More specifically, smart sensor network methods have been developed for 24/7 plant surveillance and pattern recognition is used for intelligent data processing. The latter has led to the so called intelligent prognostics and diagnostics. It should be mentioned that the most intelligent techniques are still in a research level.
3. Two-Phase Annular Flow
It should be mentioned that each phase has its own flow characteristics like velocity and direction. In case both phases flow in the same direction then the twophase flow is called co-current. In case they have the opposite direction then the flow is considered as counter-current. Transition states between co-current and countercurrent flow is of high significance in nuclear industry. Such transitions, if not detected, can lead to nonanticipated situations and severe accidents. In that direction these intermediate states have been studied and given their own terms. More specifically, transition from co-current to counter-current is the so-called flow reversal while from the opposite is named flooding. 3.2. Flooding In this section the phenomenon of flooding in vertical pipes is briefly presented. As was mentioned the notion of flooding indicates the change of flow of the two phases in the annular regime; from counter-current to co-current. Initially, we assume that we have a vertical pipe with a gas phase in the middle and a liquid film flowing on the wall. Specifically:
3.1. Introduction to Annular Flow Annular flow has been identified as a two phase flow regime. It is comprised of two phases that flow in the same medium. This concurrent flow results in the presence of some interfaces among the phases. This type of flow can be met in both vertical and horizontal pipes. The main feature that distinguishes annular flow from other regimes is the existence of a continuous flow film surrounding the second phase, as depicted in Fig.1, for vertical pipes. Annular flow has a similar shape for horizontal pipes. The usual case is that of gas flow in the centre of the pipe and of liquid film in the walls.
The liquid film has a downward direction. The gas in the center has an upward direction. The gas velocity increases. Increasing gas velocity presses the liquid film. Due to gas pressure, the liquid film starts to destabilize. Small waves appear in the surface of the liquid film. Gas starts to drag the liquid upwards. Film dries out. Film is totally dragged by gas. Flow is co-current.
An outline from a fluid dynamics point of view reports that subsequent to the initial laminar flow of the film, a turbulent layer is formed. Then the waves appear and their number increases as the flooding point is approached. When this point is reached direction of flow is totally reversed. 3.2. Flow Reversal The phenomenon of flow reversal shares the same frame with the flooding but describes the opposite flow transition. Briefly, we observe the following in a vertical pipe:
Figure 1: Annular flow in vertical pipes. On the left there is only film. On the right there is film and bubbles.
The liquid film has an upward direction. The gas in the center has an upward direction. The gas velocity decreases. Decreasing gas volume presses less the liquid film and film destabilizes with presence of waves on the interface.
Gas does not drag the liquid in the same direction. Liquid film driven by gravity starts going down. After a while total film flows down. Counter-current flow.
The sensors are connected directly to the central computer. There is no communication between them. In general the sensor network implements a star topology. The central unit utilizes a neural network for online data processing. 2.2. Neural Network for Diagnosis
At this point is should be mentioned that the prediction of flooding and flow reversal point is very difficult. There is no study that can do prognosis on such transitions very accurately. However, up to now the most widely used method for such phenomena was proposed by Wallis [8].
4. Intelligent Diagnostic System In this part, the architecture of the diagnostic system is presented and its functionality is discussed. The proposed method is specialized for diagnosis of annular flow transition phenomena in vertical pipes. 4.1. Architecture Diagnosis is based on a distribution of acoustic sensors along the pipe to be monitored (Fig.2).
The central processing unit is implemented with a pseudo neural network (PNN). The characterization as pseudo is adopted since PNN is comprised of a set of perceptrons (Fig.4) and does not need learning. The PNN has as many inputs as the sensors of the distributed system. If N is the number of sensors, then the inputs are also N. On the other side the number of perceptrons needed is N-1 (see Fig.3). Each perceptron has two inputs and one output. The inputs are the measured velocities from two sequential sensors and the output is either zero or one. Zero output is taken in case the sum of inputs is zero and one in other case. The activation function for each sensor is similar to that of a perceptron but not the same: Activation function: F(x) = 0, F(x) = 1,
if x=0 if x>0 or x
