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Research Papers

# Monitoring Pump Parameters in Small Modular Reactors Using Electric Motor Signatures1OPEN ACCESS

[+] Author and Article Information

Mem. ASME
Department of Nuclear Engineering,
University of Tennessee,
1004 Estabrook Road, Knoxville, TN 37996-2300

Chaitanya Mehta

AECOM,
10 Patewood Drive, Bldg. VI, Suite 500, Greenville, SC 29615
e-mail: Chaitanya.Mehta@aecom.com

J. Wesley Hines

Department of Nuclear Engineering,
University of Tennessee,
1004 Estabrook Road, Knoxville, TN 37996-2300
e-mail: jhines2@utk.edu

Victor B. Lollar

Department of Nuclear Engineering,
University of Tennessee,
1004 Estabrook Road, Knoxville, TN 37996-2300
e-mail: vlollar@utk.edu

Duygu Bayram

Department of Electrical Engineering,
Istanbul Technical University,
Maslak, Istanbul 34469, Turkey
e-mail: bayramd@itu.edu.tr

1A version of this manuscript was published in the Proceedings of the ASME 2014 Small Modular Reactors Symposium, “Approaches to Process Monitoring in Small Modular Reactors,” Washington, DC, April 2014.

2Corresponding author.

Manuscript received July 31, 2015; final manuscript received August 4, 2016; published online December 20, 2016. Assoc. Editor: Masaki Morishita.

ASME J of Nuclear Rad Sci 3(1), 011007 (Dec 20, 2016) (7 pages) Paper No: NERS-15-1170; doi: 10.1115/1.4034477 History: Received July 31, 2015; Accepted August 04, 2016

## Abstract

Small modular nuclear reactors (SMRs) are designed for long-term operation with minimum outages and for possible deployment in remote locations. To achieve this operational goal, the SMRs may require remote and continuous monitoring of performance parameters that contribute to operation and maintenance. This feature is also important in monitoring critical parameters during severe accidents and for postaccident recovery. Small integral light water reactors have in-vessel space constraints, and many of the traditional instrumentation are not practical in these systems. To investigate this issue, analytical and experimental researches were carried out using a flow test loop to characterize the relationship among process variables (flow rate and pressure) and pump motor signatures. The findings of this research are presented, with implications in relating electrical signatures to pump parameters. The relationship between the electrical signatures and the process variables is discussed with reference to the experimental results. The results of this work may be used for monitoring process variables in small modular reactor systems.

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## Introduction

For reliable and economic operation of a small modular nuclear reactor (SMR), it is necessary that techniques for continuous in situ monitoring of critical equipment can be developed and incorporated in the reactor design phase. This capability is attractive for remote deployment of SMRs with longer than normal fuel cycle duration and for minimizing forced outages, thus enhancing the utilization of these power generating systems in small electric grid environments. These technologies contribute to smart condition-based maintenance, reduced human resources, remote monitoring of reactor components, and semi-autonomous operation [1]. In an integral pressurized water reactor (PWR) and other designs of SMRs, most of the critical equipment used for power generation is integral to the reactor pressure vessel. Examples of such plant components include motors, coolant circulation pumps, control rod drive mechanisms (CRDMs), pressurizer, in-core instrumentation, and reactor internal structures.

Certain designs of light water SMRs have utilized the concept that is similar to the International Reactor Innovative and Secure (IRIS) [2]. Light water SMRs dominate the design; these include NuScale Power, Generation mPower, Westinghouse SMR, and system-integrated modular advanced reactor (SMART) design by South Korea [3], and the 25-MWe Central ARgentina de Elementos Modulares (CAREM) reactor design under construction in Argentina [4]. IRIS is an integral medium sized reactor with an electrical output of 335 MW. An early Westinghouse design is a PWR that utilizes an integral reactor coolant system layout. The reactor vessel houses nuclear fuel, pumps, steam generators, pressurizer, and CRDMs. This is the reason for the IRIS vessel to be larger than the traditional PWR design. There are no pipes in the primary system (except external connections to steam generators, thus, significantly reducing the overall size of the containment and minimizing loss of coolant events. Figure 1 shows a layout of the integral primary system, consisting of eight helical coil steam generators (HCSGs). The secondary water flows through the HCSG tubing, and the steam generator produces super-heated steam. The primary water is pumped from the upper plenum down through the annular space between the reactor vessel and the shielding.

The current research was undertaken to meet the special needs of light water SMRs. SMRs have components that are somewhat different from conventional PWRs. For example, the coolant pumps are internal to the vessel (no primary coolant pumps in natural circulation reactors) or mounted on the vessel without any additional piping, and therefore, component instrumentations are limited, making electrical signature analysis (ESA) one of the viable methods for ascertaining component condition [5]. Furthermore, the control rod drive mechanism is internal to the vessel (in some designs).

An independent and reliable approach for the measurement of primary core coolant flow rate is necessary in SMRs. The primary flow rate is a safety-related parameter. The outcome of research and development clearly shows a direct relationship between pump flow rate and the power drawn by the pump motor. This is facilitated by the fact that the current drawn by an induction motor is a direct function of the load on the motor; thus, the motor acts as a transducer. Additionally, this enables remote monitoring of plant condition during normal operation as well as during transient condition.

The outcome of this research demonstrates the relationship between pump motor power and discharge flow rate, and between process parameters and motor power. The latter is useful for on-line condition monitoring.

The following sections present a discussion of pump hydraulics and the relationship between motor power and pump flow rate, a description of the experimental flow control loop, the results of analysis of experimental data, repeatability of measurements, and concluding remarks. The material presented in this paper is useful in designing an implementable technique for primary coolant flow rate measurement. The experimental procedure and the results can be easily reproduced by laboratory personnel for designing an alternate flow measurement approach.

## Pump Hydraulic Power and Motor as Transducer

###### In-Vessel Monitoring in SMRs.

Based on the review of SMR design and the need for continuous operation for extended fuel cycles (4–5 years), the following in-vessel components and other equipment were identified as candidates for continuous on-line monitoring [6]:

• CRDMs.
• Motor-operated valves (MOVs) used for feed flow regulation and turbine control valves for steam flow regulation.
• Reactor coolant pumps (RCPs), located at the core upper plenum.
• Pressurizer heater banks.
• Steam generator tubing integrity. This is important because the steam generators are integral to the vessel.
• Steam generator water level monitoring.
• Validation of transmitters used for feed flow rate, hot leg and cold leg temperatures, steam flow rate, pressurizer pressure, and pressurizer measurements.

Monitoring electrical signatures of critical components could help in diagnosing faults in the components and thereby allow for timely maintenance of the components [4]. This would reduce maintenance costs and forced outages. Remote monitoring technologies would also reduce human resources. Because most of the critical components are internal to the vessel and thus not accessible during normal operation, remote monitoring via electrical signal analysis provides a means for monitoring the performance of the equipment. The electrical current drawn by induction motor changes as a function of the load and thus affects the motor power signature. The motor acts as a transducer, potentially indicative of the faults in the driven devices such as pumps, valves, and other electrically driven actuators. Hydraulic conditions of pumps can be monitored using motor signatures [7]. Under normal operation, motor signatures are also related to process parameters, such as pump flow rate. This paper demonstrates experimental method used to relate changes in the electrical signatures to changes in process variables.

###### Relationship Between Motor Power and Pump Hydraulic Power.

Motor power is one of the electrical signatures being considered in this research. The pump hydraulic power is given by [8] Display Formula

$Pump hydraulic power (kW)=(QHρ)g1000$
(1)
where $Q$ is the flow rate ($m3/s$); $H$ is the pump head (m) at the given flow rate $Q$; $ρ$ is the density of the fluid at the pumped temperature ($kg/m3$); $g$ is the acceleration due to gravity ($9.81 m/s2$). In general, the pump hydraulic power is a nonlinear function of flow rate $Q$. Note that the power delivered to the pump shaft differs from the motor power by a factor of its mechanical efficiency. The general relationship between motor power and coolant flow enables the development of a technique for relating power, flow, and pressure parameters measured in the experimental flow loop.

The mechanical efficiency of the pump is the ratio between the pump hydraulic power (power delivered to the fluid) and the power supplied to the shaft and is given by Display Formula

$ηp=PHPs=ρgQHωT$
(2)
where $PH$ is the power delivered to the fluid; $Ps$ is the power input to the shaft; $ω$ is the shaft speed ($rad/s$); $T$ is the torque ($N m$) imparted to the pump shaft. The power delivered to the pump is proportional to the power drawn by the induction motor. This power can be calculated by measurements of three-phase currents and voltages. Thus, within a factor (which may change with operating condition), the hydraulic power is proportional to the power delivered to the pump shaft. The experiments had taken advantage of this information to establish a relationship between motor electrical signatures (motor power and motor current) and pump flow rate [6].

For a three-phase induction motor (with balanced phases), the motor power is calculated as Display Formula

$Motor power=3[IrmsVrms]cosφ$
(3)
where $Irms$ is the root-mean-square (rms) value of the phase current, $Vrms$ is the rms value of the phase voltage, and $cos(φ)$ is the power factor. The phase angle $φ$ is calculated from the actual measurements of motor current and voltage. All the phases were assumed to be balanced. To calculate the pump power, three-phase voltages and currents were recorded. The efficiency of the pump is affected as the pump frequency changes. The change in the frequency will cause the load on the motor to change, thus changing the power factor.

Power and flow at different electrical frequencies are studied to establish the relationship among the variables. The relationship between flow and power is also explored by varying the main valve settings (valve position). This is useful in determining the effect of load changes on the pump power. The experiments consisted of changing the pump frequency using the variable frequency drive. The $R2$ statistic was used to define the goodness of fit between pairs of measurements. This statistic is defined as Display Formula

$R2=1−(SSres/SStotal)$
(4)
where $SSres$ is the sum of squares of the residuals and $SStotal$ is the sum of squares of the measurements. It provides a measure of linear relationship (linear fit) between any two measurements. A value close to 1 indicates a good mutual relationship between signals.

## Experimental Flow Control Loop

The experimental flow was developed at the University of Tennessee. The objective of using the test facility is to simulate anomalies in certain system devices (sensors and valves) and acquire data from instrumentation that include both process measurements and electrical signatures.

The loop consists of two coupled water tanks, control valves (MOVs), a fractional horsepower pump, orifice flow meters, turbine flow meters, water level measurement using differential pressure transmitters, a data acquisition and system monitoring computer, and digital control for valve actuation. Separate temperature and fluid pressure transmitters are also installed.

The experimental loop included instrumentation for pump-motor health monitoring. Figure 2 is a photograph of the flow control loop. Two vertical tanks are seen toward the far side of the loop with the MOVs shown by red color. A submersible pump was used to circulate the water in the loop and placed in a tank underneath the test table. The main water circulation pipes are installed on the test table and connected to the water tanks with process sensors for measuring flow rate and pressure. Some SMR designs use canned pumps. The pump is driven by a three-phase induction motor, and the current drawn by the motor reflects the changing load on the motor. Thus, the motor acts as a transducer and can be used to monitor the pump conditions. Several process and equipment performance parameters are also measured. These include water flow rate in the loop (orifice and turbine flow meters), pump outlet pressure, three-phase motor currents and voltages, vibration (underwater accelerometers), and fluid temperature (using thermocouples). A frequency converter is used to vary the frequency of the motor input power. Both steady-state and transient data are acquired during test runs.

Figure 3 is a schematic of the test loop, showing the bypass loop (1) and the main loop (2). Bypass loop (1) was used to simulate leakage in the system. The manipulation of the bypass valve changes the loop dynamics, with changes in motor current, discharge pressure, and fluid flow rate. Because motor signature can be monitored remotely (away from the machinery), this provides a method for continuous monitoring of pump behavior and changes in the reactor coolant flow, and pressure fluctuations.

###### Measurements and Data Acquisition.

The data acquisition consists of several NI-DAQ modules and the LabVIEW software. The MOVs are used to control the flow in the loop. The valve position is adjusted through interface with the data acquisition and analysis computer and a virtual instrumentation (VI) panel. The experiments were used to demonstrate the relationship between the electrical signatures (motor current and power) and process variables such as fluid pressure and flow rate. The pump-motor system vibration was monitored using accelerometers mounted on top of the assembly (vertical) near the flow outlet and on the side of the steel shell (near the pump-motor coupling) in the horizontal direction. The vibration parameters can also be related to motor current signatures.

## Results of Analysis of Test Data

###### Basic Experiments.

Experiments were performed by changing the motor frequency to observe the changes in the current and the outlet pressure. It was observed that the pump discharge pressure, motor current, and the flow rate show close correlation with each other. The pump motor was started at 55 Hz, and the frequency was decreased to 45 Hz. The motor frequency was then increased to 60 Hz and then decreased gradually to 50 Hz. The pump was then shut down after increasing the speed back to 55 Hz. This operation is explained by dividing the entire experimental run into four regions (Figs. 4 and 5).

Region 1 indicates initial start-up of the pump at 55 Hz and removal of air bubbles from the loop. After the air bubbles were removed, the bypass valve was opened completely and the flow increased to $4×10−4 m3/s$. The main loop (2) valve was closed during this test phase.

In region 2, the pump speed was first decreased to 45 Hz and then increased to 60 Hz in increments of 5 Hz to see the effect on the motor current and pressure. The motor current decreases to 1.4 A at a motor frequency of 45 Hz and increases to 2 A at 60 Hz. The discharge pressure falls as low as 51.7 kPa (7.5 psi) at 45 Hz and reaches its maximum value of 103.5 kPa (15 psi) at 60 Hz. As expected, the water flow rate also decreases to $3.3×10−4 m3/s$ and increases back to $4.5×10−4 m3/s$ at 60 Hz synchronous frequency. Note that all the frequencies refer to motor supply frequency.

Region 3 involves reducing the speed in steps to 50 Hz. Pressure changes follow the changes in flow rate and the motor current. As seen, the pressure change of 103.5 kPa (15 psi) to 58.6 kPa (8.5 psi) is also reflected in the current which decreases to 1.5 A from a value of 2 A. The bypass flow also decreases to $3.5×10−4 m3/s$. This region shows how even a small change in the pressure is reflected in the current and bypass flow. Region 4 corresponds to pump shutdown. Correlation coefficients were calculated for the three process variables to check if a relationship could be demonstrated statistically. It was observed that the correlation coefficients show strong relationship between pump discharge pressure and motor current/bypass flow rate. The motor current has a correlation of 0.90, whereas the flow rate has the correlation 0.77 with the pressure in the loop. These experiments demonstrate the causal relationship among electrical signatures and process parameters. This information can be used for system condition monitoring.

###### Relationship Between Motor Power and Pump Flow Rate.

The above results led to further investigation into the relationship of motor power with the process variables.

To test the feasibility of determining the flow rate using the power drawn by the pump, the relationship between power and flow was first explored. In this experimental run, all the flow was made to pass through the bypass valve. The measurements of interest in this experimental sequence were the motor power, bypass flow rate, and the pump discharge pressure. The input frequency was changed in steps of 0.1 Hz from 60.0 to 59.0 Hz. The rms power drawn by the pump was found to be linearly related to its rotational frequency with an $R2$ value of approximately 0.97 (Fig. 6). As observed, the relationship is fairly linear and shows the effect of change on load.

The relationship between motor power and pump discharge pressure was also studied. This experiment showed that as the frequency of the pump was increased, the pump power and the pressure were changed in a linear manner (Fig. 7). In this case, increasing pump discharge pressure is tantamount to increasing the load experienced by the motor. Once it was established that the motor power directly follows pump frequency, the relationship between flow rate and power was then investigated. The flow rate was measured using the turbine flow meter on the bypass side of the loop. The frequency was changed in 0.1 Hz increments, and the flow rate at each frequency was recorded (Fig. 8). An $R2$ value of approximately 0.87 was calculated, proving that the flow rate and power are related linearly.

###### Experiments With Flow Variation Through Control Tank.

In the next set of experiments, the motor frequency was maintained at 60 Hz and the flow rate was varied through the main loop (2) by modulating the main control valve. In this test sequence, the bypass valve was fully closed. As the control valve was opened, the outlet pressure decreased and the output power increased. The high $R2$ values indicate a linear relationship between the flow rate through and the rms power drawn by the motor (Fig. 9). As the valve opens and more water begins to flow through it, the load on the pump increases and the power increases. The relationship between the pressure and the rms power is shown in Fig. 10.

###### Control Valve Transients.

Transient change in control valve opening was also performed. The valve operates via a user-supplied voltage signal of 0—5 V, zero being completely closed, and 5 V corresponding to full open. Flow through the valve does not actually occur until the input to the control valve is at 2 V. Changes to the valve opening were performed while taking data continuously and plotting the changes in flow rate and rms power (Fig. 11). The test began with a constant flow and control valve input set to 3 V. At event 1 on the plot, the input was changed to 2.5 V. At event 2, the input was changed to 5 V. Event 3 occurred when the input was changed back to 2 V, and finally at event 4, the valve was closed. The power followed these changes closely, increasing and decreasing with the flow.

## Uncertainties of Measured and Calculated Parameters

To ensure that the results of the experiments were repeatable, the same tests were performed ten separate times on the flow loop and the results of these separate tests were compared. The testing involved collecting data at motor frequencies between 55 and 60 Hz in increments of 1 Hz. The average flow, pressure, and motor power were calculated for each frequency, and the resulting data were plotted to observe the relationships. The test runs were repeated ten separate times for each motor frequency in the range of 55–60 Hz. The results of the tests were all plotted on the same graph where the uncertainties between different results at the same frequency could then be calculated. The purpose of this was to ensure the repeatability of the experiments that were run.

The measurements at each motor frequency were repeated ten times, and the corresponding mean values and standard deviations of the following variables were calculated:

• rms motor power
• pump flow rate
• pump discharge pressure
• motor power factor

N measurements were made at each frequency and designated as ${X1,X2,…,XN}$Display Formula

$Mean value Xmean=1N∑i=1NXi$
(5)
Display Formula
$Standard deviation Xstd.–dev.=sqrt{1N∑i=1N(Xi−Xmean)2}$
(6)

A summary of the statistical variations (uncertainties) of the four measurements at the five frequency values of {55, 56, 57, 58, 59, 60} Hz is given in Table 1. The number of measurements for each frequency run is $N=10$. Figure 12 shows the plot of the mean pump flow rate as a function of mean rms power, and the uncertainty (standard deviation) at each frequency is indicated on the plot. Figure 13 shows the plot of mean pump discharge pressure as a function of mean rms power. The uncertainties of both measured (pump discharge pressure, pump flow rate) and calculated (rms power, motor power factor) parameters are listed in Table 1. The results indicate the repeatability of the experiments, thus validating the relationship among motor power, pump flow rate, and pump discharge pressure.

###### Remarks.

The analysis of the experimental data shows that there is a high degree of interrelationship among motor power, pump discharge pressure, and pump flow rate, and can be used to detect and isolate anomalies in the system. The data-based linear models are also used to create a relationship among the three variables which would assist in analyzing the faults in the system. As explained above, the leakage was introduced in the system using the bypass valve. Normal operational data were used to create empirical models. The models were then applied to measurements with leakage anomaly to detect this fault in the system by calculating the residuals between the measured and estimated values of the desired plant parameters [6,9].

## Concluding Remarks

An experimental flow control loop, with a submersible pump and a variety of transmitters, was developed to establish the feasibility of using electrical signatures for remote monitoring of reactor internals in a small modular reactor (SMR). The loop was fully instrumented to enable data acquisition during steady-state and transient operations. The results show a strong relationship between motor power and process variables such as pump flow rate and pump discharge pressure. The motor current shows a prompt change for changing loads (and frequencies). The relationships between flow rate and motor power, and between pump discharge pressure and motor power were demonstrated under different operating conditions of the experimental flow loop. Small uncertainties of both measured and calculated parameters demonstrate the repeatability of the experiments. These relationships were also useful in detecting faults in the system [6]. Monitoring motor characteristics in SMRs has the advantage of estimating primary coolant flow rate and thus adding redundancy to measurements. This also enables remote monitoring of plant condition during normal operation as well as during transient condition.

These relationships and the use of data-based models are being used for application to on-line monitoring of plant conditions at different operating levels [10].

## Acknowledgements

The authors acknowledge the technical asssitance provided by the Measurement Systems group at Oak Ridge National Laboratory (ORNL). This research was performed using funding received from the U.S. Department of Energy (DOE) Office of Nuclear Energy’s Nuclear Energy University Programs under grant DE-AC07-05IDPS07-07ID14517 with the University of Tennessee, Knoxville.

## References

Clayton, D., and Wood, R. T., 2011, “The Role of I&C Technology in Enabling the Deployment of Small Modular Reactors,” Nucl. News, 54(13), pp. 42–47.
Carelli, M. D., Conway, L., Oriani, L., Lombardi, C., Ricotti, M., Barroso, A., Collado, J., Cinotti, L., Moraes, M., Kozuch, J., Grgic, D., Ninokata, H., Boroughs, R., Ingersoll, D., and Oriolo, F., 2004, “The Design and Safety Features of the IRIS Reactor,” Nucl. Eng. Des., 230, pp. 151–167. 0029-549310
Ingersoll, D. T., 2009, “Deliberately Small Reactors and the Second Nuclear Era,” Prog. Nucl. Energy, 51, pp. 589–603. 0149-197010
Mazzi, R., 2005, “CAREM: An Innovative-Integrated PWR,” Proceedings of the 18th International Conference on Structural Mechanics in Reactor Technology, SMiRT18, Beijing, China, pp. 4407–4415.
Haynes, H. D., 1989, “Aging and Service Wear of Electric Motor-Operated Valves Used in Engineered Safety-Feature Systems of Nuclear Power Plants,” .
Upadhyaya, B. R., Hines, J. W., Damiano, B., Mehta, C., Collins, P., Lish, M., Cady, B., Lollar, V., De Wet, D., and Bayram, D., 2014, “In-situ Condition Monitoring of Components in Small Modular Reactors Using Process and Electrical Signature Analysis,” Final Report prepared for the U.S. Department of Energy, University of Tennessee, Vol. 1, .
Casada, D., and Bunch, S., 1995, “The Use of the Motor as a Transducer to Monitor Pump Conditions,” Proceedings of the P/PM Technology Conference, Indianapolis.
Shiels, S. T., 1995, Centrifugal Pump Application: Key Hydraulic and Performance Criteria, Imperial Oil Ltd., Dartmouth, Nova Scotia, Canada.
Hines, J. W., Seibert, R., and Arndt, S. A., 2006–2008, “Technical Review of On- Line Monitoring Techniques for Performance Assessment,” , U. S. Nuclear Regulatory Commission, Washington, DC.
Lollar, V. B., Upadhyaya, B. R., Hines, J. W., Coble, J. B., and De Wet, D., 2015, “Data-Based Modeling for Monitoring and Fault Detection in Small Modular Reactors,” Proceedings of the 9th ANS International Topical Meeting on NPIC & HMIT, Charlotte, NC.
View article in PDF format.

## References

Clayton, D., and Wood, R. T., 2011, “The Role of I&C Technology in Enabling the Deployment of Small Modular Reactors,” Nucl. News, 54(13), pp. 42–47.
Carelli, M. D., Conway, L., Oriani, L., Lombardi, C., Ricotti, M., Barroso, A., Collado, J., Cinotti, L., Moraes, M., Kozuch, J., Grgic, D., Ninokata, H., Boroughs, R., Ingersoll, D., and Oriolo, F., 2004, “The Design and Safety Features of the IRIS Reactor,” Nucl. Eng. Des., 230, pp. 151–167. 0029-549310
Ingersoll, D. T., 2009, “Deliberately Small Reactors and the Second Nuclear Era,” Prog. Nucl. Energy, 51, pp. 589–603. 0149-197010
Mazzi, R., 2005, “CAREM: An Innovative-Integrated PWR,” Proceedings of the 18th International Conference on Structural Mechanics in Reactor Technology, SMiRT18, Beijing, China, pp. 4407–4415.
Haynes, H. D., 1989, “Aging and Service Wear of Electric Motor-Operated Valves Used in Engineered Safety-Feature Systems of Nuclear Power Plants,” .
Upadhyaya, B. R., Hines, J. W., Damiano, B., Mehta, C., Collins, P., Lish, M., Cady, B., Lollar, V., De Wet, D., and Bayram, D., 2014, “In-situ Condition Monitoring of Components in Small Modular Reactors Using Process and Electrical Signature Analysis,” Final Report prepared for the U.S. Department of Energy, University of Tennessee, Vol. 1, .
Casada, D., and Bunch, S., 1995, “The Use of the Motor as a Transducer to Monitor Pump Conditions,” Proceedings of the P/PM Technology Conference, Indianapolis.
Shiels, S. T., 1995, Centrifugal Pump Application: Key Hydraulic and Performance Criteria, Imperial Oil Ltd., Dartmouth, Nova Scotia, Canada.
Hines, J. W., Seibert, R., and Arndt, S. A., 2006–2008, “Technical Review of On- Line Monitoring Techniques for Performance Assessment,” , U. S. Nuclear Regulatory Commission, Washington, DC.
Lollar, V. B., Upadhyaya, B. R., Hines, J. W., Coble, J. B., and De Wet, D., 2015, “Data-Based Modeling for Monitoring and Fault Detection in Small Modular Reactors,” Proceedings of the 9th ANS International Topical Meeting on NPIC & HMIT, Charlotte, NC.

## Figures

Fig. 1

Schematic of the IRIS system [2]

Fig. 2

Experimental flow control loop, showing vertical tanks, piping, motor-operated valves, and sensors [6]

Fig. 3

Schematic of the experimental flow loop [6]

Fig. 4

Motor electrical signature during pump operation and motor current–time [6]

Fig. 5

Bypass flow rate for the condition similar to that in Fig. 4 [6]

Fig. 6

Relationship between motor rms power and shaft frequency [6]

Fig. 7

Relationship between motor rms power and pump discharge pressure [6]

Fig. 8

Relationship between bypass flow rate and rms power with [6]

Fig. 9

Loop flow rate as a function of motor rms power with bypass valve closed [6]

Fig. 10

Loop pressure as a function of motor rms power with bypass valve closed [6]

Fig. 11

Transient experiments showing flow rate (top) and motor power (bottom) [6]

Fig. 12

Average values of pump flow rate as a function of average motor power, showing the uncertainty of flow rate (with respect to mean) at each test condition (motor frequency) [6]

Fig. 13

Average values of pump discharge pressure as a function of motor power at each test condition (motor frequency) [6]

## Tables

Table 1 Mean values and uncertainties of measurements at different test conditions

## Discussions

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