Download Implementing a Fully Automated Mock Circulatory Loop

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Implementing a Fully Automated Mock Circulatory Loop to
Simulate Cardiovascular Conditions
By Charles E. Taylor, Virginia Commonwealth University
Mock circulatory loops (MCLs) simulate the human circulatory system to enable testing of ventricular assist devices and other cardiac
assist technologies. When conducting tests using an MCL, investigators must adjust the settings on each component in the loop.
Components typically include a mechanical pump, a compliance chamber, and a peripheral resistance valve (Figure 1). In the past, these
setting adjustments were performed manually, a method that was imprecise, time-consuming, and susceptible to human error.
Figure 1. The mock circulatory loop, including a modified Harvard Apparatus pump (1), flow meter (2), compliance chamber (3), peripheral
resistance valve (4), reservoir tank (5), and centrifugal pump (6).
Using Simulink® and Simscape™, I modeled a fully automated MCL in which the heart pump, compliance chamber, and peripheral
resistance valve are controlled via microprocessors. With this setup, researchers can accurately simulate a wider range of the cardiac
conditions and dynamics needed to evaluate the performance of cardiac assist devices.
Inside a Mock Circulatory Loop
Each device in an MCL is designed to reproduce a specific behavior or characteristic of the human circulatory system. The
compliance chamber simulates arterial compliance, which is the volumetric expansion of cardiovascular tissue in response to
increased pressure. Arterial compliance is needed to dampen pressure waves and maintain blood pressure as the heart refills
with blood. The peripheral resistance valve simulates the resistance to blood flow produced by the transition of larger-diameter
arteries to smaller-diameter arterioles and capillaries.
By precisely controlling the pressure in the compliance chamber, the opening of the peripheral resistance valve, and the heart
pump’s rate and volume, researchers can simulate cardiac events in individuals as well as characteristics common to different
age and ethnic groups. For example, simulating the more hardened arteries and tissues typically found in a geriatric population
requires a lower compliance setting. Simulating hypertension requires a higher arterial resistance setting.
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Dynamic settings can be used to simulate cardiac and hypotensive events such as fainting or suddenly standing up—a potentially
critical event for a ventricular assist device patient.
Using Parameter Estimation to Develop an Automated Peripheral Resistance Valve
MCLs typically employ a lumped parameter model for arterial compliance and peripheral resistance. In a lumped parameter model, the
entire resistance to flow experienced by the ventricle or ventricular assist device is combined into a single resistance value. In an MCL,
this value is controlled via the peripheral resistance valve (Figure 2). As a result, this valve must have an operating range that spans the
resistance produced by all arterial vasculature for any condition that the MCL will simulate.
Figure 2. A peripheral resistance valve, including the mounting base (1), linear actuator lead screw (2), stepper motor (3), resistor motor frame
(4), rotation constraint bar (5), piston (6), threaded flow inlet (7), mounting holes (8), threaded flow outlet (9), positioning potentiometer (10),
and connector to driver board (11).
Because commercially available valves did not provide the required operating range and resolution, I had to create my own. As part of
this process, I modeled the valve using Simulink, Simscape, and SimHydraulics™ (Figure 3a). The primary purpose of this model was to
characterize key valve parameters that could not be measured directly, including the valve’s coefficient of discharge and its critical
Reynolds number. This model was integrated into a larger Simulink model of the entire MCL for system-level simulations.
Figure 3a. Simulink model of the experimental setup used for parameter estimation.
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I then compared the pressure produced by the numerical model and the physical system (Figure 3b). The experimental data set was used
as the training set for determining the parameters in the numerical model; the degree of correlation indicated the performance of the
parameter estimation routines employed.
Figure 3b. Top: Plot comparing pressure in the numerical model and the physical system. Middle and bottom: The experimental data sets.
After constructing a valve prototype, I measured the upstream and downstream pressure as I varied the flow rate and position of the
valve actuator, which determines the valve orifice area. The data set, which included the flow rate and resistor position input variables,
was used to validate the parameter estimation results from Figure 3b at a different operating state. I then used this experimental data in
Simulink and Simulink Design Optimization™ to find values for the valve’s coefficient of discharge and critical Reynolds number.
After characterizing the valve parameters, I integrated the valve model into a larger Simulink model of the MCL and ran simulations to
determine whether the valve’s operating range met requirements. The initial valve prototype did not, so I machined it to increase the
orifice size and reran the experiments and parameter estimation. After two iterations, I had the valve I needed. As a final step, I
connected the valve to a stepper motor to enable precise, automated opening and closing.
Automating a Previously Manual Blood Pump
The Harvard Apparatus pulsatile blood pump (model 1423) is the gold standard for producing flow conditions within an MCL. By
setting the heart rate, stroke volume, and percent systole (the fraction of each stroke in which blood flows out of the pump) on this
device, researchers can reproduce output similar to that of a human’s left ventricle. As with the peripheral resistance valve, however, all
setting changes must be made manually by adjusting dials and cranks on the device.
To automate this process, I wanted to bypass the dials and other manual controls with an embedded controller that would enable the
heart rate, stroke volume, and percent systole to be adjusted programmatically and remotely. I began by creating a model of the device
driveline in SolidWorks®. I then used SimMechanics Link to import this model into SimMechanics™ (Figure 4).
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Figure 4. SimMechanics model of the Harvard Apparatus pump’s driveline.
I incorporated the driveline model into a complete model of the pump, created using Simulink, Simscape, SimElectronics™, and
SimMechanics. This larger model included the device’s voltage supply, piston, electric motor, and gear box, as well as the
hydromechanical interface on the pump head (Figure 5). As with the peripheral resistance valve, I used parameter estimation to
characterize several of the model’s parameters that were not directly measurable: motor damping, motor inertia, and pump piston
friction. The complete plant model helped me better understand how the pump worked, and was vital to the development and tuning of
the pump controller software, which I deployed to a Microchip Technology PIC18F2550 microcontroller.
Figure 5. Simulink model of the Harvard Apparatus blood pump.
Developing a Real-Time Compliance Chamber Controller
The compliance chamber in my MCL includes a circular elastic membrane separating the fluid below from the air above. Compliance is
controlled by changing the air pressure in the upper compartment of the chamber. With older, manual compliance chambers, the
pressure setting is changed manually, and there is no way to determine the system’s compliance in real time. The compliance chamber
that I designed includes a real-time controller that uses input from a pressure sensor and a laser displacement sensor to monitor
compliance (Figure 6). The controller can maintain a set point and simulate dynamic changes in arterial compliance.
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Figure 6. The compliance chamber, including the pneumatic lines (1), laser displacement sensor (2), pneumatic pressure sensor (3), air bleed
line (4), hydraulic pressure sensor (5), hydraulic chamber (6), membrane cartridge (7), air chamber (8), clamps (9), and mounting rail (10).
As with the peripheral resistance valve and heart pump, I began by developing a physical model of the plant. This model, developed using
Simulink, Simscape, and SimHydraulics, incorporated several initially unknown parameter values, including membrane elasticity; I had
to test numerous materials before finding a membrane with the appropriate stiffness and durability. Using parameter estimation in
Simulink Design Optimization, I identified these model parameters based on experimental data. Once the Simulink model accurately
reflected the performance of my custom-built compliance chamber, I used it to tune the discrete proportional integral (PI) controller
gains, enabling the controller to maintain system compliance at a set point specified by the researcher. The controller tuning was
performed automatically with Control Design Toolbox™. Automated controller design and stability verification enabled me to deploy a
control architecture with minimal programming effort.
Extending This Approach
The assembled numerical model of the mock circulatory loop was completed by connecting up the subsystems. I then reevaluated the
initial steady-state condition values for the components in each of these subsystems to ensure the cohesiveness of the entire model at the
start of the simulation.
The mock circulatory loop model enables the user to control the set points of the various subsystems to orchestrate conditions of interest.
The complete Simulink model of the system also enables the user to apply parameter estimation routines to patient data to determine the
settings needed to replicate human pressure and flow waveforms. The model can also be used to rapidly evaluate a medical device’s safety
under a large number of conditions. Event sequences can be used to determine whether any of these conditions would send a device into
failure.
My current work focuses on simulating pulmonary circulation characteristics in the MCL using a centrifugal pump. For this project I
used Embedded Coder® to generate code for the control system from a Simulink model, and xPC Target™ to perform
hardware-in-the-loop testing of the controller. At each step of MCL development, the ability to simulate a device in Simulink has
deepened my understanding of the device’s operating characteristics and accelerated the development of the control software.
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About the Author
Dr. Charles E. Taylor completed this work as part of his doctoral dissertation in the Artificial Heart Laboratory at Virginia
Commonwealth University, under the direction of Dr. Gerald E. Miller. Dr. Taylor is continuing this research as an assistant
professor in the mechanical engineering department at the University of Louisiana at Lafayette.
Products Used
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MATLAB
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Simulink
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Control System Toolbox
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SimElectronics
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SimHydraulics
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SimMechanics
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Simscape
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Simulink Design Optimization
Learn More
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Model-Based Design
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Webinar: Physical Modeling with Simscape (39:48)
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Published 2013
92147v00
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