Hello,Today we have a web presentation that will involve three presenters, myself, Arlie Nuetzel also of Flowmaster, and we are joined by SudhiUppuluri. Sudhi is the principle investigator for the computational sciences Expert Group. In short, his company is a modeling expertise group in the areas of 1-D fluid flow simulation, controls, CFD, and optimization. They provide consulting services to various companies in more advanced modeling applications. Sudhi will tell you more about his company during his case study.
Here is a quick Automotive look. Here is an engine cooling system model . We have a thermostat valve, a radiator, cabin heater and oil to coolant heat exchanger. We have a picture showing the underhood airflow model as well here. Plus we can model drive cycles and have done presentations before showing how we can connect that run to a dashboard as shown here. In the real customer models would have much bigger models with more detail in the cooling system piping and lubrication system. In there we might do MC simulation on coolant pump performance, thermostat size, heat load accuracy.
A hydraulic system designer faces multiple challenges when creating a circuit. Issues of sizing like capacity, requrements, power and packaging are all important. For anything that flies, survivability is a key concern. Lastly, interactions between parts of the system like resonance and system induced controller oscillations make tools that allow you to see the big picture critical to system design.
When we think about modeling hydraulic systems, arguably the most important piece of the puzzle after the actuators is the hydraulic pump. It provides the energy that will be used throughout the system. Because Flowmaster is primarily a flow analysis tool, our default PD pump model has a shortcoming when it comes to thorough hydraulic system analysis. [click] the pump is a pseudo transient component in the sense that its output is time averaged. [click] we can vary the speed of the pump using a controller component to apply this function of pump speed vs. time that you see on the screen, measuring the output pressure with a [click] gauge component we can observe the pressure and flow change with time
Using our suite of electromechanical components, along with a simple controller and a few basic flow components, we can build a discrete model of a positive displacement pump. [click] our pump is piston based, but this piston simply represents a trapped volume, you could apply this same model to a piston pump, gear pump or rotary vane pump. [click] you would only need to change the governing equation of motion. Here, our equation is for a simple sine wave oscillation of a reciprocating pump with a chosen stroke length S, rotational speed omega, and phase angle theta. [click] we apply this equation of motion to the piston component through an earth component that defines the location of the mechanical arm of the component. [click] using a script inside a controller to define its position in time. Here you can see the equation being applied to the position of the earth component in the script itself. [click] we also have a few simple check valves modeled as very large reverse losses to keep flow moving in the correct direction as the piston moves up and down.
We can run a quick simulation to verify that our model is working properly. [click] this graph is a plot ofthe motion of our earth component, and therefore, the cylinder. You can see the sinusoidal shape and that the piston moves between zero and our desired stroke length of .2 ft. plot of position vs. time. Note that on this plot, with this component, a positive displacement pulls the cylinder DOWN[click] to demonstrate this, we can overlay piston flow rate onto the chart. everywhere that piston is rising, where position is decreasing, we are pumping fluid out. Where the position value is rising, the piston is descending and pulling fluid into the cylinder. [click] we can also plot the effective flow rate through the pump. Here you can see the effect of our check valves that restrict flow in the reverse direction as the pump falls. This plot shows why a mechanical pump model is so important, we have a periodic output of our pump that may introduce pressure fluctuations into our model that we will want to investigate. [click] our last plot here adds pump output pressure, which follows the flow rate curve.
[click] Here you can see a plot of just a few seconds of operation…the pump is running very slowly, around 70 rpm so we can see the sinusoidal motion. Overlaid on the same chart is a plot of pump outlet flow rate so we can observe how the overlapping motion has turned a periodic output into a more steady one with a constant, smaller amplitude ripple. [click] We can also look at pressure output. Here the pressure pulses do not directly line up with the piston cycles because the pressure pulses are traveling up and down the pipe we have added to the model. Waves travel up and down the pipe at a characteristic speed and will constructively and destructively interfere to create a resonant response that we see on the plot.
If we wanted to view these results on a plot of pressure vs. frequency, we could have Flowmaster generate a frequency spectrum of these results using a fast Fourier transform algorithm. [click] here we can see our two major spikes again, corresponding to 12.5 Hz and 37.5 Hz. In this plot, we somehow lose the smaller spikes visable on the pressure plot. This may be due to the short analysis time and rapid speed sweep may not give sufficient time for smaller peaks to develop on the spectrum plot.
But, Using the raw data from Flowmaster, wecan post process the data with a time averaging approach to create our own pseudo-fft plot, here using Pump RPM instead of frequency for the X axis. Looking at this plot we could make the determination that we want to operate our pump well above the speeds that lead to resonance and idealy in an area of destructive interference to minimize pressure pulsations even further. This system only consists of a single pipe. With larger more complex systems, issues like resonance only become more important to study with simulation tools.
Our flap and slat actuators use a hydraulic motor and a jack screw to convert rotational motion into linear motion. For our model, we have a [click] directional control valve or DCV to route hydraulic fluid either forward or backward through the [click] hydraulic motor. [click] a flow restrictor keeps the flow rate at an appropriate value for our system. [click] flowmaster does not have a jack screw component to convert rotation into linear motion. We can create our own simple model from scratch using a gauge component. The gauge counts the number of rotations of the hydraulic motor and we use a simple script to divide that rotation count by a user defined value of screw pitch to give a result of linear displacement. We can also feed back force through our script to pass a torque load value to our hydraulic motor for a more realistic simulation.
The landing gear door and wheel assembly actuators use simply hydraulic cylinders to create linear motion from system pressure. [click] Here we also use a DCV to route flow to our actuator [click] as well as a flow restrictor to control rate. [click] a double acting hydraulic cylinder component converts pressure and flow into force and motion. [click] the piston of the cylinder is attached to a series of mechanical components to model the mass of the moving assembly, an end stop to set the bounds of motion of the system and an earth component as a physical frame of reference. In this model, we are not modeling additional static or dynamic loads on the system like friction or the landing gear itself, but those are easily taken into account.
This plot of pressure vs time shows the effects on the system when different actuators activate and turn off. Using results like this we could determine weather our pump, accumulator and pressure relief valves were properly sized to the demands of our system. We could also model leaks in our system for survivability analysis.
Lastly, we are actively working on developing links to existing multi-body dynamics tools like MSC Adams so that instead of just visualizing the movement of parts, we will be able to accurately co-simulate between the fluid system and the dynamic mechanical systems.
Modeling Mechanical System Interactions in Flowmaster Automotive Fuel Injection and Aircraft Hydraulics Actuation System Examples Sudhi Uppuluri Shayne Ziegler Arlie Nuetzel Computational Sciences Flowmaster USA Flowmaster USA Expert Group USA Call-in Number: 1-631-267-4890 Click “Global Call-in numbers for other regions Access Code: 958 533 404
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Our Approach• We focus on the problem, combining the right tools to provide accurate answers for your simulation challenge – not the tool any one company is selling. CSEG maintains licenses for best in class COTS tools providing instant technical capability expansion to your projects.System Tools CFD Tools• Flowmaster* • Ansys Fluent• Amesim • STAR-CD• Gamma Technologies OtherOptimization Tools • Matlab/Simulink• iSight* • Can integrate your• ModeFrontier in-house software with COTS
Gasoline Direct Injection• Simplified Example of electronically controlled gasoline direct injection system* Example layout from Wikipedia, retrieved Oct 2011
The system model Engine Control Unit (Simulink)Cylinder firing FuelTiming, Voltages consumption Integrate with engine control to evaluate and optimize fuel consumption Precise engine- management software to accurately tailor fuel-injection timing and duration
1D translation of the injector * Reference from Advanced Engine Technology, Heinz Heisler
Modeling Considerations (Mechanical) Coefficient of restitution to model needle bouncing
Modeling Considerations (Fluid) Pressure waves inside the passagesCombustionchamberpressure Cd Discharge Coefficient
Which variables should I really spend a lot of time getting right ? What about: supply pressure fluctuations? Coefficient of Discharge? Needle mass?
Parameter study• Sensitivity study of select input parameters performed using Isight by Simulia.• Isight linked with Flowmaster through MS Excel• Automated input generation, runs and results extraction
Parameter Study (an example) Shows effect of select variables on amount of fuel injected Extremely Important that we get the needle port area, Cd and fluctuations in the supply pressure accurately characterized. Variables such as Coefficient of Restitution, and fuel filter loss were not that important in this case. * Analysis using Isight by Simulia
Parameter Study (an example)Fluctuations in mass of fuel injected Fluctuations in input parameters * Analysis using Isight by Simulia
Calculating Cd• Discharge coefficient directly affects the fuel flow rate through the injector.• Cd is typically a function of injector port area.• Few ways of determining Cd – Literature indicates Cd range from 0.690.73 (references avbl) with some examples much higher. – CFD is a good way to determine the Cd for your particular injector (at varying port openings) (predictive) – Can back-calculate from available test data (non- predictive)
FURTHER Sudhi Uppuluri has over 14 years of experience in the simulation industry. He worked as a consulting engineer andDISCUSSION sales manager at Flowmaster USA for 8 years .He has various technical publications on related subjects in SAE and AIAA journals. He holds a Masters in Aerospace Engineering from the University of Illinois at Urbana- Champaign and a Certificate in Strategy and Innovation from the MIT Sloan School of business. Contact: Sudhi Uppuluri Principal Investigator Sudhi.email@example.com (781) 640 2329 www.cseg.us