1. Analysis: Future Truck Thermoelectric System Customer: PBJ Xaustors Robert Wiegers Richard Statler Khurram Kemal ME430 Fall 2003 Professor Michael Anderson

2. Introduction Objective: Experimentally determine the effect of different loads on Peltier chip bank. Experimentally determine the effect of different loads on Peltier chip bank. Measure the power output of the thermal electric system constructed by the PBJ Xaustors Measure the power output of the thermal electric system constructed by the PBJ Xaustors Use calibration/regression statistical method to analyze the collected data Use calibration/regression statistical method to analyze the collected data From the analysis present opinion on the best load for chip bank From the analysis present opinion on the best load for chip bankMotivation: To help create a lower emission environment friendly vehicle To help create a lower emission environment friendly vehicle Exhaust recover system will recover the waste energy off the exhaust of Idaho Future Truck Exhaust recover system will recover the waste energy off the exhaust of Idaho Future TruckBackground: The system has been created by the PBJ Xaustors which can do the following: capture the heat on the surface of pipes and convert it into the electricity capture the heat on the surface of pipes and convert it into the electricity Does not put extra load on the engine Does not put extra load on the engine Utilizes a system of Peltier chips which produces power when a temperature gradient across the chip is present Utilizes a system of Peltier chips which produces power when a temperature gradient across the chip is present

3. Experiment Diagram Labview running on computer Variable heat source Heat sink placed on top of chips Field point module controller Chip banks

4. Data Reduction Initial Calculations: Initial Calculations: As the data is being read into the file calculations are completed using labview As the data is being read into the file calculations are completed using labview The above equation is the equation for power = P using Voltage = V and Load Resistance = R The above equation is the equation for power = P using Voltage = V and Load Resistance = R Using this equation allowed for the graph of temperature difference vs. power to be created Using this equation allowed for the graph of temperature difference vs. power to be created Calibration Regression: Calibration Regression: Experimented with more powers for fit but found over quadratic extraneous Experimented with more powers for fit but found over quadratic extraneous Data captured by Field point modules was analyzed by Labview Data captured by Field point modules was analyzed by Labview Labview data exported into Excel and read into MathCAD Labview data exported into Excel and read into MathCAD Statistical analysis was completed with MathCAD Statistical analysis was completed with MathCAD Statistical Analysis: Statistical Analysis: The above equation is the quadratic used to model the system with the betas being solved for from the data gathered The above equation is the quadratic used to model the system with the betas being solved for from the data gathered

5. Experiment Design Controls: Controls: Using Labview and field point Using Labview and field point Exported into excel and then MathCAD Exported into excel and then MathCAD This allows for small error in the measurement and saves time entering data This allows for small error in the measurement and saves time entering data Accuracy: Accuracy: The equipment being used, Field point and Labview, has high accuracy The equipment being used, Field point and Labview, has high accuracy This leads to smaller errors in the values that are collected This leads to smaller errors in the values that are collected Since all the collection and analysis is completed independent of operator the is little human error in the recording and analysis of the data. Since all the collection and analysis is completed independent of operator the is little human error in the recording and analysis of the data.

6. Measured Data 5.2 Ohm – Black Line 3.8 Ohm – Red Line 11.8 Ohm – Aqua Line Raw data taken straight from Labview and read by MathCAD. The time interval for gathering the data was once every 10 seconds. The data was captured until the temperature differential was approximately 85F Raw data taken straight from Labview and read by MathCAD. The time interval for gathering the data was once every 10 seconds. The data was captured until the temperature differential was approximately 85F A distinct difference can be noticed between the different loads on the chip bank 5.2 Ohm looks like the best from the graph but the statistical analysis needs to be completed to be confident 5.2 Ohm looks like the best from the graph but the statistical analysis needs to be completed to be confident

7. Statistical Analysis 5.2 Ohm – Black Line 3.8 Ohm – Red Line 11.8 Ohm – Aqua Line  The best fit curves for each of the different resistive loads are shown on the graph as the middle line of the three of same color  The confidence intervals are graphed as well as the outer two lines of the three  Methods including using half as many data points and looking into higher power equations. When these were investigated it was found that a quadratic curve fit was sufficient and a halving of the data points didn’t effect the outcome.

8. Conclusion Ideal Resistance Load = 5.2 Ohms

9. Questions?