How Far Does Behavioral Sink Go? :: Animals Environmental Wildlife Essays

How Far Does Behavioral Sink Go? If you ask any American what behavioral sink is, they more than likely won’t be able to define the term. It seems that everyone has become subject to this mysterious term, in once sense or another. Behavioral Sink, as described by the author, Tom Wolfe in the excerpt â€Å"O Rotten Gotham† from â€Å"A Forest of Voices†, is the study of how animals relate to their environment. In one of Wolfe’s studies he speaks of this behavioral sink in New York City. He talks about how overcrowding causes this. As observed by Wolfe, New Yorkers tended to be more aggressive and cold towards one another. When driving they were found to be screaming at each other because of traffic, speeding through a crowd getting aggravated and not really knowing why. They all seemed to be greatly stressed with a tendency to foster ulcers. He also studied the effects of overcrowding in Sika deer and rats. They all showed changes in behavior, even when there was plenty of food, water, and shelter due to this lack of personal space. When an autopsy was performed on the animals, it showed that their thyroids seem to enlarge, while their bodies looked genuinely healthy. The deer seemed to die of an adrenaline shock from their thyroids, due to the stress of no personal space. Wolfe seemed to think that if you did an autopsy on the deceased people in New York City, they would show the same general signs of thyroid enlargement. The interesting connection that humans have to rats is the grouping they exhibit. The leader-rats seem to take their own groups and then the average to below average rats gather together. Human aristocrats and wealthier people, like the leader rats, tend to live in suburbs and live in quieter, nicer places. The other people, like the average to below average rats, seemed to live in smaller apartments and much more crowded, less healthy areas like the slums. The average rats showed signs of violence, aggravation, homo and bi-sexuality, and all showed increasing signs of cancer and other diseases. The interesting connection here is that by my own observations, the same things seem to be happening in the world today. How Far Does Behavioral Sink Go? :: Animals Environmental Wildlife Essays How Far Does Behavioral Sink Go? If you ask any American what behavioral sink is, they more than likely won’t be able to define the term. It seems that everyone has become subject to this mysterious term, in once sense or another. Behavioral Sink, as described by the author, Tom Wolfe in the excerpt â€Å"O Rotten Gotham† from â€Å"A Forest of Voices†, is the study of how animals relate to their environment. In one of Wolfe’s studies he speaks of this behavioral sink in New York City. He talks about how overcrowding causes this. As observed by Wolfe, New Yorkers tended to be more aggressive and cold towards one another. When driving they were found to be screaming at each other because of traffic, speeding through a crowd getting aggravated and not really knowing why. They all seemed to be greatly stressed with a tendency to foster ulcers. He also studied the effects of overcrowding in Sika deer and rats. They all showed changes in behavior, even when there was plenty of food, water, and shelter due to this lack of personal space. When an autopsy was performed on the animals, it showed that their thyroids seem to enlarge, while their bodies looked genuinely healthy. The deer seemed to die of an adrenaline shock from their thyroids, due to the stress of no personal space. Wolfe seemed to think that if you did an autopsy on the deceased people in New York City, they would show the same general signs of thyroid enlargement. The interesting connection that humans have to rats is the grouping they exhibit. The leader-rats seem to take their own groups and then the average to below average rats gather together. Human aristocrats and wealthier people, like the leader rats, tend to live in suburbs and live in quieter, nicer places. The other people, like the average to below average rats, seemed to live in smaller apartments and much more crowded, less healthy areas like the slums. The average rats showed signs of violence, aggravation, homo and bi-sexuality, and all showed increasing signs of cancer and other diseases. The interesting connection here is that by my own observations, the same things seem to be happening in the world today.

Separation of Eddy Current and Hysteresis Losses

Laboratory Report Assignment N. 2 Separation of Eddy Current and Hysteresis Losses Instructor Name:  Ã‚  Ã‚   Dr. Walid Hubbi By: Dante Castillo Mordechi Dahan Haley Kim November 21, 2010 ECE 494 A -102 Electrical Engineering Lab Ill Table of Contents Objectives3 Equipment and Parts4 Equipment and parts ratings5 Procedure6 Final Connection Diagram7 Data Sheets8 Computations and Results10 Curves14 Analysis20 Discussion27 Conclusion28 Appendix29 Bibliography34 ObjectivesInitially, the purpose of this laboratory experiment was to separate the eddy-current and hysteresis losses at various frequencies and flux densities utilizing the Epstein Core Loss Testing equipment. However, due to technical difficulties encountered when using the watt-meters, and time constraints, we were unable to finish the experiment. Our professor acknowledging the fact that it was not our fault changed the objective of the experiment to the following: * To experimentally determine the inductance value of an in ductor with and without a magnetic core. * To experimentally determine the total loss in the core of the transformer.Equipment and Parts * 1 low-power-factor (LPF) watt-meter * 2 digital multi-meters * 1 Epstein piece of test equipment * Single-phase variac Equipment and parts ratings Multimeters: Alpa 90 Series Multimeter APPA-95 Serial No. 81601112 Wattmetters:Hampden Model: ACWM-100-2 Single-phase variac:Part Number: B2E 0-100 Model: N/A (LPF) Watt-meter: Part Number: 43284 Model: PY5 Epstein test equipment: Part Number: N/A Model: N/A Procedure The procedure for this laboratory experiment consists of two phases: A. Watt-meters accuracy determination -Recording applied voltage -Measuring current flowing into test circuit Plotting relative error vs. voltage applied B. Determination of Inductance value for inductor w/ and w/o a magnetic core -Measuring the resistance value of the inductor -Recording applied voltages and measuring current flowing into the circuit If part A of the ab ove described procedure had been successful, we would have followed the following set of instructions: 1. Complete table 2. 1 using (2. 10) 2. Connect the circuit as shown in figure 2. 1 3. Connect the power supply from the bench panel to the INPUT of the single phase variac and connect the OUTPUT of the variac to the circuit. 4.Wait for the instructor to adjust the frequency and maximum output voltage available for your panel. 5. Adjust the variac to obtain voltages Es as calculated in table 2. 1. For each applied voltage, measure and record Es and W in table 2. 2. The above sets of instructions make references to the manual of our course. Final Connection Diagram Figure 1: Circuit for Epstein core loss test set-up The above diagrams were obtained from the section that describes the experiment in the student manual. Data Sheets Part 1: Experimentally Determining the Inductance Value of Inductor Table 1: Measurements obtained without magnetic coreInductor Without Magnetic Core| V [V ]| I [A]| Z [ohm]| P [W]| 20| 1. 397| 14. 31639| 27. 94| 10| 0. 78| 12. 82051| 7. 8| 15| 1. 067| 14. 05811| 16. 005| Table 2: Measurements obtained with magnetic core Inductor With Magnetic Core| V [V]| I [A]| Z [ohm]| P [W]| 10. 2| 0. 188| 54. 25532| 1. 9176| 15. 1| 0. 269| 56. 13383| 4. 0619| 20| 0. 35| 57. 14286| 7| Part 2: Experimentally Determining Losses in the Core of the Epstein Testing Equipment Table 3: Core loss data provided by instructor | f=30 Hz| f=40 Hz| f=50 Hz| f=60 Hz| Bm| Es [Volts]| W [Watts]| Es [Volts]| W [Watts]| Es [Volts]| W [Watts]| Es [Volts]| W [Watts]| 0. | 20. 8| 1. 0| 27. 7| 1. 5| 34. 6| 3. 0| 41. 5| 3. 8| 0. 6| 31. 1| 2. 5| 41. 5| 4. 5| 51. 9| 6. 0| 62. 3| 7. 5| 0. 8| 41. 5| 4. 5| 55. 4| 7. 4| 69. 2| 11. 3| 83. 0| 15. 0| 1. 0| 51. 9| 7. 0| 69. 2| 11. 5| 86. 5| 16. 8| 103. 6| 21. 3| 1. 2| 62. 3| 10. 4| 83. 0| 16. 2| 103. 8| 22. 5| 124. 5| 33. 8| Table 4: Calculated values of Es for different values of Bm Es=1. 73*f*Bm| Bm| f=30 Hz| f=40 Hz| f=50 Hz| f =60 Hz| 0. 4| 20. 76| 27. 68| 34. 6| 41. 52| 0. 6| 31. 14| 41. 52| 51. 9| 62. 28| 0. 8| 41. 52| 55. 36| 69. 2| 83. 04| 1| 51. 9| 69. 2| 86. 5| 103. 8| 1. 2| 62. 28| 83. 04| 103. 8| 124. 56| Computations and ResultsPart 1: Experimentally Determining the Inductance Value of Inductor Table 5: Calculating values of inductances with and without magnetic core Calculating Inductances| Resistance [ohm]| 2. 50| Impedence w/o Magnetic Core (mean) [ohm]| 13. 73| Impedence w/ Magnetic Core (mean) [ohm]| 55. 84| Reactance w/o Magnetic Core [ohm]| 13. 50| Reactance w/ Magnetic Core [ohm]| 55. 79| Inductance w/o Magnetic Core [henry]| 0. 04| Inductance w/ Magnetic Core [henry]| 0. 15| The values in Table 4 were calculated using the following formulas: Z=VI Z=R+jX X=Z2-R2 L=X2 60 Part 2: Experimentally Determining Losses in the Core of the Epstein TestingEquipment Table 5: Calculation of hysteresis and Eddy-current losses Table 2. 3: Data Sheet for Eddy-Current and Hysteresis Losses|   | f=30 Hz| f=40 Hz| f=50 Hz| f=60 Hz| Bm| slope| y-intercept| Pe [W]| Ph [W]| Pe [W]| Ph [W]| Pe [W]| Ph [W]| Pe [W]| Ph [W]| 0. 4| 0. 0011| -0. 0021| 1. 01| 0. 06| 1. 80| 0. 08| 2. 81| 0. 10| 4. 05| 0. 12| 0. 6| 0. 0013| 0. 0506| 1. 19| 1. 52| 2. 12| 2. 02| 3. 31| 2. 53| 4. 77| 3. 03| 0. 8| 0. 0034| 0. 0493| 3. 07| 1. 48| 5. 46| 1. 97| 8. 53| 2. 47| 12. 28| 2. 96| 1. 0| 0. 0041| 0. 1169| 3. 72| 3. 51| 6. 62| 4. 68| 10. 34| 5. 85| 14. 89| 7. 01| 1. 2| 0. 0070| 0. 1285| 6. 6| 3. 86| 11. 12| 5. 14| 17. 38| 6. 43| 25. 02| 7. 71| Table 6: Calculation of relative error between measure core loss and the sum of the calculated hysteresis and Eddy-current losses at f=30 Hz W=Pe+Ph @ f=30 Hz| W [Watts]| Pe [Watts]| Ph [Watts]| Pe+Ph| Rel. Error| 1. 0| 1. 0125| 0. 0625| 1. 075| 7. 50%| 2. 5| 1. 1925| 1. 5174| 2. 7099| 8. 40%| 4. 5| 3. 069| 1. 479| 4. 548| 1. 07%| 7. 0| 3. 7215| 3. 507| 7. 2285| 3. 26%| 10. 4| 6. 255| 3. 855| 10. 11| 2. 79%| Table 7: Calculation of relative error between measure core los s and the sum of the calculated hysteresis and Eddy-current losses at f=40 HzW=Pe+Ph @ f=40 Hz| W [Watts]| Pe [Watts]| Ph [Watts]| Pe+Ph| Rel. Error| 1. 5| 1. 8| 0. 0833| 1. 8833| 25. 55%| 4. 5| 2. 12| 2. 0232| 4. 1432| 7. 93%| 7. 4| 5. 456| 1. 972| 7. 428| 0. 38%| 11. 5| 6. 616| 4. 676| 11. 292| 1. 81%| 16. 2| 11. 12| 5. 14| 16. 26| 0. 37%| Table 8: Calculation of relative error between measure core loss and the sum of the calculated hysteresis and Eddy-current losses at f=50 Hz W=Pe+Ph @ f=50 Hz| W [Watts]| Pe [Watts]| Ph [Watts]| Pe+Ph| Rel. Error| 3. 0| 2. 8125| 0. 1042| 2. 9167| 2. 78%| 6. 0| 3. 3125| 2. 529| 5. 8415| 2. 64%| 11. 3| 8. 525| 2. 465| 10. 99| 2. 1%| 16. 8| 10. 3375| 5. 845| 16. 1825| 3. 39%| 22. 5| 17. 375| 6. 425| 23. 8| 5. 78%| Table 9: Calculation of relative error between measure core loss and the sum of the calculated hysteresis and Eddy-current losses at f=60 Hz W=Pe+Ph @ f=60 Hz| W [Watts]| Pe [Watts]| Ph [Watts]| Pe+Ph| Rel. Error| 3. 8| 4. 05| 0. 125| 4. 175| 11. 33%| 7. 5| 4. 77| 3. 0348| 7. 8048| 4. 06%| 15. 0| 12. 276| 2. 958| 15. 234| 1. 56%| 21. 3| 14. 886| 7. 014| 21. 9| 3. 06%| 33. 8| 25. 02| 7. 71| 32. 73| 3. 02%| Curves Figure 1: Power ratio vs. frequency for Bm=0. 4 Figure 2: Power ratio vs. frequency for Bm=0. 6Figure 3: Power ratio vs. frequency for Bm=0. 8 Figure 4: Power ratio vs. frequency for Bm=1. 0 Figure 5: Power ratio vs. frequency for Bm=1. 2 Figure 6: Plot of the log of normalized hysteresis loss vs. log of magnetic flux density Figure 7: Plot of the log of normalized Eddy-current loss vs. log of magnetic flux density Figure 8: Plot of Kg core loss vs. frequency Figure 9: Plot of hysteresis power loss vs. frequency for different values of Bm Figure 10: Plot of Eddy-current power loss vs. frequency for different values of Bm Analysis Figure 11: Linear fit through power frequency ratio vs. requency for Bm=0. 4 The plot in Figure 6 was generated using Matlab’s curve fitting tool. In addition, in order to ob tain the straight line displayed in figure 6, an exclusion rule was created in which the data points in the middle were ignored. The slope and the y-intercept of the line are p1 and p2 respectively. y=mx+b fx=p1x+p2 m=p1=0. 001125 b=p2=-0. 002083 Figure 12: Linear fit through power frequency ratio vs. frequency for Bm=0. 6 The plot in figure 7 was generated in the same manner as the plot in figure 6. The slope and y-intercept obtained for this case are: m=p1=0. 001325 b=p2=0. 5058 Figure 13: Linear fit through power frequency ratio vs. frequency for Bm=0. 8 For the linear fit displayed in figure 8, no exclusion was used. The data points were well behaved; therefore the exclusion was not necessary. The slope and y-intercept are the following: m=p1=0. 00341 b=p2=0. 0493 Figure 14: Linear fit through power frequency ratio vs. frequency for Bm=1. 0 The use of exclusions was not necessary for this particular fit. The slope and y-intercept are listed below: m=p1=0. 004135 b=p2=0. 1169 Fig ure 15: Linear fit through power frequency ratio vs. frequency for Bm=1. 2The use of exclusions was not necessary for this particular fit. The slope and y-intercept are listed below: m=p1=0. 00695 b=p2=0. 1285 Figure 16: Linear fit through log (Kh*Bm^n) vs. log Bm For the plot in figure 11, exclusion was created to ignore the value in the bottom left corner. This was done because this value was negative which implies that the hysteresis loss had to be negative, and this result did not make sense. The slope of this straight line represents the exponent n and the y intercept represents log(Kh). b=logKh>Kh=10b=10-1. 014=0. 097 n=m=1. 554 Figure 17: Linear fit through log (Ke*Bm^2) vs. og Bm No exclusion rule was necessary to perform the linear fit through the data points. b=logKe>Ke=10b=0. 004487 Discussion 1. Discuss how eddy-current losses and hysteresis losses can be reduced in a transformer core. To reduce eddy-currents, the armature and field cores are constructed from laminated s teel sheets. The laminated sheets are insulated from one another so that current cannot flow from one sheet to the other. To   reduce   hysteresis   losses,   most   DC   armatures   are   constructed   of   heat-treated   silicon   steel, which has an inherently low hysteresis loss. . Using the hysteresis loss data, compute the value for the constant n. n=1. 554 The details of how this parameter was computed are under the analysis section. 3. Explain why the wattmeter voltage coil must be connected across the secondary winding terminals. The watt-meter voltage coil must be connected across the secondary winding terminals because the whole purpose of this experiment is to measure and separate the losses that occur in the core of a transformer, and connecting the potential coil to the secondary is the only way of measuring the loss.Recall that in an ideal transformer P into the primary is equal to P out of the secondary, but in reality, P into the primary is n ot equal to P out of the secondary. This is due to the core losses that we want to measure in this experiment. Conclusion I believe that this laboratory experiment was successful because the objectives of both part 1 and 2 were fulfilled, namely, to experimentally determine the inductance value of an inductor with and without a magnetic core and to separate the core losses into Hysteresis and Eddy-current losses.The inductance values were determined and the values obtained made sense. As expected the inductance of an inductor without the addition of a magnetic core was less than that of an inductor with a magnetic core. Furthermore, part 2 of this experiment was successful in the sense that after our professor provided us with the necessary measurement values, meaningful data analysis and calculations were made possible. The data obtained using matlab’s curve fitting toolbox made physical sense and allowed us to plot several required graphs.Even though analyzing the first set of values our professor provided us with was very difficult and time consuming, after receiving an email with more detailed information on how to analyze the data provided to us, we were able to get the job done. In addition to fulfilling the goals of this experiment, I consider this laboratory was even more of a success because it provided us with the opportunity of using matlab for data analysis and visualization. I know this is a valuable skill to mastery over. Appendix Matlab Code used to generate plots and the linear fits %% Defining range of variables Bm=[0. 4:. 2:1. ]; % Maximum magnetic flux density f=[30:10:60]; % range of frequencies in Hz Es1=[20. 8 31. 1 41. 5 51. 9 62. 3]; % Induced voltage on the secundary @ 30 Hz Es2=[27. 7 41. 5 55. 4 69. 2 83. 0]; % Induced voltage on the secundary @ 40 Hz Es3=[34. 6 51. 9 69. 2 86. 5 103. 8]; % Induced voltage on the secundary @ 50 Hz Es4=[41. 5 62. 3 83. 0 103. 6 124. 5]; % Induced voltage on the secundary @ 60 Hz W1=[1 2. 5 4. 5 7 10. 4]; % Power loss in the core @ 30 Hz W2=[1. 5 4. 5 7. 4 11. 5 16. 2]; % Power loss in the core @ 40 Hz W3=[3 6 11. 3 16. 8 22. ]; % Power loss in the core @ 50 Hz W4=[3. 8 7. 5 15. 0 21. 3 33. 8]; % Power loss in the core @ 60 Hz W=[W1†² W2†² W3†² W4†²]; % Power loss for all frequencies W_f1=W(1,:). /f; % Power to frequency ratio for Bm=0. 4 W_f2=W(2,:). /f; % Power to frequency ratio for Bm=0. 6 W_f3=W(3,:). /f; % Power to frequency ratio for Bm=0. 8 W_f4=W(4,:). /f; % Power to frequency ratio for Bm=1 W_f5=W(5,:). /f; % Power to frequency ratio for Bm=1. 2 %% Generating plots of W/f vs frequency for diffrent values of Bm Plotting W/f vs. frequency for Bm=0. 4 plot(f,W_f1,'rX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Ratio [W/Hz]'); grid on; title(‘Power Ratio vs. Frequency For Bm=0. 4†²); % Plotting W/f vs. frequency for Bm=0. 6 figure(2); plot(f,W_f2,'rX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(â €˜Power Ratio [W/Hz]'); grid on; title(‘Power Ratio vs. Frequency For Bm=0. 6†²); % Plotting W/f vs. frequency for Bm=0. 8 figure(3); plot(f,W_f3,'rX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Ratio [W/Hz]'); grid on; title(‘Power Ratio vs. Frequency For Bm=0. 8†²); % Plotting W/f vs. frequency for Bm=1. figure(4); plot(f,W_f4,'rX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Ratio [W/Hz]'); grid on; title(‘Power Ratio vs. Frequency For Bm=1. 0†²); % Plotting W/f vs. frequency for Bm=1. 2 figure(5); plot(f,W_f5,'rX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Ratio [W/Hz]'); grid on; title(‘Power Ratio vs. Frequency For Bm=1. 2†²); %% Obtaining Kh and n b=[-0. 002083 0. 05058 0. 0493 0. 1169 0. 1285]; % b=Kh*Bm^n log_b=log10(abs(b)); % Computing the log of magnitude of b( y-intercept) log_Bm=log10(Bm); % Computing the log of Bm Plotting log(Kh*Bm^n) vs. log(B m) figure(6); plot(log_Bm,log_b,'rX','MarkerSize',12); xlabel(‘log(Bm)'); ylabel(‘log(Kh*Bm^n)'); grid on; title(‘Log of Normalized Hysteresis Loss vs. Log of Magnetic Flux Density'); %% Obtaining Ke m=[0. 001125 0. 001325 0. 00341 0. 004135 0. 00695]; % m=Ke*Bm^2 log_m=log10(m); % Computing the log of m% Plotting log(Ke*Bm^2) vs. log(Bm) figure(7); plot(log_Bm,log_m,'rX','MarkerSize',12); xlabel(‘log(Bm)'); ylabel(‘log(Ke*Bm^2)'); grid on; title(‘Log of Normalized Eddy-Current Loss vs. Log of Magnetic Flux Density'); % Plotting W/10 vs. frequency at different values of Bm PLD1=W(1,:). /10; % Power Loss Density for Bm=0. 4 PLD2=W(2,:). /10; % Power Loss Density for Bm=0. 6 PLD3=W(3,:). /10; % Power Loss Density for Bm=0. 8 PLD4=W(4,:). /10; % Power Loss Density for Bm=1. 0 PLD5=W(5,:). /10; % Power Loss Density for Bm=1. 2 figure(8); plot(f,PLD1,'rX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Loss Density [W/Kg]'); grid on; title(‘Power Loss Density vs. Frequency'); old; plot(f,PLD2,'bX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Loss Density [W/Kg]'); grid on; title(‘Power Loss Density vs. Frequency'); plot(f,PLD3,'kX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Loss Density [W/Kg]'); grid on; title(‘Power Loss Density vs. Frequency'); plot(f,PLD4,'mX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Loss Density [W/Kg]'); grid on; title(‘Power Loss Density vs. Frequency'); plot(f,PLD5,'gX','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Power Loss Density [W/Kg]'); grid on; title(‘Power Loss Density vs.Frequency');legend(‘Bm=0. 4†²,'Bm=0. 6', ‘Bm=0. 8', ‘Bm=1. 0', ‘Bm=1. 2†²); %% Defining Ph and Pe Ph=abs(f'*b); Pe=abs(((f'). ^2)*m); %% Plotting Ph for different values of frequency % For Bm=0. 4 figure(9); plot(f,Ph(:,1),'r','MarkerSize',12); xl abel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); % For Bm=0. 6 hold; plot(f,Ph(:,2),'k','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); % For Bm=0. 8 lot(f,Ph(:,3),'g','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); % For Bm=1. 0 plot(f,Ph(:,4),'b','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); % For Bm=1. 0 plot(f,Ph(:,5),'c','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); legend(‘Bm=0. 4†²,'Bm=0. 6', ‘Bm=0. 8', ‘Bm=1. 0', ‘Bm=1. 2†²); % Plotting P e vs frequency for different values of Bm % For Bm=0. 4 figure(9); plot(f,Pe(:,1),'r','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); % For Bm=0. 6 hold; plot(f,Pe(:,2),'k','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); % For Bm=0. 8 plot(f,Pe(:,3),'g','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); For Bm=1. 0 plot(f,Pe(:,4),'b','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Hysteresis Power Loss [W]'); grid on; title(‘Hysteresis Power Loss vs. Frequency'); % For Bm=1. 0 plot(f,Pe(:,5),'c','MarkerSize',12); xlabel(‘Frequency [Hz]'); ylabel(‘Eddy-Current Power Loss [W]'); grid on; title(‘Eddy-Current Power Loss vs. Frequency'); l egend(‘Bm=0. 4†²,'Bm=0. 6', ‘Bm=0. 8', ‘Bm=1. 0', ‘Bm=1. 2'); Bibliography Chapman, Stephen J. Electric Machinery Fundamentals. Maidenhead: McGraw-Hill Education, 2005. Print. http://www. tpub. com/content/doe/h1011v2/css/h1011v2_89. htm

The Effect Of Spending Time On Nature And Its Stress...

Counselors dedicate their lives to helping people emotionally in need, and eventually this dedication begins to produce distress which, if not dealt with can lead into more serious issues. One of the more serious issues that counselors are at higher risk to experience as job-related stress increases—is burnout. In order for counselors to maintain healthy levels of compassion while maintaining their ability to help their clients, implementing a self-care plan. This self-care plan enables counselors to effectively cope with frequent job-related stress. If the self-care plan does not effectively cope with this stress, then burnout is likely to be experienced. However, there are successful strategies to prevent burnout and enables the counselor to help hurting people for the long duration. One such plausible self-care plan that effectively copes with job related stress includes frequent exposures to nature and the outdoors. In this paper, I examine the relationship between spending time in nature and its stress reducing capabilities. Because burnout is a stress induced syndrome, attempts to decrease stress levels with nature exposures, should also prevent burnout. Unfortunately, most counselors who experience burnout seldom engage in healthy coping strategies nor do they engage in them frequently enough. It is important for counselors to maintain healthy coping strategies in order to help their clients to the best of their abilities for the long duration. ByShow MoreRelatedHow Does Stress Help Facilitate Potential Positive Outcomes For Outdoor Adventure Education And Adventure Therapy Participants?2218 Words   |  9 Pages How does interacting with nature effect brain physiology, facilitating improved stress responses and overall mental and physical health within wilderness and nature based therapy programs? 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This is the un ique situation the United States and China find themselves in, with so many mutual interests, and as the global economy begins to slow, challenges such as: China’s increase in military spending and foreign tension which is rising throughout the Pacific region, highlight the importance of the U.S./China political and military cooperation. However, China’s economic agreements with neighboring countries, the U.S. and Chinese trade deficitRead MoreLong Term Development Policy Paper5988 Words   |  24 Pagessimulation model to investigate the dynamics of modernization investment policies in the development of long-term service availability for all ATC facilities. Modernization investments are meant to improve operations and efficiency primarily by reducing risk of facility and infrastructure failures. Facility infrastructure modernization should meet two goals which are higher service availability and improved resilience. General observations, however, has shown such investments have most often notRead MoreWork-Life Balance : a Comparative Study of South-East Asian Countries6004 Words   |  25 PagesABSTRACT TITLE : Work-life balance : A comparative study of South-East Asian Countries Work-life balance is a concept that has demanded attention for several years. It is highly relevant as people attempt to divide time to the myriad demands of both work and life. The multiplicity of demands that individuals have can increase this challenge as people strive to incorporate many more activities into lives. Previous empirical research has examined work-life balance in depth. The research includesRead More What Is Operations Research Essay2518 Words   |  11 Pagesimagination. Every year commute times to work take longer and longer. Since 1986 car travel has increased almost 40%, while highway capacity has barley grown. As a result most major interstate routes in Metropolitan areas are jammed during rush hours. Grid locked cost Americans almost the equivalent of $51 billion a year in lost wages and wasted fuel. And the situation is only going to get worse. (Steisand, Betsy. 1996). What causes rush hour traffic? Simply put, at specific times of the day be it in theRead MoreIndian Banking Sector10062 Words   |  41 Pagesbecame more convenient and swift. Time is given more importance than money. This resulted that Indian banking is growing at an astonishing rate, with Assets expected to reach US$1 trillion by 2010. â€Å"The banking industry should focus on having a small number of large players that can compete globally and can achieve expected goals rather than having a large number of fragmented players. KINDS OF BANKS Financial requirements in a modern economy are of a diverse nature, distinctive variety and large