Skill-Lync Launch Pad – Your Gateway to a Core Engineering Job by 2025! Only 1̶0̶0̶ +50 Seats Available.

04D 18H 02M 54S

Menu

Executive Programs

Workshops

Projects

Blogs

Careers

Placements

Student Reviews

For Business

More

Academic Training

Informative Articles

Find Jobs

We are Hiring!

All Courses

Choose a category

Mechanical

Electrical

Civil

Computer Science

Electronics

Offline Program

All Courses

All Courses

logo

CHOOSE A CATEGORY

Mechanical

Electrical

Civil

Computer Science

Electronics

Offline Program

Top Job Leading Courses

Automotive

CFD

FEA

Design

MBD

Med Tech

Courses by Software

Design

Solver

Automation

Vehicle Dynamics

CFD Solver

Preprocessor

Courses by Semester

First Year

Second Year

Third Year

Fourth Year

Courses by Domain

Automotive

CFD

Design

FEA

Tool-focused Courses

Design

Solver

Automation

Preprocessor

CFD Solver

Vehicle Dynamics

Machine learning

Machine Learning and AI

POPULAR COURSES

coursePost Graduate Program in Hybrid Electric Vehicle Design and Analysis
coursePost Graduate Program in Computational Fluid Dynamics
coursePost Graduate Program in CAD
coursePost Graduate Program in CAE
coursePost Graduate Program in Manufacturing Design
coursePost Graduate Program in Computational Design and Pre-processing
coursePost Graduate Program in Complete Passenger Car Design & Product Development
Executive Programs
Workshops
For Business

Success Stories

Placements

Student Reviews

More

Projects

Blogs

Academic Training

Find Jobs

Informative Articles

We're Hiring!

phone+91 9342691281Log in
  1. Home/
  2. Gokulkumar M/
  3. Week 3 - Adiabatic Flame Temperature calculation

Week 3 - Adiabatic Flame Temperature calculation

Objective: To calculate Adiabatic Flame Temperature for combustion with variation in equivalence ratio, type of fuel, with heat loss using the Newton-Raphson method and Cantera. Theory: Adiabatic flame temperature(AFT):          The final temperature of the product, When the combustion takes place…

    • Gokulkumar M

      updated on 23 Mar 2021

    Objective:

    • To calculate Adiabatic Flame Temperature for combustion with variation in equivalence ratio, type of fuel, with heat loss using the Newton-Raphson method and Cantera.

    Theory:

    Adiabatic flame temperature(AFT):

             The final temperature of the product, When the combustion takes place heat will be released to the products in Adiabatic condition which increases the temperature.

    To determine AFT using Newton raphson technique:

    The Newton-Raphson method is one of the most widely used methods for root finding. It can be easily generalized to the problem of finding solutions of a system of non-linear equations, which is referred to as Newton's technique.

    xi+1=xi−f(xi)f′(xi)xi+1=xi-f(xi)f′(xi)

    Equivalence ratio(Φ):

             It is the ratio of Fuel air ratio of Actual fuel to Stoichiometric fuel.

    rich mixture (Φ>1) - more fuel or less oxygen

    lean mixture (Φ<1) - less fuel or more oxygen

    Generic equation stoichiometric combustion for methane CH4 + Ar(O2 + 3.76N2) = aCO2 + bH20 + cN2 + dO2

    For Stoichiometric fuel,

              Number of air mole(Ar) = x+(y/4)

      where,     

              x - number of carbon atom & y - number of hydrogen atom in HC.

     

    '''
Adiabatic flame temperature for methane
CH4 + 2(O2 + 3.76N2) = 0.8CO2 + 1.6H20 + 7.52N2 + O2
for lean mixture
CH4 + 2/ER(O2+3.76N2) -> CO2 + 2H2O + (7.52/ER)N2 + (2/Φ- 2)O2
for rich mixture
CH4 + 2/ER(O2+3.76N2) -> ( 4/ER–3)CO2 + 2H2O + (7.52/ER)N2 + (4- 4/ER)CO
'''
import numpy as np
import matplotlib.pyplot as plt
import cantera as ct


def h(T, coeff): # fuction to determine the enthalpy
	R = 8.314 # jol/mol-k
	a1 = coeff[0]
	a2 = coeff[1]
	a3 = coeff[2]
	a4 = coeff[3]
	a5 = coeff[4]
	a6 = coeff[5]
	a7 = coeff[6]

	return (a1 + a2*T/2 + a3*pow(T,2)/3 + a4*pow(T,3)/4 + a5*pow(T,4)/5 + a6/T)*R*T

def f(T,ER): # fuction to determine total enthalpy of product & reactant

	#enthalpy of reactants
	t_sat   = 298.15
	h_CH4_r = h(t_sat,CH4_coefficient_l)
	h_O2_r  = h(t_sat,O2_coefficient_l)
	h_N2_r  = h(t_sat,N2_coefficient_l)

	H_reactants = h_CH4_r + (2/ER)*(h_O2_r + 3.76*h_N2_r)	

	#enthalpy of produce
	h_CO2_p = h(T,CO2_coefficient_h)
	h_N2_p  = h(T,N2_coefficient_h)
	h_H2O_p = h(T,H2O_coefficient_h)
	#h_CH4_p = h(T,CH4_coefficient_h)
	h_O2_p  = h(T,O2_coefficient_h)
	h_CO_p  = h(T,CO_coefficient_h)

	if ER==1: #Stoichiometric 
		H_product   = h_CO2_p + 7.52*h_N2_p + 2*h_H2O_p + 0.4*h_O2_p
	else:
	 	if ER>1: #rich mixture
	 		H_product   = ((4/ER)-3)*h_CO2_p + (7.52/ER)*h_N2_p + 2*h_H2O_p + (4-(4/ER))*h_CO_p
	 	else: 	 #lean mixture
	 		H_product   = h_CO2_p + (7.52/ER)*h_N2_p + 2*h_H2O_p + ((2/ER)-2)*h_O2_p

	return H_product-H_reactants

def fprime(T,ER): #Numerical differntiation
	return (f(T+1e-6,ER)-f(T,ER))/1e-6

#Methane Coefficient from Nasa polynomials data
CH4_coefficient_l = [ 5.14987613E+00, -1.36709788E-02,  4.91800599E-05, -4.84743026E-08,  1.66693956E-11, -1.02466476E+04, -4.64130376E+00]
#Oxygen 
O2_coefficient_l  = [ 3.78245636E+00, -2.99673416E-03,  9.84730201E-06, -9.68129509E-09,  3.24372837E-12, -1.06394356E+03,  3.65767573E+00]
O2_coefficient_h  = [ 3.28253784E+00,  1.48308754E-03, -7.57966669E-07,  2.09470555E-10, -2.16717794E-14, -1.08845772E+03,  5.45323129E+00]
#Nitrogen
N2_coefficient_h  = [ 0.02926640E+02,  0.14879768E-02, -0.05684760E-05,  0.10097038E-09, -0.06753351E-13, -0.09227977E+04,  0.05980528E+02]
N2_coefficient_l  = [ 0.03298677E+02,  0.14082404E-02, -0.03963222E-04,  0.05641515E-07, -0.02444854E-10, -0.10208999E+04,  0.03950372E+02]
#Carbon-di-oxide
CO2_coefficient_h = [ 3.85746029E+00,  4.41437026E-03, -2.21481404E-06,  5.23490188E-10, -4.72084164E-14, -4.87591660E+04,  2.27163806E+00]
#Carbon-mono-oxide
CO_coefficient_h  = [ 2.71518561E+00,  2.06252743E-03, -9.98825771E-07,  2.30053008E-10, -2.03647716E-14, -1.41518724E+04,  7.81868772E+00]
#Water vapor
H2O_coefficient_h = [ 3.03399249E+00,  2.17691804E-03, -1.64072518E-07, -9.70419870E-11,  1.68200992E-14, -3.00042971E+04,  4.96677010E+00]

ER_x      = np.linspace(0.1,2,100) #Equivalence ratio     
T_guess = 2000
tol     = 1e-5
alpha   = 1
temp = []  # empty array for newton rhapson technique
temp_cantera = [] # empty array for cantera
gas = ct.Solution('gri30.xml') # centera

for ER in ER_x:
	while (abs(f(T_guess,ER))>tol ):
		T_guess = T_guess - alpha*(f(T_guess,ER)/fprime(T_guess,ER)) # newton rhapson technique
	
	gas.TPX = 298.15,101325,{"CH4":1,"O2":2/ER,"N2":(2*3.76/ER)}
	gas.equilibrate("HP","auto")	
	print("Adiabatic Flame temperature From newton rhapson technique is ",+ T_guess)
	print("Adiabatic Flame temperature From Cantera is ",+ gas.T)
	temp.append(T_guess)
	temp_cantera.append(gas.T)
		

plt.plot(ER_x,temp,color="red")
plt.plot(ER_x,temp_cantera,color="blue")
plt.xlabel("Equivalence ratio")
plt.ylabel("Adiabatic flame temperature")
plt.grid("on")
plt.title("Adiabatic flame temperature")
plt.legend(["Newton rhapson technique","cantera"])
plt.show()

    Cantera, ER for max temperature is 1.04

    Newton raphson technique, ER for max temperature is 1.

    Heat loss:

    Heat released by one mole of certain fuel can be calculated from calorific value from experimental data.

    '''
Adiabatic flame temperature for methane
CH4 + 2(O2 + 3.76N2) = CO2 + 2H20 + 7.52N2 + energy + heat loss 
for 1 mole of methane 
Heat generation =  802.3 kJ/mol of methane
'''

def h(T, coeff):
	R = 8.314 # jol/mol-k
	a1 = coeff[0]
	a2 = coeff[1]
	a3 = coeff[2]
	a4 = coeff[3]
	a5 = coeff[4]
	a6 = coeff[5]
	a7 = coeff[6]

	return (a1 + a2*T/2 + a3*pow(T,2)/3 + a4*pow(T,3)/4 + a5*pow(T,4)/5 + a6/T)*R*T

def f(T,Heat_loss):

	#enthalpy of reactants
	t_sat = 298.15
	h_CH4_r = h(t_sat,CH4_coefficient_l)
	h_O2_r  = h(t_sat,O2_coefficient_l)
	h_N2_r  = h(t_sat,N2_coefficient_l)

	H_reactants = h_CH4_r+2*h_O2_r+2*3.76*h_N2_r

	#enthalpy of produce
	h_CO2_p = h(T,CO2_coefficient_h)
	h_N2_p  = h(T,N2_coefficient_h)
	h_H2O_p = h(T,H2O_coefficient_h)

	H_product = h_CO2_p+7.52*h_N2_p+2*h_H2O_p
	#print(H_product-H_reactants)
	energy_1mole_CH4 = 802300

	return (H_product-H_reactants)+(energy_1mole_CH4*Heat_loss)

def fprime(T,Heat_loss):
	return (f(T+1e-6,Heat_loss)-f(T,Heat_loss))/1e-6


CH4_coefficient_l = [5.14987613E+00, -1.36709788E-02, 4.91800599E-05, -4.84743026E-08, 1.66693956E-11, -1.02466476E+04, -4.64130376E+00]

O2_coefficient_l  = [3.78245636E+00, -2.99673416E-03, 9.84730201E-06, -9.68129509E-09, 3.24372837E-12, -1.06394356E+03, 3.65767573E+00 ]

N2_coefficient_h   = [0.02926640E+02, 0.14879768E-02, -0.05684760E-05, 0.10097038E-09, -0.06753351E-13, -0.09227977E+04, 0.05980528E+02]
N2_coefficient_l   = [0.03298677E+02, 0.14082404E-02, -0.03963222E-04, 0.05641515E-07, -0.02444854E-10, -0.10208999E+04, 0.03950372E+02]

CO2_coefficient_h  = [3.85746029E+00, 4.41437026E-03, -2.21481404E-06, 5.23490188E-10, -4.72084164E-14, -4.87591660E+04, 2.27163806E+00]

H2O_coefficient_h  = [3.03399249E+00, 2.17691804E-03, -1.64072518E-07, -9.70419870E-11, 1.68200992E-14, -3.00042971E+04, 4.96677010E+00]

Heat_loss = 0.35

tolerance  = 1e-5
alpha      = 1
iterations = 0
T_guess    = 1500

while (abs(f(T_guess,Heat_loss))>tolerance):
	T_guess    = T_guess - alpha*(f(T_guess,Heat_loss)/fprime(T_guess,Heat_loss))
	iterations = iterations + 1
	print("temp:  "+str(T_guess))
	
print(iterations)
print("Adiabatic Flame temperature is {}k with heat loss of {}%. ".format(int(T_guess),Heat_loss*100))

    Adiabatic flame temperature variation with respect to Alkane, Alkene, Alkyne :

    Alkane: CnH2n+2CnH2n+2

    Alkene: CnH2nCnH2n

    alkyne:CnH2n−2CnH2n-2

    Calculation of AFT with respect to Equivalence ratio for Alkane, Alkene, Alkyne using Cantera:

    import cantera as ct
import numpy as np
import matplotlib.pyplot as plt

gas = ct.Solution('gri30.xml')
ER_x = np.linspace(0.1,3,100)
alkane,alkyne,alkene = ([],[],[])

for ER in ER_x:
	gas.TPX = 298.15,101325,{"C2H2":1,"O2":2.5/ER,"N2":(2.5/ER*3.76)}
	gas.equilibrate("HP","auto")
	alkyne.append(gas.T)

	gas.TPX = 298.15,101325,{"C2H4":1,"O2":3/ER,"N2":(3/ER*3.76)}
	gas.equilibrate("HP","auto")	
	alkene.append(gas.T)

	gas.TPX = 298.15,101325,{"C2H6":1,"O2":3.5/ER,"N2":(3.5/ER*3.76)}
	gas.equilibrate("HP","auto")	
	alkane.append(gas.T)


print(len(alkyne))
plt.plot(ER_x,alkyne,color="red")
plt.plot(ER_x,alkene,color="blue")
plt.plot(ER_x,alkane,color="green")
plt.legend(["alkyne","alkene","alkane"])
plt.xlabel("Equivalence ratio")
plt.ylabel("Adiabatic flame temperature")
plt.grid("on")
plt.show()

    Calculation of AFT for stoichiometric combustion using Newton raphson technique:

    '''
Adiabatic flame temperature for methane
Alkane: C2H6 + 3.5(O2 + 3.76N2) = 2CO2 + 3H20 +  13.16N2 + energy 
Alkene: C2H4 + 3.0(O2 + 3.76N2) = 2CO2 + 2H20 + 11.28N2 + energy 
Alkyne: C2H2 + 2.5(O2 + 3.76N2) = 2CO2 +  H20 +  9.4N2  + energy 
'''
import matplotlib.pyplot as plt


def h(T, coeff):
	R = 8.314 # jol/mol-k
	a1 = coeff[0]
	a2 = coeff[1]
	a3 = coeff[2]
	a4 = coeff[3]
	a5 = coeff[4]
	a6 = coeff[5]
	a7 = coeff[6]

	return (a1 + a2*T/2 + a3*pow(T,2)/3 + a4*pow(T,3)/4 + a5*pow(T,4)/5 + a6/T)*R*T

def f(T,Ar):
	#enthalpy of reactants
	t_sat = 298.15
	h_C2H6_r = h(t_sat,C2H6_coefficient_l)
	h_C2H4_r = h(t_sat,C2H4_coefficient_l)
	h_C2H2_r = h(t_sat,C2H2_coefficient_l)
	h_O2_r   = h(t_sat,O2_coefficient_l)
	h_N2_r   = h(t_sat,N2_coefficient_l)

	#enthalpy of produce
	h_CO2_p = h(T,CO2_coefficient_h)
	h_N2_p  = h(T,N2_coefficient_h)
	h_H2O_p = h(T,H2O_coefficient_h)

	if Ar == 3.5:
		H_reactants = h_C2H6_r + Ar*(h_O2_r + 3.76*h_N2_r)
		H_product   = 2*h_CO2_p + 13.16*h_N2_p + 3*h_H2O_p
	if Ar == 3:
		H_reactants = h_C2H4_r + Ar*(h_O2_r + 3.76*h_N2_r)
		H_product   = 2*h_CO2_p + 11.28*h_N2_p + 2*h_H2O_p
	if Ar == 2.5:
		H_reactants = h_C2H2_r + Ar*(h_O2_r + 3.76*h_N2_r)
		H_product   = 2*h_CO2_p + 9.4*h_N2_p + h_H2O_p
	#print(H_product-H_reactants)

	return H_product-H_reactants

def fprime(T,Ar):
	return (f(T+1e-6,Ar)-f(T,Ar))/1e-6

#Alkane
C2H6_coefficient_l = [ 4.29142492E+00, -5.50154270E-03,  5.99438288E-05, -7.08466285E-08,  2.68685771E-11, -1.15222055E+04,  2.66682316E+00]
#Alkene
C2H4_coefficient_l = [ 3.95920148E+00, -7.57052247E-03,  5.70990292E-05, -6.91588753E-08,  2.69884373E-11,  5.08977593E+03,  4.09733096E+00]
#Alkyne
C2H2_coefficient_l = [ 8.08681094E-01,  2.33615629E-02, -3.55171815E-05,  2.80152437E-08, -8.50072974E-12,  2.64289807E+04,  1.39397051E+01]
#Oxygen 
O2_coefficient_l   = [ 3.78245636E+00, -2.99673416E-03,  9.84730201E-06, -9.68129509E-09,  3.24372837E-12, -1.06394356E+03,  3.65767573E+00]
O2_coefficient_h   = [ 3.28253784E+00,  1.48308754E-03, -7.57966669E-07,  2.09470555E-10, -2.16717794E-14, -1.08845772E+03,  5.45323129E+00]
#Nitrogen
N2_coefficient_h   = [ 0.02926640E+02,  0.14879768E-02, -0.05684760E-05,  0.10097038E-09, -0.06753351E-13, -0.09227977E+04,  0.05980528E+02]
N2_coefficient_l   = [ 0.03298677E+02,  0.14082404E-02, -0.03963222E-04,  0.05641515E-07, -0.02444854E-10, -0.10208999E+04,  0.03950372E+02]
#Carbon-di-oxide
CO2_coefficient_h  = [ 3.85746029E+00,  4.41437026E-03, -2.21481404E-06,  5.23490188E-10, -4.72084164E-14, -4.87591660E+04,  2.27163806E+00]
#Carbon-mono-oxide
CO_coefficient_h   = [ 2.71518561E+00,  2.06252743E-03, -9.98825771E-07,  2.30053008E-10, -2.03647716E-14, -1.41518724E+04,  7.81868772E+00]
#Water vapor
H2O_coefficient_h  = [ 3.03399249E+00,  2.17691804E-03, -1.64072518E-07, -9.70419870E-11,  1.68200992E-14, -3.00042971E+04,  4.96677010E+00]


Ar = 2.5  #for 2 carbon atom Ar =  3.5, 3.0, 2.5 (alkane_C2H6, alkene_C2H4, alkyne_C2H2) 
T_guess    = 2000
tolerance  = 1e-5
alpha      = 1
iterations = 0

while (abs(f(T_guess,Ar))>tolerance):
	T_guess    = T_guess - alpha*(f(T_guess,Ar)/fprime(T_guess,Ar))
	iterations = iterations + 1
	#print(T_guess)

print(iterations)
print(T_guess)

    Adiabatic flame temperature variation with respect to a number of Carbon atom chains :

     

    import cantera as ct
import matplotlib.pyplot as plt

gas = ct.Solution('gri30.xml')
alkane = []
n_x= [1,2,3]
for n in n_x:
	x = n
	y = 2*n+2
	if n ==1:
		formula = "CH{}".format(y)
	else:
		formula = "C{}H{}".format(x,y)
	Ar = x+(y/4)
	print(formula)
	gas.TPX = 298.15,101325,{formula:1,"O2":Ar,"N2":(Ar*3.76)}
	gas.equilibrate("HP","auto")
	print(gas.T)
	alkane.append(gas.T)
print(alkane)
plt.plot(n_x,alkane)
plt.xlabel("number of carbon atom")
plt.ylabel("Adiabatic flame temperature")
plt.title("Alkane")
plt.show()

    Output of AFT:

    CH4
    2224.6173595315754
    C2H6
    2258.737702688394
    C3H8
    2265.7012690691427

     

    Conclusion:

    • Cantera, equivalence ratio for max temperature is 1.04, thus rich mixture gives maximum temperature. And accuracy of Newton raphson method is lower due to not including various sub-products which are small in mass fraction.
    • Higher heat loss will reduce the Adiabatic Flame Temperature and it is linear with respect to heat loss.
    • From Alkane, Alkene, Alkyne; fuel alkyne gives the high Adiabatic Flame Temperature. which conclude that higher the hydrogen atom in HC fuel will reduce AFT.
    • High carbon fuel produces more energy than low carbon fuel.

    Leave a comment

    Thanks for choosing to leave a comment. Please keep in mind that all the comments are moderated as per our comment policy, and your email will not be published for privacy reasons. Please leave a personal & meaningful conversation.

    Please  login to add a comment

    Other comments...

    No comments yet!
    Be the first to add a comment

    Read more Projects by Gokulkumar M (17)

    Week 3 - Adiabatic Flame Temperature calculation

    Objective:

    Objective: To calculate Adiabatic Flame Temperature for combustion with variation in equivalence ratio, type of fuel, with heat loss using the Newton-Raphson method and Cantera. Theory: Adiabatic flame temperature(AFT):          The final temperature of the product, When the combustion takes place…

    calendar

    23 Mar 2021 05:17 AM IST

      Read more

      Week 10 - Simulating Combustion of Natural Gas.

      Objective:

      Objective:              To simulate the combustion in Ansys fluent with the parametric study. Theory: Combustion is a chemical process during which heat energy is generated.  During combustion, the Hydro-carbon and Oxygen combined when a spark is introduced chemical reaction takes…

      calendar

      03 Mar 2021 10:35 AM IST

      • CFD
      Read more

      Week 9 - Parametric study on Gate valve.

      Objective:

      Objective:                 To simulate the Gate valve with different opening positions using parametric study in Ansys fluent. Theory: Flow coefficient: Cv=Q√(SG∇P)Cv=Q√(SG∇P) where,        Q is the rate of flow (expressed in US gallons per minute),…

      calendar

      26 Feb 2021 03:46 PM IST

        Read more

        Week 8 - Simulating Cyclone separator with Discrete Phase Modelling

        Objective:

         Objective:                    To simulate cyclone separator with Discrete Phase Modelling in Ansys fluent. Theory: Empirical models used to calculate the cyclone separator efficiency: 1.Iozia and Leith Model Iozia and Leith logistic model is a modified version of…

        calendar

        24 Feb 2021 02:43 PM IST

        • CFD
        Read more

        Schedule a counselling session

        Please enter your name
        Please enter a valid email
        Please enter a valid number

        Related Courses

        coursecard

        Design loads considered on bridges

        Recently launched

        10 Hours of Content

        coursecard

        Design of Steel Superstructure in Bridges

        Recently launched

        16 Hours of Content

        coursecard

        Design for Manufacturability (DFM)

        Recently launched

        11 Hours of Content

        coursecard

        CATIA for Medical Product Design

        Recently launched

        5 Hours of Content

        coursecardcoursetype

        Accelerated Career Program in Embedded Systems (On-Campus) Courseware Partner: IT-ITes SSC nasscom

        Recently launched

        0 Hours of Content

        Schedule a counselling session

        Please enter your name
        Please enter a valid email
        Please enter a valid number

        logo

        Skill-Lync offers industry relevant advanced engineering courses for engineering students by partnering with industry experts.

        https://d27yxarlh48w6q.cloudfront.net/web/v1/images/facebook.svghttps://d27yxarlh48w6q.cloudfront.net/web/v1/images/insta.svghttps://d27yxarlh48w6q.cloudfront.net/web/v1/images/twitter.svghttps://d27yxarlh48w6q.cloudfront.net/web/v1/images/youtube.svghttps://d27yxarlh48w6q.cloudfront.net/web/v1/images/linkedin.svg

        Our Company

        News & EventsBlogCareersGrievance RedressalSkill-Lync ReviewsTermsPrivacy PolicyBecome an Affiliate
        map
        EpowerX Learning Technologies Pvt Ltd.
        4th Floor, BLOCK-B, Velachery - Tambaram Main Rd, Ram Nagar South, Madipakkam, Chennai, Tamil Nadu 600042.
        mail
        info@skill-lync.com
        mail
        ITgrievance@skill-lync.com

        Top Individual Courses

        Computational Combustion Using Python and CanteraIntroduction to Physical Modeling using SimscapeIntroduction to Structural Analysis using ANSYS WorkbenchIntroduction to Structural Analysis using ANSYS Workbench

        Top PG Programs

        Post Graduate Program in Hybrid Electric Vehicle Design and AnalysisPost Graduate Program in Computational Fluid DynamicsPost Graduate Program in CADPost Graduate Program in Electric Vehicle Design & Development

        Skill-Lync Plus

        Executive Program in Electric Vehicle Embedded SoftwareExecutive Program in Electric Vehicle DesignExecutive Program in Cybersecurity

        Trending Blogs

        Heat Transfer Principles in Energy-Efficient Refrigerators and Air Conditioners Advanced Modeling and Result Visualization in Simscape Exploring Simulink and Library Browser in Simscape Advanced Simulink Tools and Libraries in SimscapeExploring Simulink Basics in Simscape

        © 2025 Skill-Lync Inc. All Rights Reserved.

                    Do You Want To Showcase Your Technical Skills?
                    Sign-Up for our projects.