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Simulation of Weather Patterns Gaming Project in C++

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#include <iostream>
#include <cstdlib>
#include <ctime>

// Constants
const double INITIAL_TEMPERATURE = 20.0; // Initial temperature in degrees Celsius
const double TEMPERATURE_VARIATION = 5.0; // Max variation in temperature per day
const double INITIAL_PRECIPITATION = 0.0; // Initial precipitation in mm
const double PRECIPITATION_CHANCE = 0.3; // Chance of precipitation per day
const double MAX_PRECIPITATION = 20.0; // Max precipitation amount per day
const int NUM_DAYS = 30; // Number of days to simulate

// Function to generate a random double between min and max
double randomDouble(double min, double max) {
    return min + static_cast<double>(rand()) / (static_cast<double>(RAND_MAX / (max - min)));
}

// Function to simulate weather patterns
void simulateWeather(double initialTemperature, double temperatureVariation, double initialPrecipitation, 
                     double precipitationChance, double maxPrecipitation, int numDays) {
    double temperature = initialTemperature;
    double precipitation = initialPrecipitation;

    std::cout << "Day    Temperature (°C)   Precipitation (mm)\n";
    for (int day = 1; day <= numDays; ++day) {
        // Random temperature variation
        double tempVariation = randomDouble(-temperatureVariation, temperatureVariation);
        temperature += tempVariation;
        
        // Random precipitation event
        double dailyPrecipitation = (randomDouble(0.0, 1.0) < precipitationChance) ?
            randomDouble(0.0, maxPrecipitation) : 0.0;
        precipitation = dailyPrecipitation;
        
        // Output the weather for the day
        std::cout << day << "       " << temperature << "                " << precipitation << "\n";
    }
}

int main() {
    // Seed the random number generator
    srand(static_cast<unsigned>(time(0)));

    // Simulate weather for a given number of days
    simulateWeather(INITIAL_TEMPERATURE, TEMPERATURE_VARIATION, INITIAL_PRECIPITATION, 
                    PRECIPITATION_CHANCE, MAX_PRECIPITATION, NUM_DAYS);

    return 0;
}

#include <iostream>

#include <cstdlib>

#include <ctime>

// Constants

const double INITIAL_TEMPERATURE = 20.0; // Initial temperature in degrees Celsius

const double TEMPERATURE_VARIATION = 5.0; // Max variation in temperature per day

const double INITIAL_PRECIPITATION = 0.0; // Initial precipitation in mm

const double PRECIPITATION_CHANCE = 0.3; // Chance of precipitation per day

const double MAX_PRECIPITATION = 20.0; // Max precipitation amount per day

const int NUM_DAYS = 30; // Number of days to simulate

// Function to generate a random double between min and max

double randomDouble(double min, double max) {

return min + static_cast<double>(rand()) / (static_cast<double>(RAND_MAX / (max - min)));

}

// Function to simulate weather patterns

void simulateWeather(double initialTemperature, double temperatureVariation, double initialPrecipitation,

double precipitationChance, double maxPrecipitation, int numDays) {

double temperature = initialTemperature;

double precipitation = initialPrecipitation;

std::cout << "Day Temperature (°C) Precipitation (mm)\n";

for (int day = 1; day <= numDays; ++day) {

// Random temperature variation

double tempVariation = randomDouble(-temperatureVariation, temperatureVariation);

temperature += tempVariation;

// Random precipitation event

double dailyPrecipitation = (randomDouble(0.0, 1.0) < precipitationChance) ?

randomDouble(0.0, maxPrecipitation) : 0.0;

precipitation = dailyPrecipitation;

// Output the weather for the day

std::cout << day << " " << temperature << " " << precipitation << "\n";

}

int main() {

// Seed the random number generator

srand(static_cast<unsigned>(time(0)));

// Simulate weather for a given number of days

simulateWeather(INITIAL_TEMPERATURE, TEMPERATURE_VARIATION, INITIAL_PRECIPITATION,

PRECIPITATION_CHANCE, MAX_PRECIPITATION, NUM_DAYS);

return 0;

}

Explanation Constants: INITIAL_TEMPERATURE: Starting temperature in degrees Celsius. TEMPERATURE_VARIATION: Maximum daily variation in temperature. INITIAL_PRECIPITATION: Starting precipitation amount in millimeters. PRECIPITATION_CHANCE: Probability of precipitation occurring on any given day. MAX_PRECIPITATION: Maximum amount of precipitation that can occur in a day. NUM_DAYS: Number of days to simulate. randomDouble Function: Generates a random double between min …

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Functional requirements of Recruitment Agency Management System with non-functional

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Functional Requirements for a Recruitment Agency Management System User Authentication and Authorization: Users should be able to create accounts, log in, and manage their profiles. Role-based access control for different types of users (e.g., recruiters, candidates, administrators). Candidate Management: Ability to create, edit, and manage candidate profiles, including resumes, contact details, and application history. Support …

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Simulation of Welding Process Gaming Project in C++

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#include <iostream>
#include <vector>

// Constants
const double INITIAL_TEMPERATURE = 20.0; // Initial temperature in degrees Celsius
const double WELDING_TEMPERATURE = 1500.0; // Temperature during welding in degrees Celsius
const double COOLING_RATE = 0.05; // Cooling rate per second
const double WELDING_DURATION = 5.0; // Duration of welding in seconds
const double TIME_STEP = 0.1; // Time step for the simulation in seconds

// Function to simulate the welding process
std::vector<double> simulateWelding(double weldingTemperature, double weldingDuration, double coolingRate, double timeStep) {
    std::vector<double> temperatures;
    double currentTemperature = INITIAL_TEMPERATURE;
    double timeElapsed = 0.0;

    // Heating phase
    while (timeElapsed < weldingDuration) {
        currentTemperature = weldingTemperature;
        temperatures.push_back(currentTemperature);
        timeElapsed += timeStep;
    }

    // Cooling phase
    while (currentTemperature > INITIAL_TEMPERATURE) {
        currentTemperature -= coolingRate * timeStep;
        if (currentTemperature < INITIAL_TEMPERATURE) {
            currentTemperature = INITIAL_TEMPERATURE;
        }
        temperatures.push_back(currentTemperature);
    }

    return temperatures;
}

int main() {
    std::vector<double> temperatures = simulateWelding(WELDING_TEMPERATURE, WELDING_DURATION, COOLING_RATE, TIME_STEP);

    std::cout << "Time (s)  Temperature (°C)\n";
    double timeElapsed = 0.0;

    for (double temp : temperatures) {
        std::cout << timeElapsed << "        " << temp << "\n";
        timeElapsed += TIME_STEP;
    }

    return 0;
}

#include <iostream>

#include <vector>

// Constants

const double INITIAL_TEMPERATURE = 20.0; // Initial temperature in degrees Celsius

const double WELDING_TEMPERATURE = 1500.0; // Temperature during welding in degrees Celsius

const double COOLING_RATE = 0.05; // Cooling rate per second

const double WELDING_DURATION = 5.0; // Duration of welding in seconds

const double TIME_STEP = 0.1; // Time step for the simulation in seconds

// Function to simulate the welding process

std::vector<double> simulateWelding(double weldingTemperature, double weldingDuration, double coolingRate, double timeStep) {

std::vector<double> temperatures;

double currentTemperature = INITIAL_TEMPERATURE;

double timeElapsed = 0.0;

// Heating phase

while (timeElapsed < weldingDuration) {

currentTemperature = weldingTemperature;

temperatures.push_back(currentTemperature);

timeElapsed += timeStep;

}

// Cooling phase

while (currentTemperature > INITIAL_TEMPERATURE) {

currentTemperature -= coolingRate * timeStep;

if (currentTemperature < INITIAL_TEMPERATURE) {

currentTemperature = INITIAL_TEMPERATURE;

}

temperatures.push_back(currentTemperature);

}

return temperatures;

}

int main() {

std::vector<double> temperatures = simulateWelding(WELDING_TEMPERATURE, WELDING_DURATION, COOLING_RATE, TIME_STEP);

std::cout << "Time (s) Temperature (°C)\n";

double timeElapsed = 0.0;

for (double temp : temperatures) {

std::cout << timeElapsed << " " << temp << "\n";

timeElapsed += TIME_STEP;

}

return 0;

}

Explanation Constants: INITIAL_TEMPERATURE: The initial temperature before welding starts. WELDING_TEMPERATURE: The temperature reached during the welding process. COOLING_RATE: The rate at which the temperature decreases per second after welding stops. WELDING_DURATION: The duration of the welding process. TIME_STEP: The time increment for each step of the simulation. simulateWelding Function: Simulates the welding process, including …

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Functional requirements of Recipe Management System with non-functional

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Functional Requirements for a Recipe Management System User Authentication and Authorization: Users should be able to create accounts, log in, and manage their profiles. Role-based access control for different user types (e.g., regular users, chefs, administrators). Recipe Creation and Management: Users should be able to create, edit, and delete recipes. Support for adding detailed information …

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Simulation of Wireless Communication Gaming Project in C++

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#include <iostream>
#include <random>

// Function to generate a random noise value
double generateNoise(double mean, double stddev) {
    static std::default_random_engine generator;
    std::normal_distribution<double> distribution(mean, stddev);
    return distribution(generator);
}

// Function to simulate signal transmission and reception
double simulateCommunication(double signalStrength, double noiseMean, double noiseStddev) {
    // Generate noise
    double noise = generateNoise(noiseMean, noiseStddev);
    
    // Simulate received signal (signal + noise)
    double receivedSignal = signalStrength + noise;
    
    return receivedSignal;
}

int main() {
    double signalStrength = 100.0;   // Signal strength in dB
    double noiseMean = 0.0;          // Mean of the noise distribution
    double noiseStddev = 10.0;       // Standard deviation of the noise distribution
    
    int numSimulations = 10;         // Number of simulations to run
    
    std::cout << "Simulating wireless communication:\n";
    
    for (int i = 0; i < numSimulations; ++i) {
        double receivedSignal = simulateCommunication(signalStrength, noiseMean, noiseStddev);
        std::cout << "Simulation " << (i + 1) << ": Received Signal = " << receivedSignal << " dB\n";
    }

    return 0;
}

#include <iostream>

#include <random>

// Function to generate a random noise value

double generateNoise(double mean, double stddev) {

static std::default_random_engine generator;

std::normal_distribution<double> distribution(mean, stddev);

return distribution(generator);

}

// Function to simulate signal transmission and reception

double simulateCommunication(double signalStrength, double noiseMean, double noiseStddev) {

// Generate noise

double noise = generateNoise(noiseMean, noiseStddev);

// Simulate received signal (signal + noise)

double receivedSignal = signalStrength + noise;

return receivedSignal;

}

int main() {

double signalStrength = 100.0; // Signal strength in dB

double noiseMean = 0.0; // Mean of the noise distribution

double noiseStddev = 10.0; // Standard deviation of the noise distribution

int numSimulations = 10; // Number of simulations to run

std::cout << "Simulating wireless communication:\n";

for (int i = 0; i < numSimulations; ++i) {

double receivedSignal = simulateCommunication(signalStrength, noiseMean, noiseStddev);

std::cout << "Simulation " << (i + 1) << ": Received Signal = " << receivedSignal << " dB\n";

}

return 0;

}

Explanation generateNoise Function: Generates a random noise value using a normal distribution. The std::normal_distribution class from the C++ Standard Library is used to generate noise values with a specified mean and standard deviation. simulateCommunication Function: Simulates the wireless communication by adding noise to the transmitted signal. The received signal is calculated by adding the …

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Functional requirements of Real-time Weather Monitoring System with non-functional

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Functional Requirements for a Real-time Weather Monitoring System Data Collection: Continuous collection of weather data from various sources, including weather stations, satellites, and sensors (e.g., temperature, humidity, wind speed, precipitation). Support for integration with external APIs to fetch real-time weather data. Data Aggregation and Processing: Real-time aggregation of data from multiple sources. Processing of raw …

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Simulation of Ecosystem Dynamics Gaming Project in C++

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#include <iostream>

// Structure to represent the state of the ecosystem
struct EcosystemState {
    double prey_population;     // Population of prey
    double predator_population; // Population of predators
};

// Function to update the ecosystem state based on the Lotka-Volterra equations
void updateEcosystemState(EcosystemState& state, double alpha, double beta, double delta, double gamma, double dt) {
    // Compute the changes in population
    double dPrey = (alpha * state.prey_population - beta * state.prey_population * state.predator_population) * dt;
    double dPredator = (delta * state.prey_population * state.predator_population - gamma * state.predator_population) * dt;
    
    // Update populations
    state.prey_population += dPrey;
    state.predator_population += dPredator;
}

int main() {
    // Initial conditions
    EcosystemState ecosystem = {40.0, 9.0};  // Initial populations: 40 prey, 9 predators

    // Parameters for the Lotka-Volterra model
    double alpha = 0.1;  // Prey birth rate
    double beta = 0.02;  // Predation rate
    double delta = 0.01; // Predator reproduction rate
    double gamma = 0.1;  // Predator death rate

    double dt = 0.1;  // Time step (in arbitrary time units)
    int steps = 100;  // Number of simulation steps

    // Simulate the ecosystem dynamics
    for (int i = 0; i < steps; ++i) {
        updateEcosystemState(ecosystem, alpha, beta, delta, gamma, dt);

        // Print the current state of the ecosystem
        std::cout << "Time: " << i * dt << ", "
                  << "Prey Population: " << ecosystem.prey_population << ", "
                  << "Predator Population: " << ecosystem.predator_population << "\n";
    }

    return 0;
}

#include <iostream>

// Structure to represent the state of the ecosystem

struct EcosystemState {

double prey_population; // Population of prey

double predator_population; // Population of predators

};

// Function to update the ecosystem state based on the Lotka-Volterra equations

void updateEcosystemState(EcosystemState& state, double alpha, double beta, double delta, double gamma, double dt) {

// Compute the changes in population

double dPrey = (alpha * state.prey_population - beta * state.prey_population * state.predator_population) * dt;

double dPredator = (delta * state.prey_population * state.predator_population - gamma * state.predator_population) * dt;

// Update populations

state.prey_population += dPrey;

state.predator_population += dPredator;

}

int main() {

// Initial conditions

EcosystemState ecosystem = {40.0, 9.0}; // Initial populations: 40 prey, 9 predators

// Parameters for the Lotka-Volterra model

double alpha = 0.1; // Prey birth rate

double beta = 0.02; // Predation rate

double delta = 0.01; // Predator reproduction rate

double gamma = 0.1; // Predator death rate

double dt = 0.1; // Time step (in arbitrary time units)

int steps = 100; // Number of simulation steps

// Simulate the ecosystem dynamics

for (int i = 0; i < steps; ++i) {

updateEcosystemState(ecosystem, alpha, beta, delta, gamma, dt);

// Print the current state of the ecosystem

std::cout << "Time: " << i * dt << ", "

<< "Prey Population: " << ecosystem.prey_population << ", "

<< "Predator Population: " << ecosystem.predator_population << "\n";

}

return 0;

}

Explanation EcosystemState Structure: This structure holds the state of the ecosystem, including the populations of prey and predators. Main Function: The ecosystem is initialized with 40 prey and 9 predators. The program simulates the ecosystem for 100 steps, with each step representing an arbitrary time unit. The state of the ecosystem is printed after …

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Functional requirements of Real Estate Agent Portal with non-functional

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Functional Requirements for a Real Estate Agent Portal User Authentication and Authorization: Agents, buyers, and sellers should be able to register and log in. Role-based access control should be implemented, with different permissions for agents, buyers, and sellers. Property Listings Management: Agents can add, edit, or remove property listings. Sellers can submit properties for listing …

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Simulation of Drone Flight Gaming Project in C++

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#include <iostream>
#include <cmath>

// Structure to represent the drone's state
struct DroneState {
    double x;  // Position in the x-axis
    double y;  // Position in the y-axis
    double vx; // Velocity in the x-axis
    double vy; // Velocity in the y-axis
    double ax; // Acceleration in the x-axis
    double ay; // Acceleration in the y-axis
};

// Function to update the drone's state based on acceleration and time step
void updateDroneState(DroneState& state, double dt) {
    // Update velocity based on acceleration
    state.vx += state.ax * dt;
    state.vy += state.ay * dt;
    
    // Update position based on velocity
    state.x += state.vx * dt;
    state.y += state.vy * dt;
}

int main() {
    // Initial conditions
    DroneState drone = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};

    double dt = 0.1;  // Time step (seconds)
    int steps = 100;  // Number of simulation steps

    // Simulate the drone flight
    for (int i = 0; i < steps; ++i) {
        // Example acceleration input: hovering with slight forward acceleration
        if (i < 20) {
            drone.ax = 0.0;
            drone.ay = 0.1;  // Small upward acceleration
        } else if (i < 50) {
            drone.ax = 0.1;  // Forward acceleration
            drone.ay = 0.0;
        } else {
            drone.ax = 0.0;
            drone.ay = 0.0;  // No acceleration (coasting)
        }

        // Update drone state
        updateDroneState(drone, dt);

        // Print the current state of the drone
        std::cout << "Time: " << i * dt << " s, "
                  << "Position: (" << drone.x << ", " << drone.y << "), "
                  << "Velocity: (" << drone.vx << ", " << drone.vy << ")\n";
    }

    return 0;
}

#include <iostream>

#include <cmath>

// Structure to represent the drone's state

struct DroneState {

double x; // Position in the x-axis

double y; // Position in the y-axis

double vx; // Velocity in the x-axis

double vy; // Velocity in the y-axis

double ax; // Acceleration in the x-axis

double ay; // Acceleration in the y-axis

};

// Function to update the drone's state based on acceleration and time step

void updateDroneState(DroneState& state, double dt) {

// Update velocity based on acceleration

state.vx += state.ax * dt;

state.vy += state.ay * dt;

// Update position based on velocity

state.x += state.vx * dt;

state.y += state.vy * dt;

}

int main() {

// Initial conditions

DroneState drone = {0.0, 0.0, 0.0, 0.0, 0.0, 0.0};

double dt = 0.1; // Time step (seconds)

int steps = 100; // Number of simulation steps

// Simulate the drone flight

for (int i = 0; i < steps; ++i) {

// Example acceleration input: hovering with slight forward acceleration

if (i < 20) {

drone.ax = 0.0;

drone.ay = 0.1; // Small upward acceleration

} else if (i < 50) {

drone.ax = 0.1; // Forward acceleration

drone.ay = 0.0;

} else {

drone.ax = 0.0;

drone.ay = 0.0; // No acceleration (coasting)

}

// Update drone state

updateDroneState(drone, dt);

// Print the current state of the drone

std::cout << "Time: " << i * dt << " s, "

<< "Position: (" << drone.x << ", " << drone.y << "), "

<< "Velocity: (" << drone.vx << ", " << drone.vy << ")\n";

}

return 0;

}

Explanation DroneState Structure: This structure holds the state of the drone, including its position (x, y), velocity (vx, vy), and acceleration (ax, ay) in a 2D space. updateDroneState Function: This function updates the drone’s velocity and position based on the current acceleration and the time step (dt). The velocity is updated using the equation …

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Simulation of Double Pendulum Gaming Project in C++

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#include <iostream>
#include <cmath>
#include <vector>

// Constants
const double PI = 3.141592653589793;
const double G = 9.81;  // Acceleration due to gravity (m/s^2)
const double DT = 0.01; // Time step for the simulation (s)

// Structure to hold the state of the pendulum
struct PendulumState {
    double theta1; // Angle of the first pendulum (radians)
    double theta2; // Angle of the second pendulum (radians)
    double omega1; // Angular velocity of the first pendulum (rad/s)
    double omega2; // Angular velocity of the second pendulum (rad/s)
};

// Function to compute the derivatives for the double pendulum
PendulumState computeDerivatives(const PendulumState& state, double m1, double m2, double l1, double l2) {
    PendulumState derivatives;

    double delta = state.theta2 - state.theta1;
    double den1 = (m1 + m2) * l1 - m2 * l1 * std::cos(delta) * std::cos(delta);
    double den2 = (l2 / l1) * den1;

    derivatives.omega1 = ((m2 * l1 * state.omega1 * state.omega1 * std::sin(delta) * std::cos(delta) +
                           m2 * G * std::sin(state.theta2) * std::cos(delta) +
                           m2 * l2 * state.omega2 * state.omega2 * std::sin(delta) -
                           (m1 + m2) * G * std::sin(state.theta1)) / den1);

    derivatives.omega2 = ((-m2 * l2 * state.omega2 * state.omega2 * std::sin(delta) * std::cos(delta) +
                           (m1 + m2) * G * std::sin(state.theta1) * std::cos(delta) -
                           (m1 + m2) * l1 * state.omega1 * state.omega1 * std::sin(delta) -
                           (m1 + m2) * G * std::sin(state.theta2)) / den2);

    derivatives.theta1 = state.omega1;
    derivatives.theta2 = state.omega2;

    return derivatives;
}

// Function to update the state using Euler's method
void updateState(PendulumState& state, const PendulumState& derivatives) {
    state.theta1 += derivatives.theta1 * DT;
    state.theta2 += derivatives.theta2 * DT;
    state.omega1 += derivatives.omega1 * DT;
    state.omega2 += derivatives.omega2 * DT;
}

int main() {
    // Initial conditions
    PendulumState state;
    state.theta1 = PI / 2;  // 90 degrees
    state.theta2 = PI / 4;  // 45 degrees
    state.omega1 = 0.0;
    state.omega2 = 0.0;

    double m1 = 1.0;  // Mass of the first pendulum (kg)
    double m2 = 1.0;  // Mass of the second pendulum (kg)
    double l1 = 1.0;  // Length of the first pendulum (m)
    double l2 = 1.0;  // Length of the second pendulum (m)

    for (int i = 0; i < 1000; ++i) {
        PendulumState derivatives = computeDerivatives(state, m1, m2, l1, l2);
        updateState(state, derivatives);

        std::cout << "Time: " << i * DT << " s, "
                  << "Theta1: " << state.theta1 << " rad, "
                  << "Theta2: " << state.theta2 << " rad\n";
    }

    return 0;
}

#include <iostream>

#include <cmath>

#include <vector>

// Constants

const double PI = 3.141592653589793;

const double G = 9.81; // Acceleration due to gravity (m/s^2)

const double DT = 0.01; // Time step for the simulation (s)

// Structure to hold the state of the pendulum

struct PendulumState {

double theta1; // Angle of the first pendulum (radians)

double theta2; // Angle of the second pendulum (radians)

double omega1; // Angular velocity of the first pendulum (rad/s)

double omega2; // Angular velocity of the second pendulum (rad/s)

};

// Function to compute the derivatives for the double pendulum

PendulumState computeDerivatives(const PendulumState& state, double m1, double m2, double l1, double l2) {

PendulumState derivatives;

double delta = state.theta2 - state.theta1;

double den1 = (m1 + m2) * l1 - m2 * l1 * std::cos(delta) * std::cos(delta);

double den2 = (l2 / l1) * den1;

derivatives.omega1 = ((m2 * l1 * state.omega1 * state.omega1 * std::sin(delta) * std::cos(delta) +

m2 * G * std::sin(state.theta2) * std::cos(delta) +

m2 * l2 * state.omega2 * state.omega2 * std::sin(delta) -

(m1 + m2) * G * std::sin(state.theta1)) / den1);

derivatives.omega2 = ((-m2 * l2 * state.omega2 * state.omega2 * std::sin(delta) * std::cos(delta) +

(m1 + m2) * G * std::sin(state.theta1) * std::cos(delta) -

(m1 + m2) * l1 * state.omega1 * state.omega1 * std::sin(delta) -

(m1 + m2) * G * std::sin(state.theta2)) / den2);

derivatives.theta1 = state.omega1;

derivatives.theta2 = state.omega2;

return derivatives;

}

// Function to update the state using Euler's method

void updateState(PendulumState& state, const PendulumState& derivatives) {

state.theta1 += derivatives.theta1 * DT;

state.theta2 += derivatives.theta2 * DT;

state.omega1 += derivatives.omega1 * DT;

state.omega2 += derivatives.omega2 * DT;

}

int main() {

// Initial conditions

PendulumState state;

state.theta1 = PI / 2; // 90 degrees

state.theta2 = PI / 4; // 45 degrees

state.omega1 = 0.0;

state.omega2 = 0.0;

double m1 = 1.0; // Mass of the first pendulum (kg)

double m2 = 1.0; // Mass of the second pendulum (kg)

double l1 = 1.0; // Length of the first pendulum (m)

double l2 = 1.0; // Length of the second pendulum (m)

for (int i = 0; i < 1000; ++i) {

PendulumState derivatives = computeDerivatives(state, m1, m2, l1, l2);

updateState(state, derivatives);

std::cout << "Time: " << i * DT << " s, "

<< "Theta1: " << state.theta1 << " rad, "

<< "Theta2: " << state.theta2 << " rad\n";

}

return 0;

}

Explanation Constants: PI, G, and DT are constants representing the value of π, the gravitational constant, and the time step for the simulation, respectively. PendulumState Structure: This structure holds the state of the double pendulum, including the angles (theta1, theta2) and angular velocities (omega1, omega2) of the two pendulums. computeDerivatives Function: This function calculates …

Simulation of Double Pendulum Gaming Project in C++ Read More »