Numerical Modeling and Control of Supercritical Carbon Dioxide Power Cycles

Graphical Abstract

sCO₂ Brayton cycle

Overview

The doctoral research focuses on the numerical modeling and control of supercritical CO₂ Brayton cycles for power generation and waste heat recovery. I develop first-principles, component-level models integrated into closed-loop cycle simulations to study steady-state, part-load, off-design, and transient behavior. A key emphasis of my work is control-oriented model reduction to enable systematic analysis of classical control strategies and their limitations, followed by the design and implementation of advanced controllers, including Model Predictive Control, for load regulation and dynamic operation. This work contributes to the broader goal of improving the operability and commercial viability of sCO₂ power systems.

View dataset

Irregular verbs in Portugese: data
Irregular verbs in Italian: data
Irregular verbs in French: data
Irregular verbs in Spanish: data

Source of data

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Using data with Python

Start Python:

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import numpy as np
import pandas as pd

Open the file:

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file_path = 'data.csv'
with open(file_path, 'r') as file:

Read data:

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    lines = file.readlines()

Parse and process data:

Duis aute line_data irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur data.extend.

data = []
for line in lines:
    line_data = line.strip().split(',')  # Split the line into a list of values
    line_data = [float(value) for value in line_data]  # Convert values to floats
    data.extend(line_data)  # Extend the main list with values from the line

Compute summary statistics using NumPy:

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data_array = np.array(data)  # Convert the list to a NumPy array
mean = np.mean(data_array)
median = np.median(data_array)
std_dev = np.std(data_array)
min_value = np.min(data_array)
max_value = np.max(data_array)

Display summary statistics:

print(f"Mean: {mean}")
print(f"Median: {median}")
print(f"Standard Deviation: {std_dev}")
print(f"Minimum Value: {min_value}")
print(f"Maximum Value: {max_value}")

Description of simulation parameters

Parameter	Value	Language	Time period	Description
$\alpha$	$1/2$	French	1930–1954	Tempor dolor in
$\lambda$	$e/2$	French	1930–1954	Fugiat sint occaecat
$\gamma$	$\ln(3)$	Spanish	1833–1954	Duis officia deserunt
$\omega$	$10^{-4}$	Italian	1930–1994	Excepteur et dolore magna aliqua
$\sigma$	$1.5$	Portuguese	1990–2023	Lorem culpa qui
$\chi^2$	$\pi^2$	Portuguese	1990–2023	Labore et dolore

Graphical Abstract#

Overview#

View dataset#

Source of data#

Using data with Python#

Start Python:#

Open the file:#

Read data:#

Parse and process data:#

Compute summary statistics using NumPy:#

Display summary statistics:#

Description of simulation parameters#