This final project investigates the core principles of thermodynamics and statistical mechanics as they apply to energy conversion systems. By analyzing the relationship between entropy, temperature, and heat transfer, we aim to model the efficiency limits of idealized heat engines and compare them against real-world implementations.
The study is rooted in the four laws of thermodynamics. We focus specifically on the Second Law, exploring how entropy generation dictates the maximum work obtainable from a system. Through the use of the Boltzmann distribution, we bridge the gap between microscopic particle states and macroscopic thermodynamic variables such as pressure, volume, and temperature.
Our approach utilizes computational modeling to simulate various thermodynamic cycles, including the Carnot and Otto cycles. We employ numerical integration to calculate heat flux across boundaries and simulate gas expansion under adiabatic and isothermal conditions. Data is analyzed using statistical software to determine the margin of error when comparing theoretical maximum efficiency to measured outputs.
Initial data suggests that internal friction and non-reversible expansion processes contribute significantly to entropy production, thereby lowering the actual efficiency below the theoretical Carnot limit. Our findings indicate that as system complexity increases, the predictability of microscopic transitions becomes highly sensitive to initial state variables, aligning with the principles of statistical mechanics.
This project demonstrates that while thermal physics provides rigid mathematical boundaries for energy transformation, practical applications are consistently mediated by environmental variables. Understanding these constraints is essential for the advancement of sustainable energy technologies and high-efficiency thermal management systems.
