本研究以呼吸器CMV通氣模式下之通氣氣流為指標，利用最佳呼吸控制模式與呼吸機械與化學耗能最小化過程，模擬出最佳化之流速形態與通氣設定。使用容積控制通氣下常用之正弦波、方波、漸減波三種波形，進行數學模式化。並應用最佳呼吸控制模式，將模式化之流速形態，取代原模式中神經－機械反應器之肌神經驅動訊號，作為呼吸耗能最小化中的控制訊號，進一步產生最佳化之流速形態與尖峰氣流量、潮氣容積、呼吸頻率、尖峰壓力、吸氣末最大容積、每分鐘通氣量、肺泡通氣量、吸／吐氣時間等通氣設定。在不同呼吸環境變因與呼吸生理模式下，通氣流速形態的最佳化波形，可經由MATLAB為平台的模擬器即時顯示。在吸入二氧化碳與運動狀態下，檢視每分鐘通氣量分別與動脈中二氧化碳分壓、吸氣工作週期、與潮氣容積之間的通氣響應；也可經由呼吸道阻抗及肺順應性改變的呼吸機械特性負載模擬，觀察最佳流速形態的變化。本研究利用人體最佳呼吸控制模式中呼吸耗能最小化的概念，推導並模擬出最佳化的正弦波、方波、漸減波氣流波形與呼吸器通氣設定。透過Hypercapnia、Eucapnia、及呼吸機械特性改變的模擬實驗，系統模式與通氣行為已獲進一步之分析驗證，研究結果可作為呼吸治療與智慧型呼吸器臨床設計實務之參考。

In this study, the airflow patterns and ventilation settings are optimized with minimum effort of breathing under Continuous Mandatory Ventilation. Rather than optimizing the respiratory neural drive in the previous research, three commonly used airflow patterns, sinusoidal, square, and descending waves, of volume-controlled-ventilation are modeled into control variables and optimized through the optimal respiratory control model in conjunction with a lumped-parameter RC model for the mechanical respiratory system. Along with the optimal airflows, the ventilation settings and other respiratory patterns are also optimized. Simulations are used to perform mimic hypercapnia response, eucapnia response, and possible changes in respiratory mechanics, including continuous resistive and elastic loading. The effects of inhaled CO2, muscular exercise, and respiratory mechanic loadings on the optimal airflows and ventilation responses are also simulated and observed through the MATLAB simulator. System behavior and ventilation responses are further verified and compared with experimental results published earlier. The data and conclusion of this study demonstrate the potential for tailoring optimal airflow and ventilator settings specifically to meet the physiological needs of patients.