LTO (Landing–Take-off) Emission Modeling using Python

Customer: AI | Published: 28.11.2025

3. LTO (Landing–Take-off) Emission Modeling Title: LTO Cycle Emission Prediction Models for Commercial Aircraft Using ICAO Standard Data Focus: • Derive emission factors for each LTO phase • Fuel burn modeling Output: LTO emission calculator or nomograms. 2. ENGINE + LTO RELATIONSHIP GRAPHS (Very useful for modelling papers) 2.1 Scatter Plots • Fuel flow vs NOx (per mode) • Fuel flow vs CO₂ • Thrust setting vs emission index • Engine pressure ratio vs LTO NOx 2.2 Regression / Trendline Plots • NOx = f(fuel flow) for each mode • CO₂ = f(thrust rating) • Taxi time vs emission buildup 2.3 Multi-Scatter Plots • For comparing phases in a single figure: o Idle vs Takeoff o Climb-out vs Approach o Plot emissions against fuel flow 3. LTO FUEL BURN & TIME-IN-MODE MODELING GRAPHS 3.1 LTO Fuel Burn Maps • Fuel burn vs aircraft weight • Fuel burn vs engine rating • Fuel burn per phase shown as heatmaps or contour plots 3.2 Time-in-Mode Sensitivity Graphs • Vary taxi time (5, 10, 15, 20 min) vs NOx increase • Vary approach time vs HC and CO 3.3 Mode-Specific Curves • Thrust setting vs NOx factor • Thrust vs fuel flow • Mode time vs cumulative emissions 4. MODEL VALIDATION & CROSS-COMPARISON GRAPHS 4.1 Predicted vs Actual Comparison • Scatter plot with 1:1 line • Evaluate prediction accuracy 4.2 Error Distribution Plots • Histogram of prediction error • Error vs thrust • Error vs fuel flow 4.3 Confidence Band Plots • Regression with ±95% prediction bands • Ideal for modelling papers 5. ADVANCED MODELLING & RESEARCH-GRADE GRAPHS 5.1 Emission Surface / 3D Plots • (a) Thrust × Fuel Flow × Emission factor • (b) Weight × Taxi Time × NOx • Very useful for generating nomograms 5.2 Heatmaps • Phase-wise emission intensities • Correlation matrix: o Fuel flow o Emission index o Engine pressure ratio o Thrust level o LTO total emissions 5.3 PCA / Dimensionality Reduction • Identify main factors affecting LTO emissions • Cluster similar LTO patterns of different engines 5.4 K-Means Clustering Groups based on LTO behaviour: • Efficient engines • High-NOx engines • High fuel burn engines 6. NOMOGRAMS (Unique for this paper) Nomograms visually estimate LTO emissions by drawing straight lines across the axes. Nomogram Types: 6.1 Fuel Flow–Emission Index–Emissions Nomogram Axes: • Fuel flow • EINOx / EICO / EIHC • Emissions per minute 6.2 Thrust Setting–Fuel Flow–Time Nomogram Used to quickly estimate emissions under different loads. 6.3 Taxi Time Variation Nomogram Predict: • NOx • CO₂ • CO for different taxi durations. 6.4 Total LTO Cycle Emission Nomogram Three axes: • Engine thrust • Total LTO time • Total emissions 7. LTO EMISSION CALCULATOR PLOTS 7.1 Calculator Flowchart (Schematic) • Input: Aircraft + Engine + Taxi time • Output: Total NOx, CO₂, HC, CO 7.2 Lookup Tables (Graphically) • LTO emission lookup matrix • Taxi-time compensation curves • Fuel flow–emission lookup table as graph Tools Required (Best to Acceptable) 1. Python • matplotlib → All standard plots • seaborn → Heatmaps, statistical plots • plotly → 3D surface & interactive plots • scikit-learn → PCA / clustering • numpy/pandas → Model fitting and dataset processing