Fuel Flow Models Research Articles

Modeling of aircraft fuel consumption using machine learning algorithms

With the aid of recording systems such as the flight data recorder, information from aircraft sensors can be transmitted in-situ to airlines or maintenance providers during the flight in form of reports, or stored for subsequent analysis in the postprocessing. This allows the determination of current aircraft performance with regard to fuel consumption or emissions. At present, data in a highly aggregated form (predominantly averages) are used for statistical estimations or physical models. The metrics are calculated on a rolling basis as performance indicators of the aircraft and are then compared with book values from the manuals or with data from the performance monitoring system. However, this procedure represents only a situational, aggregated point evaluation of stable flight conditions. The data aggregation is based on strict validity limits for parameter variations. Furthermore, trigger conditions of the recording logic determine the number and quality of the transmitted reports, such, that only a few data points are available for performance analyses. To improve realistic performance analyses, the time series of all aircraft sensors over the entire flight mission (full-flight data) can be used. In contrast to physical models, this study presents data-based approaches using machine-learning tools from the field of artificial intelligence, to develop fuel flow models based on full-flight data. The proposed methods result in detailed statements for the diagnosis of aircraft fuel consumption. The paper deals with the model development and results of different analyses based on a variety of an airline’s operational flight data records. This study describes the learning methods and shows results for two different data-based models, including neural networks and decision trees. Finally, future applications for the models and an outlook on the authors’ activities will be provided.

Aircraft initial mass estimation using Bayesian inference method

Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other topics of aircraft traffic management. However, It is a common challenge for researchers, as well as air traffic control, to access this proprietary information. Previously, several studies have proposed methods to estimate aircraft weight based on specific parts of the flight. Due to inaccurate input data or biased assumptions, this often leads to less confident or inaccurate estimations. In this paper, combined with a fuel-flow model, different aircraft initial masses are computed independently using the total energy model and reference model at first. It then adopts a Bayesian approach that uses a prior probability of aircraft mass based on empirical knowledge and computed aircraft initial masses to produce the maximum a posteriori estimation. Variation in results caused by dependent factors such as prior, thrust and wind are also studied. The method is validated using 50 test flights of a Cessna Citation II aircraft, for which measurements of the true mass were available. The validation results show a mean absolute error of 4.3% of the actual aircraft mass.

Three-dimensional modeling of fuel flow with a holistic circulating fluidized bed furnace model

To study the mixing and flow of fuel inside commercial scale circulating fluidized bed (CFB) furnaces, a new model was introduced to solve the flow of fuel in an existing semi-empirical, steady state, three-dimensional Eulerian model frame. The fuel flow model is a simplified momentum equation which is based on the momentum balance considering the fuel inertia, gravity, and drag force from gaseous and solid phase. The model improves the prediction capabilities of fuel flow patterns in the existing model frame for conventional and especially renewable fuels, such as biomass. The fuel flow model improves the analysis of CFBs and can be utilized in the design and development of commercial CFB units.

Measurement of NO and NOy emission indices during SUCCESS

Exhaust measurements in the wake vortex regime of the NASA Boeing 757 aircraft were made during the SUbsonic aircraft: Contrails and Cloud Effects Special Study. Emission indices for NO and NOy were calculated from in situ measurements taken on board the NASA DC‐8 for plumes aged 20–300 seconds. The average NO emission index is 7.5 g NO2/kg fuel for mean conditions of 37 kft altitude, 0.7 Mach and 0.34 kg/s fuel flow rate. Comparison is made between measured indices and predictions based on ground engine test data and a fuel flow model. Measurements are positively correlated but are on average 22% higher than predictions, with considerable scatter and systematic deviations in measurements made under low thrust conditions. These conditions are lower than typical in commercial cruise operation of the 757, for which the model was optimized. No statistically significant change in nitrogenous emissions is observed for an order of magnitude change in fuel sulfur content. Estimation of the NO2 from photochemical calculations implies a contribution to NOx of 5–19%. Examination of exhaust composition shows that 95% of the NOy is in the form of NOx.

Four-dimensional fuel-optimal guidance in the presence of winds

The generation of optimal trajectories from cruise altitude to 10,000 ft for a typical commercial jet transport shows that the optimal absorption of delay in arrival time requires speed reduction in conjunction with altitude excursion (the latter not attempted before). Multiple regression methods are used to develop drag and fuel flow models which are smoother and more compact than those of any previous investigation. The concept of optimal cruise descent is introduced and optimal results are compared with suboptimal strategies of constant Mach number/Calibrated Airspeed (CAS) descent and constant flight path angle descent. Finally, wind effects are analyzed, and the cost of delay as a function of wind condition is presented, followed by the effects of wind on the constant flight path angle descent.