Calculation Of Fuel Consumption Research Articles

Conceptual design and optimization of a hydrogen-powered aircraft with a dorsal tank layout

Abstract The application of hydrogen as a clean energy source is revolutionizing the aerospace industry, especially in the design and optimization of next-generation aircraft. In this study, a tubular-wing hydrogen-powered aircraft is designed based on a modified Boeing 737 configuration with a dorsal fuselage tank layout. During the design process, the fuselage was parametrically reconstructed to accommodate tanks of different sizes; Subsequently, a multidisciplinary optimization of the hydrogen aircraft was carried out with fuel consumption as the optimization objective. During the optimization process, dynamic mission profiles were introduced to make the fuel consumption calculation more accurate. Finally, this paper compares and analyzes the differences between kerosene-powered and hydrogen-powered aircraft. The results show that the hydrogen airplane has an increase in weight of about 10% and a decrease in lift-to-drag ratio of 1 to 2 units. To offset these effects, a hydrogen airplane would need to be designed with a larger aspect ratio and provide more thrust during flight. Nonetheless, the results also show that, because of the high calorific value of liquid hydrogen, the total energy consumption of the designed hydrogen aircraft is lower than that of a kerosene aircraft for the same range.

Prediction of aircraft fuel consumption based on machine learning algorithm

Abstract The shortcomings of traditional aircraft fuel consumption calculation methods include insufficient theoretical analysis, complex input parameters, and poor calculation accuracy. Through the analysis of aircraft motion equations and engine working characteristics, this paper screens out the key parameters affecting aircraft fuel consumption. Based on the real flight data, three machine learning methods, BP neural network, RBF neural network, and Support Vector Machine(SVM), are used for training, and a fuel consumption prediction model is established with key parameters as inputs. The prediction accuracy and characteristics of the three models are analyzed and compared. The aircraft fuel consumption prediction model achieves the operational requirements, and the accuracy in the flight profile reaches 5%. The trained model was applied to the planning of the transport aircraft mission profile, achieving a fuel consumption error of only 1.1% across the entire mission profile, resulting in a relatively accurate outcome.

Runway assignment optimisation model for Istanbul Airport considering multiple parallel runway operations

Abstract The aviation industry has rapidly developed in recent years. Due to the increased number of flight operations, managing air traffic has become essential. The air traffic management system aims to reduce the air traffic control workload and use existing resources more efficiently. This study proposed a new mixed integer linear programming model to minimise the total fuel consumption during taxi operations for the runway assignment problem, comparing the actual Istanbul Airport runway assignment data. The average taxi times are calculated using the 30,000-flight operations data for each arrival and departure taxi route. Also, 47 different aircraft types are obtained using the data for the fuel consumption calculation. The International Civil. Aviation Organisation (IACO) aircraft engine emissions databank provides the fuel consumption values for each aircraft according to engine type. This approach allows our model to calculate more realistic fuel consumption for taxi operations, as each aircraft engine type has a different fuel consumption value. The proposed model is implemented at Istanbul Airport, the busiest airport in Turkey, where multiple parallel runway operations are applied. The results showed that the proposed model reduced total fuel consumption for taxi operations between 6.6% and 14.4% compared to the actual Istanbul Airport runway assignment data.

Comparing modelled and measured exhaust gas components from two LNG-powered ships

Bottom-up modelling is used frequently to estimate emissions produced by seagoing vessels, and the accuracy of modelling is dependent on the data the model is trained with. Observational studies can be used to increase the model accuracy. Here we compared data from two measuring campaigns conducted on board ships that use Liquefied Natural Gas (LNG) as primary fuel in internal combustion engines (ICE) in a diesel-electric setup with values obtained from the Ship Traffic Emission Assessment Model (STEAM).The power demand for propulsion calculated using Automatic Identification System (AIS) data matched observations reasonably. The root mean square error between the modelled and observed power demand was 759–914 kW (28.6–34.5%) for the measured ropax vessel and 1869–1916 kW (16.7–17.1%) for the large cruise vessel over four voyages while the ships were underway. The discrepancy is largely explained by the auxiliary power demand, which was 4 times higher on the large cruise vessel than the model prediction.Using meteorological data to estimate the increase of resistance did not improve the goodness of fit between modelled and observed engine power demand. STEAM model's base-specific fuel consumption calculation method fits observed values reasonably when the engine load is over 50%, but ICEs used in constant speed mode have increased consumption at lower engine loads compared to variable speed ICEs.The share of pilot fuel of total energy consumption was found to play a significant role in the emission factors for measured exhaust gas compounds. More accurate functions to model fuel consumption and emissions were derived using the observed data.

Research on fuel consumption calculation based on QAR and ACR data

Research on fuel consumption calculation based on QAR and ACR data

Economic and emission assessment of LNG-fuelled ships for inland waterway transportation

As the main transportation channel, the huge shipping volumes of inland rivers lead to significant emissions from ships. Inland shipping has an increasing demand for the development of green ships. Liquid natural gas (LNG) is considered to be an ideal marine fuel because of its many advantages. This paper aims to assess the economy and CO2 emission reduction of LNG-fuelled ships on inland waterway transportation, under the context that the uniqueness of inland waterway transportation, as well as cargo transportation regulations and policies are taken into account. Firstly, considering the preferential policies for LNG-fuelled ships and the mandatory use of shore power for oil-fuelled ships, we proposed a fuel consumption calculation formula and established economic and emission assessment models for inland ships. Then, we examined these models with simulation examples of three main cargo types, i.e. dry bulk, containers and dangerous goods, transported on the Yangtze River. The optimal ranges of LNG-fuelled ship size were given under various credible scenarios designed according to the transportation systems. The results show: (1) The optimal range of dead-weight tonnage (DWT) of LNG-fuelled ships for dry bulk, container and dangerous goods are between 3,000–5,000 DWT, 3,000–4,000 DWT and 2,000–3,000 DWT, respectively. (2) LNG-fuelled dry bulk ships of about 10,000 DWT or more are uneconomical compared to oil-fuelled ships of the same tonnage. (3) Smaller LNG-fuelled ships, such as dry bulk ships of less than 1,500 DWT, container and dangerous goods ships of less than 2,000 DWT, or ships serving longer routes of approximately more than 1,000 km have most significant unit CO2 emission reduction than oil-fuelled ships of the same tonnage. (4) Excessive waiting time at locks will reduce the economic feasibility and unit CO2 emission reduction of LNG-fuelled ships. Finally, we obtained a series of important management significances by simulating the changes of some variable parameters in real situations. These conclusions can provide some theoretical foundations for the development of LNG-fuelled or other green ships in inland waterways, and guide the government in formulating policies to promote the green transformation of the inland shipping industry.

Slagging characteristics in the superheater area and K/Ca distribution of biomass-fired grate boiler

The slagging on heating surfaces severely impedes the biomass combustion in grate boiler. Aiming at this situation, the slagging characteristics of a grate boiler firing straw/woody biomass has been investigated. By field sampling of deposition, the slagging mechanisms including gaseous K/Ca condensation, molten ash adhesion and viscous capture of ash particles have been clarified. The severity of slagging is ordered as: mid-temperature superheater > high-temperature superheater > low-temperature superheater > upper furnace. Ca plays a key role in initial layer formation in high-temperature superheater through condensation of gaseous sulfates. In mid-temperature superheater initial layer formed by gaseous KCl condensation and the viscous capture of ash particles lead to serious slagging. The distribution of K/Ca in the boiler during combustion were obtained based on ash collecting and characterization, counter-balance efficiency calculation and fuel consumption calculation. 44.6% of K and 35.8% of Ca contained in the biomass fuel deposited on the heating surfaces inside the boiler, indicating that more K and Ca tend to deposit in the boiler rather than remain in the discharged ash, which also requires more attention to Ca-related slagging. This study can provide basis for slagging predicting and controlling in grate boiler using high K/Ca biomass fuel.

Real-time vehicular fuel consumption estimation using machine learning and on-board diagnostics data

Instantaneous fuel consumption estimation of fleet vehicles provides essential tools for fleet operation optimization and intelligent fleet management. This study aims to develop practical and accurate models to estimate instantaneous fuel consumption based on on-board diagnostics (OBD) data. Fuel consumption data is measured by a high-precision fuel flow meter. Two machine learning algorithms of Random Forest (RF) and Artificial Neural Networks (ANN) are trained with real-world urban and highway driving data of four fleet vehicles with different types and powertrain systems. In addition, the cold-start period of the vehicle operation is included to cover the fuel consumption penalty in the warm-up period. The validation results show that the RF method is more accurate than the ANN method, and both of the machine learning models have a better accuracy compared to the existing fuel consumption calculation methods based on the engine control unit (ECU) parameters.

Mitigating stochastic uncertainty from weather routing for ships with wind propulsion

Reducing the shipping sector's contribution to climate change requires urgent emission reductions this decade. Both weather routing and wind propulsion offer immediate solutions, where combining sails with efficient routing amplifies the performance of each technology. However, while large emission savings are theoretically available, the impact of stochastic uncertainty from wind forecasts is unknown. Here, we present a novel approach that exploits fuel consumption calculations from over a thousand departures across three routes to characterise stochastic uncertainty. We show that routes with ideal wind conditions and long voyage times are most sensitive to uncertain forecast inputs, reducing savings from Flettner rotors and weather routing by up to 44% when a priori weather routing strategies are used. This paper further shows how an adaptive weather routing strategy can be used as an accurate prediction tool to reduce uncertainty on all routes investigated, reliably amplifying carbon savings from Flettner rotors by between 1.16 and 2.48 times typical great circle route savings. Overall, this paper provides greater assurance around the previously estimated carbon savings that serves to strengthen confidence in a wind-assisted decarbonisation strategy and its potential to provide essential emission reductions this decade.

Calculation of Fuel and Energy Resource Consumption during Building Demolition

Calculation of Fuel and Energy Resource Consumption during Building Demolition

Aerodynamic and Structural Aspects of a Distributed Propulsion System for Commuter Airplane

In this paper, an aerodynamic and structural computation framework was produced to develop a more efficient aircraft configuration considering a wing with a distributed electric propulsion and its use in different flight missions. For that reason, a model of a regional airplane was used as a case study. The considered model was a nine-seat light airplane with a cruise speed of 500 km/h at an altitude 9000 m. The design of the distributed system is introduced, then the aerodynamic and structural aspects of the new wing with distributed electric propulsion system are calculated, and finally flight performances are calculated for the purpose of analysis of the DEP effect. The design of the DEP system aimed at meeting the required landing conditions and the masses of its components, such as the electric motors, the control units and the power source of the DEP system were estimated. Aerodynamic calculations included computations of different wing aspect ratios. These calculations take into account the drag of the existing airplane parts such as fuselage and tail surfaces. A modified lifting-line theory was used as a computational tool for the preliminary study. It was used to calculate the wing drag in cruise regime and to determine the distribution of aerodynamic forces and moments. Next, based on aerodynamic calculations and flight envelope, the basic skeletal parts of the wing were designed and the weight of the wing was calculated. Finally, fuel consumption calculations for different wing sizes were made and compared with the original design. The results show that a wing with a 35% reduction in area can reduce fuel consumption by more than 6% while keeping the same overall weight of the aircraft.

Energy recovery potential by replacing the exhaust gases recirculation valve with an additional turbocharger in a heavy-duty engine

A simulation-based assessment of different twin-turbocharging configurations allowing energy recovery from the exhaust gas recirculation flow is performed in a heavy-duty engine from the basis of a single-stage turbocharging architecture. This baseline configuration consists of a single stage turbocharged engine with a low-pressure exhaust gas recirculation system. Experimental data from the baseline engine tests are used to calibrate the model. The novelty of this study is to evaluate the energy recovery potential from the low-pressure exhaust gas recirculation flow by replacing its control valve with a turbine so that the flow energy is not lost due to the expansion across the valve. This additional turbine is coupled to a compressor placed in the intake line so that the turbocharging system is converted into a twin-turbocharging architecture. Different twin-turbocharging configurations are tested to attain maximum exhaust gas recirculation rates at 1100, 1500, and 2200 rpm engine speeds with boost pressures ranging from 1.8 to 2.6 bar. Fuel consumption calculations have been performed at constant exhaust gas recirculation rates for all three speeds as well as 1500 rpm partial load operation to attain 11.5 bar of brake mean effective pressure. The results in 8 engine running conditions show that twin-turbocharging with the compressors in series configuration with the exhaust gas recirculation turbine discharging after the first stage benefits, in average, from increasing about 5% maximum exhaust gas recirculation rates and saves up to 7% in fuel economy compared to the single-stage turbocharging layout.

Perhitungan Jumlah Maksimum Flight Cycle Tanpa Refuelling pada Kondisi Full Tank dan Full Payload pada Jaringan Rute Commuter Pesawat ATR 72

Transportation is the process of moving or moving people and goods from one place to another with a specific purpose, ATR aircraft are very suitable for regional flights, so that fuel utilization must be calculated to make the transportation cost is minimum. Indonesian airlines have very big demands in world aviation competition. One of Indonesia's domestic route type is commuter, ATR aircraft is very suitable for regional flights with commuter route, flights with short range with small aircraft for carrying passengers between airports with low passenger capacity. One of commuter advantage is the aircraft didn't have to refueling in the transit airport. The purpose of this research is to determine maximum flight cycle without refueling based on weight variation using FCOM data and fuel consumption calculation using analytical methods based on ATR aircraft specification. Using an analytic method for calculating fuel consumption from FCOM data with full tank strategy, full payload and refueling strategy in every airport for Nusa Tenggara and Sumatera route. This research calculation has resulted for Nusa Tenggara route total fuel is 4750,57 kg and Sumatera 4474,88 kg strategy full tank. Calculation has result for Nusa Tenggara 2789,69 kg and Sumatera 2658,98 kg strategy full payload.Then obtained full tank strategy not as much as 12 flights cycle, full payload as much as 7 flights cycle in Nusa Tenggara route and full tank strategy as much as 10 flight cycle, full payload obtained 6 flight cycle in Sumatera route On the Sumatra route.

A green dynamic TSP with detailed road gradient dependent fuel consumption estimation

The rapid transition to the digitalization of firms directs companies to speed up their decision-making processes and make technology-based decisions. The increasing environmental concerns and fossil-fuel scarcity force companies to employ new logistical aims, such as reducing fuel consumption and greenhouse gas emission in addition to the traditional cost minimization and profit maximization objectives. Traditional assumptions in fuel consumption calculation have been left not only to reach the objectives of the company and make consistent delivery plans but also to increase the solution quality of real-life problems. From this point of view, this study contributes to the related literature by developing a decision support model for a Travelling Salesman Problem that considers dynamic customer requests and realistic road gradients of the entire road network while calculating fuel consumption from transportation operations. The applicability of the developed model has been shown with a real-life case study in which laboratory samples have been collected and transferred from local family clinics to a central laboratory. Several objective functions are employed to demonstrate the benefits of considering realistic road gradients. The results of the numerical analyses on static and dynamic instances illustrate the benefits of respecting realistic road gradients in fuel consumption and addressing dynamic requests during delivery operations.

The Correlation Between the Operating Conditions of an Oil Tanker and Fuel Consumption

This paper presents a calculation method to determine optimum operating conditions of power plants installations with internal combustion engines used on ships. The scope of this document is to make a minimum fuel consumption calculation using software Engineering Equation Solver. Analysing several operating regimes, the specific consumption of heavy fuel used on the main engine of the oil tanker is calculated. Vessel must operate at the parameters for which it was designed and built, thus satisfying all technical and economic aspects.

Application of Improved Multi-Objective Ant Colony Optimization Algorithm in Ship Weather Routing

This paper presents a novel intelligent and effective method based on an improved ant colony optimization (ACO) algorithm to solve the multi-objective ship weather routing optimization problem, considering the navigation safety, fuel consumption, and sailing time. Here the improvement of the ACO algorithm is mainly reflected in two aspects. First, to make the classical ACO algorithm more suitable for long-distance ship weather routing and plan a smoother route, the basic parameters of the algorithm are improved, and new control factors are introduced. Second, to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems, the related operations of crossover, recombination, and mutation in the genetic algorithm are introduced in the improved ACO algorithm. The final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization problems. In addition, the black-box model method was used to study the ship fuel consumption during a voyage; the model was constructed based on an artificial neural network. The parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship, and then the trained black-box model was used to predict the future fuel consumption of the test ship. Compared with other fuel consumption calculation methods, the black-box model method showed higher accuracy and applicability.

Modellbasierte Berechnung von Kraftstoffverbräuchen landwirtschaftlicher Verfahrensketten

Der Kraftstoffverbrauch landwirtschaftlicher Verfahrensketten wird von betriebsstrukturellen Gegebenheiten, der maschinentechnischen Ausstattung und der Art und Ausgestaltung der Verfahrensketten eines Betriebes bestimmt. Ein Simulationsmodell, das im Forschungsprojekt ,,Effiziente Kraftstoffnutzung der Agrartechnik‘‘ (EKoTech) entwickelt wurde, ermoglicht eine modellbasierte Berechnung der Kraftstoffverbrauche in der Pflanzenproduktion. Dabei dienen Modellbetriebe als Grundlage fur die Simulationsrechnungen. Mit ihnen werden relevante reprasentative Produktionsregionen in Deutschland und weiteren europaischen Landern fur verschiedene Verfahrensketten als virtuelles Abbild durchschnittlicher Betriebes der jeweiligen Region beschrieben. Die Ergebnisrechnungen aus dem Simulationsmodell beziffern die Entwicklung des Kraftstoffverbrauchs in den identifizierten Verfahrensketten von 1990 uber 2015 bis 2030 und quantifizieren die Einsparpotenziale ausgewahlter Technologien zur Kraftstoffverbrauchsreduktion.

Biomass bales infield aggregation logistics energy for tractors and automatic bale pickers — A simulation study

Infield bale aggregation is essential for bale removal and preparing the field for subsequent crops, which can be more efficiently performed using the modern automatic bale picker (ABP) that supports multiple bales/trip (BPT) than commonly used tractors. But the energy involved in the bale aggregation logistics using ABP has not been thoroughly evaluated. Therefore, the energy involved in the bale aggregation, in terms of fuel consumption, was studied for different logistic scenarios using a tractor (control) and ABP through a user-developed simulation program in R. Different variables such as field areas (8 to 259 ha), biomass yields (3–40 Mg ha−1), four outlet locations, and five equipment speeds (6.6–10.5 km h−1) using realistic equipment turning paths were used in the simulation. The Nebraska Tractor Test general method and fuel efficiency method were considered for the fuel consumption calculations. Fuel consumption for the ABP (8–259 ha) with 8 BPT on an average decreased by 72 % and 53 % compared to a tractor with 1 and 2 BPT, respectively, based on logistics distance and equipment operation. Field area, biomass yield, and BPT were the most influential variables affecting logistics distance; while, field area, biomass yield, BPT, and equipment speed affecting the operation time and fuel quantity. Convenient prediction models (multi-variate nonlinear) for logistics distance, operation time, and fuel quantity, using the influential field variables, produced very good fits (R2≥0.98). Overall, an ABP with a capacity of 8 BPT, split which can also handle 11 BPT, is recommended considering the logistics energy.

Development of a versatile depletion code AMAC

The reactor fuel consumption calculation is an important part of reactor design and analysis, which usually needs to be completed iteratively by transport calculations and point-depletion calculations. AMAC is a newly versatile point-depletion and radioactive-decay code for use in simulation nuclear fuel cycles and calculating the nuclide compositions and radiation characteristics of materials. The code implements two depletion algorithms to treat the stiff depletion systems, including Transmutation trajectory analysis (TTA) method and Chebyshev rational approximation method (CRAM). With different higher-order approximations, the IPF form of CRAM is implemented to meet the high-precision requirements for the decay calculation of long-term nuclide system. And the accuracy analysis of higher-order CRAM is also discussed in the paper. In addition, the calculation of material radiation characteristics can be provided in AMAC to extend the application in different computational and analytical requirements. Then based on the complex nuclide system and a series of efficient programming methods, the high precision and efficiency of point-depletion calculation can be guaranteed. Three different calculation modes are supported by AMAC to execute the decay, constant flux and constant power calculations. By comparing with ORIGEN and FISPACT, the performance of AMAC in nuclide compositions and varieties of material radiation characteristics is proved and discussed. Furthermore, by coupling OpenMC and AMAC, the OECD/NEA burnup credit criticality benchmark and IAEA-ADS benchmark are used to validate the code performance of efficiency and precision in coupling calculations. All the results have shown good agreements with reference values and other codes, which demonstrates AMAC is a potential tool for accurate depletion calculation of reactors.

Research on Ecological Driving Strategy of Urban Road Vehicles under Vehicle-road Collaborative Environment

With the increase of motor vehicles, the energy resources available for vehicles have become scarce. In view of the harmfulness of traffic emissions, energy conservation and emission reduction have become strategic goals for the development of the transportation industry. Guiding students to realize ecological driving and reducing vehicle fuel energy consumption and emissions have increasingly attracted the attention of researchers. We are supposed to do a research on signalized intersections at important nodes of urban transportation, use ecological driving to simulate the operation of bicycles and vehicle queues, establish an ecological driving model, and control energy conservation and emission reduction goals from multiple perspectives. This paper combines the ecological driving technology and micro fuel consumption calculation model under the vehicle-road collaborative environment to study the fleets within the signalized intersection. At the same time this paper proposes street signs, models, and case applications that can effectively reduce the average fuel consumption and parking times of the vehicles passing the signalized intersection to ensure the smoothness of operation at signalized intersections has been reduced, and rapid acceleration and deceleration operations have been performed. After verification, the establishment of the fleet’s eco-driving model has great effectiveness.