fuel consumption rate
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With the increase of the engine intensified degree, mechanical load and thermal load become to the two main factors limiting the engine to intensify. Application of Miller cycle, which can be realized by late intake valve closing (LIVC) and deeper late intake valve closing (DLIVC), has the potential to reduce the effective CR, mechanical load, and thermal load. In this paper, the effects of LIVC and DLIVC on the mechanical load and thermal load of a boosted DI diesel are experimentally compared. Compared to the original base case, the average cylinder temperature of LIVC and DLIVC is reduced by 90 and 52 K. The exhaust temperature of LIVC and DLIVC decreased by 26 and 14 K, and the maximum combustion pressure of LIVC and DLIVC decreased by 1.6 and 9.7 bar. The pumping losses of LIVC and DLIVC are reduced by more than 25%, while the actual cycle power does not decrease due to the late closing of the inlet valve. The fuel consumption rate decreased from 250.1 g/kWh of base case to 240 g/kWh of LIVC, reduced by 4.0%. The indicated thermal efficiency increased from 41.9% of base case to 43.7% and 42.5% of LIVC and DLIVC. Miller loss is only 2.55% with Miller inlet phase.

The article presents the results of a study of innovative technology on improving the rationing of diesel fuel consumption on rolling stock in the railway industry. As an object of research, fuel consumption was studied on a Automotrisa diesel editing - ADE-1, which is used in the electrification and power supply facilities of the Transenergo Directorate. Motorized carriage loading transport (MLT – 6), which is used in the economy of the track. On the basis of a number of regulatory documents of the Russian Railways company, the Samara State University of Railway Transport was entrusted with the analysis of the efficiency of fuel consumption of SSPS on these types of rolling stock at one of the railway landfills. The special self-propelled rolling stock of the Russian Railways company was studied as an object of research. The method of forming fuel consumption standards by identifying the actual values of fuel consumption and indicators of rolling stock operating modes is considered. The statistical methods used in the research allow us to set the rate of consumption in the range of 8% of the actual flow rate. Based on the use of the obtained fuel consumption rate, a method for identifying and evaluating the level of unauthorized fuel overspendings (draining) is provided.

Abstract Range prediction is a standard feature in most modern road vehicles, allowing drivers to make informed decisions about when to refuel. Most vehicles make range predictions through data- or model-driven means, monitoring the average fuel consumption rate or using a tuned vehicle model to predict fuel consumption. The uncertainty of future driving conditions makes the range prediction problem challenging, particularly for less pervasive battery electric vehicles (BEV). Most contemporary machine learning-based methods attempt to forecast the battery SOC discharge profile to predict vehicle range. In this work, we propose a novel approach using two recurrent neural networks (RNNs) to predict the remaining range of BEVs and the minimum charge required to safely complete a trip. Each RNN has two outputs which can be used for statistical analysis to account for uncertainties; the first loss function leads to mean and variance estimation (MVE), while the second results in bounded interval estimation (BIE). These outputs of the proposed RNNs are then used to predict the probability of a vehicle completing a given trip without charging, or if charging is needed, the remaining range and minimum charging required to finish the trip with high probability. Training data was generated using a low-order physics model to estimate vehicle energy consumption from historical drive cycle data collected from medium-duty last-mile delivery vehicles. The proposed method demonstrated high accuracy in the presence of day-to-day route variability, with the root-mean-square error (RMSE) below 6% for both RNN models.

Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors.

Private vehicle travel is the most basic mode of transportation, and the effective control of the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic development as well as achieving a green low-carbon society. Therefore, the impact factors of individual carbon emission must be elucidated. This study builds five different models to estimate real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the Light Gradient Boosting Machine (LightGBM) model performs better than the linear regression, Naïve Bayes regression, Neural Network regression, and Decision Tree regression models, with mean absolute error of 0.911 L/100 km, mean absolute percentage error of 10.4%, mean square error of 1.536, and R squared (R2) of 0.642. This study also assesses a large number of factors, from which three most important factors are extracted, namely, reference fuel consumption rate value, engine power and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with greater impact on real-world fuel consumption rate are vehicle brand, engine power, and engine displacement. Average air pressure, average temperature, and sunshine time are the three most important climate factors.

The quest for non-edible oil for the production of alternative fuel (bio-fuel) using homogeneous catalysts continues to supplement and replace in totality the traditional transportation fuels that are not environmentally friendly. The use of biodiesel in Compression Ignition Engines (CIE) to evaluate the engine performance is a norm and blends of biodiesel and Automotive Gas Oil (AGO) are also used in the engine performance processes to ascertain its usage in the CIE. Therefore, this study evaluated the performance of a compression-ignition engine (CIE) fuelled with biodiesel produced from sand apple oil using eggshell as a heterogeneous catalyst. Transesterification of Sand Apple Oil (SAO) with ethanol to produce ethyl ester and glycerol was optimized. Sand Apple Ethyl Esters (SAEE) was blended with Automotive Gas Oil (AGO) at 5 – 25% mix to evaluate the performance of a 3.68 kW diesel engine at five loading conditions (0, 25. 50, 75, 100%). Performance tests were carried out to determine torque, speed, exhaust gas temperature and fuel consumption rate. Data obtained were analyzed using ANOVA at P < 0.05 significant level. Results of parameters tested ranged from 6.50 – 6.60 Nm, 2795 – 2950 rpm, 385 – 400 °C and 2.93 – 5.00 × 10−6 kg/s, respectively for all the blends. The study established that the performance of the diesel engine using 5 – 25% SAEE-AGO blends was similar to using AGO alone and SAEE is therefore suitable for use in the CIE.

Abstract This paper evaluates the benefits of an SAE 30 monograde stationary gas engine oil (SGEO) in comparison with SAE 40 monograde SGEOs with the focus on two main areas. First, to demonstrate and quantify the positive impact of lower viscosity on the fuel consumption rate, and second to demonstrate the faster lubrication of hard to reach points in the engine during startup. The current industry recognized fuel efficiency test methods for passenger car and on-road diesel engine sectors are not suitable for evaluating the fuel efficiency performance of a gas engine oil because of the significant differences in fuel type, engine operating conditions, and oil formulations. This paper, therefore, describes comparative studies of three different gas engine oils in a modern MAN E3262 E302 gas engine that was carefully adapted and fully instrumented. The performance of each oil with respect to fuel efficiency was assessed in an extensive program comprising endurance testing, stationary tests on various load/speed points and dynamic tests running the engine fired as well as non-fired (motored). Another part of the test program explores the lubrication of hard to reach points in the engine, e.g. valve guide. The paper describes how the SAE 30 monograde oil results in faster lubrication of these parts during startup in comparison with the SAE 40 oils.

Abstract To ensure that the aerothermodynamic cycle design of a turbofan engine is more accurate, efficient, and provide a reliable decision-making basis for engine designers, the multi-objective particle swarm optimization (MOPSO) method was used to optimize the aerothermodynamic performance parameters of the turbofan engine at multiple design points (MDPs). Fuel consumption rate and the specific thrust were considered as optimization targets. The thrust requirements and cycle parameter constraints under each working state were comprehensively considered to obtain the optimal performance boundary of the engine, the corresponding cycle parameters, and the correlations between different requirements and constraints. The results showed that the MOPSO algorithm could accurately and completely obtain the optimal performance boundary surface of the engine in the feasible region and the corresponding cycle parameter value. The feasible region obtained by the aerothermodynamic cycle design at MDPs was more accurate and effective than the design at a single design point.

Recirculating rice dryers are suitable for a large amount of loading capacity. It generally comprises two parts, the tempering section, a drying section, and the grains are dried intermittently until the final moisture content of the grains can be achieved. Wet grains are initially dried for about 11 minutes within the drying section of the dryer. The grains then are conveyed to the tempering section, stored for about 40 to 50 minutes. At every passes, about less than 2% (wb). The moisture content can be removed from the grains. The number of passes required to accomplish a drying process in a recirculation dryer depends on the initial moisture content and the amount of rough rice to be dried. The drying and tempering duration can be adjusted through a mechanical valve. The purpose of this study was to examine the performance of recirculating dryers equipped with pneumatic conveyors instead of bucket elevators to reduce electricity cost and heated using a proper blend between kerosene and jatropha oil. Several experimental runs were conducted under a constant drying temperature of 60℃ and were controlled by adjusting the fuel consumption rate. The experimental results showed that the drying efficiency was in the range of 22.2% to 31.1%, the specific energy consumption was between 3,475-4,785 MJ/kg H2O evaporated, fuel consumption at 0.95 to 1.15 liters/hr, and the drying rate was 0.9% per day. Using 465 kg of rough rice, the entire drying operation required 10 hours of drying time with 74.3% of head yield. The mathematical model used in this study also had indicated close agreement with experimental data.

Due to depletion of conventional fuel and increasing global warming, biomass wastes have been explored and investigated by many researchers worldwide. A biomass gasification power plant is a promising conversion technology for energy sustainability. From many existing gasifiers have been developed, mostly they have high technology, large capacity, and very costly, thus unsuitable for remote area di Indonesia. The present work aims to build a simple and low cost double air-stage downdraft gasifier for a small-scale biomass power plant system. The gasifier is tested on rice husk at equivalence ratio of 0.20, 0.30, and 0.40. The parameters evaluated are axial temperature, fuel consumption rate, heating rate, thermal efficiency, and tar content. The results show that the highest gasification temperature, fuel consumption rate, heating rate, and thermal efficiency are occurs at equivalence ratio of 0.4. The values are 904.5°C, 4.14 kg/h, 25.38 kJ/h, and 63.18%, respectively. The significant findings is that the gasifier generates producer gas with low tar content, i.e. 23.9 mg/m3 at equivalence ratio of 0.4 and the producer gas is successfully used to run the 3 kW generator set. For sustainability operation of the power plant, it is important to test the gasifier on various biomass waste feedstocks.