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When optimizing signals relevant traffic agencies adopt policies to either improve mobility performance measures (e.g., delay and stops), environmental aspects, or safety of signalized intersections. One of such policies, mainly implemented through so-called Performance Index (PI), advocates that reduction of excess fuel consumption (FC) should be achieved by minimizing the PI - a linear combination of delays and stops. The key factor of such a PI is the stop penalty “K”, which represents a weighting factor, or a stop equivalency measured in seconds of delay. In the contemporary signal optimization practice, this K is given a constant value (e.g., 10 seconds) and it is not recognized as a parameter that is dependent on various operational conditions. This study challenges that common view and presents a methodology to derive the K factor and investigate impacts of various operational conditions (e.g., cruising speed) on the K value. The study uses traffic simulation model coupled with a modal fuel consumption and emission model to investigate second-by-second FC during stopping events at a signalized intersection. The experiments are performed on a hypothetical, yet realistic, intersection under several operational scenarios. The findings show that the K varies significantly with all the investigated operational conditions. More importantly, results indicate that the K factor should be much larger than used by current signal timing practices. The implications of these findings may lead to significant changes in current polices for signal timing optimization. Future research shall validate these findings with second-by-second vehicle trajectory and FC data from the field.

The selfish routing of network flow often considers a single objective, namely travel time or travel distance, and optimisation models are often guided by the principle of user equilibrium (UE). A more challenging approach is to consider multiple objectives simultaneously, as for example distance, travel time and pollution. In this paper we present a bi-criteria problem whereby the road users selfish objective of minimising their travel time is at odds with the objective of minimising the overall fuel consumption of all road users. The approach taken is to manipulate “free” parameters, namely speed limits, in an attempt to coerce the road users into behaviour which helps the latter objective. Motivated by the work done on the Price of Anarchy (PoA) into classifying the suboptimality of equilibrium with respect to the minimum total travel time we look to classify the equilibrium solutions with respect to a weighted sum model of the minimum total travel time and overall fuel consumption. Our results show that small changes to these “free” parameters can result in solutions which Pareto dominate other solutions. Whilst our measure for the suboptimality of equilibrium solutions can highlight the inefficiency of a network configuration itself, it does not allow insight into the overall quality of the solution when compared with other network configurations.

The increasing demands in modern combustion engines for low emissions and low fuel consumption improved methods for the cycle of combustion. The Beare Heads is a recent 6-hour version of the Beare Heads, recognized also as Malcolm Beare model. Beare Heads uses pistons and harbours such as a 2-hour motor and replacements for the overlap valve system of 4-hour motors. The four-stroke engine, block as well as shaft remain intact. The six-time engine was a mix of two-hit and four-hit technologies. Six Stroke is a six-stroke engine that uses two energy strokes. The six-time engine including external combustion as well as internal dual stream is the same as the real interior combustion engine, according to its mechanical design. Air heating system as well as a combustion chambers, each separate of the cylinder, are two extra chambers throughout the thermodynamic cycles. The six-stroke is heat efficient, since the power pulse pressure changes are larger than for the injection process as well as compression strokes. The major benefits of six-stroke engines included lowering fuel consumption by 40 percent, reducing emissions, versatility for multi-fuel service by 2 power strokes over the six-stroke cycles.

In the scope of the rapid technological advancements, nanoparticles (NPs) have gained prominence due to their excellent and tunable biological, and physicochemical properties. Nowadays, different methods are used for their synthesis. In particular, the green synthesis of metal precursors for the synthesis of NPs, represents a cost-effective, environmentally friendly, and hazardous chemical-free method for developing a large variety of NPs. By exploiting plant extracts, the production rate of NPs is relatively faster. Due to fossil reserves and high fuel consumption, renewable and clean energy materials are urgently needed to improve environmental sustainability. With outstanding electrochemical and physicochemical characteristics, molybdenum-based NPs (Mo-NPs) are gaining increasing attention in the fields of energy conversion and storage. Considering the significance of Mo-NPs synthesized from greener routes and their energy applications, it is necessary to review recent trends and developments in this field. This review summarizes important research studies and future research guidelines for the preparation of Mo-NPs through green routes and their applications to meet global energy and environmental demands. Moreover, future research directions are also highlighted to achieve sustainable greener precursors and Mo-NPs based energy storage devices.

The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner.

An accurate prediction of ship fuel consumption is critical for speed, trim, and voyage optimisation etc. While previous studies have focused on predicting ship fuel consumption with respect to a variety of factors, research on the impact of environmental factors on fuel consumption has been lacking. In addition, although recent research efforts have widely focused on machine learning methods to predict fuel consumption, studies on hyperparameter values that are suitable for these prediction models are limited. To compensate for this deficiency in existing literature, an adaptive hyperparameter tuning method is proposed, and the effects of maritime environmental factors on fuel consumption are taken into account. Through experimentation, the proposed adaptive hyperparameter tuning method was validated via artificial neural network (ANN), support vector regression (SVR), random forest (RF), and least absolute shrinkage and selection operator (Lasso). The hyperparameter tuning proportionally increased the amplitudes of the coefficients of determination (R2) of these algorithms. The increase of the amplitude demonstrated the following trend, in the order of the largest increase to the lowest increase: ANN, Lasso, SVM, and RF. The rates of increase were between 0.0773% and 2.1653%. Furthermore, after the environmental factors were considered, the prediction accuracies of the ANN and Lasso increased; however, the opposite was observed for the SVR and RF. As such, we confirmed that the use of Bayesian optimisation for hyperparameter tuning can effectively improve the fuel consumption prediction accuracy, and our proposed model can therefore serve as a significant reference for calculating fuel consumption.

The operation of connected platooning vehicles (CPVs) with V2V and V2I techniques has the potential to decrease the overall aerodynamic drag and reduce fuel consumption. One of the key technologies for CPVs is the spacing control which is heavily dependent on the drag force. However, the existing studies on drag coefficient of CPVs seldom pay attention to heterogeneous platoon, and most analysis only consider the effect of vehicle spacing and ignore key information such as cross-sectional area of adjacent vehicles. In addition, most research only focuses on qualitative research, without concluding a quick formula for calculating vehicle drag coefficient in a platoon. In this work, we investigate the modeling and estimation of drag coefficient in terms of the inter-vehicle distances, the platoon configurations, and the cross-sectional area of the adjacent vehicles. We calculate the drag coefficients for ten categories of vehicles with different inter-vehicle distances via numerical analysis in the software FLUENT. According to hundreds of the analysis results, we propose a model to estimate the drag correction factor online. A hybrid algorithm combining the BP neural network (BPNN) and particle swarm optimization (PSO) is employed to optimize the parameters in the model. The model is validated via three kinds of regression evaluation indexes and additional simulation tests with different platooning configurations and different types of vehicles. In terms of the comparison results, the developed model is effective to estimate the drag coefficients of CPVs.

Biodiesel from mahua is a promising source of energy in different forms like oil, gas, and char at present and for the future as well. Nations like India, which have adequate environmental conditions favouring the mahua crops, can produce oil from mahua, and this energy can be used in many efficient ways. This is an experimental approach made to test the engine performance of mahua biodiesel with nano metal oxide additives for its combustion and emission characteristics. The tests were conducted on a BSII commercial, single-cylinder, four strokes, water-cooled diesel engine. The test and operating conditions were set stringent, to generate the real-time environment. Tests were conducted at varying engine loads for determining the performance parameters like brake thermal efficiency, brake specific fuel consumption and emissions parameters like unburned hydrocarbons, CO2, CO and NOx. The results obtained from the tests showed mahua biodiesel has a higher fuel consumption rate which can be normalized to diesel by adding aluminium oxide. Moreover, improved brake thermal efficiency and reduction in ignition delay was achieved by adding nano metal oxides additives in varying proportions.

Concern over growing fossil fuel depletion has led to the search for renewable fuels with efficiencies comparable to those seen today for conventional fuels. The present article discusses the potential and opportunities to be used in various applications for alternative fuels. The properties of these fuels and their fuel efficiency are discussed in detail. Vegetable oils provide a very positive scenario for fossil fuels to be used as alternatives. The use of biodiesel leads to a major reduction of unburned hydrocarbons, carbon Monoxide, particulate matter and nitrogen oxides in traditional diesel engines. Different alternative fuels have been compared to traditional fuels and it is clear that the use of these fuels can be greatly reduced by the use of blended fuels. Many alternative fuels have properties close to those of conventional fuels, so it is already well known the technologies needed to deal with them. Hydrogen is an exception, and its ignition characteristics differ considerably from those of conventional fuels. Therefore, difficulties in the storage, transport and use of hydrogen as a fuel are important. Therefore, difficulties are essential in storing, transporting and using hydrogen as fuel. There is also discussion of other technologies, such as alternate routes to conventional fuel alternatives, such as CTL. The discussion on emulsified oils, including the use of oxygenates, eventually took place.

This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system (DES). Owing to the relevant Chinese policy of electrical transmission and distribution, combined cooling, heating, and power system (CCHP) and photovoltaic (PV) systems are not currently allowed. However, with the Chinese supply-side power grid reform, the permissions for connections between DESs and utilities are gradually evolving. By performing building simulation and using mixed integer linear programming (MILP), a real-time updated operation strategy of a DES is established. Then, considering the DES from Tianjin Eco-city as a case study, a comparative analysis between this updated strategy and the current operation strategy is performed by evaluating three factors: economic efficiency, energy consumption, and CO2 emission. The results show that the updated strategy can reduce 29.12% of electricity time-of-use cost, 10.11% of total fuel consumption, and 18.40% of CO2 emission during the cooling season. Besides, a method of “rolling load forecasting” for DES by using Support vector regression machine (SVR) is proposed and discussed. The testing shows that the Mean Absolute Percentage Error (MAPE) is below 7.5%. And when the training sample is large, the particle swarm optimization algorithm can be used to shorten the modeling time of the air conditioning load forecasting model.

Due to incredibly challenging social, economic, technical, and environmental aspects, decision-making must evaluate several conflicting agendas. Therefore, rule-based decision built on a single criterion is no longer capable of dealing with these issues. Generators, especially silent ones, are essential for industries, hospitals, schools, colleges and hotels. However, silent Genset (ŚĠ) of different companies and capacities exist in the market with various attributes/criteria such as power consumption, fuel tank capacity, fuel consumption etc. So, it becomes difficult for the firms/purchaser/wholesaler/retailer to pick the most appropriate ŚĠ. So, in this present work, multi-criteria decision making (MCDM) methodologies VIKOR (Vlsekriterijumsko kompromisno Rongiranje) and WASPAS (Weighted Aggregated Sum Product Assessment) are utilized to rank the different alternatives available in the market. First, the weights of importance to ŚĠ sets are applied identically and with objective weights. Then, the objective weights are assigned with the entropy weight method (ĖŴΜ) without the decision maker’s choice. Finally, the ranks obtained with VIKOR and WASPAS are displayed. Furthermore, more alternatives and criteria can be considered with varying MCDM techniques of weighting and ranking. As a result, for industrial engineers who play a crucial part in decision-making, the provided approach for Genset selection is convenient, practical, and productive.

Jaggery, an unrefined sweetener is prepared by continuous heating of fresh sugarcane juice in different types and capacities of old indigenous and modified plants. The old indigenous plants manufactured by the rural people without any technological advancements have very high fuel consumption (about 3 kg/kg to 4 kg/kg of jaggery produced) and low heat utilization efficiency (15% to 29%). Number of modifications have been suggested by the researchers for the optimization and improvement in the performance parameters of jaggery making processes and plants. The implementation of fins at the external bottom of pans, preheater and economiser in the chimney, efficiency booster in the furnace, freeze pre-concentration system, forced draft through the plant and controlled fuel feeding in the furnace are reported to improve the thermal efficiency in the range of 25% to 50% and reduce the bagasse consumption in the range of 1.75 kg/kg to 2.75 kg/kg of jaggery prepared. The recently published article adopting novel techniques of internal preheating of sugarcane juice in the gutter pan and automatic supply of bagasse in the furnace of a two pan jaggery making plant is claimed to improve the heat utilization efficiency up to 65% and reduce the fuel consumption up to 1.50 kg/kg of jaggery produced with an affordable investment. The present paper summarizes the various technological up-gradations adopted and tested by the researchers for the enhancement of various output parameters of jaggery making units.

The article presents the results of the development and testing of a prototype device for the combustion of liquid fuels and the disposal of liquid toxic waste based on structured microfiber catalysts. The technology is based on a two-stage approach combining homogeneous and catalytic oxidation, which is carried out in a structured spiral cartridge with a modified glass fiber platinum-containing catalyst IC-12-S111. A prototype of a catalytic device with a thermal power of 18 kW was created and tested on a model fuel (diesel fuel). The optimal combustion mode is realized at a fuel consumption of 1.8 L/h, an air flow supplied to the burner - 12.3 st.m3/h and to the mixing zone in front of the catalytic block - 67 st.m3/h. At the same time, the air leaving the device contains practically no toxic impurities (CO, nitrogen and sulfur oxides). An important advantage of the proposed technology is its modularity, that is, the possibility of implementation in the form of low-power mobile devices, from which it is possible to form transportable and quickly deployable systems of any total capacity and a wide range of purposes.

The industrial sector is one of the biggest oil consumers in Cyprus, corresponding to 20% of the fuel consumption. A parabolic trough collector (PTC) system seems the best option to produce clean thermal energy at higher temperatures than those that can be achieved from the already widely used flat plate collectors on the island. This paper presents a simulation model built to investigate the performance of the first industrial PTC system in Cyprus, installed at the Cyprus biggest soft drinks factory. The simulation model is built in TRNSYS and is validated using data from the real PTC system installed at the factory. The results show a very good fitting between the operating parameters and the power output of the Solar Field (SF), Concrete Thermal Storage System (CTES), and the Steam Generator (SG). The average percentage relative error of the system's contribution to the process is less than 6.32% for the daily steam production and during a week did not exceed 6.45%. The novelty of this study is the development of a design tool that can be used by potential interested industries to identify the suitable system that fits their needs. All data are provided in the form of graphs and allow anyone to use as input data the thermal energy demand and required steam temperature of the industry to retrieve information about the size of a suitable system which satisfies these requirements depending on each case. The payback period for all cases examined varies from 2 to 6 years, depending on the size of the system.

In this paper, role of heavy duty vehicles in contributing pollution is addressed with identified areas of improvement for enhancing their existing fuel efficiency. Potentials of thermo-electric generator (TEG), piezoelectric generator and regenerative braking are studied. It is suggested to develop integrated solution having all three technologies to harvest the waste energy through various means. This solution brings the individual little energy harvested through the three discussed solutions together and supply back the significant amount of energy back to the HDV’s. Combined energy harvest is suggested to optimize the cost involved and reduce the losses as well as maintenance expenses by indirectly increasing the life of associated components of the vehicles. These technologies have several limitations related to efficiency and materials which may further get improvised with the research in future.

Future asteroid landing and sample return missions will seek for landing sites with high scientific value, such as hazardous terrains, which means that the spacecraft needs the ability of autonomous hazard avoidance. Nowadays, the guidance algorithm based on artificial potential function plays an increasingly important role in hazard avoidance, but the local minimum problem makes that the spacecraft cannot reach the desired target landing point in some complex terrains. In this paper, a novel hazard avoidance guidance method which improves the traditional artificial potential function is developed. This method focuses on the impact zone analysis of hazards and defines anti-collision zone. In addition, a new repulsive potential function in logarithmic form is designed with continuity and fast numerical change rate in the anti-collision zone on this basis, and the problem of spacecraft falling into local minimum zone in complex terrains can be effectively avoided. Considering the practical application of constant thrust engine, the hazard avoidance control law with constant thrust sliding mode is designed to reduce switching frequency and fuel consumption. The performance of the proposed guidance law is verified through a set of simulations, as well as the global stability of the control system under uncertainty and perturbation conditions.

The growing price of fossil oils, the gradual decline of petroleum supplies and the unsustainable emissions of motor vehicles led researchers to look for substitute fuel, such as waste lubricant oil and waste transformation oil (WTO). In power transformers, welding transformers, and electromotive systems, a significant volume of transformer oil is used as a coolant. The electrical transformer is an integral part of electrical power transmission and distribution equipment used in smaller, intermediate and larger electrical distribution stations. The efficiency and lifespan of an electrical transformer depend upon the transformer’s efficient cooling and insulation. During the working of a transformer, transformer oil is exposed to electrical and thermal stress due to which it started to degrade after some time. Thus a huge amount of degraded transformer oil is rejected as waste. The paper details the utility of waste transformer oil by using simple batch distillation operation at atmospheric pressure by analyzing the physiochemical properties of the product. The treated transformer oil is further tested in the diesel engine to examine fuel efficiency. Diesel engine runs were performed with various combinations of blends consists of diesel fuel and treated transformer oil. Results reveal that brake thermal efficiency of the blends found at 10%, 20%, 30% and 40% is 28.19%, 27.52% 26.21% and 25.98% respectively. The brake specific fuel consumption of blends at 10%, 20%, 30% and 40% is 0.29 KJ/Kwhr, 0.30 KJ/Kwhr 0.31 KJ/Kwhr and 0.315 KJ/Kwhr respectively.

Heavy duty vehicles e.g., trucks are one of the important mediums of ground transportation in every sector like defence, road transport etc. which provides immediate support whenever required. Innovative technological development in the area of automobile has been growing very rapidly, especially in the developing scenario. This development leads to the requirement of reducing the weight of the vehicle specially the chassis frame which serves as a skeleton to provide the proper structural safety to a vehicle & hence affects the vehicle’s performance along with the requirement of fuel consumption. It is a critical part in the vehicle, and various sorts of failures happen because of static loading conditions. Decreasing the heaviness of the vehicle parts is an ideal answer for the issue. The presented work focuses on optimizing a heavy-duty TATA 1612 truck chassis (ladder type frame) by substituting its material from conventional steel with potential metal matrix composite light weight Graphite Al GA 7–230 MMC material without compromising the safety and strength. CAD modelling & structural analysis is accomplished on the modelled frame using ANSYS and optimal space filling design scheme of response surface method respectively. Analysis shows around 70% reduction in mass of chassis and thus subsequently affect the functioning of the vehicle in a positive approach.

In today's world, the transportation industry is globally known for its high energy consumption and intensive use of fossil fuels. However, the search for more sustainable, less resource-carrying, and environmentally friendly transportation alternatives is growing. In this sense, this paper aims to analyze the eco-efficiency of transports using Data Envelopment Analysis (DEA) in the Brazilian automotive industry and so to propose a method to measure the performance of transports from the perspective of sustainable development. In this case study, there were considered eleven alternatives and four variables: load capacity, fuel efficiency, total emissions, and energetic consumption. As a reference, it was used data provided by the Brazilian program for the rationalization of the use of petroleum derivatives and natural gas, Inmetro and Petrobras, and by the automobile manufacturers of the vehicles analyzed. Findings indicate that the use of DEA provided evidence of a closer view of operational reality, and proved to be an adequate and complementary tool, allowing greater discrimination for eco-efficiency analysis of vehicles. Thus, the proposed approach can bring important insights to improve the Government evaluation regarding the energy efficiency of vehicles and is up to enrich the body of knowledge in organizational sustainability management and its performance measurement in the transports sector. Finally, the main contributions of this study include: (i) it is an extension of previous DEA works that assesses the efficiency of the road transport sector, considering also undesirable variables; and it (ii) allows a comparative assessment of car eco-efficiency using DEA model, combining desirable and undesirable results, and provides the unified efficiency of the Brazilian transportation industry.

This paper investigated the techno-economic viability assessment of solar PV/wind/diesel generator (DG)/battery hybrid energy systems (HES) for powering an isolated rural health clinic in northern Nigeria. HOMER–software tool developed by the US National Renewable Energy Laboratory (NREL) has been utilized for the techno-economic assessment of the proposed HES. The results of the simulation reveal that PV/DG/battery HES with 5.43 kW PV, 2 kW DG, 3.06 kW power converter, and 10 units of batteries emerged as the optimum system and most preferable with the minimum Net Present Cost (NPC) of $16,457 and Cost of Energy (COE) of $0.259/kWh compared to other system cases. The outcome also shows that the optimized solution is environmentally friendly as it presented an acceptable carbon dioxide emission of 1304 kg/year, which was about 80% and 82.5% less than that of system case 3 (DG/battery) and system case 5 (DG-Only). To have a good understanding of the operation of various system configurations considered, details of the system’s battery storage status and power flow are discussed via the energy balance of the various system configurations. This analysis shows operating cost, fuel cost, COE, fuel consumption, and renewable fraction are sensitive to the variation in all the considered sensitivity parameters.

Many biomass power plants are not flexible, using only one fuel type, and have high CO2 emissions. In this study, a new methodology to assess the multi-biomass utilization for a power plant is proposed. The results provide that the proportions of mixed biomass residues in different regions of Thailand are appropriate with lower chloride content and slagging inclination than the standard specification. However, high alkali metal oxides in biomass in the southern region lead to high fouling inclination with high fly ash deposition. The electricity production using multi-biomass fuel is more efficient with lower fuel consumption than that with single-type biomass. To generate 103.43 MW electricity production, 107.89 t h−1 consumption of mixed biomass fuels in the central is lower than that with multi-biomass in other regions. However, it generates a maximum of 12.5 mol% CO2 in exhaust gases which can be captured by suitable amine-based absorbents with 90.82% efficiency.

Minimizing the amount of fuel consumed by a moving vehicle can be formulated as an optimal control problem that determines the speed profile that the vehicle should follow. The fuel consumption is generally a function of speed and acceleration, and is optimized under external parameters (e.g., road grade or surrounding traffic conditions) known to affect fuel economy. Uncertainty in the traffic conditions, and in particular traffic speed, has seldom been investigated in this context, which may prevent the vehicle from following the optimal speed profile and consequently affect the fuel economy and the journey time. This paper describes two stochastic optimal speed control models for minimizing the fuel consumption of a vehicle traveling over a given stretch of road under a given time limit, where the maximum speed that can be achieved by the vehicle over the journey is assumed to be random and follow a certain probability distribution. The models include chance constraints that either (i) limit the probability that the desired vehicle speed exceeds the traffic speed, or (ii) bound the probability that the journey time limit is violated. The models are then extended into distributionally robust formulations to capture any uncertainties in the probability distribution of the traffic speed. Computational results are presented on the performance of the proposed models and to numerically assess the impact of traffic speed variability and journey duration on the desired speed trajectories: The results affirm that uncertainty in traffic speeds can significantly increase the amount of fuel consumption and the journey time of the speed profiles created by deterministic model. Such increase in journey duration can be mitigated by incorporating the stochasticity at the planning stage using the models described in this paper, and more so with the distributionally robust formulations particularly with higher levels of uncertainty. The solutions themselves generally exhibit low levels of speeds, which ensure the feasibility of the speed profile against any variabilities in the traffic speed.

An on-board guidance generation approach for Mars pinpoint landing has been developed based on sequential convex programming. To optimize the flight time in the formulation, a discretized form of the landing problem was constructed under the conditions of linearized states, controls, and time increment between consecutive time steps. To implement this strategy in real time, on-board demonstrations were conducted with the GR740, which is the next-generation on-board processor of choice for the European Space Agency. The total number of time steps was determined based on the results of these demonstrations. The numerically simulated results also indicate that the solution obtained via the proposed strategy is close to the optimized GPOPS-II solution under the condition of no disturbance. Even under disturbances such as navigation errors, initial prediction errors, and perturbation forces, the proposed strategy ensures that the spacecraft reaches its target position with near-optimal fuel consumption.

This study proposes an autonomous truck (AT) based scheduling problem for container transshipment between two seaport terminals, in which ATs can travel in platoons with short inter-vehicle distance. The following vehicles in a platoon can save fuel consumption because of the reduced air resistance. Based on a fuel consumption function with respect to travel speed and platoon formation, we formulate a mixed-integer second-order-cone programming (MISOCP) model to minimize the total operation cost. To address the extra computational challenges caused by the requirements of platoon coordination, we develop a column-generation-based heuristic method in which two types of columns representing AT schedules and platoon schemes are generated and added into the master problem. According to the practice of container transshipment between two port terminals in Singapore, we conduct extensive numerical experiments and evaluate the performance of the proposed method up to the instance of 50 requests in a two-hour time limit. The results show that the proposed CG heuristic can outperform the state-of-the-art solver by around 10% for the instances with 40–50 requests.

In this study, a new hybrid solar preheating intercooled gas turbine (SP-IcGT), is presented, in which a parabolic trough solar technology is used to preheat the compressed air before entering the combustor. The performance of the new hybrid gas turbine was evaluated and compared with the conventional hybrid solar preheating gas turbine (SP-GT). Several performance indicators were used in the analysis under Guangzhou (China) weather data. It is observed that the SP-IcGT is superior to the SP-GT system as it can boost the fuel-based efficiency by 19.35% versus 0.26% for the SP-GT system. In addition, the SP-IcGT has a much lower specific fuel consumption (about 7017 kJ/kWh) compared with the 10362 kJ/kWh for SP-GT. The highest fuel-based efficiency of 51.4% is obtained for the SP-IcGT with 47.4% improvement over the SP-GT, which exhibits a levelized electricity cost of 4.58 US/kWh. Meanwhile, fuel consumption and greenhouse gas emissions can be reduced greatly by integrating solar energy with the intercooled gas turbine. The SP-IcGT is more economical than applying carbon capture to the equivalent conventional gas turbine plant combined while achieving the same reduction of CO2 emissions. Overall, the SP-IcGT is an attractive system under different climates.

The source of electricity supply in isolated areas is commonly from diesel or other fossil fuel-based generation systems. However, the increasing cost and harmful emissions of these fuels have led researchers to seek alternative solutions in providing electricity. Hence, harnessing renewable resources can be a favourable solution in lowering the dependency on fossil fuels. The Strait of Malacca is blessed with tidal current energy, a research was conducted to design a hybrid renewable energy system (HRES) that consists of tidal current and solar energy as its sources along with backup power such as generators to support the electrical demand of a lighthouse. A simulation method is used to determine and compare the optimal configuration of the HRES with the diesel generator-based system. An evaluation of all possible scenarios of standalone systems was carried out by considering the amount of fuel consumption and pollutant emittance, capacity shortage, excess electricity production, unmet energy and renewable fraction parameters of each system scenario. From the findings, the hybrid energy system of Scenario A that consists of tidal turbines, PVs, generator, battery, and converter produces better results where the diesel fuel consumption can be reduced up to 84% in comparison with the standalone generator-based system.

Representative driving cycles and specific vehicle types play an important role in analyzing energy consumption to provide valuable references for manufacturers and government inspections. In this study, a parallel plug-in hybrid electric vehicle with different driven configurations is employed to analyze energy consumption under a typical driving cycle. Specifically, a typical driving cycle of Tianjin is constructed by integrating the clustering and Markov chain algorithms. Considering the various layouts of energy systems in plug-in hybrid electric vehicles, three different driven configurations (P2, P3, P4) of the parallel plug-in hybrid electric vehicle are established. Finally, the energy consumptions of the different configurations are analyzed under the proposed typical driving cycle and the compared China Automotive Test Cycle. Results of two driving cycles demonstrate that the fuel consumption of the P3 configuration is lower than that of the P2 and P4 configurations, respectively, while the purely electric driving range of the P3 configuration is longer than the other two configurations, respectively. These results provide a basis for the formulation of China’s new light vehicle emission standards and reduce exhaust emission by making the vehicles more economically suitable for the road conditions in Tianjin.

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. The framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

In this study, experiments were carried out in two stages. In the first stage, n-heptane (in 4, 8, 12, 16, and 20% volumes) was added to biodiesel (C0) which was produced from refined cottonseed oil via the transesterification method. The optimal n-heptane ratio was determined to be 8% in volume as a result of engine experiments. In CH8 fuel compared to C0 fuel, there was a 7.52% increase in brake power, 7.84% in engine torque, 2.57% in brake thermal efficiency, and 3.08% decrease in brake specific fuel consumption. When n-heptane additive fuels were analyzed, it was observed that the cylinder pressure in CH8 fuel were higher in all loading cases. The minimum ignition delay at full load was 12.88 o CA in CH8 fuel. When the exhaust emission was analyzed, in n-heptane added fuels compared to C0 fuel in CH8 fuels, there was a 29.99% decrease in CO emissions and a 3.16% decrease in THC emissions. In NO X emissions, there was a 1.18% increase in CH8 fuel and a 1.19% increase in smoke emissions. As a result of the analysis of engine performance and the experimental data regarding emission characteristics, the optimal n-heptane additive fuel was determined to be CH8 fuel. In the second phase of the experiment, an organic based manganese addition (4 ppm, 8 ppm, 12ppm, and 16 ppm) was added to 8% n-heptane additive (CH8) fuel. The optimal experiment results were obtained at a ratio of 12 ppm organic based manganese additive. Compared to CH8 fuel, in CH8Mn12 fuel, brake power increased by 3.42%, engine torque by 7.12%, and brake thermal efficiency by 5.35% while brake-specific fuel consumption decreased by 5.91%. In the evaluation of exhaust emissions, in CH8Mn12 fuel compared to CH8 fuel there was an 8.28% decrease in CO emissions, a 5.54% decrease in THC emissions, and a 3.67% decrease in smoke emissions while there was a 10.67% increase in NO X emissions. Depending on its properties, the additive reacted directly with carbon atoms and caused a serious reduction in emissions.

Currently micro/nano satellites have become the protagonists in most formation flying missions, and there is an urgent demand to propose a reliable approach for quick fuel estimation of multiple-impulsive scheme on-board to achieve formation reconfiguration problems. This paper presents an optimal control approach based on multiple impulses for satellite formation in-plane reconfiguration issue in near circular orbit. Based on initial small deviation in the cylindrical coordinates system, a relative orbit motion expression is investigated, and a time-varying propagate system without perturbation is presented in this paper, which is suitable to calculate the solution of relative orbital maneuver by multiple impulses. The formation reconfiguration problem of this relative orbit motion is considered, and orbital motion equations with initial deviations are presented for relative orbital transfer calculation. Through different combinations of method from normal four-impulsive solution, the optimal three-impulsive method for relative orbital transfer in plane is obtained by analysis solution based on graphical and numerical way. In the end of paper, the effectivity and optimality of method is validated, and fuel consumption is analyzed through simulations of assumptive different formation reconfiguration missions.

Application of diesel fueled partially premixed compression ignition (PPCI) engines are limited due to its excessive pressure rise rate at high load. Multiple premixed compression ignition (MPCI) engines fueled with low octane number gasoline (LON) has the potential to operate at both low and high load, while effects of split injection strategy on performance of such engines have not been discovered yet. In the present study, comparisons of LON and commercial diesel are first conducted in a heavy-duty single cylinder engine. Then, pilot injection timing and main injection timing are varied individually to reveal their effects on the combustion, emission, and economic performance of the MPCI engine. Comparing with diesel, MPCI engines with LON merits a low pollutants emission due to the long ignition delay and good volatility of LON. Split injection strategy can reduce the NO x and PM emissions simultaneously for MPCI engine with LON, and also reduce fuel consumption.

Water injection in internal combustion engines is mainly used for additional cooling of the combustion chamber and knock suppression. The main advantages of the water injection are the potential for the enhancement of the performance of the engine. Studies conducted so far have aimed to track engine performance at maximum load and fuel consumption (brake-specific fuel consumption BSFC). Specialized literature is less consistent as regards to the injection of water at medium and low loads. This work monitors the effects of water injection at medium loads and the evolution processes of the four-strokes in the engine with and without water injection. A side-valve, air cooled natural aspirated engine with water and fuel injections in the intake manifold was used for this study. The work during expansion was increased, but this trend was reversed by the higher loads required during intake, compression and exhaust strokes, leading to a reduction in the indicated efficiency when water was injected.

A high precision and compact IOT based digital instrumentation setup to measure, display and record various tractor and implement system performance parameters was developed and installed on a 28.3 kW Tractor. The setup was capable of continuous monitoring and wirelessly transmitting tractor-implement performance parameters on a cloud platform such as engine speed, radiator fan speed, fuel consumption, draft, forward speed, lift arm angle, wheel slip, wheel slip, PTO speed, geo-location/position of the tractor, choking of seeds in the implement and vibrations experienced by the implement. For precision measurements, commercial transducers used in the system were calibrated and assessed under both static and dynamic conditions. The average calibration constant for fuel consumption, forward speed, lift arm angle and load cell were 0.00009804 L/pulse, 0.01610306 km/h/pulse, 0.056 mA/degree and 0.2575 mV/kN respectively. The system based on DataTaker DT 85 Data logger connected to a micro-computer through transducers capable of transferring data wirelessly was installed on John Deere 5038 tractor and was tested with a Spatially Modified No-Till Drill in agricultural field with varied implement depth.

Natural gas with H2 is widely used for lean-burn combustion, which leads to NOx emission as the main problem for it. For decreasing NOx emission and increasing thermal efficiency, the investigation on seeking the influence of H2 fractions on the mixture of CH4 and CO2 was conducted. Firstly, the ignition timing was decided through thermal efficiency and brake mean effective pressure (BMEP) for CH4 only. Then, combustion characteristics of CH4, CH4+CO2 and CH4+CO2+H2 were compared with volume percentage of H2 changing from 5% to 30%. Finally, the H2 injection strategy was checked between closed and open valve injections. Among these discussions, thermal efficiency, power output, BMEP and fuel consumption were evaluated. Results show that CO2 addition decreases power output and BMEP, leading to much more fuel consumption and lower thermal efficiency. When H2 is added, at the rich mixture conditions (λ＜1.0), power output and thermal efficiency decrease sharply as the mixture is enriched. However, at the lean-burn conditions (λ＞1.0), the decrease in flow rate of lower heating value (LHV) and increase in power output finally result in the higher efficiency with H2 addition. Moreover, when λ＞1.0, both low fuel consumption and high efficiency can be obtained with H2 addition to achieve the high BMEP. Furthermore, the open valve injection could obtain higher thermal efficiency, power output and BMEP with lower fuel consumption, suggesting that the H2 injection strategy should be well controlled with the ignition timing.

Contactless space debris removal systems are the promising direction for solving the space debris problem. An ion beam transportation method is based on blowing the space debris with a high-speed plasma flow generated by the engine of an active spacecraft. The aim of the work is to evaluate the effect of controlling the attitude motion of space debris on the amount of fuel consumption during the contactless transportation. Four control strategies for an active spacecraft are proposed and compared. The first strategy involves transporting the object without considering its attitude motion. The second strategy involves stabilizing the object in a stable angular equilibrium position. The third strategy assumes transportation the space debris in the oscillation mode with the maximum average force. The fourth strategy involves stabilizing the object in an angular position corresponding to the maximum ion force. The control laws for the spacecraft's thrusters and for the direction of the ion beam axis, implementing these strategies, are proposed. The simulation of the transportation of the rocket stage is carried out and the fuel costs for various strategies are calculated. The most preferable from the point of view of minimizing fuel consumption is the second strategy. The fourth strategy is ineffective. The results obtained in this work can be used in the preparation of contactless space debris removal missions.

This paper conducts a comparison study of constrained powered descent guidance laws for planetary pin-point landing from the viewpoints of guidance performance and algorithm structure. All considered algorithms are reformulated to incorporate the critical elements of the powered descent problem, such as the free flight time, thrust limitation and terrain constraint. With these requirements to meet, the performance of guidance laws is evaluated and compared via simulations. Specifically, the nominal landing simulation verifies the guidance accuracy, fuel consumption, and real-time performance, as well as the hazard avoidance capability. Meanwhile, the robustness of guidance laws is evaluated by the Monte Carlo simulation, and the performance difference in final landing dispersion is interpreted by analyzing the algorithm structure and characteristics. Lastly, the discussion on practicability indicates the promise of optimization-based algorithms in future planetary landing missions.

Most routing decisions assume that routing costs are modelled as a weighted sum of total distance and time traveled by delivery vehicles. However, this assumption does not apply for logistics operations in cities with significant road grades. We study an extension to the VRP model that plans vehicle routes considering the combined impact of detailed road grade information and vehicle load-weight in fuel consumption cost. We refer to this model as the VRP with Steep Roads (VRP-SR), which is formulated as an integer linear program and solved heuristically. In simulated experiments performed for a mountainous metropolitan region in Chile, we estimate operating cost reductions up to 12.4 % when compared to the real cost of a plan disregarding road grade information. In addition, we obtain valuable managerial insights from our routing plans; our planned routes tend to initially use roads with relatively small grades to avoid abrupt altitude changes with a loaded vehicle. Higher altitude changes are planned after the vehicle unloads a significant fraction of its cargo. Also, we identify instances in which it is cheaper to insert an intermediate depot visit to drop off weight and split a feasible route in two subroutes to travel over mountainous areas with a lighter vehicle.

The aim of this study is to find the effect of motorcycle tyre rolling resistance coefficient on the saving of fuel consumption. Five motorcycle tyres on the market was selected to determine the rolling resistance coefficient according to ISO18164-2005. These tyres had rolling resistance coefficient of 8.9, 10.3, 11.4, 12.4 and 13.7 (N/kN) respectively. Each tyre was installed on the motorcycle and performed the test on a chassis dynamometer with the constant motorcycle speed of 50 kilometres per hour. The average fuel consumption from each tyre was 0.0202, 0.0206, 0.0211, 0.0215 and 0.0218 respectively. The higher rolling resistance coefficient tyre will increase the fuel consumption. With a 10% decrease in rolling resistance coefficient, the fuel consumption will decrease by approximately 1%. By using rolling resistance coefficient of 12.0, the total fuel saving potential in Thailand would be about 16.6 million litres per year.

The vehicle fuel consumption gauge is a vehicle’s basic device that usually records the instantaneous as well as average fuel consumption of the vehicle, which brings a lot of convenience during the driving process. The individual real-time fuel consumption data are meaningless, but its continuous real-time fuel consumption data contains some information that may reveal the user’s privacy, which is sensitive information for users. There are hot topics on privacy protection of sensitive user information, however, most of the studies have focused on privacy protection at the time of data release and the need for a trusted third party. In this paper, we propose a negative survey-based approach that can be utilized to protect aggregated vehicle fuel consumption data against time-series-based differential attacks. Its anonymous algorithm on the user side and estimation algorithm on the server side are able to protect the user’s privacy regardless of changes in the user’s fuel consumption data. The experiment results show that our method can achieve better privacy protection with better application prospects in a simpler and more effective way.

Transportation is one of the most influential factors in greenhouse gas emissions and global warming. This paper studies a network with an automotive supply chain that includes one supplier and one manufacturer next to a fuel supply chain consisting of a fuel manufacturer, operating under government intervention. The car manufacturer sells its green and non-green products under the dual-channel system with stochastic demand and demand leakage. To decrease greenhouse gas emissions, the car manufacturer invests in fuel-efficient technology for green products to reduce car fuel consumption. Moreover, due to increasing public awareness of environmental issues, the supplier invests in greening efforts to provide raw materials for fuel-efficient cars. The fuel manufacturer determines the fuel price under the government supervision and the government considers subsidies to support it. The fuel manufacturer has the opportunity to export the surplus fuel to other markets at a higher price. The government supports the production of fuel-efficient cars with two policies of subsidy-taxation and granting loans to customers. By doing so, the car manufacturer improves the level of fuel-efficient technology and the greening efforts of the supplier are increased. Additionally, the amount of fuel consumption in the internal market is reduced and the fuel manufacturer can export more fuel to the external market. As a result, the profit of all members increases simultaneously and environmental conditions improve. This model is studied under two scenarios for the distribution function of stochastic part of demand: (1) the Stackelberg scenario with complete information as a uniform distribution function and (2) the Nash scenario with partial information on a distribution-free approach. Some numerical examples based on real cases are provided to examine the capability of the proposed models. Finally, some sensitivity analyses are applied to the main parameters to extract several in-depth managerial concepts.

Road transport has a significant effect on air pollution. Therefore, the article focuses on the impact of addition of “oxyhydrogen”, known as HHO, on selected vehicle features. These features, on which this study is focused, include the engine power and torque, composition of the exhaust gases and fuel consumption. There was also an impact of HHO on the value of pre-ignition as well as engine cleaning observed. HHO has been obtained from the generator designed and constructed for the purposes of this article. The measurements were performed under conditions of taking electric energy needed for HHO operation from the vehicle used in the measurements and also from the other source. The measurements were performed with vehicles of different kilometres driven, different technical conditions and different fuels and they were conducted under laboratory conditions to ensure a higher accuracy of the results. The results have shown a low impact of HHO addition of 2 dm3 min−1 on concentration of particular components of the exhaust gases. With HHO added, it was measured a mild decrease in the engine power and torque. Taking electric energy needed for HHO operation from the vehicle used in the measurement has evinced more considerably than addition of HHO into the engine.

This paper proposes a fuel-optimal deorbit scheme for space debris deorbit using tethered space tug. The scheme contains three stages named respectively as dragging, maintenance and swinging. In the first stage, the tug, propelled by continuous thrust, tows deorbit to a transfer orbit with a tether. Then in the second stage, the combination of the tug and the debris flies unpowered and uncontrolled to a swing point on the transfer orbit. Finally, in the third stage, the tug is propelled at the swing point and the rotation speed of the tethered system increases such that the debris obtains enough velocity increment. The trajectory optimization of the first stage is established considering the total fuel consumption of the three stages, whereas the dynamic model is simplified for computation efficiency. The solution to the optimal problem is obtained using a direct method based on Gauss pesudospectral discretization. Then a model predictive controller is designed to track the open-loop optimal reference trajectories, reducing the states’ deviations caused by model simplification and ignorance of perturbations. Furthermore, it is proved that the fuel-optimal swing point is the apogee of the transfer orbit. The paper analyzes the fuel consumption of a typical scenario and demonstrates effectiveness of the proposed deorbit scheme numerically.

We evaluate the impact of a grass-roots campaign promoting adoption of simple modifications to cooking fires in rural communities in Western Kenya. The modifications consist of adding rock-beds to open fires or simple woodstoves, increasing airflow, improving combustion efficiency, and decreasing the distance between the fire and cooking pot, which improves heat transfer. The campaign was sponsored by Sun24, a US-based non-profit organization. They trained women leaders active in a local Anglican diocese consisting of 9200 families. The women returned to their communities and held their own trainings at local parishes. We randomly sampled 1362 households from those communities to understand the impact of the campaign. 67% of surveyed families were aware of rockbeds: 51% had attended trainings themselves and 16% heard about rockbeds from relatives or neighbors. 85% of the families that had heard about rockbeds installed them at home and continued to use them for many months after first hearing about them. Nearly all users express a high degree of satisfaction with rockbeds and 67% of trained respondents report cooking two or more meals daily on with rockbeds. We examined variation in weekly and daily frequency of use and find larger households are twice as likely to use rockbeds daily and to cook all their meals with rockbeds than smaller households (p

Fuel opening injection pressure and injection timing are important injection parameters, and they have a significant influence on engine combustion, performance, and emissions. The focus of this work is to improve the performance and emissions of single-cylinder diesel engines by using injection parameters in engines running with rice bran biodiesel 10% blend (RB10+H₂) and 20% blend (RB20+H₂) with a fixed hydrogen flow rate of 7 lpm. In addition, hydrogen and biodiesel are excellent alternatives to conventional fuels, which can reduce energy consumption and strict emission standards. The investigation is conducted for three different opening injection pressure of 220, 240, 260 bar, and four different injection timings of 20°, 22°, 24°, and 26° bTDC. Results indicate that the sample ‘RB10+H₂’ provides 3.32% higher BTE and reduces the fuel consumption by 13% as diesel fuel. The blend RB10+H₂ attributes a maximum cylinder pressure of 68.7 bar and a peak HRR value of 49 J/oCA. Further, compared to diesel, RB10+H₂ blend emits lower CO, HC, and smoke opacity by 17%, 22%, and 16%, respectively. However, an almost 12% increase of nitrogen oxides for the RB10+H₂ blend is observed. However, with advanced injection timing and higher opening injection pressure, NOx emissions is slightly increased.

Platooning connected and autonomous vehicles (CAVs) can improve traffic and fuel efficiency. However, scalable platooning operations require junction-level coordination, which has not been well studied. In this paper, we study the coordination of vehicle platooning at highway junctions. We consider a setting where CAVs randomly arrive at a highway junction according to a general renewal process. When a CAV approaches the junction, a system operator determines whether the CAV will merge into the platoon ahead according to the positions and speeds of the CAV and the platoon. We formulate a Markov decision process to minimize the discounted cumulative travel cost, i.e., fuel consumption plus travel delay, over an infinite time horizon. We show that the optimal policy is threshold-based: the CAV will merge with the platoon if and only if the difference between the CAV’s and the platoon’s predicted times of arrival at the junction is less than a constant threshold. We also propose two ready-to-implement algorithms to derive the optimal policy. Comparison with the classical value iteration algorithm implies that our approach explicitly incorporating the characteristics of the optimal policy is significantly more efficient in terms of computation. Importantly, we show that the optimal policy under Poisson arrivals can be obtained by solving a system of integral equations. We also validate our results in simulation with a Real-time Strategy (RTS) using real traffic data. The simulation results indicate that the proposed method yields better performance compared with the conventional method.

Ni catalysts with preferred orientation and microsphere were fabricated by a facile one-step pulsed voltage electrodeposition (PVE) method, while the crystal plane orientation and catalytic activity for borohydride oxidation reaction (BOR) were researched systematically. The results indicate that adjusting pulse anode potential can effectively regulate the morphology and crystal plane preferred orientation of Ni catalysts. The optimal Ni-PVE0.15 catalyst exhibits the highest catalytic activity, stability, and fuel efficiency to BOR. The improvement of catalytic activity of Ni and fuel efficiency is attributed to the more exposure of the (220) crystal plane, which blocks the competing hydrogen evolution reaction (HER), increases the intrinsic activity of the catalytic sites to BOR, and strengthens the adsorption of escaping hydrogen to further electrooxidation. Meanwhile, the no-cracks feature of the microsphere increases the active sites and avoids the shedding of catalysts. The derived insights are important for the design of efficient nickel catalysts for practical applications of direct borohydride fuel cells.

The demand for Liquefied Natural Gas (LNG) in the global markets has changed significantly. As a result, industries have been forced to consider investing significantly in supply chains to achieve an efficient distribution of LNG for cost efficiency and carbon footprint reduction. To minimize the contribution of LNG maritime transportation to global climate change, there is a need to quantify the carbon footprints systematically. In this research, we developed a novel and practical model for estimating the carbon footprint for LNG maritime transport. Using the MATLAB program, an uncertainty-based carbon footprint accounting framework is created. The Monte Carlo simulation model is built to conduct a carbon footprint analysis while the main input parameters were changed within a reliable range. Later, a multivariate sensitivity analysis is performed using the Risk Solver software to estimate the most significant parameters on the net carbon footprints. The sensitivity analysis results showed that that steam process day and steaming fuel consumption are found to be the most sensitive parameters for the overall carbon footprint for both Laden and Ballast trips. Furthermore, it was found that the Q-Max vessel produces more carbon emissions when compared to the Q-Flex, although both are traveling the same distance and are using the same fuel type. The type of fuel is also significantly affecting the emission values due to the relevant carbon content in the fuel. Like the case of the two conventional vessels, the one that is running with the only LNG is found to have fewer emissions when compared to the one run with dual-mode.

Recently, the number of accessories in vehicle powertrains has been significantly increased. These accessories are necessary for vehicle operation. Accessories are driven by the engine, so their power demands result in a reduction of the vehicle's useful power. For heavy-duty vehicles, the most demanding accessories are the alternator; fan drive; heating, ventilation, and air-conditioning compressor, and brake air compressor. Their power demands vary according to the engine speed, and this may lead to notable energy losses especially in certain conditions, such as engine idling. This paper aims to evaluate the accessories power demands on a tractor equipped with an engine rating of 192 kW. In this tractor, a data logger together with auxiliary sensors were installed to monitor their power demands. During idling conditions, accessories power demands reach 38% of the power provided by the engine and this leads to non-negligible impact on fuel consumption. The fuel consumption caused by accessories and their potential fuel savings by introducing electrically-driven accessories into tractor powertrains were estimated. A potential fuel saving of 232 l per year could be attained. This may lead to a yearly saving of 278 million litres of fuel and 747 thousand tons of CO2 in the US. These savings were calculated assuming that all tractors are used with the same duty cycle. Even if it may not occur, thus the actual savings may be significantly different than that reported in this study, it permits to estimate if farming may benefit from tractors with electrically driven accessories.

Fuels obtained from waste in seeking of sustainable and environmentally friendly fuel are promising for internal combustion engines. In this study, an environmental pollution cost analysis was performed for a diesel engine fueled with blends of pyrolytic oil – biogas - neat diesel fuel. Five different test fuels were studied. Neat diesel fuel (DF), the fuel mixture prepared by blending 10% pyrolytic oil to 90% neat diesel fuel by volume (DF90P10). While the DF90P10 fuel was supplied to the engine from the injector, the experiments were carried out with different fuel combinations created by delivering gaseous biogas at constant flow rates of 1, 3 and 5 L/min from the intake manifold (DF90P10B1, DF90P10B3, DF90P10B5). The experiments were carried out in a single-cylinder, air-cooled, direct injection diesel engine, with a constant engine speed of 3000 rpm and four different engine loads ranging from 0.25 to 1 kW, with prepared fuel blends. Fuel consumption, exhaust emissions, exhaust and engine block temperatures were measured to make environmental pollution cost analysis. In these tests, it was found that the DF90P10B1 test fuel performs better results as compared to those of neat diesel fuel which is reference fuel and other test fuels in terms of environmental pollution cost analysis. Pyrolytic oil – biogas – diesel fuel mixtures in variable ratios, can be used as an alternative fuel instead of neat diesel in diesel engines without any engine modifications.

Motivated by the increasingly popular mixed traffic flow modeling, a novel heterogeneous continuum traffic flow model is proposed in this paper, which considers the differences of the driver's psychological headway and the driver's self-stabilizing effect on the optimal velocity related to the psychological headway. Leveraging the linear and nonlinear analysis approaches, the linear stability condition and the KdV-Burgers equation are yielded to describe the characteristic of traffic flow evolution. The bifurcation analysis on the proposed model is executed to discuss the existence and stability of Hopf bifurcation from a theoretical perspective. Numerical simulations are performed to verify the rationality of the abovementioned theory and explore the density evolution of heterogeneous traffic flow, including local cluster effect, fuel consumption and exhaust emissions and Hopf bifurcation, in which it is notable that the heterogeneous continuum traffic flow generates significantly different phenomena under different density conditions and the evolution of Hopf bifurcation in traffic flow is also involved.