Search results

Combustion of fuel with oxidizer inside a combustion chamber of an internal combustion engine forms inevitable oxides of nitrogen (NOx) due to high temperature at different locations of the combustion chamber. This study aims to quantify NOx formed inside the combustion chamber using two fuels, a conventional diesel (n-heptane) and a biodiesel (methyl oleate).

This research uses a computational fluid dynamics simulation of chemically reacting fluid flow to quantify and compare oxides of nitrogen (NOx) in a compression ignition (CI) engine. The study expends species transport model of ANSYS FLUENT. The simulation model has provided the temperature profile inside the combustion chamber, which is subsequently used to calculate NOx using the NOx model. The simulation uses a single component hydrocarbon and oxygenated hydrocarbon to represent fuels; for instance, it uses n-heptane (C₇H₁₆) for diesel and methyl-oleate (C₁₉H₃₆O₂) for biodiesel. A stoichiometric air–fuel mixture is used for both fuels. The simulation runs a single cylinder CI engine of 650 cm³ swept volume with inlet and exhaust valves closed.

The pattern for variation of velocity, an important flow parameter, which affects combustion and subsequently oxides of nitrogen (NOx) formation at different piston locations, is similar for the two fuels. The variations of in-cylinder temperature and NOx formation with crank angles have similar patterns for the fuels, diesel and biodiesel. However, the numerical values of in-cylinder temperature and mass fraction of NOx are different. The volume averaged static peak temperatures are 1,013 K in case of diesel and 1,121 K in case of biodiesel, while the mass averaged mass fractions of NOx are 15 ppm for diesel and 141 ppm for biodiesel. The temperature rise after combustion is more in case of biodiesel, which augments the oxides of nitrogen formation. A new parameter, relative mass fraction of NOx, yields 28% lower value for biodiesel than for diesel.

This work uses a new concept of simulating simple chemical reacting system model to quantify oxides of NOx using single component fuels. Simplification has captured required fluid flow data to analyse NOx emission from CI engine while reducing computational time and expensive experimental tests.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The purpose of this study is to assess the performance of fuel blends containing ethanol and gasoline in spark ignition engines. The aim is to explore alternative fuels that can enhance performance while minimizing or eliminating adverse environmental impacts, particularly in the context of limited fossil fuel availability and the need for sustainable alternatives.

The authors used the Ricardo Wave software to evaluate the performance of fuel blends with varying ethanol content (represented as E0, E10, E25, E40, E55, E70, E85 and E100) in comparison to gasoline. The assessment involved different composition percentages and was conducted at various engine speeds (1,500, 3,000, 4,500 and 6,000 rpm). This methodology aims to provide a comprehensive understanding of how different ethanol-gasoline blends perform under different conditions.

The study found that, across all fuel blends, the highest brake power (BP) and the highest brake-specific fuel consumption (BSFC) were observed at 6,000 rpm. Additionally, it was noted that the presence of ethanol in gasoline fuel blends has the potential to increase both the BP and BSFC. These findings suggest that ethanol can positively impact the performance of spark-ignition engines, highlighting its potential as an alternative fuel.

This research contributes to the ongoing efforts in the automotive industry to find sustainable alternative fuels. The use of Ricardo Wave software for performance assessment and the comprehensive exploration of various ethanol-gasoline blends at different engine speeds add to the originality of the study. The emphasis on the potential of ethanol to enhance engine performance provides valuable insights for motor vehicle manufacturers and researchers working on alternative fuel solutions.

The case study has been prepared for management students/business executives to understand electric vehicle (EV) business, business environment, industry competition and strategic planning and strategy implementation.

The size of the Indian passenger vehicle market was valued at US$32.70bn in 2021; it was projected to touch US$54.84bn by 2027 with a Compound Annual Growth Rate (CAGR) of more than 9% during the period 2022–2027. The passenger vehicle industry, a part of the overall automotive industry, was expected to grow at a rapid pace, as the Indian economy was rising at the fastest rate. However, the Government of India (GoI) had put a condition on the growth scenario by mandating that 100% of vehicles produced would be EVs by 2030. Tata Motors (TaMo), a domestic player in the market, had been facing a challenging competitive environment. Although it had been incurring losses, it had successfully ventured into the EV business. TaMo had taken advantage of the first mover by creating an electric mobility business vertical to enable the company to deliver on its aspiration of providing innovative and competitive e-mobility solutions. TaMo leadership had been putting efforts to scale up the electric mobility business, thus, contributing to GoI’s plan for electric mobility. Shailesh Chandra, president of electric mobility business, had a big task in hand. He had to scale up EV production and sales despite insufficient infrastructure for charging and shortages of electronic components for manufacturing.

The case study has been prepared for management students/business executives for strategic management class. It is recommended that the case study is distributed in advance so that the students can prepare well in advance for classroom discussions. Groups will be created to delve into details for a specific question. While one group will make their presentation, the other groups will question the solution provided and give suggestions.

Teaching notes are available for educators only.

CSS 11: Strategy.

This paper aims to assess the impact of electrifying the environmental control system (ECS) and ice protection system (IPS), the primary pneumatic system consumers in a conventional commercial transport aircraft, on aircraft weight, range, and fuel consumption.

The case study was carried out on Airbus A321-200 aircraft. Design, modelling and analysis processes were carried out on Pacelab SysArc software. Conventional and electrical ECS and IPS architectures were modelled and analysed considering different temperature profiles.

The simulation results have shown that the aircraft model with ±270 VDC ECS and IPS architecture is lighter, has a more extended range and has less relative fuel consumption. In addition, the simulation results showed that the maximum range and relative fuel economy of all three aircraft models increased slightly as the temperature increased.

Considering the findings in this paper, it is seen that the electrification of the conventional pneumatic system in aircraft has positive contributions in terms of weight, power consumption and fuel consumption.

The positive contributions in terms of weight, power consumption and fuel consumption in aircraft will be direct environmental and economic contributions.

Apart from the conventional ECS and IPS of the aircraft, two electrical architectures, 230 VAC and ±270 VDC, were modelled and analysed. To see the effects of the three models created in different temperature profiles, analyses were done for cold day, ISA standard day and hot day temperature profiles.

There is a lot of talk about the electric car today, but these vehicles are not new. Indeed, thebeginning of the 20th century saw electricity and the automobile take hold in North American society, so that by 1910, the electric car was everywhere. Until the turn of the 1920s, a new era dawned for transportation in the USA, but without the electric car. The purpose of this study is to question Why did it happen.

This paper develops such a comparison, not of the cars themselves, through a detailed engineering analysis, but rather of the marketing of electric vehicles in the USA in 1910 and 2010, as it appeared in the marketing strategies of the manufacturers.

There are many technical and economic reasons for this, but not only; there are also commercial strategy reasons. The position of manufacturers, especially through advertising and the press, can tell us about this golden age of the electric car, what precipitated its fall, and its reappearance a century later.

It is a comparison of images, of how electric vehicles had been and are proposed to the public, through the exploration of mainly promotional material and newspaper articles.

This study explores how titanium oxide (TiO₂) filler influences the specific wear rate (SWR) in flax fiber-reinforced epoxy composites (FFRCs) through a Taguchi approach. It aims to boost abrasive wear resistance by incorporating TiO₂ filler, promoting sustainable and eco-friendly materials.

This study fabricates epoxy/flax composites with TiO₂ particles (0–8 wt%) using hand layup. Composites were tested for wear following American Society for Testing and Materials (ASTM) G99-05. Statistical analysis used Taguchi design of experiments (DOE), with ANOVA identifying key factors affecting SWR in abrasive sliding conditions.

The study illuminates how integrating TiO2 filler particles into epoxy/flax composites enhances abrasive wear properties. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, wt% of TiO₂ and sliding distance. Grit size has the highest effect at 43.78%, and wt% TiO₂ filler contributes 15.61% to SWR according to ANOVA. Notably, the Taguchi predictive model closely aligns with experimental results, validating its reliability.

This paper integrates TiO₂ filler and flax fibers to form a novel hybrid composite with enhanced tribological properties in epoxy composites. The use of Taguchi DOE and ANOVA offers valuable insights for optimizing control variables, particularly in natural fiber-reinforced composites (NFRCs).

The purpose of this study is to investigate the impact of titanium oxide (TiO₂) filler on the abrasive wear properties of bamboo fiber reinforced epoxy composites (BFRCs) using a Taguchi approach. The study aims to enhance the abrasive wear resistance of these composites by introducing TiO₂ filler as a potential reinforcement, thus contributing to the development of sustainable and environmentally friendly materials.

This study focuses on the fabrication of epoxy/bamboo composites infused with TiO₂ particles within the Wt.% range of 0–8 Wt.% using hand layup techniques. The resulting composites were subjected to wear testing according to ASTM G99-05 standards. Statistical analysis of the wear results was carried out using the Taguchi design of experiments (DOE). Additionally, an analysis of variance (ANOVA) was used to determine the influential control factors impacting the specific wear rate (SWR) and coefficient of friction (COF).

The study illuminates how integrating TiO₂ filler enhances abrasive wear in epoxy/bamboo composites. Statistical analysis of SWR highlights abrasive grit size (grit) as the most influential factor, followed by normal load, Wt.% of TiO₂ and sliding distance. Analysis of the COF identifies normal load as the primary influential factor, followed by grit, Wt.% of TiO₂ and sliding distance. The Taguchi predictive model closely aligns with experimental results, validating its reliability. The morphological study revealed significant differences between the unfilled and TiO₂-filled composites. The inclusion of TiO₂ improved wear resistance, as evidenced by reduced surface damage and wear debris.

This research paper aims to integrate TiO₂ filler and bamboo fibers to create an innovative hybrid composite material. TiO₂ micro and nanoparticles show promise as filler materials, contributing to improved tribological properties of epoxy composites. The utilization of Taguchi’s DOE and ANOVA for statistical analysis provides valuable guidance for academic researchers and practitioners in optimizing control variables, especially in the context of natural fiber reinforced composites.

This paper aims to focus on a medical goods distribution problem and pharmacological waste collection by plug-in hybrid vehicles with some real-world restrictions. In this research, considering alternative energy sources and simultaneous pickup and delivery led to a decrease in greenhouse gas emissions and distribution costs, respectively.

Here, this problem has been modeled as mixed-integer linear programming with the traveling and energy consumption costs objective function. The GAMS was used for model-solving in small-size instances. Because the problem in this research is an NP-hard problem and solving real-size problems in a reasonable time is impossible, in this study, the artificial bee colony algorithm is used.

Then, the algorithm results are compared with a simulated annealing algorithm that recently was proposed in the literature. Finally, the results obtained from the exact solution and metaheuristic algorithms are compared, analyzed and reported. The results showed that the artificial bee colony algorithm has a good performance.

In this paper, medical goods distribution with pharmacological waste collection is studied. The paper was focused on plug-in hybrid vehicles with simultaneous pickup and delivery. The problem was modeled with environmental criteria. The traveling and energy consumption costs are considered as an objective function.

Entrepreneurial ventures aspiring to disrupt existing market incumbents often use business-model innovation to increase the attractiveness of their offerings. A value proposition is the central element of a business model, and is critical for this purpose. However, how entrepreneurial ventures modify their value propositions to increase the attractiveness of their comparatively inferior offerings is not well understood. The purpose of this paper is to analyze the value proposition innovation (VPI) of aspiring disruptors.

The authors used a flexible pattern matching approach to ground the inductive findings in extant theory. The authors conducted 21 semi-structured interviews with managers from startups in the global electric vehicle industry.

The authors developed a framework, showing two factors, determinants and tactics, that play a key role in VPI connected by a continuous feedback loop. Directed by the determinants of cognitive antecedents, development drivers and realization capabilities, aspiring disruptors determine the scope, focus and priorities of various configuration and support tactics to enable and secure the success of their value proposition.

The authors contribute to theory by showing how cognitive antecedents, development drivers and capabilities determine VPI tactics to disrupt existing market incumbents, furthering the understanding of configuration tactics. The results have important implications for disruptive innovation theory, and entrepreneurship research and practice, as they offer an explanatory framework to analyze strategies of aspiring disruptors who increase the attractiveness of sustainable technologies, thereby accelerating their diffusion.