| 1. |
- Babayev, Rafig, 1995, et al.
(författare)
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Double compression-expansion engine (DCEE) fueled with hydrogen: Preliminary computational assessment
- 2022
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Ingår i: Transportation Engineering. - : Elsevier BV. - 2666-691X. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Hydrogen (H2) is currently a highly attractive fuel for internal combustion engines (ICEs) owing to the prospects of potentially near-zero emissions. However, the production emissions and cost of H2 fuel necessitate substantial improvements in ICE thermal efficiency. This work aims to investigate a potential implementation of H2 combustion in a highly efficient double compression-expansion engine (DCEE). DICI nonpremixed H2 combustion mode is used for its superior characteristics, as concluded in previous studies. The analysis is performed using a 1D GT-Power software package, where different variants of the DICI H2 and diesel combustion cycles, obtained experimentally and numerically (3D CFD) are imposed in the combustion cylinder of the DCEE. The results show that the low jet momentum, free jet mixing dominated variants of the DICI H2 combustion concept are preferred, owing to the lower heat transfer losses and relaxed requirements on the fuel injection system. Insulation of the expander and removal of the intercooling improve the engine efficiency by 1.3 and 0.5%-points, respectively, but the latter leads to elevated temperatures in the high-pressure tank, which makes the selection of its materials harder but allows the use of cheaper oxidation catalysts. The results also show that the DCEE performance is insensitive to combustion cylinder temperatures, making it potentially suitable for other high-octane fuels, such as methane, methanol, ammonia, etc. Finally, a brake thermal efficiency of 56% is achieved with H2 combustion, around 1%-point higher than with diesel. Further efficiency improvements are also possible with a fully optimized H2 combustion system.
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| 2. |
- Gupta, S., et al.
(författare)
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Assessing whether artificial intelligence is an enabler or an inhibitor of sustainability at indicator level
- 2021
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Ingår i: Transportation Engineering. - : Elsevier BV. - 2666-691X. ; 4, s. 100064-
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Tidskriftsartikel (refereegranskat)abstract
- Since the early phase of the artificial-intelligence (AI) era expectations towards AI are high, with experts believing that AI paves the way for managing and handling various global challenges. However, the significant enabling and inhibiting influence of AI for sustainable development needs to be assessed carefully, given that the technology diffuses rapidly and affects millions of people worldwide on a day-to-day basis. To address this challenge, a panel discussion was organized by the KTH Royal Institute of Technology, the AI Sustainability Center and MIT Massachusetts Institute of Technology, gathering a wide range of AI experts. This paper summarizes the insights from the panel discussion around the following themes: The role of AI in achieving the Sustainable Development Goals (SDGs); AI for a prosperous 21st century; Transparency, automated decision-making processes, and personal profiling; and Measuring the relevance of Digitalization and Artificial Intelligence (D&AI) at the indicator level of SDGs. The research-backed panel discussion was dedicated to recognize and prioritize the agenda for addressing the pressing research gaps for academic research, funding bodies, professionals, as well as industry with an emphasis on the transportation sector. A common conclusion across these themes was the need to go beyond the development of AI in sectorial silos, so as to understand the impacts AI might have across societal, environmental, and economic outcomes. The recordings of the panel discussion can be found at: https://www.kth.se/en/2.18487/evenemang/the-role-of-ai-in-achieving-the-sdgs-enabler-or-inhibitor-1.1001364?date=2020–08–20&length=1&orglength=185&orgdate=2020–06–30 Short link: https://bit.ly/2Kap1tE
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| 3. |
- Saeed, Nausheen, et al.
(författare)
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A multimodal deep learning approach for gravel road condition evaluation through image and audio integration
- 2024
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Ingår i: Transportation Engineering. - : Elsevier. - 2666-691X. ; 16
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Tidskriftsartikel (refereegranskat)abstract
- This study investigates the combination of audio and image data to classify road conditions, particularly focusingon loose gravel scenarios. The dataset underwent binary categorisation, comprising audio segments capturinggravel sounds and corresponding images. Early feature fusion, utilising a pre-trained Very Deep ConvolutionalNetworks 19 (VGG19) and Principal component analysis (PCA), improved the accuracy of the Random Forestclassifier, surpassing other models in accuracy, precision, recall, and F1-score. Late fusion, involving decisionlevelprocessing with logical disjunction and conjunction gates (AND and OR) in combination with individualclassifiers for images and audio based on Densely Connected Convolutional Networks 121 (DenseNet121),demonstrated notable performance, especially with the OR gate, achieving 97 % accuracy. The late fusionmethod enhances adaptability by compensating for limitations in one modality with information from the other.Adapting maintenance based on identified road conditions minimises unnecessary environmental impact. Thismethod can help to identify loose gravel on gravel roads, substantially improving road safety and implementing aprecise maintenance strategy through a data-driven approach.
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| 4. |
- Shahroz, M., et al.
(författare)
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COVID-19 digital contact tracing applications and techniques : A review post initial deployments
- 2021
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Ingår i: Transportation Engineering. - : Elsevier BV. - 2666-691X. ; 5
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Tidskriftsartikel (refereegranskat)abstract
- The coronavirus disease 2019 (COVID-19) is a severe global pandemic that has claimed millions of lives and continues to overwhelm public health systems in many countries. The spread of COVID-19 pandemic has negatively impacted the human mobility patterns such as daily transportation-related behavior of the public. There is a requirement to understand the disease spread patterns and its routes among neighboring individuals for the timely implementation of corrective measures at the required placement. To increase the effectiveness of contact tracing, countries across the globe are leveraging advancements in mobile technology and Internet of Things (IoT) to aid traditional manual contact tracing to track individuals who have come in close contact with identified COVID-19 patients. Even as the first administration of vaccines begins in 2021, the COVID-19 management strategy will continue to be multi-pronged for the foreseeable future with digital contact tracing being a vital component of the response along with the use of preventive measures such as social distancing and the use of face masks. After some months of deployment of digital contact tracing technology, deeper insights into the merits of various approaches and the usability, privacy, and ethical trade-offs involved are emerging. In this paper, we provide a comprehensive analysis of digital contact tracing solutions in terms of their methodologies and technologies in the light of the new data emerging about international experiences of deployments of digital contact tracing technology. We also provide a discussion on open challenges such as scalability, privacy, adaptability and highlight promising directions for future work.
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| 5. |
- Xu, Yu, 1996, et al.
(författare)
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Improved efficiency with adaptive front and rear axle independently driven powertrain and disconnect functionality
- 2023
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Ingår i: Transportation Engineering. - 2666-691X. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Front and rear axle independently driven (FRID) powertrains are becoming a popular solution for electric vehicles (EVs) due to torque distribution capability which can enhance powertrain energy efficiency. Typically, permanent magnet synchronous machines (PMSMs) are used for FRID powertrains due to their high torque, and power density. However, the drive-cycle efficiency of FRID powertrains with PMSMs is typically reduced in comparison to single motor drives. This is due to the unwanted no-load losses of PMSMs in the field weakening region. To overcome this drawback of PMSM FRIDs, this paper proposes an adaptive front- and rear-axle independently driven (AFRID) powertrain, utilizing two dog clutches, so that the powertrain can be operated in different modes (rear, front, and all-wheel drive) by adaptively connecting and disconnecting the front and/or rear electric drive unit (EDU). A rule-based mode selection strategy is developed to utilize the flexibility of different powertrain operating modes of the powertrain for maximizing the energy efficiency of the EDU. The simulation results show that the suggested AFRID powertrain, in comparison to a common FRID powertrain, can improve the WLTC drive-cycle consumption from 22.17 kWhh to 20.50 kWhh per 100 km. Based on the route and road-load information, the energy-saving potential of the AFRID powertrain can be further improved to 20.37 kWhh per 100 km by a suggested predictive mode selection strategy, achieving an optimal mode selection.
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| 6. |
- Zhu, Xinda, et al.
(författare)
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Performance of new and aged injectors with and without fuel additives in a light duty diesel engine
- 2020
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Ingår i: Transportation Engineering. - : Elsevier BV. - 2666-691X. ; 1
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Tidskriftsartikel (refereegranskat)abstract
- Two sets of diesel injectors are tested in combination with common fuel additives in a multi-cylinder light-duty diesel engine. One set consists of new injectors and the other is aged by over 100,000 km use in a vehicle. Four fuels are tested with these injector sets to investigate the impact of fuel additives on combustion and emission characteristics. The results show that the aged injectors consistently deliver larger quantities of fuel for a given injection strategy, leading to a higher power output and deviating emissions. This is hypothesized to be due to drift in the injector actuating characteristics. The fuels tested are a baseline diesel quality, and blends of this fuel with three additives: a cetane number improver (2-ethylhexyl nitrate), a soot reducer (tripropylene-glycol monomethyl ether), and a flow improver consisting of quaternary ammonium salts. At the selected low and medium load operating conditions, these additives had a smaller effect on the emissions than the injector ageing, the most notable effect being that TPGME reduces the soot emissions even at the oxygen-rich conditions studied here. These studies will be followed by optical investigations of the in-cylinder effects on spray and combustion characteristics.
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