| 1. |
- Abuella, Mohamed, 1980-, et al.
(författare)
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Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping
- 2023
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Ingår i: Machine Learning and Knowledge Discovery in Databases. - Cham : Springer. - 9783031434297 - 9783031434303 ; , s. 226-241
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Konferensbidrag (refereegranskat)abstract
- The maritime industry is under pressure to increase energy efficiency for climate change mitigation. Navigational data, combining vessel operational and environmental measurements from onboard instruments and external sources, are critical for achieving this goal. Short-sea shipping presents a unique challenge due to the significant influence of surrounding landscape characteristics. With high-resolution onboard data increasingly accessible through IoT devices, appropriate data representations and AI/ML analytical tools are needed for effective decision support. The aim of this study is to investigate the fuel consumption estimation model’s role in developing an energy efficiency decision support tool. ML models that lacking explainability may neglect important factors and essential constraints, such as the need to meet arrival time requirements. Onboard weather measurements are compared to external forecasts, and our findings demonstrate the necessity of eXplainable Artificial Intelligence (XAI) techniques for effective decision support. Real-world data from a short-sea passenger vessel in southern Sweden, consisting of 1754 voyages over 15 months (More of data description and code sources of this study can be found in the GitHub repository at https://github.com/MohamedAbuella/ST4EESSS), are used to support our conclusions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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| 2. |
- Abuella, Mohamed, Postdoktor, 1980-, et al.
(författare)
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Spatial Clustering Approach for Vessel Path Identification
- 2024
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Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 12, s. 66248-66258
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Tidskriftsartikel (refereegranskat)abstract
- This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five clusters achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation. © 2013 IEEE.
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| 3. |
- Andric, Jelena, 1979, et al.
(författare)
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Development and Calibration of One Dimensional Engine Model for Hardware-in-the-Loop Applications
- 2018
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2018-April
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Tidskriftsartikel (refereegranskat)abstract
- The present paper aims at developing an innovative procedure to create a one-dimensional (1D) real-time capable simulation model for a heavy-duty diesel engine. The novelty of this approach is the use of the top-level engine configuration, test cell measurement data, and manufacturer maps as opposite to common practice of utilizing a detailed 1D engine model. The objective is to facilitate effective model adjustments and hence further increase the application of Hardware-in-the-Loop (HiL) simulations in powertrain development. This work describes the development of Fast Running Model (FRM) in GT-SUITE simulation software. The cylinder and gas-path modeling and calibration are described in detail. The results for engine performance and exhaust emissions produced satisfactory agreement with both steady-state and transient experimental data. Therefore, the presented methodology shows a great potential for testing and validation of new control strategies in Engine Management System (EMS) and for optimizing engine performance using HiL systems. The model has been successfully used in powertrain testing and calibration in the VIRtual TEst Cell (VIRTEC) system at Volvo Penta.
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| 4. |
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| 5. |
- Faghani, Ethan, et al.
(författare)
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Toward an Effective Virtual Powertrain Calibration System
- 2018
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2018-April
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Tidskriftsartikel (refereegranskat)abstract
- Due to stricter emission regulations and more environmental awareness, the powertrain systems are moving toward higher fuel efficiency and lower emissions. In response to these pressing needs, new technologies have been designed and implemented by manufacturers. As a result of increasing complexity of the powertrain systems, their control and optimization become more and more challenging. Virtual powertrain calibration, also known as model-based calibration, has been introduced to transfer a part of test bench testing into a virtual environment, and hence considerably reduce time and cost of product development process while increasing the product quality. Nevertheless, virtual calibration has not yet reached its full potential in industrial applications. Volvo Penta has recently developed a virtual test cell named VIRTEC, which is used in an ongoing pilot project to meet the Stage V emission standards. The integrated powertrain system includes engine, Exhaust Aftertreatment System (EATS), and Engine Management System (EMS). The objective of this paper is to describe the essential aspects required to increase the contribution of virtual testing in powertrain calibration activities. These aspects comprise the following: Hardware-in-the-Loop (HiL) system, simulation models, and working process for joint virtual and physical testing to facilitate efficient powertrain development process. The current paper describes the design, test and verification of a calibration platform based on the requirements of the project. The future phases in the current project (Virtual Calibration at Volvo Penta) will cover validation of the platform by performing calibrations in industrial scales on the virtual system.
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| 6. |
- Ghanaati, Ali, 1979, et al.
(författare)
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Design of a virtual test cell based on GMDH-type neural network for a heavy-duty diesel engine
- 2021
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Ingår i: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. - : SAGE Publications. - 2041-2991 .- 0954-4070. ; 235:2-3, s. 436-445
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Tidskriftsartikel (refereegranskat)abstract
- The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.
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| 7. |
- Sediako, Anton D., et al.
(författare)
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Heavy Duty Diesel Engine Modeling with Layered Artificial Neural Network Structures
- 2018
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2018-April
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Tidskriftsartikel (refereegranskat)abstract
- In order to meet emissions and power requirements, modern engine design has evolved in complexity and control. The cost and time restraints of calibration and testing of various control strategies have made virtual testing environments increasingly popular. Using Hardware-in-the-Loop (HiL), Volvo Penta has built a virtual test rig named VIRTEC for efficient engine testing, using a model simulating a fully instrumented engine. This paper presents an innovative Artificial Neural Network (ANN) based model for engine simulations in HiL environment. The engine model, herein called Artificial Neural Network Engine (ANN-E), was built for D8-600 hp Volvo Penta engine, and directly implemented in the VIRTEC system. ANN-E uses a combination of feedforward and recursive ANNs, processing 7 actuator signals from the engine management system (EMS) to provide 30 output signals. To improve the accuracy in predicting exhaust emissions, the ANNs were arranged into two layers, such that engine temperature and pressure output signals and their average rate of change act as extra inputs for exhaust emission signals. The simulation results show that the ANN-E model accurately predicts engine performance, engine temperatures and pressures along the flow path, as well as exhaust emissions. In addition, the modular nature of ANN-E makes it possible for fast rebuild of the model if engine components are changed. Therefore, the layered modular ANN modeling approach represents a powerful tool for virtual engine testing and calibration optimization.
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| 8. |
- Sjöblom, Jonas, 1968, et al.
(författare)
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Intrinsic Design of Experiments for Modeling of Internal Combustion Engines
- 2018
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2018-April
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Tidskriftsartikel (refereegranskat)abstract
- In engine research and development there are often different engine parameters that produce similar effects on the end-point results. When calibrating modern engines, a huge number of parameters needs to be set, which also includes compensation parameters for model imperfections. In this context, simpler, more robust, and physically based models should be beneficial both for calibration work load and powertrain performance. In this study, we present an experimental methodology that uses intermediate ("intrinsic") variables instead of engine parameters. By using simple thermodynamic models, the engine parameters EGR, IVC, and P Boost could be translated into oxygen concentration, temperature and gas density at the start of injection. The reason for this transformation of data is to "move" the Design of Experiment (DoE) closer to the situation of interest (i.e. the combustion) and to be able to construct simpler and more physically based models. In this example, the system was a diesel engine. However, the method can be applied to any experimental system that shares the non-intrinsic nature (e.g. the internal combustion engine), which makes this methodology general. The approach was demonstrated for a heavy-duty diesel engine and five design variables were investigated. Regression models were made using either the engine variables or the intrinsic variables and the resulting regression coefficients were compared and contrasted. By using exactly the same experiments but described in a different way (using the intrinsic variables), the optimization task becomes facilitated. Furthermore, by using physical properties instead of engine settings, these models should be more general and more robust during powertrain optimization.
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