Enhancing the accuracy of engine calibration through a computer aided calibration algorithm

Abstract This paper addresses a novel Computer Aided Calibration software developed by the authors to overcome a critical issue of the traditional calibration process: improve the calibration accuracy. The algorithm includes some innovative features aimed at error minimization through a complete parametric analysis of a target ECU functions. Therefore, it is possible to assess if further quantities that are not considered as calibration parameters within the current ECU function model actually affect the quantity estimated by the function itself. If so, a more accurate physical model can be implemented within the ECU function to increase the accuracy of the calibration process

Colorectal cancer after bariatric surgery (Cric-Abs 2020): Sicob (Italian society of obesity surgery) endorsed national survey

Background The published colorectal cancer (CRC) outcomes after bariatric surgery (BS) are conflicting, with some anecdotal studies reporting increased risks. The present nationwide survey CRIC-ABS 2020 (Colo-Rectal Cancer Incidence-After Bariatric Surgery-2020), endorsed by the Italian Society of Obesity Surgery (SICOB), aims to report its incidence in Italy after BS, comparing the two commonest laparoscopic procedures-Sleeve Gastrectomy (SG) and Roux-en-Y gastric bypass (GBP). Methods Two online questionnaires-first having 11 questions on SG/GBP frequency with a follow-up of 5-10 years, and the second containing 15 questions on CRC incidence and management, were administered to 53 referral bariatric, high volume centers. A standardized incidence ratio (SIR-a ratio of the observed number of cases to the expected number) with 95% confidence intervals (CI) was calculated along with CRC incidence risk computation for baseline characteristics. Results Data for 20,571 patients from 34 (63%) centers between 2010 and 2015 were collected, of which 14,431 had SG (70%) and 6140 GBP (30%). 22 patients (0.10%, mean age = 53 +/- 12 years, 13 males), SG: 12 and GBP: 10, developed CRC after 4.3 +/- 2.3 years. Overall incidence was higher among males for both groups (SG: 0.15% vs 0.05%; GBP: 0.35% vs 0.09%) and the GBP cohort having slightly older patients. The right colon was most affected (n = 13) and SIR categorized/sex had fewer values < 1, except for GBP males (SIR = 1.07). Conclusion Low CRC incidence after BS at 10 years (0.10%), and no difference between procedures was seen, suggesting that BS does not trigger the neoplasm development

Internal combustion engine base calibration: computer aided tools and methodologies for the experimental effort reduction

Over the last decades, internal combustion engines have undergone a continuous evolution to achieve better performance, lower pollutant emissions and reduced fuel consumption. The pursuit of these often-conflicting goals involved changes in engine architecture in order to carry out advanced management strategies. Therefore, Variable Valve Actuation, Exhaust Gas Recirculation, Gasoline Direct Injection, turbocharging and powertrain hybridization have found wide application in the automotive field. However, the effective management of such a complex system is due to the contemporaneous development of the on-board Engine Electronic Control Unit. In fact, the additional degrees of freedom available for the engine regulation highly increased the complexity of engine control and management, resulting in a very expensive and long calibration process. Indeed, the functions of the engine control units are calibrated trying to reduce the error between the quantity obtained from the ECU algorithms and the experimental quantity in a wide range of engine working conditions. To this aim, extensive experimental campaigns are carried out on the test bench, in which thousands of operating conditions are analyzed, resulting in high costs for the realization of the process. Figure - A schematizes the traditional base engine calibration process. Figure - A - main stages of the traditional base engine calibration process (on the left), and the proposed methodologies (on the center and on the right) With the aim to overcome the above issues, two methodologies have been investigated. The purpose of the proposed methodologies is to reduce the number of the experimental bench tests without reducing the performance of the control unit functions. A first effective methodology is based on the use of Neural Networks (NN) to overcome some critical issues concerning the calibration of engine control parameters. NN are adopted to provide a detailed engine data sheet starting from a reduced number of experimental data. The potential of the proposed methodology has been verified using this detailed data set as input to a specific Computer Aided Calibration algorithm developed in this work and evaluating the achievable calibration performance. In particular, the calibration performance has been assessed with reference to a specific ECU function. The research clearly demonstrates the effectiveness of the proposed approach since the calibration performance falls within acceptable limits even after a 60% cut of the experimental data usually acquired for calibration purposes, highlighting how the use of neural networks can allow a significant reduction of the experimental effort along with its related times and costs. A second methodology based on the adoption of 1D thermo-fluid dynamic analysis is proposed. In particular, starting from a complete experimental set of data currently used for the base calibration of a reference spark ignition engine, a novel procedure based on vector optimization approach is used to reliably calibrate a 1D engine model starting from a reduced experimental dataset. Once validated, the engine model is used as a virtual test bench to reproduce the experimental campaign numerically, thus obtaining a detailed and complete dataset exploitable for calibration purposes, here called numerical or virtual dataset. The potential of the proposed methodology has been verified by comparing the experimental and virtual dataset. The research clearly demonstrates the effectiveness of the proposed approach since the mean errors are comparable with the measurement errors. Therefore, the methodology shows promising results concerning the use of numerical dataset obtained from reliable 1D CFD engine models as input to computer aided calibration software. In this way, a significant cut to the experimental campaign required for calibration purposes is achieved, with their related times and costs

Engine Valvetrain Lift Prediction Using a Physic-based Model for The Electronic Control Unit Calibration

The electronic control has an increasingly important role in the evolution of the internal combustion engine (ICE) and the vehicle. Research in the automotive sector, in this historical period, is dictated by three main guidelines: reducing polluting emissions and fuel consumption while maintaining high performance. The Electronic Control Unit (ECU) has made it possible, complicating the engine both in terms of architecture and in terms of strategies, controlling, through simplified functions, physical phenomena in an ever more precise way. The ECU functions are experimentally calibrated, reducing the error between the quantity estimated by the function and the experimental quantity over the entire operating range of the engine, developing extensive experimental campaigns. The calibration process of the ECU functions is one of the longest and most expensive processes in the development of a new vehicle. Some lines of research have been explored to reduce the experimental tests to be carried out on the test bench. The use of neural networks (NN) has proven to be effective, leading to a reduction in experimental tests from 40 to 60%. Another methodology consists in the use of 1D/0D Thermo-fluid dynamic models of the ICE. These models are used as virtual test benches and through them it is possible to carry out the experimental campaigns necessary for the calibration of the control unit functions. At the real test bench, only the few experimental tests necessary for the validation of the model must be carried out. One of the simplifications that is usually made in the 1D/0D ICE models consists in assigning a single intake and exhaust valve lift, without taking into account the effect of the engine speed on the valve lift in early intake valve closure (EIVC) mode for engines equipped with VVA. This phenomenon has a not negligible effect on engine performance, especially at high engine speeds. In the case of engine models equipped with VVA, the valve lift cannot be imposed, since it is unique for each closing angle at each engine speed. Indeed, in order to assign the correct valve lift for a given engine speed and EIVC, numerous experimental tests should be carried out, making vain the beneficial effects of the method. In this work, the authors propose the use of a 0D/1D CFD model of the entire electro-hydraulic valvetrain VVA module, coupled with 1D lumped mass for reproducing the linear displacements of the intake valve, and for simulating the interactions between flow and mechanical systems of the solenoid hydro-mechanical valve. Thus, model simulations allow to predict the valve lift in all the necessary conditions in the experimental campaigns for the calibration of the control unit functions. Starting from geometric valvetrain data, the model has been validated with a parametric analysis of some variables on which there was greater uncertainty, by comparing the valve lift obtained by the model with the experimental ones in certain engine speeds. Subsequently, the authors have obtained the valve lifts in conditions not used for model validation, comparing them with their respective experimental lifts. The model has proven to be sensitive to the effect of the variation of the engine speed, reproducing the valve lift with a low error. In this way it is possible to reduce the experimental effort aimed to the calibration process considering that the virtual experimental campaign has proven to be reliable

A Model-Based Computer Aided Calibration Methodology Enhancing Accuracy, Time and Experimental Effort Savings Through Regression Techniques and Neural Networks

In the last few years, the automotive industry had to face three main challenges: compliance with more severe pollutant emission limits, better engine performance in terms of torque and drivability and simultaneous demand for a significant reduction in fuel consumption. These conflicting goals have driven the evolution of automotive engines. In particular, the achievement of these mandatory aims, together with the increasingly stringent requirements for carbon dioxide reduction, led to the development of highly complex engine architectures needed to perform advanced operating strategies. Therefore, Variable Valve Actuation (VVA), Exhaust Gas Recirculation (EGR), Gasoline Direct Injection (GDI), turbocharging, powertrain hybridization and other solutions have gradually and widely been introduced into modern internal combustion engines, enhancing the possibilities of achieving the required goals. However, none of the improvements would have been possible without the contextual development of electronics. In fact, that solutions have highly increased the complexity of engine control and management because of the degrees of freedom available for the engine regulation, thus resulting in a long calibration time. In particular, base calibration is the most onerous phase of the engine control, both in terms of experimental and computational effort and costs. This paper addresses some critical issues concerning the calibration of control parameters through the use of a specific Model-Based Computer Aided Calibration algorithm developed by the authors to automate the calibration process and minimize calibration errors. The proposed methodology is also based on the use of neural networks (NN). In particular, starting from a reduced number of experimental data, NN provide a detailed engine data sheets used as input to the actual calibration process itself. The proposed algorithm provides optimal portability and reduced calibration time. The research also highlights how the developed methodology could be useful to identify possible enhancements for specific ECU engine models that can improve the accuracy of the calibration process by using more detailed physically based functions. The results of the proposed research clearly highlight how, in engine control, more accurate physical modeling may lead to promising results and better performance, ultimately enhancing the accuracy, time, experimental effort and cost savings of the calibration process

Volumetric efficiency estimation based on neural networks to reduce the experimental effort in engine base calibration

Over the last decades, internal combustion engines have undergone a continuous evolution to achieve better performance, lower pollutant emissions and reduced fuel consumption. This evolution involved changes in the engine architecture needed to perform advanced management strategies. Therefore, Variable Valve Actuation, Exhaust Gas Recirculation, Gasoline Direct Injection, turbocharging and powertrain hybridization have widely equipped modern internal combustion engines. However, the effective management of a such complex system is due to the contemporaneous development of the on-board Engine electronic Control Unit. In fact, the additional degrees of freedom available for the engine regulation highly increased the complexity of engine control and management, resulting in a very expensive and long calibration process. For this reason, this study proposes an effective methodology based on the use of Neural Networks to overcome some critical issues concerning the calibration of engine control parameters. NN are adopted to provide a detailed engine data sheet starting from a reduced number of experimental data. To verify the potential of the proposed methodology, this detailed data set is subsequently used as input to a specific Computer Aided Calibration algorithm developed by the authors and the achievable calibration performance are evaluated. In particular, the calibration performance was assessed with reference to a specific ECU function in this paper. The research clearly demonstrates the effectiveness of the proposed approach since the calibration performance falls within acceptable limits even after a 60% cut of the experimental data usually acquired for calibration purposes, highlighting how the use of neural networks can allow a significant reduction of the experimental effort along with its related times and costs

Early Stage Calibration of a Formula SAE Engine 1-D Fluid Dynamic Model with Limited Experimental Data

This work addresses the early stage calibration of a Formula SAE engine 1-D fluid dynamic model starting from limited experimental data. The availability of an engine model since the early stages of the development of a new Formula SAE vehicle allows to carry out preliminary analyses or ECU calibration. A few experimental tests have been executed at wide open throttle and variable engine speed. Then, a 1D thermo-fluid dynamic engine model has been developed starting from the geometry data of the engine. A vector optimization problem has been then solved to calibrate the engine model. In particular, the error minimization between numerical and experimental values of the torque in different engine operating conditions has been set as objective of the optimization process. Finally, starting from the results of the proposed calibration methodology, a decision-making criterion allowed the identification of a single optimal solution within the Pareto optimal front together with the related values for the set of calibration parameters. The results highlight how the proposed calibration procedure could be usefully adopted to set an early stage engine model which could be properly adopted to preliminarily detect the effects of geometric changes or control parameters variations on the main engine performance