Academic literature on the topic 'DoE calibration optimization'
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Journal articles on the topic "DoE calibration optimization"
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Stubler, Timo, Matthias Niegl, Martin Förster, Peter E. Pfeffer, and Ravi Abhishek. "Development and model-based calibration of the lateral controllers “steer-by-angle” and “steer-by-torque” of a lane keeping assistance system." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 8 (January 24, 2021): 2136–47. http://dx.doi.org/10.1177/0954407020987437.
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Advanced Driver Assistance Systems (ADAS) such as Lane Keeping Assistance (LKA) systems are in the focus of current vehicle developments. Of special interest is the calibration task, which plays an increasingly decisive role in early development stages. At this point it is essential to analyze the pre-calibrated controller concepts by appropriate simulation methods. A software toolchain is introduced, using Model-in-the-Loop (MiL) for the evaluation and calibration of the LKA system. Within, a Design-of-Experiment (DoE) tool is integrated together with the simulation environment. This enables the examination of the two different lateral controller concepts Steer-by-Angle (SbA) and Steer-by-Torque (SbT). Their lateral guidance performance is characterized by Key Performance Indicators (KPI), which are derived from simulation quantities of straight-line and cornering maneuvers. Further, statistical behavior models are generated based on those KPI and controller design variables (DV). The subsequent optimization process leads to high performances of both controllers. In particular, the SbA controller shows higher efficiency under the impact of disturbances as well as the reference reaction with lower settling times. Overall, the achieved lateral guidance performances indicate the potential of both control algorithms in an early development stage. With the software toolchain, a platform for further LKA system calibration and lateral guidance performance optimization is established.
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Wang, Jun, Lizhong Shen, Yuhua Bi, Shaohua Liu, and Mingding Wan. "Power recovery of a variable nozzle turbocharged diesel engine at high altitude by response surface methodology and sequential quadratic programming." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 4 (February 21, 2018): 810–23. http://dx.doi.org/10.1177/0954407017753913.
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Based on a review of the research methods about diesel engine performance recovery at high altitude and an experimental investigation, by optimizing variable nozzle turbocharger (VNT) and fuel supply system calibration parameters a novel method is proposed to enhance the performance of a turbocharged diesel engine at high altitude. At an altitude of 1920 m, four calibration parameters deeply affecting performance of the diesel engine were selected at the rated power condition, that is, injection quantity, injection timing, injection pressure, and VNT nozzle opening. In order to reduce thermal load of the diesel engine running in the plateau environment, reasonable coded levels of Design of Experiments (DoE) factors were chosen, and an experimental design matrix was selected based on the Box–Behnken design. The interaction effects of the four calibration parameters on engine performance were investigated using the response surface methodology. Power recovery optimization was carried out by means of sequential quadratic programming under a minimum smoke limit and durability constraints. The results show that this performance optimization method can effectively recover engine performance at high altitude. Moreover, it can, to an extent, alleviate the problems such as deterioration of fuel consumption and high thermal load induced by the rise in elevation. With optimized calibration parameters, the rated power of the diesel engine at an altitude of 1920 m proved to be recovered to that at sea level, and there was an increase of brake specific fuel consumption by less than 3% compared with that in the plain area, which met the performance and durability requirements for general turbocharged internal combustion engines at altitudes lower than 2000 m.
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Huang, Chao-Tsai, Rui-Ting Xu, Po-Hsuan Chen, Wen-Ren Jong, and Shia-Chung Chen. "Investigation on the machine calibration effect on the optimization through design of experiments (DOE) in injection molding parts." Polymer Testing 90 (October 2020): 106703. http://dx.doi.org/10.1016/j.polymertesting.2020.106703.
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Di Blasio, Gabriele, Mauro Viscardi, and Carlo Beatrice. "DoE Method for Operating Parameter Optimization of a Dual-Fuel BioEthanol/Diesel Light Duty Engine." Journal of Fuels 2015 (January 21, 2015): 1–14. http://dx.doi.org/10.1155/2015/674705.
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In recent years, alcoholic fuels have been considered as an alternative transportation biofuel even in compression ignition engines either as blended in diesel or as premixed fuel in the case of dual-fuel configuration. Within this framework, the authors investigated the possibility to improve the combustion efficiency when ethanol is used in a dual-fuel light duty diesel engine. In particular, the study was focused on reducing the HC and CO emissions at low load conditions, acting on the most influential engine calibration parameters. Since this kind of investigation would require a significant number of runs, the statistical design of experiment methodology was adopted to reduce significantly its number. As required by the DoE approach, a set of factors (injection parameters, etc.) were selected. For each of them, two levels “high” and “low” were defined in a range of reasonable values. Combining the levels of all the factors, it was possible to evaluate the effects and the weight of each factor and of their combination on the outputs. The results identified the rail pressure, the pilot, and post-injection as the most influential emission parameters. Significant reductions of unburnt were found acting on those parameters without substantial penalties on the global engine performances.
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Albers, Albert, Alexander Schwarz, Rolf Hettel, and Matthias Behrendt. "Time Efficient Testing of Hybrid Electric Vehicles Using Automated Identificated Physical Model Structures." Applied Mechanics and Materials 391 (September 2013): 118–22. http://dx.doi.org/10.4028/www.scientific.net/amm.391.118.
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Validation and optimization of technical systems are central activities in the product development process. One part of it is the calibration and validation on a level, which covers the whole vehicle. The aspect, that plays the most important role in both validation and optimization, is the driving condition. Especially in the case of hybrid vehicles, state variables like the state of charge (SOC) have great influence on the operating strategy and therefore on assessment criteria.The article’s objective is to present a procedure, which performs the conditioning and brings the planned maneuver into an order, which reduces the total needed conditioning duration. Thereby a lot of time can be saved, according to the type and amount of the possible maneuver and state values. In addition to optimizing the order of conventional maneuver, the procedure can be used to optimize the list of maneuver in a DOE-Plan. Thereby the maneuver of the individual criteria can be re-sorted as well as the designparametervariation. The IPEK-X-in-the-Loop framework (XiL) is the basis for the approach and will be used as a validation environment in an acoustic roller test bench with vehicle-in-the-loop technology.
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Galindo, José, Héctor Climent, Joaquín de la Morena, David González-Domínguez, Stéphane Guilain, and Thomas Besançon. "Experimental and modeling analysis on the optimization of combined VVT and EGR strategies in turbocharged direct-injection gasoline engines with VNT." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 10-11 (March 19, 2021): 2843–56. http://dx.doi.org/10.1177/09544070211004502.
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The combination of a growing number of complex technologies in internal combustion engines (ICE) is commonplace, due to the need of complying with the tight pollutant regulations and achieving high efficiencies. Hence the work of calibration engineers is led by a constant increase in degrees of freedom in ICE design. In this research work, a wide analysis on the optimization of combined variable valve timing (VVT) and exhaust gases recirculation (EGR) strategies is developed, in order to reduce fuel consumption in a EURO 6 1.3l 4-stroke 4-cylinder, gasoline, turbocharged, direct-injection engine, also equipped with a variable nozzle turbine (VNT). For that purpose, a methodology which combines 1D engine simulations with limited experimental work was applied. First, the data from 25 experimental tests distributed into three steady engine operating conditions was used to calibrate a 1D model. Then, modeling parametric studies were performed to optimize VVT and EGR parameters. A total of 150 cases were simulated for each operating point, in which VVT settings and EGR rate were varied at iso-air mass flow and iso-intake manifold temperature. The optimization was based on finding the configuration of VVT and EGR systems which maximizes the indicated efficiency. All different cases modeled were also evaluated in terms of pumping and heat losses. Moreover, a deep assessment of instantaneous pressure traces and mass flows in intake and exhaust valves was given, to provide insights about the optimization procedure. Finally, the findings obtained by simulation were compared with the results from a design of experiments (DOE) composed of more than 300 tests, and the impact on engine fuel consumption was analyzed.
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Tekeste, Mehari Z., Mohammad Mousaviraad, and Kurt A. Rosentrater. "Discrete Element Model Calibration Using Multi-Responses and Simulation of Corn Flow in a Commercial Grain Auger." Transactions of the ASABE 61, no. 5 (2018): 1743–55. http://dx.doi.org/10.13031/trans.12742.
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Abstract. Grain augers are primary grain conveying equipment in agriculture. Quantitative prediction of dynamic grain flow in grain augers using discrete element modeling (DEM) has potential to support simulation-based engineering design of grain handling equipment. The objective of this study was to develop a DEM corn model using a multi-response calibration methodology and validation of combine-harvested corn flow in a commercial grain auger. Using a Latin hypercube design of experiment (DOE) sampling from four particle interaction DEM parameters values, 27 DEM simulations were generated for four DEM corn shape approximations (1-sphere, 2-spheres, 5-spheres, and 13-spheres) to create virtual DEM experiments of bucket-discharged and anchor-lifted angle of repose (AOR) tests. A surface meta-model was developed using the DEM interaction parameters as predictor variables, and normalized AOR expressed as a mean square error (MSE), i.e., the sum of square differences between DEM simulations and laboratory-measured AOR. Analysis of the MSE percentiles with lower error differences between DEM simulations and laboratory AOR and the computational effort required per simulation (h per simulation) showed that the 2-spheres DEM model had better performance than the 1-sphere, 5-spheres, and 13-spheres models. Using the best stepwise linear regression models of bucket AOR MSE (R2 of 0.9423 and RMSE of 94.56) and anchor AOR MSE (R2 of 0.5412 and RMSE of 78.02) and a surface profiler optimization technique, an optimized 2-spheres DEM corn model was generated. The DEM predicted AOR with relative errors of 8.5% for bucket AOR and 7.0% for anchor AOR. A DEM grain auger simulation used as a validation step also showed good agreement with the laboratory-measured steady-state mass flow rate (kg s-1) and static AOR (degrees) of corn piled on a flat surface, with DEM prediction relative error ranging from 2.8% to 9.6% and from 8.55% to 1.26%, respectively. Keywords: Corn, DEM, Discharge angle of repose, Discrete element modeling, Grain auger, Lift angle of repose.
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Korsunovs, Aleksandrs, Felician Campean, Gaurav Pant, Oscar Garcia-Afonso, and Efe Tunc. "Evaluation of zero-dimensional stochastic reactor modelling for a Diesel engine application." International Journal of Engine Research 21, no. 4 (April 29, 2019): 592–609. http://dx.doi.org/10.1177/1468087419845823.
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Prediction of engine-out emissions with high fidelity from in-cylinder combustion simulations is still a significant challenge early in the engine development process. This article contributes to this fast evolving body of knowledge by focusing on the evaluation of NO x emission prediction capability of a probability density function–based stochastic reactor engine models for a Diesel engine. The research implements a systematic approach to the study of the stochastic reactor engine model performance, underpinned by a detailed space-filling design of experiments (DoE)-based sensitivity analysis of both external and internal parameters, evaluating their effects on the accuracy in matching physical measurements of both in-cylinder conditions and NO x output. The approach proposed in this article introduces an automatic stochastic reactor engine model calibration methodology across the engine operating envelope, based on a multi-objective optimization approach. This aims to exploit opportunities for internal stochastic reactor engine model parameters tuning to achieve good overall modelling performance as a trade-off between physical in-cylinder measurements accuracy and the output NO x emission predictions error. The results from the case study provide a valuable insight into the effectiveness of the stochastic reactor engine model, showing good capability for NO x emissions prediction and trends, while pointing out the critical sensitivity to the external input parameters and modelling conditions.
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A. Razak, J., N. Mohamad, M. A. Mahamood, R. Jaafar, I. S. Othman, M. M. Ismail, L. K. Tee, R. Junid, and Z. Mustafa. "On the preparation of EPDM-g-MAH compatibilizer via melt-blending method." Journal of Mechanical Engineering and Sciences 13, no. 3 (September 27, 2019): 5424–40. http://dx.doi.org/10.15282/jmes.13.3.2019.14.0440.
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This paper presents an experimental investigation to determine the optimum composition of maleic anhydride (MAH) and dicumyl peroxide (DCP) as initiator for ethylene-propylene-diene-monomer grafted MAH (EPDM-g-MAH) compatibilizer preparation, using response surface methodology (RSM) approach. EPDM-g-MAH was prepared in the laboratory scale by melt blending method using an internal mixer. For this study, the effects of MAH (2.50 – 7.50 wt.%) and DCP (0.10 – 0.30 wt.%) towards grafting efficiency was determined. Two level full factorial design of experiment (DOE) is applied to establish the relationship between these two independent factors of raw materials. Analysis of variance (ANOVA) and the optimization menu were utilized to decide the raw materials formulation with maximum grafting efficiency. Quantitative analysis based on infra-red (IR) spectral intensity supported by 1H-NMR spectral are used to propose for EPDM-g-MAH grafting mechanism. Standard calibration curve for quantity ratio plot was exponential with R2 = 89.19%. It was found that an optimum about 8.52% of MAF grafting efficiency has been yielded with DCP factor has contributed larger effect at 67.45% of contribution effect. Anhydride stretching of grafted C=O as confirmed by FTIR peak at 1713 cm-1 and 1770 – 1792 cm-1 has responsible for MAH grafting into EPDM rubber. Based on FTIR, 1H-NMR and 2D-COSY spectral analysis, reaction mechanism for EPDM-g-MAH grafting was successfully proposed with two possible termination steps.
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Vidović, Tino, Ivan Tolj, Gojmir Radica, and Natalia Bodrožić Ćoko. "Proton-Exchange Membrane Fuel Cell Balance of Plant and Performance Simulation for Vehicle Applications." Energies 15, no. 21 (October 31, 2022): 8110. http://dx.doi.org/10.3390/en15218110.
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In this study, a newly developed zero-dimensional electrochemical model was used for modeling and controlling proton-exchange membrane fuel cell (PEMFC) performance. Calibration of the model was performed with measurements from the fuel cell stack. Subsequently, a compressor and a humidifier on the cathode side were sized and added to the existing model. The aim of this work was to model the PEMFC stack and balance of plant (BoP) components in detail to show the influence of operating parameters such as cathode pressure, stack temperature and cathode stoichiometric ratio on the performance and efficiency of the overall system compared to the original model using a newly developed real-time model. The model managed to predict the profile of essential parameters, such as temperature, pressure, power, voltage, etc. The most important conclusions from this particular case are: the cell power output is only slightly changed with the variations in stoichiometric ratio of the cathode side and adding an external compressor is valid only for high current applications, but in those cases, there is 10–22% power gain. Stack temperature is a very influential parameter. Optimal temperatures were determined through design of experiments (DoE) and for this case are in the 40–60 °C range, where for low current applications lower temperatures are better due lower activation loss (8% difference between 80 °C and 40 °C at 20 A current). For high current applications, due to lower ohmic losses, higher temperatures are desirable.
Dissertations / Theses on the topic "DoE calibration optimization"
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MANCARELLA, ALESSANDRO. "Experimental analysis of an early diesel PCCI concept and strategies to limit its application constraints." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2846611.
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Kong, Qianyin. "Calibration and Estimation of Dog Teeth Positions in Synchronizers for Minimizing Noise and Wear during Gear Shifting." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-282763.
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Electric motors are used more widely in automotive to reducing emissions in vehicles. Due to the decreased usage of internal combustion engines which used to be the main noise source, impacts from synchronizers cannot be ignored during gear shifting, not only causing noise and wear but also delaying gear shifting completion. To minimize the impacts during gear shifting, a dog teeth position sensor is required but the high calculation frequency leads to a high cost, due to the high velocity of synchronizer portions and the dog teeth number. In this thesis, the gear shifting transmission is being modelled, in order to study the process of gear shifting and engagement. The transmission model, which is expressed with electrics and dynamics formulations. In order to avoid the impact without the dog teeth position sensor, this thesis proposes an estimation algorithm based on the transmission model to approve the gear engagement if the first and second portions of synchronizers are engaged in the mating position without impacts. Two different learning algorithms: direct comparison and particle swarm optimization application, are presented in the thesis as well, which are used to calibrate a parameter in the off-time test as part of the end of the calibration line, the so-called relevant initial phase being used in the real-time estimation. The transmission model is simulated in Simulink and different algorithms are running in MATLAB. All these results are plotted and analyzed for further evaluation in different aspects in the result chapter. The direct comparison algorithm has a simpler structure of computation but the quantity of required actuation is uncertain in this algorithm with a probability of failure to find the solution. The application of particle swarm optimization in this case succeeds in calibrating the objective parameter with a small error than the other algorithm. Actuation quantity affects the accuracy of the solutions and errors but not the failure rate.Elektriska motorer används i allt större utsträckning inom fordonsindustrin för att minska utsläppen från fordon. Den minskade användningen av förbränningsmotorer, som tidigare varit den främsta bullerkällan, gör att kollisioner från synkroniserare inte kan bli ignorerade under växlingen. Dessa kollisioner orsakar inte bara buller och nötningar utan även fördröjer slutförandet av växlingen. För att minimera kollisioner under växlingen krävs det en positionssensor för dog-teeth, men den höga beräkningsfrekvensen leder till hög kostnad på grund av den höga hastigheten hos synkroniseringsdelarna samt antalet dog-teeth. I den här avhandlingen görs en modell av växellåda för att studera växlingsprocessen och kugghjulsingreppet. Transmissionsmodellen uttrycks med elektriska och dynamiska formuleringar. För att undvika kollisioner utan positionssensor för dog-teeth, föreslås det en uppskattningsalgoritm baserad på transmissionsmodellen för att godta kugghjulsingreppet om den första and andra delen av synkroniseraren är inkopplade i parningsläget utan kollisioner. Två olika inlärningsalgoritmer, direkt jämförelsemetoden och partikelsvärmoptimeringsmetoden presenteras även i avhandlingen. De används för att kalibrera en parameter i off-time test som en del av slutet av produktionslinjen. Denna parameter kallas för den relevanta initialfasen och används vid realtidsuppskattningen. Transmissionsmodellen är simulerad i Simulink och de olika algoritmerna exekveras i Matlab. Alla resultat är plottade och analyserade för vidare utvärdering av olika aspekter i resultatkapitlet. Den direkta jämförelsealgoritmen har en enklare beräkningsstruktur, men mängden av nödvändig exekveringar är oklar för denna algoritm med en sannolikhet att det inte går att hitta lösningen. Däremot visar det sig att partikelsvärmoptimeringsmetoden lyckas med att kalibrera målparametern med dessutom ge mindre fel än den andra algoritmen. Antalet exekveringar påverkar lösningen samt noggrannheten hos lösningarna men påverkar inte själva felfrekvensen.
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Kianifar, Mohammed R., I. Felician Campean, and D. Richardson. "Sequential DoE framework for steady state model based calibration." Thesis, 2013. http://hdl.handle.net/10454/9676.
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noThe complexity of powertrain calibration has increased significantly with the development and introduction of new technologies to improve fuel economy and performance while meeting increasingly stringent emissions legislation with given time and cost constraints. This paper presents research to improve the model-based engine calibration optimization using an integrated sequential Design of Experiments (DoE) strategy for engine mapping experiments. This DoE strategy is based on a coherent framework for a model building - model validation sequence underpinned by Optimal Latin Hypercube (OLH) space filling DoEs. The paper describes the algorithm development and implementation for generating the OLH space filling DoEs based on a Permutation Genetic Algorithm (PermGA), subsequently modified to support optimal infill strategies for the model building - model validation sequence and to deal with constrained non-orthogonal variables space. The development, implementation and validation of the proposed strategy is discussed in conjunction with a case study of a GDI engine steady state mapping, focused on the development of an optimal calibration for CO₂ and particulate number (Pn) emissions. The proposed DoE framework applied to the GDI engine mapping task combines a screening space filling DoE with a flexible sequence of model building - model validation mapping DoEs, all based on optimal DoE test plan augmentation using space filling criteria. The case study results show that the sequential DoE strategy offers a flexible way of carrying out the engine mapping experiments, maximizing the information gained and ensuring that a satisfactory quality model is achieved.
Book chapters on the topic "DoE calibration optimization"
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Okkan, Umut, Nuray Gedik, and Halil Uysal. "Usage of Differential Evolution Algorithm in the Calibration of Parametric Rainfall-Runoff Modeling." In Advances in Computational Intelligence and Robotics, 481–99. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-4766-2.ch022.
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In recent years, global optimization algorithms are used in many engineering applications. Calibration of certain parameters at conceptualization of hydrological models is one example of these. An important issue in interpreting the effects of climate change on the basin depends on selecting an appropriate hydrological model. Not only climate change impact assessment studies, but also many water resources planning studies refer to such modeling applications. In order to obtain reliable results from these hydrological models, calibration phase of the models needs to be done well. Hence, global optimization methods are utilized in the calibration process. In this chapter, the differential evolution algorithm (DEA), which has rare application in the hydrological modeling literature, was explained. As an application, the use of the DEA algorithm in the hydrological model calibration phase was mentioned. DYNWBM, a lumped model with five parameters, was selected as the hydrological model. The calibration and then validation period performances of the DEA based DYNWBM model were tested and also compared with other global optimization algorithms. According to the results derived from the study, hydrological model appropriately reflects the rainfall-runoff relation of basin for both periods.
Conference papers on the topic "DoE calibration optimization"
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Wu, Guangqiang, Lu Sun, Sheng Zhu, and Kuankuan Zhang. "Research on an Approximate Model Based Virtual Calibration Method With DoE and Optimization Algorithm for Transmission Control Unit." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3735.
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In order to solve the problems arising from the manual calibration method in the developing process of vehicle automatic transmission control unit (TCU), known as time-consuming, heavy workload, high cost and over-dependence on subjective experience, this article researches on a virtual calibration method based on an approximate model to obtain optimal parameters for TCU. The neural network approximate model is established from the test data chosen with the method of DoE (Design of Experiment). The virtual calibration method is then conducted through Optimal Latin Hypercube Design (OLHD) and multi-island genetic algorithm (MIGA) to search the optimal parameters. By comparing the new calibration method with original manual one on the condition of gear 1 up to gear 2, the result shows that the new method can increase the efficiency significantly.
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Olson, Karen, Joshua Merritt, Rair Barraez, Garrett Fowler, Jackson Haffener, and Kyle Haustveit. "Sealed Wellbore Pressure Monitoring (SWPM) and Calibrated Fracture Modeling: The Next Step in Unconventional Completions Optimization." In SPE Hydraulic Fracturing Technology Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212367-ms.
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Abstract Sealed Wellbore Pressure Monitoring (SWPM) has been utilized across North and South America Basins with over 16,000 stages monitored as of June 2022. Since May 2020, the analysis procedure has been automated using a cloud-based software platform designed to ingest, process, and analyze high-frequency hydraulic fracturing data (Iriarte et al., 2021). A real time option of SWPM was also developed to aid in real time fracturing decisions (Ramirez et al., 2022). The latest development is the added capability of a fracture model that can automatically history match the volume to first responses (VFRs) determined from SWPM. This next level allows for the matching of the VFRs and the visualization of the resulting fracture geometries from a fully-coupled fracture propagation, reservoir, and geomechanics simulator. The simulator is capable of accounting for complex processes such as poroelastic stress changes from depletion, allowing for evaluation of complex interactions of fracture propagation and depletion. Insights gained from this process allows the operator to optimize their completion design faster and with fewer field trials. This paper’s focus is a case study of the DOE Eagle Ford refracturing project where a range of completion designs were trialed while monitoring offset SWPM and fiber optic strain. The resulting VFRs of the SWPM project were compared to the fiber data and then used to calibrate the fracture model. Fracture model calibration was first performed assuming that restimulation fractures propagated independently of the previously created fractures. The VFR of each stage design was calculated and summarized. The model is constructed with three stage designs primarily identified by cluster count: 7-clusters, 12-clusters, and 22-clusters. The VFR for the 7-cluster stage design was then used as an objective in an automated history matching algorithm employing the fracture model. The resulting best fit model was then evaluated on VFRs for the 12 and 22-cluster stage designs. The results demonstrate the model calibrated to the VFR of the 7-cluster stage design was able to predict VFRs in the far field for 12 and 22-cluster stage designs. Further, it is shown that including the original fractures in the model and allowing crossflow between the original and newly created fractures can match the rapid VFRs observed on a minority of stages. These same results were confirmed by the fiber data (not shared with modelers prior to calibration). Conclusions of the DOE project will show the optimum cluster spacing, cluster count and stage spacing as confirmed by the SWPM analysis and the fracture modeling.
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Cozzolini, A., M. C. Besch, M. Ardanese, R. Ardanese, M. Gautam, A. Oshinuga, and M. Miyasato. "Determination of Optimal Engine Parameters for Exhaust Emissions Reduction Using the Taguchi Method." In ASME 2011 Internal Combustion Engine Division Fall Technical Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/icef2011-60134.
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In order to meet the ever more stringent exhaust emissions regulations and improve fuel consumption, heavy-duty Diesel engines (HDDE) have been equipped with electronically controlled components, including Exhaust Gas Recirculation systems (EGR), Variable Geometry Turbochargers (VGT) and advanced Fuel Injection Equipment (FIE) allowing for more flexible engine optimization. The introduction of such components increased the number of parameters influencing the optimization procedure; thus, significantly increasing the required amount of test-cell time to achieve an optimal engine calibration. Moreover, the adoption of aftertreatment systems, such as Selective Catalytic Reduction (SCR) technology or Diesel Particulate Filter (DPF) systems, required to comply with latest US-2010 and EURO V emissions legislations, requires flexible engine calibrations to address their efficiency dependency upon the thermodynamic conditions of the engine exhaust. The primary objective of this study was to develop and implement a simple multivariate optimization technique to program any given engine with multiple calibrations, both for steady-state and transient conditions, capable of modifying exhaust properties in order to guarantee optimal aftertreatment efficiencies during a wide range of engine operation. Four engine parameters, each at three levels, were selected for the optimization process, namely, EGR rate, VGT position, Start of Injection (SOI) and Nozzle Opening Pressure (NOP) as a surrogate for fuel injection pressure. Changes in control parameters which lead to an improvement in one specific emissions component may however often result in the deterioration of another. Thus, a good understanding of the relationship between individual control parameter effects is of utmost importance to correctly attain the optimum condition in short time and simultaneously reduce the number of experiments to be performed. Therefore, Design of Experiment (DOE) via factorial design, using the Taguchi method, was adopted to simultaneously study multiple factors and isolate the effects of changes in a single engine parameter on exhaust emissions. Different engine calibrations were obtained for an 11-liter Volvo engine by performing a set of only nine experiments for each engine speed/load point, which were selected to be equally distributed underneath the engine’s lug-curve. The main engine calibrations proved to be test cycle independent since comparable emission levels were observed over the European Steady-State Cycle (ESC) as well as the Federal Test Procedure (FTP). Reductions in Oxides of Nitrogen (NOx) on the order of 20% were achieved, while limiting the fuel consumption penalty to below 3%. Several high-efficiency calibrations were generated, achieving fuel consumption reductions close to 6%. Thus, the Taguchi method was found to be a viable way for simultaneous optimization of key engine parameters leading to a significant reduction in test-cell time; hence, relative development costs.
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Jeong, Jay il, Dongsoo Kang, and Jongwon Kim. "Kinematic Calibration for Redundantly Actuated Parallel Mechanisms: Theory and Application for 2-DOF Mechanism." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57554.
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We present a new kinematic calibration algorithm for redundantly actuated parallel mechanisms. The calibration algorithm for a non-redundant case does not apply for a redundantly actuated parallel mechanism, because the angle error of the actuating joint varies with position and the geometrical constraint fails to be consistent. Such change of joint angle error comes from constraint torque variation with each kinematic pose. To calibrate a redundant parallel mechanism, one therefore has to consider constraint torque equilibrium and the relationship of constraint torque to torsional deflection, in addition to geometric constraint. In this paper, we develop the calibration algorithm for a redundantly actuated parallel mechanism using these three relationships, and formulate cost functions for an optimization algorithm. As a case study, we executed the calibration of a 2-degree of freedom (DOF) parallel mechanism with three actuators using the developed algorithm. Coordinate values of tool plate were measured using a laser ball bar and the actual kinematic parameters were identified with a new cost function of the optimization algorithm. Experimental results showed that the accuracy of the tool plate improved by 82% after kinematic calibration in a redundant actuation case.
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Brooks, T., G. Lumsden, and H. Blaxill. "Improving Base Engine Calibrations for Diesel Vehicles Through the Use of DoE and Optimization Techniques." In Powertrain & Fluid Systems Conference & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2005. http://dx.doi.org/10.4271/2005-01-3833.
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Lawrence, Edwin, Marie Bjoerdal Loevereide, Sanggeetha Kalidas, Ngoc Le Le, Sarjono Tasi Antoneus, and Tu Le Mai Khanh. "Production Optimization in Mature Field Through Scenario Prediction Using a Representative Network Model: A Rapid Solution Without Well Intervention." In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/205662-ms.
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Abstract As part of the production optimization exercise in J field, an initiative has been taken to enhance the field production target without well intervention. J field is a mature field; the wells are mostly gas lifted, and currently it is in production decline mode. As part of this optimization exercise, a network model with multiple platforms was updated with the surface systems (separator, compressors, pumps, FPSO) and pipelines in place to understand the actual pressure drop across the system. Modelling and calibration of the well and network model was done for the entire field, and the calibrated model was used for the production optimization exercise. A representative model updated with the current operating conditions is the key for the field production and asset management. In this exercise, a multiphase flow simulator for wells and pipelines has been utilized. A total of ∼50 wells (inclusive of idle wells) has been included in the network model. Basically, the exercise started by updating the single-well model using latest well test data. During the calibration at well level, several steps were taken, such as evaluation of historical production, reservoir pressure, and well intervention. This will provide a better idea on the fine-tuning parameters. Upon completion of calibrating well models, the next level was calibration of network model at the platform level by matching against the platform operating conditions (platform production rates, separator/pipeline pressure). The last stage was performing field network model calibration to match the overall field performance. During the platform stage calibration, some parameters such as pipeline ID, horizontal flow correlation, friction factor, and holdup factor were fine-tuned to match the platform level operating conditions. Most of the wells in J field have been calibrated by meeting the success criterion, which is within +/-5% for the production rates. However, there were some challenges in matching several wells due to well test data validity especially wells located on remote platform where there is no dedicated test separator as well as the impact of gas breakthrough, which may interfere to performance of wells. These wells were decided to be retested in the following month. As for the platform level matching, five platforms were matched within +/-10% against the reported production rates. During the evaluation, it was observed there were some uncertainties in the reported water and gas rates (platform level vs. well test data). This is something that can be looked into for a better measurement in the future. By this observation, it was suggested to select Platform 1 with the most reliable test data as well as the platform rate for the optimization process and qualifying for the field trial. Nevertheless, with the representative network model, two scenarios, reducing separator pressure at platform level and gas lift optimization by an optimal gas lift rate allocation, were performed. The model predicts that a separator pressure reduction of 30 psi in Platform 1 has a potential gain of ∼300 BOPD, which is aligned with the field results. Apart from that, there was also a potential savings in gas by utilizing the predicted allocated gas lift injection rate.
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Suggust, Alister Albert, and Jing Zhi Kueh. "Gas Lift Optimization Study in Brownfields: A Review on Dual String Gas Lift Injection Allocation In Malaysian Offshore Fields." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210400-ms.
Full textAbstract:
Abstract Sarawak offshore wells are mostly completed with dual string completion and are heavily relying on gas lift as the primary artificial lift. Dual string gas lift is an economical way to selectively produce from multi-stacked reservoirs in Sarawak fields. However, it poses great challenges in terms of operations, troubleshooting, allocation and optimization as both strings share a common annulus. Dual string gas lift performance diagnosis need to be done from time to time to ensure the strings production are optimized at well level. Gas injection rate is a critical input in predicting the well performance based on the gas lift performance curve. However, the gas lift injection rate for dual string is measured at well – not at string level. The gas lift injection rate into each string needs to be allocated correctly either through well modelling calibration approach, testing one string while shutting its neighbor or well tracer application. After allocating the gas lift injection rate into each string correctly, well modelling prediction run at done to mitigate multipointing issues, design optimum point of injection, establish optimum injection rate at well level and determine the optimum casing head pressure. The operator has proposed for a workflow to correct the dual gas lift injection allocation based on well modelling calibration. The workflow was implemented and resulted in multiple optimizations in terms of gas lift valve change program, choke optimization and gas lift rate optimization. Apart from that, the paper will also share on the findings from the well tracer application in correcting the gas injection allocation. This paper will focus on the production performance check on dual string gas lift performance at well level. The findings from the study are subsequently monetized as quick-gain opportunities while the operator is embarking into long term study on assessing the alternative artificial lift strategy suitable for a brownfield. The lessons learned will also be applicable to oil fields with similar situations to further improve the fields’ production.
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Suggust, Alister Albert, and Jing Zhi Kueh. "Brilliant at the Basics: A Review on Dual String Gas Lift Injection Performance in Malaysian Offshore Fields." In International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-22806-ms.
Full textAbstract:
Abstract Sarawak offshore wells are mostly completed with dual string completion and are heavily relying on gas lift as the primary artificial lift. Dual string gas lift is an economical way to selectively produce from multi-stacked reservoirs in Sarawak fields. However, it poses great challenges in terms of operations, troubleshooting, allocation and optimization as both strings share a common annulus. Dual string gas lift performance diagnosis need to be done from time to time to ensure the strings production are optimized at well level. Gas injection rate is a critical input in predicting the well performance based on the gas lift performance curve. However, the gas lift injection rate for dual string is measured at well – not at string level. The gas lift injection rate into each string needs to be allocated correctly either through well modelling calibration approach, testing one string while shutting its neighbor or well tracer application. After allocating the gas lift injection rate into each string correctly, well modelling prediction run at done to mitigate multipointing issues, design optimum point of injection, establish optimum injection rate at well level and determine the optimum casing head pressure. The operator has proposed for a workflow to correct the dual gas lift injection allocation based on well modelling calibration. The workflow was implemented and resulted in multiple optimizations in terms of gas lift valve change program, choke optimization and gas lift rate optimization. Apart from that, the paper will also share on the findings from the well tracer application in correcting the gas injection allocation. This paper will focus on the production performance check on dual string gas lift performance at well level. The findings from the study are subsequently monetized as quick-gain opportunities while the operator is embarking into long term study on assessing the alternative artificial lift strategy suitable for a brownfield. The lessons learned will also be applicable to oil fields with similar situations to further improve the fields’ production.
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Drignei, Dorin, Zissimos P. Mourelatos, Michael Kokkolaras, Vijitashwa Pandey, and Grzegorz Koscik. "A Variable-Size Local Domain Approach for Concurrent Design Optimization and Model Validation Using Parametric Bootstrap." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47234.
Full textAbstract:
A common approach to the validation of simulation models focuses on validation throughout the entire design space. In a more recent methodology, we proposed to validate designs as they are generated during a simulation-based optimization process, relying on validating the simulation model through calibration in a sequence of local domains. In that work, the size of the local domains was held fixed and not linked to uncertainty, and the confidence in designs was quantified using Bayesian hypothesis testing. In this article, we present an improved methodology where the size and shape of the local domain at each stage of a sequential design optimization process, are determined from a parametric bootstrap methodology involving maximum likelihood estimators of unknown model parameters. Validation through calibration is carried out in the local domain at each stage. The sequential process continues until the local domain does not change from stage to stage during the design optimization process, ensuring convergence to an optimal design. The proposed methodology is illustrated with the design of a thermal insulator using one-dimensional, linear heat conduction in a solid slab with heat flux boundary conditions.
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Drignei, Dorin, Zissimos P. Mourelatos, Vijitashwa Pandey, and Michael Kokkolaras. "Concurrent Design Optimization and Calibration-Based Validation Using Local Domains Sized by Bootstrapping." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70423.
Full textAbstract:
The design optimization process relies often on computational models for analysis or simulation. These models must be validated to quantify the expected accuracy of the obtained design solutions. It can be argued that validation of computational models in the entire design space is neither affordable nor required. In previous work, motivated by the fact that most numerical optimization algorithms generate a sequence of candidate designs, we proposed a paradigm where design optimization and calibration-based model validation are performed concurrently in a sequence of variable-size local domains that are relatively small compared to the entire design space. A key element of this approach is how to account for variability in test data and model predictions in order to determine the size of the local domains at each stage of the sequential design optimization process. In this paper, we discuss two alternative techniques for accomplishing this: parametric and nonparametric bootstrapping. The parametric bootstrapping assumes a Gaussian distribution for the error between test and model data and uses maximum likelihood estimation to calibrate the prediction model. The nonparametric bootstrapping does not rely on the Gaussian assumption providing therefore, a more general way to size the local domains for applications where distributional assumptions are difficult to verify, or not met at all. If distribution assumptions are met, parametric methods are preferable over nonparametric methods. We use a validation literature benchmark problem to demonstrate the application of the two techniques, emphasizing that results cannot be compared. Which technique to use depends on whether the Gaussian distribution assumption is appropriate based on available information.
Reports on the topic "DoE calibration optimization"
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Lieth, J. Heiner, Michael Raviv, and David W. Burger. Effects of root zone temperature, oxygen concentration, and moisture content on actual vs. potential growth of greenhouse crops. United States Department of Agriculture, January 2006. http://dx.doi.org/10.32747/2006.7586547.bard.
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Soilless crop production in protected cultivation requires optimization of many environmental and plant variables. Variables of the root zone (rhizosphere) have always been difficult to characterize but have been studied extensively. In soilless production the opportunity exists to optimize these variables in relation to crop production. The project objectives were to model the relationship between biomass production and the rhizosphere variables: temperature, dissolved oxygen concentration and water availability by characterizing potential growth and how this translates to actual growth. As part of this we sought to improve of our understanding of root growth and rhizosphere processes by generating data on the effect of rhizosphere water status, temperature and dissolved oxygen on root growth, modeling potential and actual growth and by developing and calibrating models for various physical and chemical properties in soilless production systems. In particular we sought to use calorimetry to identify potential growth of the plants in relation to these rhizosphere variables. While we did experimental work on various crops, our main model system for the mathematical modeling work was greenhouse cut-flower rose production in soil-less cultivation. In support of this, our objective was the development of a Rose crop model. Specific to this project we sought to create submodels for the rhizosphere processes, integrate these into the rose crop simulation model which we had begun developing prior to the start of this project. We also sought to verify and validate any such models and where feasible create tools that growers could be used for production management. We made significant progress with regard to the use of microcalorimetry. At both locations (Israel and US) we demonstrated that specific growth rate for root and flower stem biomass production were sensitive to dissolved oxygen. Our work also identified that it is possible to identify optimal potential growth scenarios and that for greenhouse-grown rose the optimal root zone temperature for potential growth is around 17 C (substantially lower than is common in commercial greenhouses) while flower production growth potential was indifferent to a range as wide as 17-26C in the root zone. We had several set-backs that highlighted to us the fact that work needs to be done to identify when microcalorimetric research relates to instantaneous plant responses to the environment and when it relates to plant acclimation. One outcome of this research has been our determination that irrigation technology in soilless production systems needs to explicitly include optimization of oxygen in the root zone. Simply structuring the root zone to be “well aerated” is not the most optimal approach, but rather a minimum level. Our future work will focus on implementing direct control over dissolved oxygen in the root zone of soilless production systems.
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Belkin, Shimshon, Sylvia Daunert, and Mona Wells. Whole-Cell Biosensor Panel for Agricultural Endocrine Disruptors. United States Department of Agriculture, December 2010. http://dx.doi.org/10.32747/2010.7696542.bard.
Full textAbstract:
Objectives: The overall objective as defined in the approved proposal was the development of a whole-cell sensor panel for the detection of endocrine disruption activities of agriculturally relevant chemicals. To achieve this goal several specific objectives were outlined: (a) The development of new genetically engineered wholecell sensor strains; (b) the combination of multiple strains into a single sensor panel to effect multiple response modes; (c) development of a computerized algorithm to analyze the panel responses; (d) laboratory testing and calibration; (e) field testing. In the course of the project, mostly due to the change in the US partner, three modifications were introduced to the original objectives: (a) the scope of the project was expanded to include pharmaceuticals (with a focus on antibiotics) in addition to endocrine disrupting chemicals, (b) the computerized algorithm was not fully developed and (c) the field test was not carried out. Background: Chemical agents, such as pesticides applied at inappropriate levels, may compromise water quality or contaminate soils and hence threaten human populations. In recent years, two classes of compounds have been increasingly implicated as emerging risks in agriculturally-related pollution: endocrine disrupting compounds (EDCs) and pharmaceuticals. The latter group may reach the environment by the use of wastewater effluents, whereas many pesticides have been implicated as EDCs. Both groups pose a threat in proportion to their bioavailability, since that which is biounavailable or can be rendered so is a priori not a threat; bioavailability, in turn, is mediated by complex matrices such as soils. Genetically engineered biosensor bacteria hold great promise for sensing bioavailability because the sensor is a live soil- and water-compatible organism with biological response dynamics, and because its response can be genetically “tailored” to report on general toxicity, on bioavailability, and on the presence of specific classes of toxicants. In the present project we have developed a bacterial-based sensor panel incorporating multiple strains of genetically engineered biosensors for the purpose of detecting different types of biological effects. The overall objective as defined in the approved proposal was the development of a whole-cell sensor panel for the detection of endocrine disruption activities of agriculturally relevant chemicals. To achieve this goal several specific objectives were outlined: (a) The development of new genetically engineered wholecell sensor strains; (b) the combination of multiple strains into a single sensor panel to effect multiple response modes; (c) development of a computerized algorithm to analyze the panel responses; (d) laboratory testing and calibration; (e) field testing. In the course of the project, mostly due to the change in the US partner, three modifications were introduced to the original objectives: (a) the scope of the project was expanded to include pharmaceuticals (with a focus on antibiotics) in addition to endocrine disrupting chemicals, (b) the computerized algorithm was not fully developed and (c) the field test was not carried out. Major achievements: (a) construction of innovative bacterial sensor strains for accurate and sensitive detection of agriculturally-relevant pollutants, with a focus on endocrine disrupting compounds (UK and HUJ) and antibiotics (HUJ); (b) optimization of methods for long-term preservation of the reporter bacteria, either by direct deposition on solid surfaces (HUJ) or by the construction of spore-forming Bacillus-based sensors (UK); (c) partial development of a computerized algorithm for the analysis of sensor panel responses. Implications: The sensor panel developed in the course of the project was shown to be applicable for the detection of a broad range of antibiotics and EDCs. Following a suitable development phase, the panel will be ready for testing in an agricultural environment, as an innovative tool for assessing the environmental impacts of EDCs and pharmaceuticals. Furthermore, while the current study relates directly to issues of water quality and soil health, its implications are much broader, with potential uses is risk-based assessment related to the clinical, pharmaceutical, and chemical industries as well as to homeland security.