scholarly journals Problems of Modelling Toxic Compounds Emitted by a Marine Internal Combustion Engine in Unsteady States

2015 ◽  
Vol 21 (4) ◽  
pp. 57-65 ◽  
Author(s):  
Jacek Rudnicki ◽  
Ryszard Zadrąg
Keyword(s):  
Combustion Engine ◽  
Neural Model ◽  
Multiple Regression Model ◽  
Diagnostic Tools ◽  
Model Parameters ◽  
Single Output ◽  
Frequent Use ◽  
Measurement Results ◽  
Sample Application ◽  
Analytical Tools

Abstract Contemporary engine tests are performed based on the theory of experiment. The available versions of programmes used for analysing experimental data make frequent use of the multiple regression model, which enables examining effects and interactions between input model parameters and a single output variable. The use of multi-equation models provides more freedom in analysing the measured results, as those models enable simultaneous analysis of effects and interactions between many output variables. They can also be used as a tool in preparing experimental material for other advanced diagnostic tools, such as the models making use of neural networks which, when properly prepared, enable also analysing measurement results recorded during dynamic processes. The article presents advantages of the use of the abovementioned analytical tools and a sample application of the neural model developed based on the results of examination carried out on the engine research rig.

scholarly journals Technical State Assessment of Charge Exchange System of Self-Ignition Engine, Based On the Exhaust Gas Composition Testing

2017 ◽  
Vol 24 (s1) ◽  
pp. 203-212 ◽  
Author(s):  
Jacek Rudnicki ◽  
Ryszard Zadrąg
Keyword(s):  
Charge Exchange ◽  
Measurement Data ◽  
Multiple Regression Model ◽  
State Assessment ◽  
Technical State ◽  
Diagnostic Tools ◽  
Exhaust Gas ◽  
Gas Composition ◽  
Exchange System ◽  
Analytical Tools

Abstract This paper presents possible use of results of exhaust gas composition testing of self - ignition engine for technical state assessment of its charge exchange system under assumption that there is strong correlation between considered structure parameters and output signals in the form of concentration of toxic compounds (ZT) as well as unambiguous character of their changes. Concentration of the analyzed ZT may be hence considered to be symptoms of engine technical state. At given values of the signals and their estimates it is also possible to determine values of residues which may indicate a type of failure. Available tool programs aimed at analysis of experimental data commonly make use of multiple regression model which allows to investigate effects and interaction between model input quantities and one output variable. Application of multi-equation models provides great freedom during analysis of measurement data as it makes it possible to simultaneously analyze effects and interaction of many output variables. It may be also implemented as a tool for preparation of experimental material for other advanced diagnostic tools such as neural networks which, in contrast to multi-equation models, make it possible to recognize a state at multistate classification and - in consequence - to do diagnostic inference. Here , these authors present merits of application of the above mentioned analytical tools on the example of tests conducted on an experimental engine test stand.

Supporting the Empiric Research of Diesel Engines with Multi-Equation Models

2013 ◽  
Vol 196 ◽  
pp. 74-81 ◽  
Author(s):  
Ryszard Zadrąg
Keyword(s):  
Experimental Data ◽  
Regression Models ◽  
Combustion Engine ◽  
Engine Test ◽  
Output Variable ◽  
Software Applications ◽  
Single Output ◽  
Measurement Results ◽  
Multiple Regression Models ◽  
Paper Author

Contemporary empirical researches on the object, which is combustion engine, are processed basing on the theory of experiment. Available software applications to analyze the experimental data commonly use the multiple regression models, which enables studying effects and interactions between input values of the model and single output variable. Using multi-equation models gives free hand at analyzing measurement results because it enables analysis of effects and interaction of many output variables. It also allows analysis of the measurement results during dynamic process. In this paper author presents advantages of using the multidimensional regression model on example of researches conducted on engine test stand.

scholarly journals Online Estimation of Terminal Airspace Sector Capacity from ATC Workload

2021 ◽  
Author(s):  
Majeed Mohamed
Keyword(s):  
Neural Network ◽  
Dynamical System ◽  
Experimental Data ◽  
Neural Model ◽  
Model Parameters ◽  
Air Traffic Controller ◽  
Single Output ◽  
Estimated Parameters ◽  
Model Response ◽  
Relative Standard

Neural Partial Differentiation (NPD) approach is applied to estimate terminal airspace sector capacity in real-time from the ATC (Air Traffic Controller) dynamical neural model with permissible safe separation and affordable workload. A neural model of a multi-input-single-output (MISO) ATC dynamical system is primarily established and used to estimate parameters from the experimental data using NPD. Since the relative standard deviations of these estimated parameters are lesser, the predicted neural model response is well matched with the intervention of ATC workload. Moreover, the proposed neural network-based approach works well with the experimental data online as it does not require the initial values of model parameters that are unknown in practice.

scholarly journals Modeling of German Low Voltage Cables with Ground Return Path

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1265 ◽  
Author(s):  
Johanna Geis-Schroer ◽  
Sebastian Hubschneider ◽  
Lukas Held ◽  
Frederik Gielnik ◽  
Michael Armbruster ◽  
...  
Keyword(s):  
Low Voltage ◽  
Measurement Data ◽  
Analytical Calculation ◽  
Laboratory Measurement ◽  
Model Parameters ◽  
Calculation Methods ◽  
Modeling Approach ◽  
Measurement Results ◽  
Simulation Results ◽  
Return Path

In this contribution, measurement data of phase, neutral, and ground currents from real low voltage (LV) feeders in Germany is presented and analyzed. The data obtained is used to review and evaluate common modeling approaches for LV systems. An alternative modeling approach for detailed cable and ground modeling, which allows for the consideration of typical German LV earthing conditions and asymmetrical cable design, is proposed. Further, analytical calculation methods for model parameters are described and compared to laboratory measurement results of real LV cables. The models are then evaluated in terms of parameter sensitivity and parameter relevance, focusing on the influence of conventionally performed simplifications, such as neglecting house junction cables, shunt admittances, or temperature dependencies. By comparing measurement data from a real LV feeder to simulation results, the proposed modeling approach is validated.

scholarly journals Mathematical Model of Communication Delays in Wireless Networks

2016 ◽  
Vol 62 (1) ◽  
pp. 61-64
Author(s):  
Beata Krupanek ◽  
Ryszard Bogacz
Keyword(s):  
Wireless Networks ◽  
Probabilistic Models ◽  
Experimental System ◽  
Delta Function ◽  
Access Time ◽  
Model Parameters ◽  
Communication Delays ◽  
Proposed Model ◽  
Measurement Results ◽  
Wireless Medium

Abstract The paper presents a new conception of building probabilistic models of communication delays in wireless networks that basis on using a delta function sequence to describe retransmissions between a transmitter and a receiver. It is assumed that the access time of the transmitter is described by a probability density function and the communication channel established in the wireless medium is disturbed by passive or active factors which cause that the transmission can be not correct and the sent data have to be retransmitted. Theoretical considerations have been verified by measurement results obtained by using the experimental system developed for investigating delays caused by external disturbances influencing the wireless transmission. A method of identification of the proposed model parameters and verification of the identified values has been presented.

The Operation Monitoring Condition of the Production Machinery and Facilities Using the Tools of Technical Diagnostics

2013 ◽  
Vol 308 ◽  
pp. 105-109 ◽  
Author(s):  
Stanislava Šoltésová ◽  
Petr Baron
Keyword(s):  
Professional Practice ◽  
Main Tool ◽  
Diagnostic Tools ◽  
Technical Diagnostics ◽  
Effective Management ◽  
New Methods ◽  
Operation And Maintenance ◽  
Measurement Results ◽  
Reliability Of Measurement Results ◽  
Monitoring Behavior

Technical diagnostics is currently the most important factor of reliability, dealing with forms of disorder manifestation, developing of disorders, their detection and working principles. As tool it has great value in operation and maintenance of facilities. It denotes great interest not only in scientific sphere but also in sphere of results application in the new developing methods and their using in professional practice. Using methods of technical diagnostics taking into account the reliability of measurement results both with new methods IT is one of the main tool for effective management of facilities, elimination of unacceptable conditions and other aspects. The article describes the application of technical and diagnostic tools on workplaces for monitoring behavior of the production machines and facilities.

Characteristics of the flow field in the combustion chamber of the internal combustion test engine

2011 ◽  
Vol 32 (3) ◽  
pp. 203-214 ◽  
Author(s):  
Wojciech Tutak ◽  
Arkadiusz Jamrozik
Keyword(s):  
Flow Field ◽  
Combustion Chamber ◽  
Flow Velocity ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Maximal Velocity ◽  
Maximum Value ◽  
Measurement Results ◽  
Test Engine ◽  
Fresh Charge

Characteristics of the flow field in the combustion chamber of the internal combustion test engine Results of a research study into the velocity field in combustion chamber of internal combustion engine are presented in the paper. Measurements of fresh charge flow velocity in the cylinder axis and near the cylinder squeezing surface were performed. The hot-wire anemometer was used. The measurement results were used for analysis of turbulence field in the examined combustion chamber. It turned out that in the axis of cylinder the maximum of velocity occurs 30 deg before TDC and achieves 6 m/s. In the studied combustion chamber, the maximum value of turbulence intensity was close to 0.2 and it was achieved 35 deg BTDC. Additionally, the maximal velocity dispersion in the following cycles of the researched engine was at the level of 2 m/s, which is 35% of the maximum value of flow velocity. At a point located near the squeezing surface of the piston, a similar level of turbulence, but a the smaller value of the average velocity was achieved. The turbulence field turned out to be inhomogeneous in the combustion chamber.

Parameter Adaptation by Parameter Signature Isolation in the Time-Scale Domain

2008 ◽  
Author(s):  
Kourosh Danai ◽  
James R. McCusker
Keyword(s):  
Error Estimates ◽  
Time Scale ◽  
Prediction Error ◽  
Dynamic Models ◽  
Noise Suppression ◽  
Model Parameters ◽  
Isolation Method ◽  
Parameter Adaptation ◽  
Single Output ◽  
Parameter Error

It is shown that delineation of output sensitivities with respect to model parameters in dynamic models can be enhanced in the time-scale domain. This enhanced differentiation of output sensitivities then provides the capacity to isolate regions of the time-scale plane wherein a single output sensitivity dominates the others. Due to this dominance, the prediction error can be attributed to the error of a single parameter at these regions so as to estimate each model parameter error separately. The proposed Parameter Signature Isolation Method (PARSIM) that uses these parameter error estimates for parameter adaptation has been found to have an adaptation precision comparable to that of the Gauss-Newton method for noise-free cases. PARSIM, however, appears to be less sensitive to input conditions, while offering the promise of more effective noise suppression by the capabilities available in the time-scale domain.

Identification of thermal parameters of a building envelope based on the cooling process of a building object

2019 ◽  
Vol 43 (6) ◽  
pp. 503-527 ◽  
Author(s):  
Iwona Pokorska-Silva ◽  
Artur Nowoświat ◽  
Lidia Fedorowicz
Keyword(s):  
Thermal Conductivity ◽  
Regression Model ◽  
Multiple Regression ◽  
Analytical Model ◽  
Multiple Regression Model ◽  
Building Envelope ◽  
Thermal Parameters ◽  
Cooling Process ◽  
Building Envelopes ◽  
Measurement Results

Thermal properties of building envelopes are often described using thermal conductivity or thermal resistance. And the opposite task involves the identification of thermal parameters of building envelopes based on the measurements of their cooling process. In this article, the authors proposed a method of identifying thermal parameters of a building envelope based on cooling measurements, using a multiple regression model for this purpose. To satisfy the research objectives, two basic experiments were carried out. The first experiment was performed in laboratory conditions. The research model was a cube of the dimensions of 1.1 m × 1.1 m × 1.1 m. The second experiment was carried out in semi-real conditions, and the used model was a small house of the dimensions of 6.0 m × 4.15 m × 5.2 m. The measurement results were also used to calibrate numerical models made in the ESP-r program. The research studies have demonstrated that the model can be used to identify thermal parameters of a building envelope. Based on the measurements and simulations, the cooling equations of the object were determined and the 95% confidence interval for the heat retention index was estimated. On that basis, using the multiple regression model, such parameters of the model as density, specific heat, and thermal conductivity were estimated. It turned out that using the Gauss–Newton approximation, we obtained the correlation of the measurement results and the analytical model with the correlation coefficient of 0.9971 (for the laboratory scale). And the multiple regression improved not only the correlation between the measurement and the analytical model, but it also allowed to obtain “almost identical” results. Similarly, promising results were obtained for the semi-real scale.

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