Journal articles: 'Estimation of parameters tool'

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Cortés-Benito, I., H. Rodríguez-Cortés, M. Martínez-Ramírez, Y. Tlatelpa-Osorio, and J. G. Romero. "Quadrotor physical parameters online estimation." Memorias del Congreso Nacional de Control Automático 5, no. 1 (October 17, 2022): 133–39. http://dx.doi.org/10.58571/cnca.amca.2022.005.

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Online estimation of unmanned aerial vehicles' physical parameters is an essential tool for aerodynamic and control design. This paper numerically evaluates two methods for physical parameter estimation for a quadrotor. The estimation methods are based on recently introduced techniques that relax the persistency of excitation constraint in the least-squares estimation methods.

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Balakrishnan, P., and M. F. DeVries. "Sequential Estimation of Machinability Parameters for Adaptive Optimization of Machinability Data Base Systems." Journal of Engineering for Industry 107, no. 2 (May 1, 1985): 159–66. http://dx.doi.org/10.1115/1.3185980.

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Mathematical model type machinability data base systems require suitable model building procedures to estimate the model parameters. The estimation procedure should be capable of using subjective prior information about the models and must also be capable of adapting the model parameters to the particular machining environment for which the data are needed. In this paper, the sequential Maximum A Posteriori (MAP) estimation procedure is proposed as the mathematical tool for performing these functions. Mathematical details of this estimation procedure are presented. The advantages of this method over conventional regression analysis are discussed based on the analysis of an experimental tool life data set. Details regarding the selection of the various initial values needed for starting the sequential procedure are presented. The use of prior information about the models in order to improve the parameter estimates is investigated. The adaptive capability of the procedure is analyzed using simulated tool life data. The results of this analysis indicate that the proposed sequential estimation procedure is a valuable tool for estimating machinability parameters and for the adaptive optimization of machinability data base systems.

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Shamine, D. M., S. W. Hong, and Y. C. Shin. "Experimental Identification of Dynamic Parameters of Rolling Element Bearings in Machine Tools." Journal of Dynamic Systems, Measurement, and Control 122, no. 1 (May 11, 1998): 95–101. http://dx.doi.org/10.1115/1.482432.

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In-situ identification is essential for estimating bearing joint parameters involved in spindle systems because of the inherent interaction between the bearings and spindle. This paper presents in-situ identification results for rolling element bearing parameters involved in machine tools by using frequency response functions (FRF’s). An indirect estimation technique is used for the estimation of unmeasured FRF’s, which are required for identification of joint parameters but are not available. With the help of an index function, which is devised for indicating the quality of estimation or identification at a particular frequency, the frequency region appropriate for identification is selected. Experiments are conducted on two different machine tool spindles. Repeatable and accurate joint coefficients are obtained for both machine tool systems. [S0022-0434(00)02501-6]

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Rajeev, D., D. Dinakaran, and S. C. E. Singh. "Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 4 (August 1, 2017): 553–59. http://dx.doi.org/10.1515/bpasts-2017-0060.

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AbstractNowadays, finishing operation in hardened steel parts which have wide industrial applications is done by hard turning. Cubic boron nitride (CBN) inserts, which are expensive, are used for hard turning. The cheaper coated carbide tool is seen as a substitute for CBN inserts in the hardness range (45–55 HRC). However, tool wear in a coated carbide tool during hard turning is a significant factor that influences the tolerance of machined surface. An online tool wear estimation system is essential for maintaining the surface quality and minimizing the manufacturing cost. In this investigation, the cutting tool wear estimation using artificial neural network (ANN) is proposed. AISI4140 steel hardened to 47 HRC is used as a work piece and a coated carbide tool is the cutting tool. Experimentation is based on full factorial design (FFD) as per design of experiments. The variations in cutting forces and vibrations are measured during the experimentation. Based on the process parameters and measured parameters an ANN-based tool wear estimator is developed. The wear outputs from the ANN model are then tested. It was observed that as the model using ANN provided quite satisfactory results, and that it can be used for online tool wear estimation.

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Panić, Branislav, Jernej Klemenc, and Marko Nagode. "Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation." Mathematics 8, no. 3 (March 7, 2020): 373. http://dx.doi.org/10.3390/math8030373.

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A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

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Santos, Tiago, Florian Lemmerich, and Denis Helic. "Bayesian estimation of decay parameters in Hawkes processes." Intelligent Data Analysis 27, no. 1 (January 30, 2023): 223–40. http://dx.doi.org/10.3233/ida-216283.

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Hawkes processes with exponential kernels are a ubiquitous tool for modeling and predicting event times. However, estimating their decay parameter is challenging, and there is a remarkable variability among decay parameter estimates. Moreover, this variability increases substantially in cases of a small number of realizations of the process or due to sudden changes to a system under study, for example, in the presence of exogenous shocks. In this work, we demonstrate that these estimation difficulties relate to the noisy, non-convex shape of the Hawkes process’ log-likelihood as a function of the decay. To address uncertainty in the estimates, we propose to use a Bayesian approach to learn more about likely decay values. We show that our approach alleviates the decay estimation problem across a range of experiments with synthetic and real-world data. With our work, we support researchers and practitioners in their applications of Hawkes processes in general and in their interpretation of Hawkes process parameters in particular.

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Khangura, RajbirKaur, Keya Sircar, and DilpreetSingh Grewal. "Four odontometric parameters as a forensic tool in stature estimation." Journal of Forensic Dental Sciences 7, no. 2 (2015): 132. http://dx.doi.org/10.4103/0975-1475.146367.

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Zhang, Liang, Wei Yang, Shuaifeng Zhi, and Chen Yang. "Parameter Estimation Processor for K-distribution Clutter Based on Deep Learning." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012095. http://dx.doi.org/10.1088/1742-6596/2290/1/012095.

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Abstract This paper concerns the problem of parameter estimating of K-distribution. In previous work only the shape parameter of K-distribution is estimated from which the scale parameter is calculated. Therefore, the accuracy of the estimated scale parameter is largely determined by the accuracy of shape parameter estimation. In order to decouple the estimation of scale and shape parameters, in this work, deep learning is considered as the main tool to achieve K-distribution parameters estimation as a regression task. Specifically, a parameter estimation processor combining CNN with LSTM is constructed. The ground truth of the two parameters are taken as labels, and the weighted losses of the two parameters construct the total loss function of the network training. The effectiveness and superiority of the proposed estimation processor are verified on the simulated data and the real sea clutter data.

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SaraerToosi, Ali, and Avery E. Broderick. "Autoencoding Labeled Interpolator, Inferring Parameters from Image and Image from Parameters." Astrophysical Journal 967, no. 2 (May 29, 2024): 140. http://dx.doi.org/10.3847/1538-4357/ad3e76.

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Abstract The Event Horizon Telescope (EHT) provides an avenue to study black hole accretion flows on event-horizon scales. Fitting a semianalytical model to EHT observations requires the construction of synthetic images, which is computationally expensive. This study presents an image generation tool in the form of a generative machine-learning model, which extends the capabilities of a variational autoencoder. This tool can rapidly and continuously interpolate between a training set of images and can retrieve the defining parameters of those images. Trained on a set of synthetic black hole images, our tool showcases success in interpolating both black hole images and their associated physical parameters. By reducing the computational cost of generating an image, this tool facilitates parameter estimation and model validation for observations of black hole systems.

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Elanayar, Sunil, and Yung C. Shin. "Robust Tool Wear Estimation With Radial Basis Function Neural Networks." Journal of Dynamic Systems, Measurement, and Control 117, no. 4 (December 1, 1995): 459–67. http://dx.doi.org/10.1115/1.2801101.

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In this paper, a unified method for constructing dynamic models for tool wear from prior experiments is proposed. The model approximates flank and crater wear propagation and their effects on cutting force using radial basis function neural networks. Instead of assuming a structure for the wear model and identifying its parameters, only an approximate model is obtained in terms of radial basis functions. The appearance of parameters in a linear fashion motivates a recursive least squares training algorithm. This results in a model which is available as a monitoring tool for online application. Using the identified model, a state estimator is designed based on the upperbound covariance matrix. This filter includes the errors in modeling the wear process, and hence reduces filter divergence. Simulations using the neural network for different cutting conditions show good results. Addition of pseudo noise during state estimation is used to reflect inherent process variabilities. Estimation of wear under these conditions is also shown to be accurate. Simulations performed using experimental data similarly show good results. Finally, experimental implementation of the wear monitoring system reveals a reasonable ability of the proposed monitoring scheme to track flank wear.

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Lan, Ming-Shong, and Yngve Naerheim. "In-Process Detection of Tool Breakage in Milling." Journal of Engineering for Industry 108, no. 3 (August 1, 1986): 191–97. http://dx.doi.org/10.1115/1.3187063.

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An adaptive signal processing scheme for the cutting force signal was used to detect the fracture and chipping of a cutting tool during milling operation. The cutting force signal was modeled by a discrete autoregressive model where parameters were estimated recursively at each sampling instant using a parameter adaptation algorithm based on a model reference adaptive system approach. The sensitivity of the prediction error and the estimated parameters to the frature and chipping of a cutting tool are presented. The influence of the adaptation algorithm parameters on the estimation results is discussed. The effect of the change in cutting conditions on the estimation results is also investigated.

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Ghasempoor, A., T. N. Moore, and J. Jeswiet. "On-line wear estimation using neural networks." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 212, no. 2 (February 1, 1998): 105–12. http://dx.doi.org/10.1243/0954405971515537.

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In this paper, a neural network based system for ‘on-line’ estimation of tool wear in turning operations is introduced. The system monitors the cutting force components and extracts the tool wear information from the changes occurring over the cutting process. A hierarchical structure using multilayered feedforward static and dynamic neural networks is used as a specialized subsystem, for each wear component to be monitored. These subsystems share information about the tool wear components they are monitoring and their error in estimating the cutting force components is used to update the dynamic neural networks. The adaptability property of neural networks ensures that changes in machining parameters can be accommodated. Simulation studies are undertaken using experimental data available from manufacturing literature. The results are promising and show good estimation ability.

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Alajmi, Mahdi S., and Abdullah M. Almeshal. "Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization." Materials 14, no. 14 (July 6, 2021): 3773. http://dx.doi.org/10.3390/ma14143773.

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Cutting tool wear reduces the quality of the product in production processes. The optimization of both the machining parameters and tool life reliability is an increasing research trend to save manufacturing resources. In the present work,, we introduced a computational approach in estimating the tool wear in the turning process using artificial intelligence. Support vector machines (SVM) for regression with Bayesian optimization is used to determine the tool wear based on various machining parameters. A coated insert carbide tool 2025 was utilized in turning tests of 709M40 alloy steel. Experimental data were collected for three machining parameters like feed rate, depth of cut, and cutting speed, while the parameter of tool wear was calculated with a scanning electron microscope (SEM). The SVM model was trained on 162 experimental data points and the trained model was then used to estimate the experimental testing data points to determine the model performance. The proposed SVM model with Bayesian optimization achieved a superior accuracy in estimation of the tool wear with a mean absolute percentage error (MAPE) of 6.13% and root mean square error (RMSE) of 2.29%. The results suggest the feasibility of adopting artificial intelligence methods in estimating the machining parameters to reduce the time and costs of manufacturing processes and contribute toward greater sustainability.

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Kutuzov, S. A. "Estimation of Galactic Model Parameters by Interval Method." Symposium - International Astronomical Union 169 (1996): 713–14. http://dx.doi.org/10.1017/s0074180900230635.

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The interval method of estimating model parameters (MPs) for the Galaxy was suggested earlier (Kutuzov 1988). Intervals are proposed to be used both for observational estimates of galactic parameters (GPs) and for the values of MPs. In this work we consider a model as a tool for studying mutual interaction of GPs. Two-component model is considered (Kutuzov, Ossipkov 1989). We have to estimate the array P of eight MPs.

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Doungpan, Satawat, Vanvisa Chatchavong, and Kanok Janchitrapongvej. "Applicability of Weibull distribution in Generating Model for Evaluating Lifespan Closed Circuit Television System (CCTV)." Journal of Physics: Conference Series 2559, no. 1 (August 1, 2023): 012007. http://dx.doi.org/10.1088/1742-6596/2559/1/012007.

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Abstract CCTV is an expedient tool for monitoring and investigating. Since it can record emergency and routine events in memory of the electronic circuit board. However, in the process of operating CCTV, it is necessary to carry out a sampling inspection on its lifetime to predicate its availability and reliability. According to previous literature, most of the parameter estimation methods used only one parameter estimation method, which is not sufficient for identifying two explicit parameters of Weibull distribution. Moreover, the bad parameter estimation method cannot achieve reliable analysis results. Therefore, this paper presented the four methods of estimation in both graphical and analytical methods for estimating parameters of Weibull distribution. These methods have measured the accuracy by using mean square error (MSE). In addition, the real data set confirmed that there is the Weibull distribution by using the Kolmogorov-Smirnov test. The simulation results found that the maximum likelihood method (MLM) can outperform the other estimator because it can provide a minimum MSE, there is an optimal parameter in which the shape is 4.3881 and the scale is 86.8207. Finally, the two optimal parameters are applied to real data sets of the CCTV. As a result, the optimal parameter fits with real data sets, and it can evaluate the lifespan of CCTV by using the mean time to failure (MTTF) as 6.6 years.

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Bhawana Verma, Satish Kumar Alaria. "Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 1 (January 31, 2019): 27–34. http://dx.doi.org/10.17762/ijritcc.v7i1.5222.

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Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.

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Yang, Xinyi, Wei Shen, Shan Pang, Benwei Li, Keyi Jiang, and Yonghua Wang. "A Novel Gas Turbine Engine Health Status Estimation Method Using Quantum-Behaved Particle Swarm Optimization." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/302514.

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Accurate gas turbine engine health status estimation is very important for engine applications and aircraft flight safety. Due to the fact that there are many to-be-estimated parameters, engine health status estimation is a very difficult optimization problem. Traditional gas path analysis (GPA) methods are based on the linearized thermodynamic engine performance model, and the estimation accuracy is not satisfactory on conditions that the nonlinearity of the engine model is significant. To solve this problem, a novel gas turbine engine health status estimation method has been developed. The method estimates degraded engine component parameters using quantum-behaved particle swarm optimization (QPSO) algorithm. And the engine health indices are calculated using these estimated component parameters. The new method was applied to turbine fan engine health status estimation and is compared with the other three representative methods. Results show that although the developed method is slower in computation speed than GPA methods it succeeds in estimating engine health status with the highest accuracy in all test cases and is proven to be a very suitable tool for off-line engine health status estimation.

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Arditty, P. C., F. Mathieu, and P. Staron. "EVALUATION OF FORMATION PROPERTIES FROM PROCESSING AND INTERPRETATION OF THE EVA TOOL LOGS." APPEA Journal 26, no. 1 (1986): 187. http://dx.doi.org/10.1071/aj85018.

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With the development of full waveform acoustic tools, geophysicists have tried to extract more and more information from acoustic parameters and to relate to the data to formation propertiesThe new tool, EVA (Evaluation of Velocity and Attenuation), is a 4-transmitter/12-receiver long spacing tool permitting recording of the complete waveform and processing of all information contained in the acoustic signal.The key point for optimum results is a robust and automatic processing which allows quantitative estimation of different parameters such as velocity, amplitude, and period of all the three main types of waves, i.e. compressional (P), shear (S), and Stoneley (ST) waves.In parallel with the processing phase, the analysis of the raw data leads to a qualitative and very rapid interpretation of the recordings.Once the processing is completed and all parameters obtained, these may be applied to a quantitative interpretation of the EVA data. Such interpretation can then be utilised for automatic lithology estimation, estimation of porosity and shale content, and to the detection of fractures and eventually the estimation of permeability.Further applications such as the detection of invaded zones, estimation behind casing, estimation of elastic moduli, and the reconstitution of a density log can provide significant data for exploration and production engineers.

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Kalina, J. "Three contributions to robust regression diagnostics." Journal of Applied Mathematics, Statistics and Informatics 11, no. 2 (December 1, 2015): 69–78. http://dx.doi.org/10.1515/jamsi-2015-0013.

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Abstract Robust regression methods have been developed not only as a diagnostic tool for standard least squares estimation in statistical and econometric applications, but can be also used as self-standing regression estimation procedures. Therefore, they need to be equipped by their own diagnostic tools. This paper is devoted to robust regression and presents three contributions to its diagnostic tools or estimating regression parameters under non-standard conditions. Firstly, we derive the Durbin-Watson test of independence of random regression errors for the regression median. The approach is based on the approximation to the exact null distribution of the test statistic. Secondly, we accompany the least trimmed squares estimator by a subjective criterion for selecting a suitable value of the trimming constant. Thirdly, we propose a robust version of the instrumental variables estimator. The new methods are illustrated on examples with real data and their advantages and limitations are discussed.

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Mohamed Nuri Hussin, Ibrahim S Nasir, and Ahmed E Hamza. "Estimation of Solar Cell Equivalent Circuit Parameters for Photovoltaic Module Using Differential Evolution Algorithm." Journal of Pure & Applied Sciences 22, no. 3 (October 2, 2023): 31–34. http://dx.doi.org/10.51984/jopas.v22i3.2732.

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The accurate estimation of solar module parameters is crucial for predicting the energy production of photovoltaic modules under different environmental conditions. In this paper, we present an approach for estimating the optimum parameters of a one-diode model using the Differential Evolution (DE) algorithm implemented in MATLAB. Our system enables the determination of these parameters at any solar irradiance and module temperature conditions, making it easy to apply the model for predicting the energy production of a photovoltaic module. The results demonstrate that the DE algorithm can estimate the parameters accurately. The numerical estimation technique based on a mathematical algorithm approximately fits all the points on the I-V curve at various environmental conditions. This work is a valuable tool for improving solar cells’ performance and efficiency and applies to many photovoltaic applications.

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Deng, Xiang Li, Chuan Qi Wang, Zhe Zhang, and Xiang Gen Yin. "Studies on the Estimation of Transformer Winding Parameters." Advanced Materials Research 354-355 (October 2011): 953–58. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.953.

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This paper presents a new winding parameter model of UHV transformer used by On-line Least- Squares Identification Algorithm (OLSIA) to estimate winding parameters. This paper also provides the criteria to estimate the accuracy of identification model. With EMTP simulation tool, this paper establishes the model according to parameters of the first UHV power transmission system in China, simulates various situations such as UHV transformer energizing, external fault, internal fault, normal operation in the model, and demonstrates that the OLSIA based on the new transformer model is capable to estimate the winding parameters accurately in various conditions, especially in stable-state condition.

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Ahmed, Shamim, Marian Krüger, Christian Willomitzer, and Golam A. Zakaria. "Numerical evaluation of image parameters of ETR-1." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 489–91. http://dx.doi.org/10.1515/cdbme-2016-0108.

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AbstractIn this work, we developed a method to handle the image quality test-tool precisely. This test-tool is important to evaluate the quality of the medical images for pre-treatment planning phase. But the achieved images are estimated by naked eyes, which does not provide the precise result. Our main goal is to get the desired image parameters numerically. This numerical estimation overcomes the limitation of naked eye observation. Hence, it enhances the pre-treatment planning. The ETR-1 test-tool is considered here. The contrast, the low contrast details and line-pairs (lp/mm) were estimated.

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Li, Peng Zhong, and Wei Min Zhang. "Tool Life Estimation for High-Speed Machining of Composite Material." Advanced Materials Research 143-144 (October 2010): 1075–80. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.1075.

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Tool life is an important index to judge machinability of cutting tool. Wear standard is a necessary considered factor to give tool life. Microwave printed board is composite material, wear standard should be found by tool wear experiment under definite machining environment. Life-span data of cutting tools with different combination of cutting parameters is collected through machining experiment. And then based 2 mathematic models: extended Taylor empirical equation and quadratic response surface equation, with regression analysis method, the mathematical models of relationship between cutting parameters and tool life were given respectively. Through comparison of two regression methods, response surface equation based wear equation is given, determining calculating method of tool life for High-speed Machining of Aluminum Base Microwave Printed Circuit Board. Described method can be popularized according to change of machining conditions.

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Das, Santanu, A. B. Chattopadhyay, and A. S. R. Murthy. "Force Parameters for On-line Tool Wear Estimation: A Neural Network Approach." Neural Networks 9, no. 9 (December 1996): 1639–45. http://dx.doi.org/10.1016/s0893-6080(96)00036-6.

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Bazaz, Sara M., Juho Ratava, Mika Lohtander, and Juha Varis. "An Investigation of Factors Influencing Tool Life in the Metal Cutting Turning Process by Dimensional Analysis." Machines 11, no. 3 (March 17, 2023): 393. http://dx.doi.org/10.3390/machines11030393.

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This article uses dimensional analysis to formulate the tool life in the turning process of metal cutting for small-lot production by considering the impacts of the most important parameters. The estimation of tool life specifies process efficiency, machining productivity, resource consumption, machining time, and cost. Many parameters influence tool life on the real shop floor in small-lot production. This literature review studies 29 parameters affecting tool life directly or indirectly. The results of this research are represented as a graph-based analysis in the form of a web of interdependencies and a relationship matrix. The relationship matrix illustrates the direct and indirect interdependencies of the parameters which influence tool life in the turning process. The graph visualizes the weight of the parameters for the estimation of tool life in small-lot production. A cause-and-effect diagram is extracted from the relationship matrix to study the parameters affecting tool life in small-lot production. A dimensional analysis is executed based on the cause-and-effect diagram in order to calculate the tool life. The functions of tool life involve the cutting conditions, tool and workpiece hardness, cutting force, and cutting temperature. The dimensional analysis shows that the cutting speed, feed rate, and workpiece hardness are the most effective factors impacting tool life in the turning process.

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Joudivand Sarand, Mohammadhasan, Mohammadreza Shabgard, Mohammad Jodari Saghaie, and Laya Joudivand Sarand. "Tool wear estimating model considering modeling improvement factors in electrical discharge machining process based on physical properties of tool electrodes." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 5 (October 24, 2016): 850–66. http://dx.doi.org/10.1177/0954405416673114.

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A comprehensive numerical model is introduced in this article for estimating the tool wear based on finite element model and inverse heat conduction techniques considering the interactive and direct effects of the experimental factors of recast layer thickness, anode energy fraction and plasma flushing efficiency. The individual and interactive effects of the major thermo-physical and electro-physical parameters have been modeled to improve the introduced tool wear estimation procedure. Comparison of the numerical results with the experimental observations indicated that the developed finite element model and inverse heat conduction process was capable of predicting the tool wear with high accuracy. Additionally, application of analysis of variance technique showed that the introduced mathematical models for estimating anode energy fraction, plasma flushing efficiency and recast layer thickness are statistically significant and adequate which means that the proposed equations can be used for a variety of physical, electrical and thermal variables’ combinations.

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Molodtsov, Vladimir Vladimirovich, and Andrey Yurevich Barbin. "Reasonable Choice of Motorized Spindles with HSK Tool Connection." Applied Mechanics and Materials 607 (July 2014): 229–34. http://dx.doi.org/10.4028/www.scientific.net/amm.607.229.

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The article describes the problem of reasonable choice of parameters of main motion drive of modern multi-purpose machine tools. Authors propose a multi-purpose methodological model for reasonable estimation of operational capabilities of motorized spindles and efficiency of equipment usage for performing of required technological operations. Dependencies are provided considering influence of HSK connection on operational parameters of drives.

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Rong, Zhiming, Yuxiong Li, Li Wu, Chong Zhang, and Jialin Li. "An Advanced Tool Wear Forecasting Technique with Uncertainty Quantification Using Bayesian Inference and Support Vector Regression." Sensors 24, no. 11 (May 24, 2024): 3394. http://dx.doi.org/10.3390/s24113394.

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Tool wear prediction is of great significance in industrial production. Current tool wear prediction methods mainly rely on the indirect estimation of machine learning, which focuses more on estimating the current tool wear state and lacks effective quantification of random uncertainty factors. To overcome these shortcomings, this paper proposes a novel method for predicting cutting tool wear. In the offline phase, the multiple degradation features were modeled using the Brownian motion stochastic process and a SVR model was trained for mapping the features and the tool wear values. In the online phase, the Bayesian inference was used to update the random parameters of the feature degradation model, and the future trend of the features was estimated using simulation samples. The estimation results were input into the SVR model to achieve in-advance prediction of the cutting tool wear in the form of distribution densities. An experimental tool wear dataset was used to verify the effectiveness of the proposed method. The results demonstrate that the method shows superiority in prediction accuracy and stability.

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Huang, Pao-Ming, and Ching-Hung Lee. "Estimation of Tool Wear and Surface Roughness Development Using Deep Learning and Sensors Fusion." Sensors 21, no. 16 (August 7, 2021): 5338. http://dx.doi.org/10.3390/s21165338.

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This paper proposes an estimation approach for tool wear and surface roughness using deep learning and sensor fusion. The one-dimensional convolutional neural network (1D-CNN) is utilized as the estimation model with X- and Y-coordinate vibration signals and sound signal fusion using sensor influence analysis. First, machining experiments with computer numerical control (CNC) parameters are designed using a uniform experimental design (UED) method to guarantee the variety of collected data. The vibration, sound, and spindle current signals are collected and labeled according to the machining parameters. To speed up the degree of tool wear, an accelerated experiment is designed, and the corresponding tool wear and surface roughness are measured. An influential sensor selection analysis is proposed to preserve the estimation accuracy and to minimize the number of sensors. After sensor selection analysis, the sensor signals with better estimation capability are selected and combined using the sensor fusion method. The proposed estimation system combined with sensor selection analysis performs well in terms of accuracy and computational effort. Finally, the proposed approach is applied for on-line monitoring of tool wear with an alarm, which demonstrates the effectiveness of our approach.

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Sun, Wenchao, Xiaolei Yao, Na Cao, Zongxue Xu, and Jingshan Yu. "Integration of soil hydraulic characteristics derived from pedotransfer functions into hydrological models: evaluation of its effects on simulation uncertainty." Hydrology Research 47, no. 5 (January 27, 2016): 964–78. http://dx.doi.org/10.2166/nh.2016.150.

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Aimed at reducing simulation uncertainty of hydrological models in data-sparse basins where soil hydraulic data are unavailable, a method of estimating soil water parameters of soil and water assessment tool (SWAT) from readily available soil information using pedotransfer functions was introduced. The method was evaluated through a case study of Jinjiang Basin, China and was performed based on comparison between two model calibrations: (1) soil parameters estimated from pedotransfer functions and other parameters obtained from calibration; and (2) all parameters derived from calibration. The generalized likelihood uncertainty estimation (GLUE) was used as a model calibration and uncertainty analysis tool. The results show that information contained in streamflow data is insufficient to derive physically reasonable soil parameter values via calibration. The proposed method can reduce simulation uncertainty, resulting from greater average performance of behavioral parameter sets identified by GLUE. Exploring the parameter space reveals that the means of estimating soil parameters has little influence on other parameters. These facts indicate the decrease in uncertainty most likely results from a more realistic description of soil water characteristics than calibration. Thus, the proposed method is superior to calibration for estimating soil parameters of SWAT model when basin data are sparse.

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Reichert, Peter, Reto von Schulthess, and Daniel Wild. "The use of aquasim for estimating parameters of activated sludge models." Water Science and Technology 31, no. 2 (January 1, 1995): 135–47. http://dx.doi.org/10.2166/wst.1995.0089.

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The capabilities of a new computer program for the identification and simulation of aquatic systems (AQUASIM) with respect to parameter estimation of activated sludge models are outlined. The example of the evaluation of two denitrification experiments is used to demonstrate the flexibility of the program with respect to model definition, the possibility of combining several experiments with universal and experiment-specific parameters and several target variables to give a single parameter estimation, and the utility of sensitivity functions for assessing parameter identifiability. The provision for all these tasks makes AQUASIM a very useful tool for parameter estimation of activated sludge models.

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Tian, Tianhai, Yanli Zhou, Yonghong Wu, and Xiangyu Ge. "Estimation of Parameters in Mean-Reverting Stochastic Systems." Mathematical Problems in Engineering 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/317059.

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Stochastic differential equation (SDE) is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.

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Morse, Llewellyn, Zahra Sharif Khodaei, and Ferri M. H. Aliabadi. "Statistical Inference of the Equivalent Initial Flaw Size Distribution Using the Boundary Element Method under Multiple Sources of Uncertainty." Key Engineering Materials 774 (August 2018): 613–18. http://dx.doi.org/10.4028/www.scientific.net/kem.774.613.

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In this work, a method for determining the Equivalent Initial Flaw Size (EIFS) distribution using the Boundary Element Method (BEM) is proposed. Maximum Likelihood Estimation (MLE) is used to infer the EIFS distribution of a cracked stiffened panel under multiple sources of uncertainty, including uncertainty in the loading conditions, fatigue crack growth model parameters, and in the measurement of crack size found from routine inspections. Results suggest that MLE is an effective tool for estimating the parameters of an EIFS distribution when no prior knowledge is available regarding the EIFS distribution or its parameters.

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Gopal, Kathiresan, Lai Soon Lee, and Hsin-Vonn Seow. "Parameter Estimation of Compartmental Epidemiological Model Using Harmony Search Algorithm and Its Variants." Applied Sciences 11, no. 3 (January 26, 2021): 1138. http://dx.doi.org/10.3390/app11031138.

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Epidemiological models play a vital role in understanding the spread and severity of a pandemic of infectious disease, such as the COVID-19 global pandemic. The mathematical modeling of infectious diseases in the form of compartmental models are often employed in studying the probable outbreak growth. Such models heavily rely on a good estimation of the epidemiological parameters for simulating the outbreak trajectory. In this paper, the parameter estimation is formulated as an optimization problem and a metaheuristic algorithm is applied, namely Harmony Search (HS), in order to obtain the optimized epidemiological parameters. The application of HS in epidemiological modeling is demonstrated by implementing ten variants of HS algorithm on five COVID-19 data sets that were calibrated with the prototypical Susceptible-Infectious-Removed (SIR) compartmental model. Computational experiments indicated the ability of HS to be successfully applied to epidemiological modeling and as an efficacious estimator for the model parameters. In essence, HS is proposed as a potential alternative estimation tool for parameters of interest in compartmental epidemiological models.

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Abdullah, Syamsul Bahari, Zakaria Man, L. Ismail, Mohammed Ibrahim Abdul Mutalib, and Mohamad Azmi Bustam. "Solubility Parameters Based on Refractive Index Data of Ionic Liquid." Advanced Materials Research 917 (June 2014): 45–55. http://dx.doi.org/10.4028/www.scientific.net/amr.917.45.

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This article reports on the estimation of solubility parameters using refractive index for a series of imidazolium-based ionic liquids (ILs) and their dependencies on the carbon number of alkyl side-chain of cation and anion. Gathering about 35 ILs of refractive indices data from the literature, the solubility parameters estimation fit well with the suggested correlation. The precision of the estimated solubility parameters obtained indicates that the refractive index-solubility parameters correlation can be an alternative tool to estimate solubility parameter value for ILs.

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Susyanto, Nanang, and Jayrold P. Arcede. "Unveiling SIR Model Parameters: Empirical Parameter Approach for Explicit Estimation and Confidence Interval Construction." Jambura Journal of Biomathematics (JJBM) 5, no. 1 (July 18, 2024): 54–62. http://dx.doi.org/10.37905/jjbm.v5i1.26287.

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We propose a simple parameter estimation method for the Susceptible-Infectious-Recovered (SIR) model. This method offers explicit estimates of parameters using second-order numerical derivatives to construct empirical parameters. In addition, the method constructs confidence intervals, providing a robust assessment of parameter uncertainty. To validate the accuracy of our method, we applied it to simulated data, in order to demonstrate its effectiveness in accurately estimating the true model parameters. Furthermore, we applied this method to actual COVID-19 case data from the USA, Indonesia, and the Philippines. This application enables the estimation of parameters and reproductive numbers, along with their confidence intervals, thus underscoring the efficacy of our technique. Notably, the parameter estimates obtained through our approach successfully predicted the case numbers in all three countries, confirming its predictive reliability. Our method offers significant advantages in terms of simplicity and accuracy, making it an invaluable tool for epidemiological modeling and public health planning.

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Tian, Zhuo, and Bai Cheng Li. "Optimize Preview Model Parameters Evaluation of RANSAC." Applied Mechanics and Materials 687-691 (November 2014): 3984–87. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3984.

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The Random Sample Consensus (RANSAC) algorithm is a popular tool for robust estimation problems in computer vision, primarily due to its ability to tolerate a tremendous fraction of outliers. In this paper, we propose an approach for optimizing the preview model parameters evaluation of RANSAC that has the benefit of offering fast and accurate RANSAC. With guaranteeing the same confidence of the solution as RANSAC, a very large number of erroneous model parameters obtained from the contaminated samples are discarded in the preview evaluation selection. And use local optimization step apply to selected models. The combination of these two strategies results in a robust estimation procedure that provides a significant speed and accuracy RANSAC techniques, while requiring no prior information to guide the sampling process.

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Adamopoulos, Panagiotis A., Abraham Pouliakis, Aris T. Spathis, Christine Kottaridi, and Petros Karakitsos. "Gene Polymorphisms and their Transcripts as Factors for Computerized Assessment of Fracture Risk." International Journal of Reliable and Quality E-Healthcare 4, no. 3 (July 2015): 27–46. http://dx.doi.org/10.4018/ijrqeh.2015070103.

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The aim of this study is the evaluation of osteoporosis related genes and transcription products, demographic and medication data for the estimation of fracture risk in women with osteoporosis. Such estimations are important for the creation of computerized applications similar to the Fracture Risk Assessment Tool (FRAX®). Low cost Internet based tools were used for the creation of anonymised patient record stored in the Cloud, and for an interdisciplinary team to decide on patient inclusion via voice and video conference. 100 female patients with an osteoporotic fracture, on medication or not were included in the study. A series of seven biochemical parameters using the Luminex® technique were measured and genetic characteristics using the microarray Metabone® technique were analysed. The statistical analysis indicated that these new parameters can be important for the estimation of fracture risk; thus computer based risk estimation for fractures could be based on additional molecular data.

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Li, Hui Liang. "A Reliability Analysis of NC Machine Tools Based on the Weibull Distribution." Applied Mechanics and Materials 741 (March 2015): 763–67. http://dx.doi.org/10.4028/www.scientific.net/amm.741.763.

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In this paper, a series of numerical control machine tool truncated failure data collect.The data are assumed to be two parameter weibull distribution and related parameters is obtained and relevant inspection and test of hypothesis, it is concluded that the mathematical model of batch of nc machine tool failure interval time distribution.Relevant indicators to determine the reliability of nc machine tools, machine tool's observations, Machine tool observations, point estimation and interval estimation index value provide a theoretical basis for the research on reliability of machine tool.

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Mijajlovic, Miroslav, Sonja Vidojkovic, and Milos Milosevi]a. "Temperature dependent effective friction coefficient estimation in friction stir welding with the bobbin tool." Thermal Science 20, suppl. 5 (2016): 1321–32. http://dx.doi.org/10.2298/tsci16s5321m.

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The friction coefficient in many friction stir welding researches is generally used as an effective, constant value without concern on the adaptable and changeable nature of the friction during welding sequence. This is understandable because the main problem in analyzing friction in friction stir welding are complex nature of the friction processes, case-dependent and time dependent contact between the bodies, influence of the temperature, sliding velocity, etc. This paper is presenting a complex experimental-numerical-analytical model for estimating the effective friction coefficient on contact of the bobbin tool and welding plates during welding, considering the temperature at the contact as the most influencing parameter on friction. The estimation criterion is the correspondence of the experimental temperature and temperature from the numerical model. The estimation procedure is iterative and parametric - the heat transport parameters and friction coefficient are adapted during the estimation procedure in a realistic manner to achieve relative difference between experimental and model?s temperature lower than 3%. The results show that friction coefficient varies from 0.01 to 0.21 for steel-aluminium alloy contact and temperature range from 406?C to 22?C.

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Lin, Shen-Yung, and Guan-Chen Li. "Estimation and Adjustment of Interface Stiffnesses for Machine-Tool." Applied Sciences 12, no. 23 (December 3, 2022): 12384. http://dx.doi.org/10.3390/app122312384.

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This study adopts a practical approach to establish the estimation and adjustment methods of the interface stiffness for the machine-tool through the finite element analysis (FEA). First, numerical and experimental modal analysis (FMA and EMA) for each single subsystem of the machine-tool structure are performed. Then, the parameters obtained from EMA are used as the objective criterion function, and the FMA is conducted iteratively to solve the material Young’s modulus and Poisson’s ratio for each single subsystem structure, in which the geometrical model is simplified and FE mesh convergence is performed to ensure the quality and efficiency of the numerical analysis. Next, by considering that the machine-tool is assembled by subsystem stacking and that loading on each contact surface is deduced, the FE method is used to calculate the deformation of each contact interface after the single subsystem is stacked successively. The initial value of the interface stiffness estimated by the deformation formula in the mechanics of material is utilized as the initial condition for the iterative calculation in FMA. The changes of the modal parameters are observed in the analysis and the interface, which have a significant impact on the natural frequency variations of the whole machine-tool is selected as the main adjustment object. Then, the adjustment method proposed in this study is applied repeatedly to modify this interface stiffness. The results show that footing interface stiffnesses play the most important role that intensively affects the numerical analysis results of modal parameter. After the repeated adjustments of the interface stiffnesses of the footing, the error of the natural frequency of the whole machine-tool is less than 5%, which is calculated from the comparison between EMA and FMA results. It indicates that the proposed adjustment method in this study for footing interface stiffness determination has a valuable reference in practical use.

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Majchrzak, Ewa, and Jerzy Mendakiewicz. "Sensitivity Analysis as a Tool of Optimal Sensors Location for Solidification Parameters Estimation." Materials Science Forum 638-642 (January 2010): 2640–45. http://dx.doi.org/10.4028/www.scientific.net/msf.638-642.2640.

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The paper deals with the inverse problems appearing in the thermal theory of foundry processes. In particular, the problems connected with identification of casting and mould thermophysical parameters can be considered. To estimate the parameters determining heat transfer processes in the system casting-mould, the additional information concerning the changes of temperature at the points selected from the domain considered is necessary. The localization of sensors should assure the best conditions of identification process. Generally speaking, the sensor should be located at the point for which the sensitivity of temperature field with respect to parameter estimated achieves maximum. In the case of simultaneous identification of several parameters, the proper choice of sensors localization is more complicated. The main goal of the paper is to discuss a theoretical base connected with the problem formulated, to present the possibilities of numerical methods application, to solve the problem, and finally to show the examples of inverse problems solutions (in particular, the latent heat of binary alloy will be estimated).

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Zheng, Jianqin, Yuanhao Dai, Yongtu Liang, Qi Liao, and Haoran Zhang. "An online real-time estimation tool of leakage parameters for hazardous liquid pipelines." International Journal of Critical Infrastructure Protection 31 (December 2020): 100389. http://dx.doi.org/10.1016/j.ijcip.2020.100389.

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Zaghbani, I., and V. Songmene. "Estimation of machine-tool dynamic parameters during machining operation through operational modal analysis." International Journal of Machine Tools and Manufacture 49, no. 12-13 (October 2009): 947–57. http://dx.doi.org/10.1016/j.ijmachtools.2009.06.010.

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Roney, James P., Jeremy Ferlic, Franziska Michor, and Thomas O. McDonald. "ESTIpop: a computational tool to simulate and estimate parameters for continuous-time Markov branching processes." Bioinformatics 36, no. 15 (May 19, 2020): 4372–73. http://dx.doi.org/10.1093/bioinformatics/btaa526.

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Abstract Summary ESTIpop is an R package designed to simulate and estimate parameters for continuous-time Markov branching processes with constant or time-dependent rates, a common model for asexually reproducing cell populations. Analytical approaches to parameter estimation quickly become intractable in complex branching processes. In ESTIpop, parameter estimation is based on a likelihood function with respect to a time series of cell counts, approximated by the Central Limit Theorem for multitype branching processes. Additionally, simulation in ESTIpop via approximation can be performed many times faster than exact simulation methods with similar results. Availability and implementation ESTIpop is available as an R package on Github (https://github.com/michorlab/estipop). Supplementary information Supplementary data are available at Bioinformatics online.

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Kushnir, Dmitry Yu, Nikolay N. Velker, Darya V. Andornaya, and Yuriy E. Antonov. "NEURAL NETWORK INVERSION OF RESISTIVITY DATA FOR DETERMINATION OF DISTANCE TO A BED BOUNDARY." Interexpo GEO-Siberia 2, no. 2 (May 21, 2021): 95–102. http://dx.doi.org/10.33764/2618-981x-2021-2-2-95-102.

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Accurate real-time estimation of a distance to the nearest bed boundary simplifies the steering of directional wells. For estimation of that distance, we propose an approach of pointwise inversion of resistivity data using neural networks based on two-layer resistivity formation model. The model parameters are determined from the tool responses using a cascade of neural networks. The first network calculates the resistivity of the layer containing the tool measure point. The subsequent networks take as input the tool responses and the model parameters determined with the previous networks. All networks are trained on the same synthetic database. The samples of that database consist of the pairs of model parameters and corresponding noisy tool responses. The results of the proposed approach are close to the results of the general inversion algorithm based on the method of the most-probable parameter combination. At the same time, the performance of the proposed inversion is several orders faster.

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Sick, Bernhard. "Tool Wear Monitoring in Turning: A Neural Network Application." Measurement and Control 34, no. 7 (September 2001): 207–22. http://dx.doi.org/10.1177/002029400103400704.

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Tool wear monitoring is the most difficult task in the area of tool condition monitoring for metal-cutting manufacturing processes. The main objective is to improve the process reliability, but the production costs need to be reduced as well. This article summarises a new approach for online and indirect tool wear estimation or classification in turning using neural networks. This technique uses a physical process model describing the influence of cutting conditions (such as the feed rate) on measured process parameters (here: cutting force signals) in order to separate signal changes caused by variable cutting conditions from signal changes caused by tool wear. Features extracted from the normalised process parameters are taken as inputs of a dynamic, but nonrecurrent neural network that estimates the current state of the tool. It is shown that the estimation error can be reduced significantly with this combination of a hard computing and a soft computing technique. The article represents an extended summary of the author's investigations and publications in the area of online and indirect tool wear monitoring in turning by means of artificial neural networks.

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(Gadbail), Mugdha Mankar. "Odontometric and Skull Anthropometric Parameters as a Forensic Tool in Stature Estimation: A Cross-sectional Analysis." World Journal of Dentistry 8, no. 3 (2017): 202–6. http://dx.doi.org/10.5005/jp-journals-10015-1438.

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ABSTRACT Background The aim of the study was to evaluate the relation between height of an individual with the circumference of skull and combined mesiodistal width (CMDW) of the permanent maxillary anterior teeth in forensic and medicolegal investigations. Materials and methods In this study, 100 persons were evaluated. Mesiodistal widths of the permanent maxillary anterior teeth and height and circumference of the skull were measured. Linear regression analysis was used for the known heights of the combined data and data for males and females against the odontometric and anthropometric variables. Results Highly significant correlation was observed between height and other parameters for combined data and data for males. There was statistically significant difference between equation relating height to the CMDW of the maxillary anterior teeth and equation relating height to head circumference. Conclusion It can be stated that the skull with teeth may provide accurate clues to stature from an individual's fragmentary remains and thus provide a reliable method of estimation of height from skeletal remains during forensic investigations. Clinical significance Odontometric parameters along with regression equations may be used as a supplementary approach for stature estimation, especially in situations of unavailability of extremities. In the advancement of field of forensic odontology, skull anthropometry including maxillary extent, mesiodistal dimension of anteriors, and circumference of the skull are beneficial for stature estimation. How to cite this article Gondivkar SM, Gadbail AR, Vedpathak PR, Sarode S, Sarode G, Mankar M, Patil S. Odontometric and Skull Anthropometric Parameters as a Forensic Tool in Stature Estimation: A Cross-sectional Analysis. World J Dent 2017;8(3):202-206

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Bujak, Janusz, Piotr Sitarz, Krzysztof Bujak, Sebastian Majkowski, and Rafał Pasela. "Estimation Complete Combustion Coefficient in Rotary Kilns." Energies 15, no. 3 (February 3, 2022): 1143. http://dx.doi.org/10.3390/en15031143.

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This paper presents a model-based analysis of variability of thermodynamic and chemical parameters in a rotary kiln (RK) during thermal treatment of animal waste. The core process of chemical treatment of waste takes place in RKs; the process involves heating, gasification and partial combustion of the waste. Control over these parameters, and especially the level of complete combustion, determines the quality and efficiency of the process. In operational practice, control and analysis of the variability of process parameters is complicated by the high degree of simultaneity of individual transformations, random disruptions of the process and metrological difficulties resulting from high temperature and chemical activity of the materials being processed. The purpose of preparing the model was to obtain a tool for predicting variability of selected process parameters. By definition, model calculations assume no influence of disturbances on output values, which makes it possible to acquire accurate results that can be compared with corresponding empirically obtained data. The result of the analyses conducted is a theoretical model of the analysed process and a graphical presentation of the calculation results in the form of graphs and charts. A formula for calculating the level of complete combustion and the results of calculation of this index on the basis of empirical data from an industrial waste incineration plant are also presented herein. The presented model is a useful tool providing an insight into interdependencies between selected process parameters and facilitating design of corrective actions oriented towards process optimisation.

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Kveselis, Vaclovas, Sigitas Masaitis, and Petras Urbonas. "QUALITY ASSURANCE FOR HOT WATER SUPPLY FROM DH SYSTEM: SIMULATION TOOL AND APPLICATION." JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT 12, no. 1 (March 31, 2006): 15–19. http://dx.doi.org/10.3846/13923730.2006.9636367.

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Widely developed DH systems in Lithuanian towns and small settlements undergo modernisations aimed to increase their efficiency and heat supply quality. Typically, modernisations include the replacement of building heat substations, the reduction of heat losses in pipes and more efficient heat production, including the use of indigenous fuel resources. It was notified that new heat substations equipped with automatic heating control devices have backward impact on the technological parameters of DH networks. Modelling tools for simulation of hydraulic and thermal parameters in the network allow estimating of potential deviation of heat carrier temperature with loading variations not only during the season of the year, but also over day and night. Special technical measures can be taken to avoid unduly low heat carrier supply temperatures and violation of hot water supply quality requirements. Cost estimation of such measures versus the option of full network renovation is presented in the article.

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Journal articles on the topic 'Estimation of parameters tool'

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