DYNAMIC VIBRATION DAMPERS FOR CONSOLE INSTRUMENTS

The analyses performed in this study relate to the method of quasigeoid modelling which uses the geophysical gravity data inversion technique (GGI method). The purpose of the analyses was to estimate the optimal values of quantitative parameters of selected input data used in the calculations. In particular, the following parameters were investigated: extents of a digital elevation model and the Moho depth model, the extent of the area covered by gravity data and density of gravity data. In order to perform the analyses, a series of local quasigeoid models was determined with different values of the above-mentioned parameters. The optimal values of the parameters were subsequently estimated based on the analyses of changes of quasigeoid models.

For pt.I see ibid., vol.5, no.2, p.366-73 (1990). A comprehensive analysis of the transient performance of a permanent magnet (PM) synchronous motor operating from a variable source is described. Internal damping is modeled, and optimum values of design parameters which improve transient performance are obtained. The effects of varying the supply voltage and frequency on the optimum values of these parameters are demonstrated. Simulated nonlinear response to a load change and starting performance are discussed. The results illustrate that significant improvement in transient performance can be achieved over a wide range of voltage and frequencies when some parameters are properly chosen. >

Inconel 718 (IN 718) powder is used for a laser powder bed fusion (LPBF) printer, but the mechanical properties of the as-built object are not suited to cold deep drawing applications. This study uses the Taguchi method to design experimental groups to determine the effect of various factors on the mechanical properties of as-built objects produced using an LPBF printer. The optimal printing parameters are defined using the result for the factor response to produce an as-built object with the greatest ultimate tensile strength (UTS), and this is used to produce a specimen for post-processing, including heat treatment (HT) and surface finishing. The HT parameter value that gives the maximum UTS is the optimal HT parameter. The optimal printing and HT parameter values are used to manufacture a die and a punch to verify the suitability of the manufactured tool for deep drawing applications. The experimental results show that the greatest UTS is 1091.33 MPa. The optimal printing parameters include a laser power of 190 W, a scanning speed of 600 mm/s, a hatch space of 0.105 mm and a layer thickness of 40 μm, which give a UTS of 1122.88 MPa. The UTS for the post-processed specimen increases to 1511.9 MPa. The optimal parameter values for HT are heating to 720 °C and maintaining this temperature for 8 h, decreasing the temperature to 620 °C and maintaining this temperature for 8 h, and cooling to room temperature in the furnace. Surface finishing increases the hardness to HRC 55. Tools, including a punch and a die, are manufactured using these optimized parameter values. The deep drawing experiment demonstrates that the manufactured tools that are produced using these values form a round cup of Aluminum alloy 6061. The parameter values that are defined can be used to manufacture IN 718 tools with a UTS of more than 1500 MPa and a hardness of more than 50 HRC, so these tools are suited to cold deep drawing specifications.

The rationale for the choice of a technical solution to the issue of vibration protection of gyroscopic angular velocity meters, built on the basis of dynamically tuned gyroscopes (DTG) is presented. The proposed vibration protection system consists of shock absorbers with high elasticity and dynamic vibration dampers (DVD) with nonlinear elastic and damping characteristics. The main factors that determine the peculiarities of choosing a vibration protection system for precision gyroscopic devices are indicated. Keywords dynamically tuned gyroscope (DTG); dynamic vibration damper (DVD); vibration protection system; gyroscopic angular velocity meter

Purpose: Regularization of inverse treatment planning can improve dose uniformity and decrease delivery time of intensity‐modulated radiotherapy (IMRT) thus improving the radiobiological effect. We attempt to select an optimal value of the regularization parameter which improves the smoothness of beam intensity functions without deterioration of accuracy of fit in the IMRT optimization problem. Method and Materials: We apply to inverse treatment planning the L‐curve method which was developed to determine an optimal value of regularization parameter in the discrete ill‐posed problems. The method is based on finding the regularization parameter which minimizes the residual norm which is a measure of accuracy of fit and the solution norm which is a measure of smoothness of solution. The main idea of the L‐curve method is to plot the smoothing norm as a function of the residual norm for all values of the regularization parameter. Remarkably, this characteristic curve has an L‐shaped dependence. The optimal value of regularization parameter can be found at the “corner” of the L‐curve. Results: We apply the L‐curve criterion to the optimization problem of the prostate radiotherapy cancer treatment with intensity‐modulated beams. We show the distinctive L‐shaped dependence of the smoothing norm as a function of the residual norm. Of great practical importance is the vertical part of the L‐curve because the smoothing norm is decreasing and the residual norm remains approximately of the same value. The corner of the L‐curve is located using criteria of 1) maximum curvature and 2) minimum total of the residual and smoothing norm. Conclusion: The L‐curve method can be an important graphical tool for selection of the regularization parameter in inverse treatment planning. The regularization parameter determined using the L‐curve provides the optimal trade‐off between the smoothness of beam intensity functions and the accuracy of fit.

Formulas for calculating the dimensions of a dynamic low-frequency vibration damper are derived. The damper is designed as a circularly curved thin-walled plate. Bench tests demonstrated that the damper effectively reduces the amplitude of vibrations of the bearing of a high-power turbine-generator set in two directions simultaneously.

In helicopter rotorcraft design, two main approaches as passive and active control methods widely used to decline vibration. In this study passive control methods are used to reduce vibration in the helicopter rotor blade. The most common passive control devices are dynamic vibration dampers, isolators and structural distribution of the composite blades. A surrogate optimization formula is used as the objective function of vibration reduction which includes vibratory hub loads and bending moments. In optimization model, composite ply angles are design variables and spar frequency-placement, autorotation and stress conditions are constraints. As the optimization method, a hybrid solution is chosen. The gradient-based algorithms generate accurate results in trust region and heuristic methods scan very large area of solution space. Due to the aforementioned advantages, these algorithms are hybridized. As a result of the comparison of the optimization outcomes with the baseline UH-60 rotor blades, approximately 38 % vibration reduction is observed in the new design.

The article is devoted to the urgent problem of protecting buildings and structures from dynamic influences of various nature and origin. An effective means of vibration suppression is being considered namely dynamic vibration dampers. A generalized model of a system of multi-mass dampers and a rational, engineering-oriented method for calculating their parameters at conditions of polyharmonic descriptions of complex dynamic excitations on construction objects are proposed, using the concepts and methods of the theory of regulating the stress-strain state of structures, where dampers play the role of regulators. Based on the decomposition approach, an analytical solution to the problem of determining the parameters of a complex of dampers according to pre-formulated requirements for the results of regulating the dynamic state of a deformable system (buildings, structures) is obtained. The test tasks were used to evaluate and analyze the accuracy of the results of calculations of model systems according to the developed methods, algorithms and formulas.

Soft magnetoactive elastomers (MAEs) are smart materials that respond to external magnetic fields by dynamically altering their mechanical properties. They are composed of magnetically responsive microparticles embedded within a soft polymer matrix, exhibiting an effective shear modulus of up to 100 kPa. In recent decades, MAEs' bulk properties have been successfully exploited for applications such as dynamic vibration damping, vibration sensing, and actuation in soft robotics. Recent research has shifted to their surface properties, revealing promising results on tunable surface features such as roughness, adhesion, and wetting. Even the transport of small solid and fluid objects was demonstrated. The associated surface effects can be significantly enhanced through the precise engineering of surface topography. In this article, an efficient laser micromachining technique, with a resolution of 15 µm, is presented, which enables rapid prototyping of MAE surfaces. It allows the creation of various complex shapes and offers functionality beyond the one achievable with traditional molding techniques. Additionally, the approach is versatile and can be applied to any polymer that sufficiently absorbs the laser light. As an example, a lamellar surface micro-pattern fabrication process and its characterization by optical and scanning electron microscopies are shown. Its response to a magnetic field is demonstrated. The technique provides a flexible and fast solution for optimizing polymer surface design across a wide range of applications.

The problem of parametric synthesis of an electronic control unit for a brake system of a car is considered, which consists in the process of choice of values of the varied parameters of electronic CU. The selected parameters should provide the maximum margin of stability and maximum velocity of the closed-loop system of automatic control of a given direction of car movement in braking mode. This is important because modern high-velocity cars, operating in conditions of low-quality road surfaces of highways, often become objects of road traffic accidents, despite the fact that they are equipped with anti-lock braking and traction control systems. In this regard, the design development of the latest models of automotive equipment is aimed at creating directional stability systems, increased vehicle dirigibility in case of loss of wheel adhesion with the road surface. The problem is solved using the method of frozen coefficients, which is based on the iterative process of sequential selection of numerical values of the varied parameters of the electronic unit. In particular, it is found that the characteristic equation of a closed-loop stability control system contains three variable parameters, and the system itself is a two-channel one. The first control channel for the inclination angle of the car body contains variable parameters, and the second control channel for side skid contains a variable parameter. It is shown that 5–6 steps of the iterative process are sufficient to obtain optimal values of the varied parameters that provide the maximum safety margin and maximum performance of the closed-loop vehicle stability program. The work proves the effectiveness of the developed algorithms for parametric synthesis of the electronic control unit for a car, the simplicity of their implementation, as well as the high quality of stabilization processes of the car body. The optimal values of the varied parameters are In this case, the margin of stability of the system is

Forecasting skills of the complex weather and climate models have been improved by tuning the sensitive parameters that exert the greatest impact on simulated results based on more effective optimization methods. However, whether the optimal parameter values are still work when the model simulation conditions vary, which is a scientific problem deserving of study. In this study, a highly-effective optimization method, adaptive surrogate model-based optimization (ASMO), was firstly used to tune nine sensitive parameters from four physical parameterization schemes of the Weather Research and Forecasting (WRF) model to obtain better summer precipitation forecasting over the Greater Beijing Area in China. Then, to assess the applicability of the optimal parameter values, simulation results from the WRF model with default and optimal parameter values were compared across precipitation events, boundary conditions, spatial scales, and physical processes in the Greater Beijing Area. The summer precipitation events from 6 years were used to calibrate and evaluate the optimal parameter values of WRF model. Three boundary data and two spatial resolutions were adopted to evaluate the superiority of the calibrated optimal parameters to default parameters under the WRF simulations with different boundary conditions and spatial resolutions, respectively. Physical interpretations of the optimal parameters indicating how to improve precipitation simulation results were also examined. All the results showed that the optimal parameters obtained by ASMO are superior to the default parameters for WRF simulations for predicting summer precipitation in the Greater Beijing Area because the optimal parameters are not constrained by specific precipitation events, boundary conditions, and spatial resolutions. The optimal values of the nine parameters were determined from 127 parameter samples using the ASMO method, which showed that the ASMO method is very highly-efficient for optimizing WRF model parameters.

There is a need to solve the problems of diffusion-convection in practice. Initial-boundary problems for the diffusion-convection equation serve as basic models for hydro- and gas-mechanical processes, heat and mass transfer, population dynamics, pollution transport in water and air, etc. An algorithm is proposed for finding the optimal weight value that ensures the minimum approximation error at the solution of the initial initial-boundary value problem for given value of the steps of the time grid for difference schemes with weights that approximate the problem data. The solution function and the approximation error were decomposed to find the value of the optimal parameter of the scheme with weights, depending on the time step along the trigonometric basis. The optimal value of the weight parameter was obtained, based on the numerical minimization of the relative magnitude of the approximation error, which depending on the time grid spacing in the range of 0.5-0.6. The efficiency of the algorithm was demonstrated on the base of the test problem.

Density based clustering algorithms are one of the primary method for data mining. The clusters which are formed using density clustering are easy to understand and it does limit itself to shapes of clusters. Existing density based algorithms have trouble because they are not capable of finding out all meaningful clusters whenever the density is so much varied. VDBSCAN is introduced to compensate this problem. It is same as DBSCAN (Density Based Spatial Clustering of Applications with Noise) but only the difference is VDBSCAN selects several values of parameter Eps for different densities according to k-dist plot. The problem is the value of parameter k in k-dist plot is user defined. This paper introduces a new method to find out the value of parameter k automatically based on the characteristics of the datasets. In this method we consider spatial distance from a point to all others points in the datasets. The proposed method has potential to find out optimal value for parameter k .In this paper a synthetic database with two dimensional data is used for demonstration.

SVM is a one of the most favorite and efficient supervised-classification technique. As it is supervised so learning of the model directly affects the result. Learning of SVM is done with the help of sample data points and model; it can be either linear or non-linear. Number of models has been used so far for multiclass classification but which one is better with optimal value of parameters? If we have several models for SVM and each model has some parameters so one could confuse about to select the model and optimal values of its parameters. SVM is broadly used for binary classification or multiclass classification with linear model. In this article we will describe linear as well as non-linear models and their accuracy with focus on the optimal value of parameters used in each model and tolerating cost of SVM. In this paper class agreement between model and particular class is explained so that one can understand better that which model is responding well for which particular class. So this paper will provide comparative study between various models, for hyper-plane, used with SVM for LULC classification of digital satellite image. The kappa coefficient is calculated to compare the classified result and standard error is evaluated under the 95% confidence interval to estimate the error in result.

This paper presents Pareto-based multi-objective optimization for speed control of induction motor with fractional order proportional integral ( $$\hbox {FOPI}^{\lambda }$$ ) controller. The aim of this study is to find optimum values of tuning parameters of $$\hbox {FOPI}^{\lambda }$$ by using Elman neural network (ENN) and Pareto-based multi-objective optimization. In this context, proportional gain $$K_{\mathrm{p}}$$ , integral gain $$K_{\mathrm{i}}$$ and the order of fractional integral $$\lambda $$ are selected as tuning parameters while settling time $${T}_{\mathrm{s}}$$ and overshoot $${M}_{\mathrm{o}}$$ are chosen as objective functions. Firstly, experiments have been carried out to obtain training and test data. Then, ENN has been trained to construct mathematical model which is necessary for multi-objective optimization. In the next step, accuracy and reliability of ENN model are tested by using test data taken from experimental set-up. Finally, Pareto-based multi-objective optimization method has been used to find the optimum values of tuning parameters that minimize both $${T}_{\mathrm{s}}$$ and $${M}_{\mathrm{o}}$$ values. The different three conditions of the Pareto solution set are applied to the experimental set-up to verify the effectiveness of the proposed method. Results show that ENN is well modelled for induction motor and Pareto solution is an effective method to find optimal values of controller coefficients according to desired $${T}_{\mathrm{s}}$$ and $${M}_{\mathrm{o}}$$ values.