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The purpose of this paper is to determine the time-dependent heat transfer coefficient during FC-72 flow boiling in a 1.7-mm-deep vertical and asymmetrically heated minichannel.

The temperature of the minichannel heated wall was recorded continuously with the use of thermocouples. The heat transfer coefficients for the subcooled and saturated boiling regions at the heated wall–fluid contact surface were calculated from the Robin boundary condition. Both the wall and fluid temperatures were obtained from the solution of the inverse nonstationary problems in two adjacent domains: the heated wall and flowing fluid. The FEM with Trefftz-type basis functions was applied to solve the inverse problem.

The obtained time-dependent heat transfer coefficient in subcooled boiling achieved rather low values, whereas in saturated boiling, the coefficient was the highest at the channel inlet. The boiling curves were plotted to illustrate the results.

The results of experiments are the best source of information for the design of minichannel cooling systems used for thermoregulation of components and heat exchangers. High-tech minichannel heat exchangers are applied in various industrial applications as microelectronics devices, gas turbines, internal combustion engines, nuclear reactors, X-ray sources and organic rankine cycle (ORC) modules.

In the study, the Trefftz functions for the nonstationary Fourier–Kirchhoff equation with the factor describing void fraction were determined and then used to construct the time-dependent basis functions in FEM.

The purpose of this paper is to establish a cheap but accurate approximation of the forward map in electrical capacitance tomography in order to approach robust real‐time inversion in the framework of Bayesian statistics based on Markov chain Monte Carlo (MCMC) sampling.

Existing formulations and methods to reduce the order of the forward model with focus on electrical tomography are reviewed and compared. In this work, the problem of fast and robust estimation of shape and position of non‐conducting inclusions in an otherwise uniform background is considered. The boundary of the inclusion is represented implicitly using an appropriate interpolation strategy based on radial basis functions. The inverse problem is formulated as Bayesian inference, with MCMC sampling used to efficiently explore the posterior distribution. An affine approximation to the forward map built over the state space is introduced to significantly reduce the reconstruction time, while maintaining spatial accuracy. It is shown that the proposed approximation is unbiased and the variance of the introduced additional model error is even smaller than the measurement error of the tomography instrumentation. Numerical examples are presented, avoiding all inverse crimes.

Provides a consistent formulation of the affine approximation with application to imaging of binary mixtures in electrical tomography using MCMC sampling with Metropolis‐Hastings‐Green dynamics.

The proposed cheap approximation indicates that accurate real‐time inversion of capacitance data using statistical inversion is possible.

The proposed approach demonstrates that a tolerably small increase in posterior uncertainty of relevant parameters, e.g. inclusion area and contour shape, is traded for a huge reduction in computing time without introducing bias in estimates. Furthermore, the proposed framework – approximated forward map combined with statistical inversion – can be applied to all kinds of soft‐field tomography problems.

This study aims to reveal the essential characteristics of nonstationary signals and explore the high-concentration representation in the joint time–frequency (TF) plane.

In this paper, the authors consider the effective TF analysis for nonstationary signals consisting of multiple components.

To make it, the authors propose the combined multi-window Gabor transform (CMGT) under the scheme of multi-window Gabor transform by introducing the combination operator. The authors establish the completeness utilizing the discrete piecewise Zak transform and provide the perfect-reconstruction conditions with respect to combined TF coefficients. The high-concentration is achieved by optimization. The authors establish the optimization function with considerations of TF concentration and computational complexity. Based on Bergman formulation, the iteration process is further analyzed to obtain the optimal solution.

With numerical experiments, it is verified that the proposed CMGT performs better in TF analysis for multi-component nonstationary signals.

This paper aims to considered the problem of identification of temperature-dependent thermal conductivity in the nonstationary, nonlinear heat equation. To describe the heat transfer in the furnace charge occupied by a homogeneous porous material, the heat equation is formulated. The inverse problem consists in finding the heat conductivity parameter, which depends on the temperature, from the measurements of the temperature in fixed points of the material.

A numerical method based on the finite-difference scheme and the least squares approach for numerical solution of the direct and inverse problems has been recently developed.

The influence of different numerical scheme parameters on the accuracy of the identified conductivity coefficient is studied. The results of the experiment carried out on real measurements are presented. Their results confirm the ones obtained earlier by using other methods.

Novelty is in a new, easy way to identify thermal conductivity by known temperature measurements. This method is based on special finite-difference scheme, which gives a resolvable system of algebraic equations. The results sensitivity on changes in the method parameters was studies. The algorithms of identification in the case of a purely mathematical experiment and in the case of real measurements, their differences and the practical details are presented.

A novel frequency domain approach, which combines the pseudo excitation method modified by the authors and multi-domain Fourier transform (PEM-FT), is proposed for analyzing nonstationary random vibration in this paper.

For a structure subjected to a nonstationary random excitation, the closed-form solution of evolutionary power spectral density of the response is derived in frequency domain.

The deterministic process and random process in an evolutionary spectrum are separated effectively using this method during the analysis of nonstationary random vibration of a linear damped system, only modulation function of the system needs to be estimated, which brings about a large saving in computational time.

The method is general and highly flexible as it can deal with various damping types and nonstationary random excitations with different modulation functions.

There is a growing literature in nonparametric econometrics in the recent two decades. Given the space limitation, it is impossible to survey all the important recent developments in nonparametric econometrics. Therefore, we choose to limit our focus on the following areas. In Section 2, we review the recent developments of nonparametric estimation and testing of regression functions with mixed discrete and continuous covariates. We discuss nonparametric estimation and testing of econometric models for nonstationary data in Section 3. Section 4 is devoted to surveying the literature of nonparametric instrumental variable (IV) models. We review nonparametric estimation of quantile regression models in Section 5. In Sections 2–5, we also point out some open research problems, which might be useful for graduate students to review the important research papers in this field and to search for their own research interests, particularly dissertation topics for doctoral students. Finally, in Section 6 we highlight some important research areas that are not covered in this paper due to space limitation. We plan to write a separate survey paper to discuss some of the omitted topics.

The interplay between prediction and estimation is crucial in adaptive prediction problems. In its general form, the adaptive prediction problem is a difficult multiperiod optimization problem and thus too complex for practical applications. By minimizing the one‐step‐ahead forecasting error, subject to a constraint on the (weighted) trace of the inverse on the one‐step‐ahead information matrix we arrive at a very simple approach, which should be interesting for more complex situations also. In the context suggesting the problem, a pollution dispersion model with time‐varying parameters is considered, and the target is to obtain a sensible and easily implementable adaptive predictor.

Purpose — The purpose of this paper is to present a new nonstationary, random vibration method for the analysis of coupled vehicle‐bridge systems with vertical track irregularity. Design/methodology/approach — The vehicle is modeled using a two‐layer suspension system and hence possesses ten degrees of freedom. The bridge is simulated using a Bernoulli‐Euler beam and the longitudinal track irregularity is taken as a uniformly modulated, evolutionary random process that includes phase lags between successive wheels. The pseudo‐excitation method (PEM) is extended to include time‐dependent systems for the first time, thus making it possible to compute the nonstationary random vibration of coupled vehicle‐bridge systems. Additionally, the precise integration method (PIM) is adapted to simulate continuous vehicle force variations in both time and space. Findings — The accuracy and effectiveness of the proposed PEM‐PIM method are confirmed by comparisons with Monte Carlo simulations. The influence of vehicle speed and track irregularity on system random responses are evaluated, and it is shown that the first and second derivatives of the track irregularity should not be arbitrarily ignored, as is usually the case. Originality/value — PEM and PIM are relatively new tools for the numerical solution of complicated random vibration problems and direct dynamic analyses. Until now, they have only been applied to time‐independent systems. However, it is shown herein that the proposed PEM‐PIM method performs nonstationary random vibration analysis of time‐dependent coupled vehicle‐bridge systems efficiently and accurately.

The discrete Fourier transform (dft) of a fractional process is studied. An exact representation of the dft is given in terms of the component data, leading to the frequency domain form of the model for a fractional process. This representation is particularly useful in analyzing the asymptotic behavior of the dft and periodogram in the nonstationary case when the memory parameter d≥12. Various asymptotic approximations are established including some new hypergeometric function representations that are of independent interest. It is shown that smoothed periodogram spectral estimates remain consistent for frequencies away from the origin in the nonstationary case provided the memory parameter d < 1. When d = 1, the spectral estimates are inconsistent and converge weakly to random variates. Applications of the theory to log periodogram regression and local Whittle estimation of the memory parameter are discussed and some modified versions of these procedures are suggested for nonstationary cases.