Studies With Transient Compact Models of Packages and Heat Sinks

2003 ◽  
Author(s):  
M. Rencz ◽  
V. Sze´kely ◽  
B. Courtois
Keyword(s):  
Linear Models ◽  
Heat Sinks ◽  
Simulation Experiments ◽  
Small Temperature ◽  
Thermal Models ◽  
Linear And Nonlinear ◽  
Compact Models ◽  
Board Level

This paper deals with the application of dynamic compact thermal models of packages. First an algorithm and a methodology is presented, that was developed for the inclusion of compact electrical RC thermal models of packages into field solvers, to enable fast board level simulation with compact models of packages. Application examples demonstrate the advantages of the method. In the second part of the paper a method is presented for the generation of nonlinear compact models. Simulation experiments comparing linear and nonlinear compact models show that for small temperature excursions the use of linear models is acceptable, but in case of larger than 80°C temperature increases the use of linear models results in an about 20–30% regular error for the usual package structures.

Simulation-based site amplification model for shallow bedrock sites in Korea

2021 ◽  
pp. 875529302098198
Author(s):  
Muhammad Aaqib ◽  
Duhee Park ◽  
Muhammad Bilal Adeel ◽  
Youssef M A Hashash ◽  
Okan Ilhan
Keyword(s):  
Linear Models ◽  
Site Response ◽  
Site Amplification ◽  
One Dimensional ◽  
Nonlinear Component ◽  
Nonlinear Site Response ◽  
Simulation Based ◽  
Linear And Nonlinear ◽  
Input Motion ◽  
Nonlinear Components

A new simulation-based site amplification model for shallow sites with thickness less than 30 m in Korea is developed. The site amplification model consists of linear and nonlinear components that are developed from one-dimensional linear and nonlinear site response analyses. A suite of measured shear wave velocity profiles is used to develop corresponding randomized profiles. A VS30 scaled linear amplification model and a model dependent on both VS30 and site period are developed. The proposed linear models compare well with the amplification equations developed for the western United States (WUS) at short periods but show a distinct curved bump between 0.1 and 0.5 s that corresponds to the range of site natural periods of shallow sites. The response at periods longer than 0.5 s is demonstrated to be lower than those of the WUS models. The functional form widely used in both WUS and central and eastern North America (CENA), for the nonlinear component of the site amplification model, is employed in this study. The slope of the proposed nonlinear component with respect to the input motion intensity is demonstrated to be higher than those of both the WUS and CENA models, particularly for soft sites with VS30 < 300 m/s and at periods shorter than 0.2 s. The nonlinear component deviates from the models for generic sites even at low ground motion intensities. The comparisons highlight the uniqueness of the amplification characteristics of shallow sites that a generic site amplification model is unable to capture.

scholarly journals Modeling Nonlinear Transfer Functions from Speech Envelopes to Encephalography with Neural Networks

2019 ◽  
Vol 11 (4) ◽  
pp. 1 ◽  
Author(s):  
Tobias de Taillez ◽  
Florian Denk ◽  
Bojana Mirkovic ◽  
Birger Kollmeier ◽  
Bernd T. Meyer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Linear Models ◽  
Transfer Functions ◽  
Event Related Potentials ◽  
Stimulus Presentation ◽  
Related Potentials ◽  
Linear And Nonlinear ◽  
Artificial Neural

Diferent linear models have been proposed to establish a link between an auditory stimulus and the neurophysiological response obtained through electroencephalography (EEG). We investigate if non-linear mappings can be modeled with deep neural networks trained on continuous speech envelopes and EEG data obtained in an auditory attention two-speaker scenario. An artificial neural network was trained to predict the EEG response related to the attended and unattended speech envelopes. After training, the properties of the DNN-based model are analyzed by measuring the transfer function between input envelopes and predicted EEG signals by using click-like stimuli and frequency sweeps as input patterns. Using sweep responses allows to separate the linear and nonlinear response components also with respect to attention. The responses from the model trained on normal speech resemble event-related potentials despite the fact that the DNN was not trained to reproduce such patterns. These responses are modulated by attention, since we obtain significantly lower amplitudes at latencies of 110 ms, 170 ms and 300 ms after stimulus presentation for unattended processing in contrast to the attended. The comparison of linear and nonlinear components indicates that the largest contribution arises from linear processing (75%), while the remaining 25% are attributed to nonlinear processes in the model. Further, a spectral analysis showed a stronger 5 Hz component in modeled EEG for attended in contrast to unattended predictions. The results indicate that the artificial neural network produces responses consistent with recent findings and presents a new approach for quantifying the model properties.

scholarly journals Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models

2021 ◽  
Vol 10 (5) ◽  
pp. 293
Author(s):  
Blerina Vika ◽  
Ilir Vika
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Linear Models ◽  
Smooth Transition ◽  
Parameter Method ◽  
Economic Time Series ◽  
Nonlinear Methods ◽  
Linear And Nonlinear ◽  
Artificial Neural

Albanian economic time series show irregular patterns since the 1990s that may affect economic analyses with linear methods. The purpose of this study is to assess the ability of nonlinear methods in producing forecasts that could improve upon univariate linear models. The latter are represented by the classic autoregressive (AR) technique, which is regularly used as a benchmark in forecasting. The nonlinear family is represented by two methods, i) the logistic smooth transition autoregressive (LSTAR) model as a special form of the time-varying parameter method, and ii) the nonparametric artificial neural networks (ANN) that mimic the brain’s problem solving process. Our analysis focuses on four basic economic indicators – the CPI prices, GDP, the T-bill interest rate and the lek exchange rate – that are commonly used in various macroeconomic models. Comparing the forecast ability of the models in 1, 4 and 8 quarters ahead, we find that nonlinear methods rank on the top for more than 75 percent of the out-of-sample forecasts, led by the feed-forward artificial neural networks. Although the loss differential between linear and nonlinear model forecasts is often found not statistically significant by the Diebold-Mariano test, our results suggest that it can be worth trying various alternatives beyond the linear estimation framework.   Received: 19 June 2021 / Accepted: 25 August 2021 / Published: 5 September 2021

Compact Modeling of Unshrouded Plate Fin Heat Sinks

2003 ◽  
Author(s):  
Sridhar Narasimhan ◽  
Avram Bar-Cohen
Keyword(s):  
Thermal Conductivity ◽  
Pressure Drop ◽  
Effective Thermal Conductivity ◽  
Heat Sink ◽  
Heat Sinks ◽  
Compact Modeling ◽  
Base Temperature ◽  
Computational Domain ◽  
Porous Block ◽  
Compact Models

The present work considers the compact modeling of unshrouded parallel plate heat sinks in laminar forced convection. The computational domain includes three heat sinks in series, cooled by an intake fan. The two upstream heat sinks are represented as “porous blocks”, each with an effective thermal conductivity and a pressure loss coefficient, while the downstream heat sink, assumed to be the component requiring the most accurate characterization, is modeled in detail. A large parametric space covering three typical heat sink geometries, as well as a range of common inlet velocities, separation distances between the heat sinks, and bypass clearances is considered in the development and evaluation of the compact models. The current study uses a boundary layer-based methodology, accounting for both the viscous dissipation and form drag losses, to determine the pressure drop characteristics, and an effective conductivity methodology, using a flow bypass model and Nusselt number correlation, to determine the effective thermal conductivity, for the porous block representation of the heat sink. The results indicate that the introduction of compact heat sinks has little influence on the pressure drop of the critical heat sink. Good agreement in pressure drops, typically in the range of 5%, is also obtained between “detailed” heat sink models and their corresponding porous block representation. The introduction of the compact models is found to have little influence (typically less than 1°C) on the base temperature of the critical heat sinks. For the compact heat sinks, the agreement is again within a typical difference of 5% in thermal resistance. Dramatic improvements were observed in the mesh count (factor &gt; 10X) and solution time (factor &gt;20X) required to achieve a high-fidelity simulation of the velocity, pressure, and temperature fields.

scholarly journals Information criteria for nonlinear time series models

2016 ◽  
Vol 20 (3) ◽  
Author(s):  
Saskia Rinke ◽  
Philipp Sibbertsen
Keyword(s):  
Error Term ◽  
Linear Models ◽  
Nonlinear Models ◽  
General Information ◽  
Information Criteria ◽  
Order Selection ◽  
Simulation Studies ◽  
Variance Estimator ◽  
Linear And Nonlinear ◽  
The Impact

AbstractIn this paper the performance of different information criteria for simultaneous model class and lag order selection is evaluated using simulation studies. We focus on the ability of the criteria to distinguish linear and nonlinear models. In the simulation studies, we consider three different versions of the commonly known criteria AIC, SIC and AICc. In addition, we also assess the performance of WIC and evaluate the impact of the error term variance estimator. Our results confirm the findings of different authors that AIC and AICc favor nonlinear over linear models, whereas weighted versions of WIC and all versions of SIC are able to successfully distinguish linear and nonlinear models. However, the discrimination between different nonlinear model classes is more difficult. Nevertheless, the lag order selection is reliable. In general, information criteria involving the unbiased error term variance estimator overfit less and should be preferred to using the usual ML estimator of the error term variance.

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