Multi-regime Models Research Articles

Integrated experimental and computational analysis of porous media combustion by combining gas-phase synchrotron [formula omitted]CT, IR-imaging, and pore-resolved simulations

Porous media combustion is a promising combustion technology that enables operation over an extended range of conditions, with a variety of fuels and low pollutant emissions. However, the lack of understanding of the interstitial combustion processes remains a major obstacle towards the application of this technology and the development of predictive models. To understand these processes, this study presents an integrated experimental and computational effort to examine porous media combustion. Specifically, a new experimental diagnostic method is employed that simultaneously combines synchrotron X-ray micro-computed tomography (μCT) and short-wavelength infrared thermometry. With this approach, the surface temperature and the 3D gas-phase temperature field are simultaneously measured inside a porous burner with micrometer spatial resolution. These measurements are then integrated into 3D pore-resolved simulations. This integrated approach enables a direct evaluation of current modeling capabilities, and a unique analysis of interstitial combustion in porous foams under realistic heating conditions. In light of these results, we discover that about 80% of the fuel consumption inside the matrix pores occurs far enough from the porous surface to be characteristic of a decoupled regime of preheated, but adiabatic flamelets. The remaining fuel consumption near the pore surface occurs in a non-adiabatic combustion mode. This insight and the proposed approach contribute to our fundamental understanding of porous media combustion, and suggest that multi-regime models could more accurately describe porous burners.

Local combustion regime identification using machine learning

A new combustion regime identification methodology using the neural networks as supervised classifiers is proposed and validated. As a first proof of concept, a binary classifier is trained with labelled thermochemical states obtained as solutions of prototypical one-dimensional models representing premixed and nonpremixed regimes. The trained classifier is then used to associate the regime to any given thermochemical state originating from a multi-dimensional reacting flow simulation that shares similar operating conditions with the training problems. The classification requires local information only, i.e. no gradients are required, and operates on reduced-dimension thermochemical states, in order to cope with experimental data as well. The validity of the approach is assessed by employing a two-dimensional laminar edge flame data as a canonical configuration exhibiting multi-regime combustion behaviour. The method is readily extendable to additional classes to identify criticality phenomena, such as local extinction and re-ignition. It is anticipated that the proposed classifier tool will be useful in the development of turbulent multi-regime combustion closure models in large scale simulations.

Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment

The fundamental relationship of traffic flow and bivariate relations between speed and flow, speed and density, and flow and density are of great importance in transportation engineering. Fundamental relationship models may be applied to assess and forecast traffic conditions at uninterrupted traffic flow facilities. The objective of the article was to analyze and compare existing models of the fundamental relationship. To that end, we proposed a universal and quantitative method for assessing models of the fundamental relationship based on real traffic data from a Polish expressway. The proposed methodology seeks to address the problem of finding the best deterministic model to describe the empirical relationship between fundamental traffic flow parameters: average speed, flow, and density based on simple and transparent criteria. Both single and multi-regime models were considered: a total of 17 models. For the given data, the results helped to identify the best performing models that meet the boundary conditions and ensure simplicity, empirical accuracy, and good estimation of traffic flow parameters.

Calibration of traffic stream models on high-speed urban roads with heterogeneous traffic

The basic objective of this work was to identify best-fitting traffic stream models for the varying traffic flows on high-speed urban roads under heterogeneous traffic in India. To this end, traffic data on ten-lane, eight-lane and six-lane divided roadways were collected. An iterative process was developed to fit traffic stream models by optimising constant parameters using optimisation techniques. Different single-regime models, multi-regime models and models based on the car-following approach were fitted to the field-observed data set. Among the single-regime models, the Northwestern model fitted the data set with the highest level of accuracy. The three-regime model was found to be more accurate than the other multi-regime models. Various multi-variate car-following models were also applied and found to fit uncongested and congested data with higher accuracy than the use of a single equation. Among all the models examined, the model proposed by Van Aerde and Rakha provided the highest accuracy. This observation was also validated for roadway sections with varying carriageway widths. The outcome of this research may be useful for the development of correct trends of speed–flow relationships for similar kinds of roadway facilities in the Asia-Pacific nations. Using the most suitable trend, level-of-service boundaries can be delineated more effectively.

Novel Approach for Calibrating Freeway Highway Multi-Regimes Fundamental Diagram

For almost a century, several models have been developed to calibrate the pairwise relationship between traffic flow variables, that is, speed, density, and flow. Multi-regime models are well known for being superior over single-regime models in fitting the speed–density relationship. However, in modeling multi-regime models, breakpoints that separate the regimes are visually established based on the subjective judgment of data characteristics. Thus, this study proposes a data-driven approach to estimate the breakpoints of multi-regime models. It applies the Bayesian model for calibrating multi-regime models (two and three-regime models) for fitting traffic flow fundamental diagram. Furthermore, the analysis presented accounts for the random characteristics associated with the flow. To demonstrate the application of the proposed algorithm, traffic flow data from Interstate 10 (I-10) freeway in Jacksonville, Florida, were used in the analysis. The results demonstrate the potential benefit of using the proposed model in calibrating the fundamental diagram. The proposed approach can also quantify uncertainty and encode prior knowledge about the breakpoints in the model if the model developer wishes.

Mispricing in Single Stock Futures: Empirical Examination of Indian Markets

ABSTRACT We examine the determinants of mispricing in single stock futures traded in the National Stock Exchange of India, the second largest global trading venue for such contracts. We compute mispricing bounds using multi-regime models for over one hundred stocks. The size of the mispricing window—defined as the distance between these bounds—increases with decrease in liquidity. Liquidity of the futures market has a larger impact on the size of the mispricing window compared to that of the spot market. After controlling for these liquidity effects, the size of the mispricing window is found to increase with increase in volatility. This suggests that concerns related to margin calls and execution shortfalls dominate early exit options. Volatility has an asymmetrical effect on mispricing bounds. We attribute this to short-sale constraints as they make the early exit option difficult to exercise when futures are underpriced.

Development of traffic stream characteristics models for intercity roads in Egypt

Literatures revealed that there are no accurate models that describe the relationship between traffic steam parameters on intercity roads in Egypt. Therefore, the main objective of this research was to develop these relationships. Traffic data of flow rate, speed, and density were collected from three study sections in Cairo-Ismailia desert road and Cairo-Suez desert road, using video-based measurement technique. Traffic data were collected during typical weekdays and weekend. Samples were recorded in different times of the day to represent all conditions of traffic flow on the selected road sections. Using the collected traffic data, the traffic stream models were obtained. Different types of single and multi-regime models have been checked. Two criteria have been flowed to differentiate between these models: statistical tests and logic of the output parameters. Based on these tests, three-regime statistical models were found the best models to describe the relationships among the different traffic stream characteristics. Analysis of these models showed that, the jam and optimum densities are 167.77 and 61.72pcu/km/lane respectively. The free-flow and optimum speeds are 109.58 and 28.08km/h respectively, and the lane capacity is about 1732.9pcu/h/lane.

Exporters’ Exposures to Currencies: Beyond the Loglinear Model*

Abstract We extend the constant-elasticity regression that is the default choice when equities’ exposure to currencies is estimated. In a proper real-option-style model for the exporters’ equity exposure to the foreign exchange rate, we argue, the convexity of the relationship implies that the elasticity should depend on the exchange rate level. For instance, it should shrink to zero when the option to export becomes worthless, and that should happen at a critical exchange rate that is still strictly positive. We propose a class of tractable multi-regime regression models featuring, in line with the real-options logic, smooth transitions and within-regime dynamics in the foreign exchange exposure. We then analyze the exchange rate exposure of Chinese exporting firms and find that the model in which the moneyness of the export option has a positive impact on the exchange rate exposure detects a significantly positive and convex exposure for 40% and 65% of the firms depending on whether the market return is included in the regression or not.

Characterization of Time-Varying Regimes in Remote Sensing Time Series: Application to the Forecasting of Satellite-Derived Suspended Matter Concentrations

The spatial and temporal coverage of satellites provides data that are particularly well suited for the analysis and characterization of space–time-varying geophysical relationships. The latent-class models aim here to identify time-varying regimes within a dataset. This is of particular interest for geophysical processes driven by the seasonal variability. As a case example, we study the daily concentration of mineral suspended particulate matters estimated from satellite-derived datasets, in coastal waters adjacent to the French Gironde river mouth. We forecast this high-resolution dataset using environmental data (wave height, wind strength and direction, tides, and river outflow) and four latent-regime models: homogeneous and nonhomogeneous Markov-switching models, with and without an autoregressive term (i.e., the mineral suspended matter concentration observed the day before). Using a validation dataset, significant improvements are observed with the multiregime models compared to a classical multiregression and a state-of-the-art nonlinear model [support vector regression (SVR) model]. The best results are reported for a mixture of three regimes for the autoregressive model using nonhomogeneous transitions. With the autoregressive models, we obtain at day+1 for the mixture model forecasting performances of 93% of the explained variance, compared to 83% for a standard linear model and 85% using an SVR. These improvements are more important for the nonautoregressive models. These results stress the potential of the identification of geophysical regimes to improve the forecasting. We also show that nonhomogeneous transition probabilities and estimated autoregressive terms improve forecasting performances when observation data is lacking for short-time period of 1–15 days.

Regime-dependent Characteristics of KOSPI Return

Stylized facts on asset return are fat-tail, asymmetry, volatility clustering and structure changes. This paper simultaneously captures these characteristics by introducing a multi-regime models: Finite mixture distribution and regime switching GARCH model. Analyzing the daily KOSPI return from <TEX>$4^{th}$</TEX> January 2000 to <TEX>$30^{th}$</TEX> June 2014, we find that a two-component mixture of t distribution is a good candidate to describe the shape of the KOSPI return from unconditional and conditional perspectives. Empirical results suggest that the equality assumption on the shape parameter of t distribution yields better discrimination of heterogeneity component in return data. We report the strong regime-dependent characteristics in volatility dynamics with high persistence and asymmetry by employing a regime switching GJR-GARCH model with t innovation model. Compared to two sub-samples, Pre-Crisis (January 2003 ~ December 2007) and Post-Crisis (January 2010 ~ June 2014), we find that the degree of persistence in the Pre-Crisis is higher than in the Post-Crisis along with a strong asymmetry in the low-volatility (high-volatility) regime during the Pre-Crisis (Post-Crisis).

A Comparison of Detrending Models and Multi-Regime Models for Traffic Flow Prediction

Short-term traffic flow prediction received considerations from different fields, because of its essentialness in traffic engineering and its theoretical difficulties. In this paper, we studied two important approaches of traffic flow prediction: detrending methods and multi-regime methods. First, we compared their differences in modeling philosophy and compared their merits as well as shortcomings. Then, we tested several representative prediction models of these two approaches on the openly accessible PeMS traffic flow database to find their merits and shortcomings. The obtained results threw some interesting light on how to select the appropriate traffic prediction models in practices.

New Fraction Jetting Model for Distillation Sieve Tray Efficiency Prediction

The two phases on distillation trays can function in several different regimes depending on the physical properties of the system and the liquid-to-vapor ratio. Predicting the fraction of the vapor transported as jets, or fraction jetting, on a tray operating in the mixed-froth regime can bridge froth and spray regime models, explain the gradual changes in tray efficiency during the froth–spray transition, and eliminate the need to predict the froth–spray transition point when separate froth and spray regime models are used. Fraction jetting models will also facilitate multiregime models, such as the Syeda et al. (Chem. Eng. Res. Dev.2007, 85, 269–277) sieve tray efficiency model, that are valid for both froth and spray regimes. Current fraction jetting models are empirical and developed from limited data. In this paper, the concept of fraction jetting on a sieve tray is discussed and a new semiempirical model is presented. The fraction jetting model was developed using the existing data of Raper et al. (Chem. Eng. Sci.1982, 37, 501–506) and no new data were generated. Furthermore, the applicability of the fraction jetting model to tray efficiency models that incorporate jetting is demonstrated using the Syeda et al. sieve tray efficiency model.

Multi-regime nonlinear capital asset pricing models

A multiple-regime threshold generalized autoregressive conditionally heteroskedastic capital asset pricing model is introduced. The model captures asymmetric risk through allowing market beta to change discretely between regimes that are driven by market information. Asymmetric volatility and mean equation dynamics are also captured. We confirm the time-varying nature of market risk, in response to changes in the market, and that this discrete time variation can differ across assets. These findings could have important implications for optimizing investment decisions: e.g. in risk assessment, portfolio selection and hedging decisions.

Multi–regime models for nonlinear nonstationary time series

Nonlinear nonstationary models for time series are considered, where the series is generated from an autoregressive equation whose coefficients change both according to time and the delayed values of the series itself, switching between several regimes. The transition from one regime to the next one may be discontinuous (self-exciting threshold model), smooth (smooth transition model) or continuous linear (piecewise linear threshold model). A genetic algorithm for identifying and estimating such models is proposed, and its behavior is evaluated through a simulation study and application to temperature data and a financial index.

Time-varying multi-regime models fitting by genetic algorithms

Many time series exhibit both nonlinearity and non-stationarity. Though both features have been often taken into account separately, few attempts have been proposed for modelling them simultaneously. We consider threshold models, and present a general model allowing for different regimes both in time and in levels, where regime transitions may happen according to self-exciting, or smoothly varying or piecewise linear threshold modelling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The performance of the proposed procedure is illustrated with a simulation study and applications to some real data.

Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data

The research reported in this paper develops a heuristic automated tool (SPD_CAL) for calibrating steady-state traffic stream and car-following models using loop detector data. The performance of the automated procedure is then compared to off-the-shelf optimization software parameter estimates, including the MINOS and Branch and Reduce Optimization Navigator (BARON) solvers. The model structure and optimization procedure is shown to fit data from different roadway types and traffic regimes (uncongested and congested conditions) with a high quality of fit (within 1% of the optimum objective function). Furthermore, the selected functional form is consistent with multiregime models, without the need to deal with the complexities associated with the selection of regime breakpoints. The heuristic SPD_CAL solver, which is available for free, is demonstrated to perform better than the MINOS and BARON solvers in terms of execution time (at least 10 times faster), computational efficiency (better match to field data), and algorithm robustness (always produces a valid and reasonable solution).

Characterizing regimes in daily cycles of urban traffic using smooth-transition regressions

During the past few years, researchers have found evidence indicating that various time series representing daily cycles of traffic, such as volumes, speeds and occupancies, may be nonlinear. In this paper it is shown that such nonlinearities can be adequately described by smooth-transition regression (STR) models which may characterize distinct regimes for free flow, congestion, and asymmetric behavior in the transition phases from free flow to congestion and vice versa. STR models are advantageous compared to the – frequently adopted in traffic modeling – Artificial Neural Networks models because their parameters are interpretable. An exposition of smooth transition models is presented with a focus on logistic multi-regime models that are deemed to be most appropriate for modeling traffic variables. The methodology is illustrated by an application to data on speeds volumes and occupancies, obtained from two loop detectors located in a major arterial of Athens, Greece. Tests on nonlinearity provided ample evidence of regime-dependent dynamics for all traffic time series examined. The following research questions are examined using STR models: How many regimes, described by linear dynamics, characterize the cycle of traffic for a typical weekday? Which time interval corresponds to each regime and how fast is the transition from one regime to another? Are the estimated dynamics of daily cycles stable across weekdays?

Validation of Van Aerde's simplified steadystate car-following and traffic stream model

The research reported in this paper validates Van Aerde's simplified steady-state car-following and traffic stream motion model, identifies valid input parameter ranges, and compares the model performance to other single and multi-regime models. In addition, the paper demonstrates that the Pipes linear car-following model produces the commonly known inverted V-shaped flow-density relationship. The paper also derives the relationship between the kinematic wave speed and the facility capacity, jam density, and free-flow speed for the Pipes and Van Aerde models. The study demonstrates that the Van Aerde functional form offers a superior fit to field data when compared to Newell's nonlinear single-regime model and Pipes linear dual-regime model. Specifically, the Van Aerde model is shown to fit data from different roadway types using different data aggregation intervals with a quality of fit that is superior to multi-regime models, without the need to deal with the complexities associated with the selection of regime break points.

Development of Multiregime Speed–Density Relationships by Cluster Analysis

Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.

Development of Multiregime Speed-Density Relationships by Cluster Analysis

Empirical speed-density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed-density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed-density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represe...