Dynamic prediction models and optimization of polyacrylonitrile (PAN) stabilization processes for production of carbon fiber

Thermal stabilization process of polyacrylonitrile (PAN) is the slowest and the most energy-consuming step in carbon fiber production. As such, in industrial production of carbonfiber, this step is considered as amajor bottleneck in the whole process. Stabilization process parameters are usually many in number and highly constrained, leading to high uncertainty. The goal of this paper is to study and analyze the carbon fiber thermal stabilization process through presenting several effective dynamic models for the prediction of the process. The key point with using dynamic models is that using an evolutionary search technique, the heat of reaction can be optimized. The employed components of the study are Levenberg–Marquardt algorithm (LMA)-neural network (LMA-NN), Gauss–Newton (GN)-curve fitting, Taylor polynomial method, and a genetic algorithm. The results show that the procedure can effectively optimize a given PAN fiber heat of reaction based on determining the proper values of heating rampand temperature

Nonlinear dynamic modeling of ionic polymer conductive network composite actuators using rigid finite element method

Ionic polymer conductive network composite (IPCNC) actuators are a class of electroactive polymer composites that exhibit some interesting electromechanical characteristics such as low voltage actuation, large displacements, and benefit from low density and elastic modulus. Thus, these emerging materials have potential applications in biomimetic and biomedical devices. Whereas significant efforts have been directed toward the development of IPMC actuators, the establishment of a proper mathematical model that could effectively predict the actuators' dynamic behavior is still a key challenge. This paper presents development of an effective modeling strategy for dynamic analysis of IPCNC actuators undergoing large bending deformations. The proposed model is composed of two parts, namely electrical and mechanical dynamic models. The electrical model describes the actuator as a resistive-capacitive (RC) transmission line, whereas the mechanical model describes the actuator as a system of rigid links connected by spring-damping elements. The proposed modeling approach is validated by experimental data, and the results are discussed. © 2014 Elsevier B.V. All rights reserved.

Tourism, guest workers, and resident immiserization

This paper analyses the impact of a boom in tourism on the welfare of the residents in the presence of guest workers. Guest workers are employed in the tourism industry and they consume non-traded goods and services. This consumption by guest workers converts non-traded goods into
exportables and creates guest worker generated monopoly power in trade in the home country. It is established that under certain plausible conditions a tourist boom (in the presence of guest workers) results in the immiserization of the resident population. This result arises due to an adverse movement in the terms-of-trade, specifically those associated with the guest workers consumption of non-traded goods. These results are based on a static model of trade and may not be necessarily
valid in a growth model with guest workers, tourism and labor shortages. It is not the object of the paper to be either anti-tourism or anti-guest worker, but only to show a possible source of resident immiserization that is associated with guest workers. This possibility may require correction via a suitable policy both in static and dynamic models.

Social networks in changing environments

Social network analysis (SNA) has become a widespread tool for the study of animal social organisation. However despite this broad applicability, SNA is currently limited by both an overly strong focus on pattern analysis as well as a lack of dynamic interaction models. Here, we use a dynamic modelling approach that can capture the responses of social networks to changing environments. Using the guppy, Poecilia reticulata, we identified the general properties of the social dynamics underlying fish social networks and found that they are highly robust to differences in population density and habitat changes. Movement simulations showed that this robustness could buffer changes in transmission processes over a surprisingly large density range. These simulation results suggest that the ability of social systems to self-stabilise could have important implications for the spread of infectious diseases and information. In contrast to habitat manipulations, social manipulations (e.g. change of sex ratios) produced strong, but short-lived, changes in network dynamics. Lastly, we discuss how the evolution of the observed social dynamics might be linked to predator attack strategies. We argue that guppy social networks are an emergent property of social dynamics resulting from predator–prey co-evolution. Our study highlights the need to develop dynamic models of social networks in connection with an evolutionary framework.

Integrating network analysis, sensor tags, and observation to understand shark ecology and behavior

Group living in animals is a well-studied phenomenon, having been documented extensively in a wide range of terrestrial, freshwater, and marine species. Although social dynamics are complex across space and time, recent technological and analytical advances enable deeper understanding of their nature and ecological implications. While for some taxa, a great deal of information is known regarding the mechanistic underpinnings of these social processes, knowledge of these mechanisms in elasmobranchs is lacking. Here, we used an integrative and novel combination of direct observation, accelerometer biologgers, and recent advances in network analysis to better understand the mechanistic bases of individual-level differences in sociality (leadership, network attributes) and diel patterns of locomotor activity in a widespread marine predator, the lemon shark (Negaprion brevirostris). We found that dynamic models of interaction based on Markov chains can accurately predict juvenile lemon shark social behavior and that lemon sharks did not occupy consistent positions within their network. Lemon sharks did however preferentially associate with specific group members, by sex as well as by similarity or nonsimilarity for a number of behavioral (nonsimilarity: leadership) and locomotor traits (similarity: proportion of time swimming "fast," mean swim duration; nonsimilarity: proportion of swimming bursts/transitions between activity states). Our study provides some of the first information on the mechanistic bases of group living and personality in sharks and further, a potential experimental approach for studying fine-scale differences in behavior and locomotor patterns in difficult-to-study organisms.

Streaming analysis in wireless sensor networks

Two new incremental models for online anomaly detection in data streams at nodes in wireless sensor networks are discussed. These models are incremental versions of a model that uses ellipsoids to detect first, second, and higher-ordered anomalies in arrears. The incremental versions can also be used this way but have additional capabilities offered by processing data incrementally as they arrive in time. Specifically, they can detect anomalies 'on-the-fly' in near real time. They can also be used to track temporal changes in near real-time because of sensor drift, cyclic variation, or seasonal changes. One of the new models has a mechanism that enables graceful degradation of inputs in the distant past (fading memory). Three real datasets from single sensors in deployed environmental monitoring networks are used to illustrate various facets of the new models. Examples compare the incremental version with the previous batch and dynamic models and show that the incremental versions can detect various types of dynamic anomalies in near real time.

Comparison of static and dynamic engine models on the transient performance of a passenger vehicle powertrain

This paper presents experimental and computational results obtained on the Ford Barra 190 4.0 litres I6 gasoline engine and on the Ford Falcon car equipped with this engine. Measurements of steady engine performance, fuel consumption and exhaust emissions were first collected using an automated test facility for a wide range of cam and spark timings vs. throttle position and engine speed. Simulations were performed for a significant number of measured operating points at full and part load by using a coupled Gamma Technologies GT-POWER/GT-COOL engine model for gas exchange, combustion and heat transfer. The fluid model was made up of intake and exhaust systems, oil circuit, coolant circuit and radiator cooling air circuit. The thermal model was made up of finite element components for cylinder head, cylinder, piston, valves and ports and wall thermal masses for pipes. The model was validated versus measured steady state air and fuel flow rates, cylinder pressure parameters, indicated and brake mean effective pressures, and temperature of metal, oil and coolant in selected locations. Computational results agree well with experiments, demonstrating the ability of the approach to produce fairly accurate steady state maps of BMEP and BSFC, as well as to optimize engine operation changing geometry, throttle position, cam and spark timing. Measurements of the transient performance and fuel consumption of the full vehicle were then collected over the NEDC cycle. Simulations were performed by using a coupled Gamma Technologies GT-POWER/GT-COOL/GT-DRIVE model for instantaneous engine gas exchange, combustion and heat transfer and vehicle motion. The full vehicle model is made up of transmission, driveshaft, axles, and car components and the previous engine model. The model was validated with measured fuel flow rates through the engine, engine throttle position, and engine speed and oil and coolant temperatures in selected locations. Instantaneous engine states following a time dependent demand for torque and speed differ from those obtained by interpolating steady state maps of BSFC vs. BMEP and speed. Computational results agree well with experiments, demonstrating the utility of the approach in providing a more accurate prediction of the fuel consumption over test cycles.

Dynamic interactions in models with Kaleckian lags

Provides a careful assessment of previous research on lags in economic models. Several interesting lines of research are opened up. Chief among them is the analysis of bubbles and their bursting in the financial components of economic models.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part II: proofs of results

In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption.

A factor analytical method to interactive effects dynamic panel models with or without unit root

In a recent study, Bai (Fixed-Effects Dynamic Panel Models, A Factor Analytical Method. Econometrica 81, 285-314, 2013a) proposes a new factor analytic (FA) method to the estimation of dynamic panel data models, which has the unique and very useful property that it is completely bias-free. However, while certainly appealing, it is restricted to fixed effects models without a unit root. In many situations of practical relevance this is a rather restrictive consideration. The purpose of the current study is therefore to extend the FA approach to cover models with multiple interactive effects and a possible unit root.

Dynamic simulation of collisions of heavy high-speed trucks with concrete barriers

Real vehicle collision experiments on full-scale road safety barriers are important to determine the outcome of a vehicle versus barrier impact accident. However, such experiments require large investment of time and money. Numerical simulation has therefore been imperative as an alternative method for testing concrete barriers. In this research, spring subgrade models were first developed to simulate the ground boundary of concrete barriers. Both heavy trucks and concrete barriers were modeled using finite element methods (FEM) to simulate dynamic collision performances. Comparison of the results generated from computer simulations and on-site full-scale experiments demonstrated that the developed models could be applied to simulate the collision of heavy trucks with concrete barriers to provide the data to design new road safety barriers and analyze existing ones.

A physical based modeling approach for the dynamic behavior of ultrafine grained structures

This paper discusses some experimental results on the influence of grain refinement on the final mechanical properties of IF and microalloyed steels designed for auto-body components. It shows also some modeling approaches to understanding the dynamic behavior of fine-rained materials. The Zerilli–Armstrong (Z–A) and Khan–Huang–Liang (KHL) models for studied steels were implemented into FEM code in order to simulate the dynamic compression tests with different strain rates.

Study of the effect of grain size on the dynamic mechanical properties of microalloyed steels

In the present paper the effect of grain refinement on the dynamic response of ultra fine-grained (UFG) structures for C–Mn and HSLA steels is investigated. A physically based flow stress model (Khan-Huang-Liang, KHL) was used to predict the mechanical response of steel structures over a wide range of strain rates and grain sizes. However, the comparison was restricted to the bcc ferrite structures. In previous work [K. Muszka, P.D. Hodgson, J. Majta, A physical based modeling approach for the dynamic behavior of ultra fine-grained structures, J. Mater. Process. Technol. 177 (2006) 456–460] it was shown that the KHL model has better accuracy for structures with a higher level of refinement (below 1 μm) compared to other flow stress models (e.g. Zerrili-Armstrong model). In the present paper, simulation results using the KHL model were compared with experiments. To provide a wide range of the experimental data, a complex thermomechanical processing was applied. The mechanical behavior of the steels was examined utilizing quasi-static tension and dynamic compression tests. The application of the different deformation histories enabled to obtain complex microstructure evolution that was reflected in the level of ferrite refinement.