Real Behavior Research Articles

Minimal model identification of drum brake squeal via SINDy

The industrial standard in the design and development process of NVH(Noise Vibration Harshness) characteristic of brakes is the application of Finite Element(FE) models with a high number of degrees of freedom in the range of one or several millions. Nevertheless, parallel experimental investigations are still indispensable. On the other hand, minimal models with, due to the inclusion of the self-excitation process, at least two degrees of freedom are well known to be capable to explain qualitatively phenomena as instability of the desired non-vibrating solution or limit cycle oscillation but are in general very inaccurate in predicting the dynamics of a specific real brake. This is because the underlying physical assumptions are already too restrictive and model parameters (especially those referring to nonlinearities) are widely unknown. To overcome this problem, the data-driven modeling approach SINDy(Sparse Identification of Nonlinear Dynamics) is applied to identify appropriate nonlinear functions for a brake squeal minimal model. A problem thereby is the limited database. It turns out that the naive implementation of the method yielding the lowest possible residuum does not necessarily provide physically meaningful models and results, respectively. Instead, a constrained model that incorporates physical knowledge is used to robustly identify parameters and reproduce realistic dynamic behavior. Thereby, several appropriate models with coexisting limit cycles and stationary equilibrium are identified. In particular, it was found that the angular position of the brake drum has a significant influence on the model parameters and therefore must be taken into account in a model with long-term validity.

Prediction of the free energy of binding for cyclodextrin-steroid complexes: phase solubility and molecular dynamics studies

Steroid hormones play a crucial role in the body by acting as chemical messengers. They are, however, poorly soluble in water, and cyclodextrins can increase their solubility thus leading to increased bioavailability when used in drug formulations. Accuracy in the prediction of the free energy of binding of cyclodextrin/steroid inclusion complexes with simulation is important because of the potential value it brings by providing low-cost predictions of the real-life behavior of the cyclodextrin/steroid inclusion complex and the potential for high-through-put screening. Many computational methods exist, and it is therefore important to understand the ability of current theoretical models to accurately predict the free energy of binding for these inclusion complexes. We focused specifically on the estimation of the free energy of binding of inclusion complexes of four steroids: Hydrocortisone, dexamethasone, prednisolone, and 6α-methylprednisolone with native α-CD, β-CD, γ-CD, (2-hydroxy)propyl-β-CD, and sulfobutylether-β-CD by phase solubility as well as with α, β, and γ-CD by simulations. The simulations were assessed with both docking and the molecular mechanics combined with the generalized Born and surface area (MM/GBSA) continuum solvation approach. Considering the phase solubility diagram, (2-hydroxy)propyl-β-CD and sulfobutylether-β-CD dissolved more steroids in the higher concentration range as expected. The assessment of the free energy of binding obtained from the phase solubility and theory showed that the MM/GBSA method has shown promise in reliably generating accurate predictions in the field of calculating the free energy of binding of steroids/cyclodextrins with a correlation coefficient (R2) = 0.94.

FAN PARAMETER ANALYS OF HEAT PUMP AIR CONDITIONING SYSTEMS WITH DIFFERENT HEAT EXCHANGERS CONFIGURATIONS.

The residential sector accounts for 27% of the EU's total energy consumption, and more than half is used for space conditioning, so improvements in this sector would contribute to achieving the sustainability goals set. Heat pumps (HP) have been presented as a good solution, as they are systems capable of satisfying air-conditioning needs in a more efficient way than systems based on fossil fuels; moreover, the REPowerEU Communication established the objective of doubling the current HP deployment rate in the next years. In this sense, designing and optimizing air conditioning systems is crucial. This study presents a method to determine the power and airflow parameters of a fan in a heat pump air conditioning system. The calculation is performed considering the pressure drop associated with the design of the heat exchangers using the IMST-ART simulation tool. This modeling aims to obtain simulations closer to the real behavior under different design conditions for the development of more efficient air conditioning systems

The Effect of Nonlinear Sloshing Response of Water on Seismic Behavior of Reinforced Concrete Elevated Water Tanks

In this study, the fluid-structure interactive behavior by using the Westergaard approach and the Smoothed Particle Hydrodynamics (SPH) method of a 1000 m3 reinforced concrete (RC) elevated water tank under different seismic activities i.e., Kocaeli, Van, Kahramanmaraş and Kobe earthquakes was investigated. In the SPH method, the resulting hydrodynamic pressures were applied to the interior of the wall of the structure as an external force which is made as mass additions to the tank walls in Westergaard approach. With the help of software developed, the load carrying system of the elevated water tank was modeled using the MATLAB Partial Differential Equation Toolbox (PDE) software, based on the finite element method, and its verification was made by comparing with ANSYS software model. Dynamic condensation method was used to perform time history analyses for the cited earthquakes. The time-dependent solutions of partial differential equations are solved with the help of ODE45 functions based on Runge-Kutta method, by arranging the equation of motion in state-space format. The cited tank was analyzed linearly under different seismic activities for empty, half-filled (50%) and full-filled (100%) cases and the obtained results were compared with each other. It has been concluded that considering nonlinear behavior of the fluid during the earthquake with the SPH method provided consistency with the real behavior of RC elevated water tanks and gave more realistic results than the traditional Westergaard approach.

Multiscale modelling of microstructure evolution, and local solidification behaviours of the AlSi10Mg build component in laser powder bed fusion process

Laser powder bed fusion (L-PBF) process is an additive manufacturing process, in which rapid heating and cooling takes place. The microstructure and the solidification behaviour of L-PBF components have significant impacts on their mechanical properties. The solidification takes place in a very short duration which is difficult to capture experimentally, therefore the finite-element (FE) simulation is the best feasible solution. In this paper, to study the evolution of the AlSi10Mg alloy’s microstructure during the L-PBF process, a model composed of macroscopic finite element (FE) model and microscopic phase field (PF) model was built. This model is suitable to predict a more realistic thermal behaviour involving the possible physical phenomena and the thermal behaviour for various heat inputs was calculated. The PF model was then updated with these results in order to simulate the chemical compositions’ concentration field and solidified microstructure. According to the simulation’s results, the primary dendrite arm spacing (PDAS) is significantly influenced by the laser line energy. To validate the FE model along with the Phase field (PF) model, the results were compared with the analytical and experimental findings; and the FE model and PF results exhibited good agreement with the experimental results. Additionally, the solute distribution analysis demonstrated that during the solidification process, micro-segregation developed and the process parameters affected the micro-segregation. Finally, the specimens were subjected to metallographic and element distribution analyses using an energy-dispersive spectrometer (EDS) to compare the results with those from the PF simulation and establish the relationship between the process parameters.

Kinetic modeling of methane dehydroaromatization over Mo/ZSM-5 catalysts

We propose a novel kinetic model for the methane dehydroaromatization (MDA) reaction over the Mo/ZSM-5 catalyst to express the practical behavior of the catalytic reaction. Unlike existing MDA kinetic models, we introduced a deactivation model involving carbon deposit formation to simulate the more realistic behavior of the MDA reaction. The proposed kinetic model integrated the “apparent kinetic model,” which focuses on detectable components, and the “micro-kinetic model,” which incorporates the acidic properties and active sites of the catalyst. To validate this model, we conducted fixed-bed experiments across a wide range of operating conditions, including temperature, gas velocity, and metal loading. Compared with experimental data, our model predictions exhibited a remarkable error of 5 %. Specifically, by leveraging the relationship between the deactivation behavior and carbon deposit formation, the model accurately predicted the CH4 conversion rates, component yields, and coke production throughout the reaction. This model sheds light on catalytic behavior during carbon deposit generation under diverse operating conditions, contributing to a comprehensive understanding of the MDA reaction mechanism. The developed kinetic model can be applied to estimate reactor performance across various scenarios. By providing a kinetic model that incorporates information regarding the catalytic properties of Mo/ZSM-5 and the apparent gas concentration, our study offers a potent tool for estimating practical reactor performance and advancing MDA catalyst development.

Weakly q-deformed Heisenberg algebra and non-Hermitian Hamiltonians: Application in statistical physics

We have investigated a weakly q-deformed algebra, which leads to non-Hermitian operators. It has been explicitly shown that the harmonic oscillator Hamiltonian is quasi-Hermitian, and that the corresponding physical Hilbert-space metric Θ differs from that obtained in Ref. [1], by hermitizing the position operator. The reality of the Hamiltonian eigenvalues has been illustrated by analytically computing the first order correction to the energy spectrum of the harmonic oscillator (HO). Furthermore, we have shown that this q-deformation leads to a GUP with minimal uncertainties in both position and momentum measurements. Moreover, the physical implications of this q-deformation, have been investigated by studying the thermostatistics of a system of HOs and an ideal gas. For both systems, we computed the partition function, and then we derived some thermodynamic functions. In addition, for the ideal gas, a modified equation of state and a generalized Mayer's equation, consistent with the real behavior of gases, have been established. The obtained results show that the impact of this model would be significant at high temperatures. However, unlike earlier research, we observe that this q-deformation induced the similar corrections regardless of the system under study.

Analyzing Data Streams from Cyber-Physical-Systems: A Case Study

We show that conducting a process-mining-centric analysis concerning cyber-physical systems provides insights into usage behavior. To show that, we perform our analysis on connected-vehicle data. We transform connected-vehicle data into an event log. We analyze the resulting event log using various process-mining techniques. In particular, we apply basic statistical analysis as well as process-discovery and conformance-checking techniques to receive a well-representative process model. We apply various process-enhancement techniques to get deeper insights. Finally, we capture a multi-perspective view using a state-based approach. We show deviations between a de-jure model and our picked process model, leading to better knowledge concerning real user behavior. We observed that the predefined escalation of warning states does not happen. Additionally, we verified system requirements. Furthermore, we show that the reasons for drivers’ behavior are not related to system issues. Applying process-mining techniques to data concerning cyber-physical systems provides valuable insights into their functionality in a real-world setting. By utilizing process-mining techniques, we can extract insights to a human-understandable level and provide a well-studied access point.

Numerical characterization of high-pressure hydrogen jets for compression–ignition engines applying real gas thermodynamics

The development of hydrogen-fueled direct-injection compression–ignition engines requires adequate models that can accurately predict the spatial development and heat release rate of a jet at high pressure. It is found in literature that the combustion process benefits from minimizing flame–wall interaction, but it is unresearched how to achieve this. Jet theory can greatly reduce research efforts, only its validity is uncertain for hydrogen injections at relevant conditions. This work checks the validity via a parametric study performed with Computational Fluid Dynamics simulations. Sonic conditions are imposed at the nozzle exit, using different equations of state. It is found that real gas behavior needs to be used to compute these conditions and to model the jet dynamics. Furthermore, jet theory shows qualitative agreement with the simulation results. Finally, the heat release rate of a multi-hole injector appears insensitive to injection properties, but jet penetration can be controlled.

Real-Time Multi-Map Saliency-Driven Gaze Behavior for Non-Conversational Characters.

Gaze behavior of virtual characters in video games and virtual reality experiences is a key factor of realism and immersion. Indeed, gaze plays many roles when interacting with the environment; not only does it indicate what characters are looking at, but it also plays an important role in verbal and non-verbal behaviors and in making virtual characters alive. Automated computing of gaze behaviors is however a challenging problem, and to date none of the existing methods are capable of producing close-to-real results in an interactive context. We therefore propose a novel method that leverages recent advances in several distinct areas related to visual saliency, attention mechanisms, saccadic behavior modelling, and head-gaze animation techniques. Our approach articulates these advances to converge on a multi-map saliency-driven model which offers real-time realistic gaze behaviors for non-conversational characters, together with additional user-control over customizable features to compose a wide variety of results. We first evaluate the benefits of our approach through an objective evaluation that confronts our gaze simulation with ground truth data using an eye-tracking dataset specifically acquired for this purpose. We then rely on subjective evaluation to measure the level of realism of gaze animations generated by our method, in comparison with gaze animations captured from real actors. Our results show that our method generates gaze behaviors that cannot be distinguished from captured gaze animations. Overall, we believe that these results will open the way for more natural and intuitive design of realistic and coherent gaze animations for real-time applications.

Modelling stress-strain curves and forming limit curves using deep learning

Abstract The sheet metal forming process stands one of the most principal applications in production engineering with the goal of producing defect free products possessing the mechanical properties that met the working conditions requirements. The efficient metal forming process relies on a deep understanding of the material behaviour during the forming process. To achieve this, feasibility studies for new incoming components helps determine if a part is feasible for manufacturing or if design modifications are required. This simulation requires an accurate material card to replicate the real material behaviour, requiring extensive material testing data such as stress-strain curves and forming limit curves (FLC). Artificial intelligence, specifically deep learning, is a promising tool to overcome such challenges. In this study, one of the deep learning algorithms, the artificial neural network (ANN), is utilized to create material testing data for building the material cards for feasibility simulation.

A Data-Driven Pandemic Simulator with Reinforcement Learning

After the coronavirus disease 2019 (COVID-19) outbreak erupted, it swiftly spread globally and triggered a severe public health crisis in 2019. To contain the virus’s spread, several countries implemented various lockdown measures. As the governments faced this unprecedented challenge, understanding the impact of lockdown policies became paramount. The goal of addressing the pandemic crisis is to devise prudent policies that strike a balance between safeguarding lives and maintaining economic stability. Traditional mathematical and statistical models for studying virus transmission only offer macro-level predictions of epidemic development and often overlook individual variations’ impact, therefore failing to reflect the role of government decisions. To address this challenge, we propose an integrated framework that combines agent-based modeling (ABM) and deep Q-network (DQN) techniques. This framework enables a more comprehensive analysis and optimization of epidemic control strategies while considering real human behavior. We construct a pandemic simulator based on the ABM method, accurately simulating agents’ daily activities, interactions, and the dynamic spread of the virus. Additionally, we employ a data-driven approach and adjust the model through real statistical data to enhance its effectiveness. Subsequently, we integrated ABM into a decision-making framework using reinforcement learning techniques to explore the most effective strategies. In experiments, we validated the model’s effectiveness by simulating virus transmission across different countries globally. In this model, we obtained decision outcomes when governments focused on various factors. Our research findings indicate that our model serves as a valuable tool for decision-makers, enabling them to formulate prudent and rational policies.

A Stealthy Communication Model with Blockchain Smart Contract for Bidding Systems

With the widespread adoption of blockchain technology, its public ledger characteristic enhances transaction transparency but also amplifies the risk of privacy breaches. Attackers can infer users’ real identities and behaviors by analyzing public transaction patterns and address relationships, posing a severe threat to users’ privacy and security, and thus hindering further advancements in blockchain applications. To address this challenge, covert communication has emerged as an effective strategy for safeguarding the privacy of blockchain users and preventing information leakage. But existing blockchain-based covert communication schemes rely solely on the immutability of blockchain itself for robustness and suffer from low transmission efficiency. To tackle these issues, this paper proposes a stealthy communication model with blockchain smart contract for bidding systems. The model initiates by preprocessing sensitive information using a secret-sharing algorithm-the Shamir (t, n) threshold scheme-and subsequently embeds this information into bidding amounts, facilitating the covert transfer of sensitive data. We implemented and deployed this model on the Ethereum platform and conducted comprehensive performance evaluations. To assess the stealthiness of our approach, we employed a suite of statistical tests including the CDF, the Kolmogorov–Smirnov test, Welch’s t-test and K–L divergence. These analyses confirmed that amounts carrying concealed information were statistically indistinguishable from regular transactions, thus validating the effectiveness of our solution in maintaining the anonymity and confidentiality of information transmission within the blockchain ecosystem.

Environmental or Personal Benefit? The Role of Message Appeal and Information Type in Destination Social Media Advertisements

Destinations are increasingly focusing on sustainability awareness through tourism campaigns, but effective communication remains challenging. This research, rooted in consumption value theory, delves into the influence of message appeal (environmental vs. personal benefit) and information type (objective vs. subjective) on tourists’ behavioral intentions through two online experiments. Study 1 demonstrates the positive impact of environmental benefit message appeals on behavioral intentions toward destinations, both directly and indirectly, by enhancing destination brand quality and perceived green value. Study 2 validates these findings and extends the analysis to include click-through behavior as a proxy for real behavior. Our research emphasizes the importance of environmental benefits alongside objective information in destinations’ sustainability messages within social media advertisements. This approach serves to bolster destination brand quality, perceived green value, and ultimately, positive behavioral intentions.

Steady Hand at the Wheel: How Perceived Movement Influences Consumer Responses to Brand Failures

Paratextual communication methods, such as the use of emojis and other visual messaging cues, offer firms a unique opportunity to repair relationships with consumers after brand failures. The current research demonstrates that visual messaging cues associated with lower perceived movement lead to greater brand loyalty relative to more dynamic cues in response to brand failure. Across five studies, we show that lower perceived movement in visual messaging cues leads to more favorable consumer outcomes (e.g., subsequent real choice behavior and brand loyalty intentions). This effect is mediated by brand trust. Furthermore, we examine theoretically and practically relevant boundary conditions. The findings presented offer theoretical contributions to research on visual communication cues in brand recovery contexts. Additionally, the current research provides managerially relevant insights for communication strategies during recovery.

How a food scanner app influences healthy food choice

The use of mobile applications to assist with food decision making has increased significantly. Although food scanner applications provide nutritional information to consumers in the marketplace, little is known about their effects on users’ intentions and behavior. This research investigates whether a mobile food scanner app can influence consumers toward healthier food choices. Four studies tested whether information displayed through a food scanner app (as opposed to no information or front-of-packaging label information) influenced purchase intentions for food products (Studies 1–3) or led consumers to make healthier food choices (Study 4). Application-provided information enhanced hypothetical choice and purchase intentions of healthy products in comparison no information, but it did not influence real behavior when participants made choices in an experimental supermarket. Information provided through a food scanner app was systematically outperformed by front-of-packaging label information.

The In-Plane Seismic Response of Infilled Reinforced Concrete Frames Using a Strut Modelling Approach: Validation and Applications

Reinforced Concrete (RC) buildings often rely on masonry walls to increase their rigidity and strength, distinguishing them from bare frames. Consequently, the lateral capacity of the RC frames is significantly impacted by the presence or absence of these walls. Numerical models are fundamental to understanding this behavior interaction, but the development of robust simplified models is still scarce. Based on this motivation, the reliability of a simplified numerical modelling approach was examined in this study and compared to several experimental tests. An optimized approach was implemented to determine the strut parameters, rather than relying on existing empirical formulae. The reliability of the initial stiffness, maximum strength, and energy dissipation was studied. From the results, the accuracy of the considered modelling strategy can be observed in different types of masonry elements (strong and weak units) with and without openings. The validated simulation approach reveals that the adopted macro-modelling procedure can accurately represent the behavior of infilled masonry frames. The maximum deviation of the prediction of the initial stiffness and maximum strength was found to be around 23% and 14%, respectively. These findings illustrate that the strut model effectively replicates real behavior with a satisfactory level of accuracy. However, using a consistent formula to define the strut can result in significant errors, particularly in strut width.

Modeling coupled driving behavior during lane change: A multi-agent Transformer reinforcement learning approach

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with interactions, and the Transformer can process the observation-action memory accurately and efficiently. The MA-TDDPG algorithm can learn the sequential decision-making process over continuous action space during LC with the accommodation of the multi-vehicle interaction and the driver’s memory effect. Compared to traditional supervised learning and reinforcement learning methods, it demonstrates superior performance in imitating the longitudinal and lateral actions of the lane changer and the follower. The findings of this study provide insights into the development of microscopic simulations by producing realistic LC behaviors, and assistance/automation LC systems by generating LC motions and responses conforming to human driving habits. The model also creates an interactive simulation environment and lays the foundation for optimizing driving strategies.

Insights into nearshore sandbar dynamics through process-based numerical and logistic regression modeling

Process-based nearshore morphodynamic models are commonly used tools by coastal engineers and planners to predict the nearshore morphology change of sandy beaches across various spatiotemporal scales. Accurate modeling of the morphological response on medium and long time scales is imperative for quantitative assessments of coastal infrastructure over a project’s intended life-span. However, most previous modeling applications have focused on single/sub-seasonal storm events and are often limited to an assessment of the subaerial beach (i.e. berm and dune). This not only leaves uncertainty concerning the quality of morphology predictions on extended (> weeks) time scales, but also the capacity of process-based models to emulate realistic nearshore sandbar dynamics and the corresponding exchange of sediment between the nearshore-beach system. To shed light on these meso-scale dynamics, CSHORE, a 1D phase-averaged, process-based nearshore morphodynamic model, was applied on an annual scale to a multi-barred, dissipative beach in Oysterville, WA, USA. Thousands of unique sediment transport and hydrodynamic parameter combinations were executed during model calibration. A large portion of these simulations displayed physically realistic sandbar dynamics, including the growth, decay, and migration of intertidal and subtidal sandbars. To explore the model mechanisms enabling realistic bar behavior, the binary and multinomial logistic regression model were used to quantify the relationship between model parameter selection and the probability of various categorical bar configurations occurring in the final predicted profile. The results indicate the most sensitive parameters associated with barred morphology, in this study, and support the use of separate sediment transport parameters for low and high wave energy conditions. The co-utilization of numerical and statistical modeling outlined in this publication is generalizable to future exploratory modeling and/or calibration routines concerned with categorical outcomes.

Combined radiation and convection in developing flow in a parallel plate channel with real gas behavior: Contrast between gas cooling and heating

Laminar thermally developing gas flow in a two-dimensional parallel plate channel with real gas radiation has been investigated numerically. Real gas spectral radiation effects are accounted for by using the Spectral Line Weighted-sum-of-gray-gases model. Gas mixtures of H2O and CO2, either alone in mixture with N2 or as a mixture of H2O and CO2 are considered. The phenomena are investigated for a range of differences between wall and inlet gas temperature, mole fraction of the radiating species, and total pressure. Predictions for the cases of gas heating and gas cooling are contrasted with results presented by the classical no-radiation case, commonly known as the Graetz solution. The impact of gas radiation on the local mean temperature, convective and radiative wall heat fluxes, and the convective, radiative, and total local Nusselt numbers are studied.The predictions reveal that the inclusion of participating gas radiation results in a much more rapid change in temperature of the gas in the channel than for the convection-only case, for both the gas heating and gas cooling cases. Further, unique differences in the heat transfer behavior for gas cooling (wall temperature below the inlet gas temperature) and gas heating (wall temperature above the inlet gas temperature) scenarios are shown. However, significant differences between the cases of heating and cooling exist. In the classical Graetz solution, for constant thermophysical properties and using appropriately nondimensionalized heat transfer parameters (e.g., Nusselt number, inverse Graetz number) the predictions for fluid heating and cooling are identical. However, the results of this study including the influence of gas radiation demonstrate that the heat transfer behavior depends heavily on whether the gas is heated or cooled. An apparent thermally fully developed condition may exist for the case of gas cooling, whereas the variation in local Nusselt number with dimensionless axial location exhibits a local minimum for the case of gas heating. Further, the case of gas heating results in a more rapid streamwise change in mean temperature in the channel than for the cooling case. The thermal physics of the developing flow for combined convective-radiative transfer with real gas effects are explored for a range of wall and gas inlet temperatures, radiating species mole fractions, and total pressures.