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Achieving anti-roll bar effect through air management in commercial vehicle pneumatic suspensions
Chen, Yang; Peterson, Andrew W.; Ahmadian, Mehdi (Taylor & Francis, 2019-12-02)
This paper introduces the concept of managing air in commercial vehicle suspensions for reducing body roll. A conventional pneumatic suspension is re-designed to include higher-flow air hoses and dual levelling valves for improving the dynamic response of the suspension to the body roll, which commonly happens at relatively low frequencies. The improved air management allows air to get from the air tank to the airsprings quicker, and also changes the side-to-side suspension air pressure such that the suspension forces can more readily level the vehicle body, much in the same manner as an anti-roll bar (ARB). The results of a multi-domain simulation study in AMESim and TruckSim indicate that the proposed suspension configuration is capable of providing balanced airflow to the truck’s drive-axle suspensions, resulting in balanced suspension forces in response to single lane change and steady-state cornering steering maneuvers. The simulation results further indicate that a truck equipped with the reconfigured suspension experiences a uniform dynamic load sharing, smoother body motion (less roll angle), and improved handling and stability during steering maneuvers commonly occurring in commercial trucks during their intended use.
Capillary forces on a small particle at a liquid-vapor interface: Theory and simulation
Tang, Yanfei; Cheng, Shengfeng (American Physical Society, 2018-09-24)
ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements
Chen, Xi; Neuwald, Andrew F.; Hilakivi-Clarke, Leena; Clarke, Robert; Xuan, Jianhua (PLoS, 2021-07-01)
Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIPseq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on co-localization of ChIP-seq peaks, often many weak binding events are missed, especially for mediators, resulting in incomplete identification of modules. To address this problem, we develop a ChIP-seq data-driven Gibbs Sampler to infer Modules (ChIP-GSM) using a Bayesian framework that integrates ChIP-seq profiles of multiple TFs. ChIP-GSM samples read counts of module TFs iteratively to estimate the binding potential of a module to each region and, across all regions, estimates the module abundance. Using inferred module-region probabilistic bindings as feature units, ChIP-GSM then employs logistic regression to predict active regulatory elements. Validation of ChIPGSM predicted regulatory regions on multiple independent datasets sharing the same context confirms the advantage of using TF modules for predicting regulatory activity. In a case study of K562 cells, we demonstrate that the ChIP-GSM inferred modules form as groups, activate gene expression at different time points, and mediate diverse functional cellular processes. Hence, ChIP-GSM infers biologically meaningful TF modules and improves the prediction accuracy of regulatory region activities.
Crossover From Self-Similar to Self-Affine Structures in Percolation
Frey, E.; Täuber, Uwe C.; Schwabl, Franz (Editions Physique, 1994-05-20)
We study the crossover from self-similar scaling behavior to asymptotically self-affine (anisotropic) structures. As an example, we consider bond percolation with one preferred direction. Our theory is based on a field-theoretical representation, and takes advantage of a renormalization group approach designed for crossover phenomena. We calculate effective exponents for the connectivity describing the entire crossover region from isotropic to directed percolation, and predict at which scale of the anisotropy the crossover should occur. We emphasize the broad range of applicability of our method.
Droplet Evaporation on Hot Micro-Structured Superhydrophobic Surfaces: Analysis of Evaporation from Droplet Cap and Base Surfaces
Huang, Wenge; He, Xukun; Liu, Cong; Li, Xiaojie; Liu, Yahua; Collier, C. Patrick; Srijanto, Bernadeta R.; Liu, Jiansheng; Cheng, Jiangtao (Elsevier, 2022-04-01)
In this study, evaporation of sessile water droplets on hot micro-structured superhydrophobic surfaces is experimentally and theoretically investigated. Water droplets of 4 µL are placed on micro-pillared silicon substrates with the substrate temperature heated up to 120°C. A comprehensive thermal circuit model is developed to analyze the effects of substrate roughness and substrate temperature on the sessile droplet evaporation. For the first time, two components of heat and mass transfer, i.e., one from the droplet cap surface and the other from the droplet base surface, during droplet evaporation are distinguished and systematically studied. As such, the evaporation heat transfer rates from both the droplet cap surface and the interstitial liquid-vapor interface between micropillars at the droplet base are calculated in various conditions. For droplet evaporation on the heated substrates in the range of 40°C – 80°C, the predicted droplet cap temperature matches well with the experimental results. During the constant contact radius mode of droplet evaporation, the decrease of evaporation rate from the droplet base contributes most to the continuously decreasing overall evaporation heat transfer rate, whereas the decrease of evaporation rate from the droplet cap surface is dominant in the constant contact angle mode. The influence of internal fluid flow is considered for droplet evaporation on substrates heated above 100°C, and an effective thermal conductivity is adopted as a correction factor to account for the effect of convection heat transfer inside the droplet. Temperature differences between the droplet base and the substrate base are estimated to be about 2°C, 5°C, 8°C, 13°C and 18°C for droplet evaporation on substrates heated at 40°C, 60°C, 80°C, 100°C, and 120°C, respectively, elucidating the delayed or depressed boiling of water droplets on a heated rough surface due to evaporative cooling.
Failure mode and effects analysis of dual levelling valve airspring suspensions on truck dynamics
Chen, Yang; Hou, Yunbo; Peterson, Andrew W.; Ahmadian, Mehdi (Taylor & Francis, 2019-04-03)
Failure mode and effects analysis are performed for a dual levelling valve pneumatic suspension to determine the effect of suspension failure on tractor–semi-trailer dynamics, using a detailed model of suspension pneumatics coupled with a truck dynamic model. A key element of failure analysis in suspensions with one or two levelling valves is determining the effect on the vehicle body roll when one or more failures occur. The failure modes considered are mainly the suspension pneumatic components, including clogged levelling valve, bent control rod, disabled lever arm, and punctured or leaking connectors and pipes. The pneumatic suspension is modelled in AMESim, with critical parameters established through component testing. Upon validating the AMESim component model experimentally, the pneumatic suspension model is integrated into TruckSim for studying the consequences of suspension failure on truck dynamics. The simulation results indicate that the second levelling valve in a dual-valve arrangement brings a certain amount of failure redundancy to the system, in the sense that when one side fails, the other side can compensate for the failure. Equipping the trailer with dual levelling valves brings an additional stabilising effect to the vehicle in the event of tractor suspension failure.
Frame-independent vector-cloud neural network for nonlocal constitutive modeling on arbitrary grids
Zhou, Xu-Hui; Han, Jiequn; Xiao, Heng (Elsevier, 2022-01-01)
Constitutive models are widely used for modeling complex systems in science and engineering, where first-principle-based, well-resolved simulations are often prohibitively expensive. For example, in fluid dynamics, constitutive models are required to describe nonlocal, unresolved physics such as turbulence and laminar–turbulent transition. However, traditional constitutive models based on partial differential equations (PDEs) often lack robustness and are too rigid to accommodate diverse calibration datasets. We propose a frame-independent, nonlocal constitutive model based on a vector-cloud neural network that can be learned with data. The model predicts the closure variable at a point based on the flow information in its neighborhood. Such nonlocal information is represented by a group of points, each having a feature vector attached to it, and thus the input is referred to as vector cloud. The cloud is mapped to the closure variable through a frame-independent neural network, invariant both to coordinate translation and rotation and to the ordering of points in the cloud. As such, the network can deal with any number of arbitrarily arranged grid points and thus is suitable for unstructured meshes in fluid simulations. The merits of the proposed network are demonstrated for scalar transport PDEs on a family of parameterized periodic hill geometries. The vector-cloud neural network is a promising tool not only as nonlocal constitutive models and but also as general surrogate models for PDEs on irregular domains.
John Cardy's scale-invariant journey in low dimensions: a special issue for his 70th birthday Preface
Calabrese, Pasquale; Fendley, Paul; Täuber, Uwe C. (IOP, 2018-07-13)
Requirements for the containment of COVID-19 disease outbreaks through periodic testing, isolation, and quarantine
Mukhamadiarov, Ruslan I.; Deng, Shengfeng; Serrao, Shannon R.; Priyanka; Childs, Lauren M.; Täuber, Uwe C. (IOP, 2022-01-21)
We employ individual-based Monte Carlo computer simulations of a stochastic SEIR model variant on a two-dimensional Newman–Watts small-world network to investigate the control of epidemic outbreaks through periodic testing and isolation of infectious individuals, and subsequent quarantine of their immediate contacts. Using disease parameters informed by the COVID-19 pandemic, we investigate the effects of various crucial mitigation features on the epidemic spreading: fraction of the infectious population that is identifiable through the tests; testing frequency; time delay between testing and isolation of positively tested individuals; and the further time delay until quarantining their contacts as well as the quarantine duration. We thus determine the required ranges for these intervention parameters to yield effective control of the disease through both considerable delaying the epidemic peak and massively reducing the total number of sustained infections.
The role of the non-linearity in controlling the surface roughness in the one-dimensional Kardar-Parisi-Zhang growth process
Priyanka; Täuber, Uwe C.; Pleimling, Michel J. (IOP, 2021-04-16)
We explore linear control of the one-dimensional non-linear Kardar-Parisi-Zhang (KPZ) equation with the goal to understand the effects the control process has on the dynamics and on the stationary state of the resulting stochastic growth kinetics. In linear control, the intrinsic non-linearity of the system is maintained at all times. In our protocol, the control is applied to only a small number nc of Fourier modes. The stationary-state roughness is obtained analytically in the small-nc regime with weak non-linear coupling wherein the controlled growth process is found to result in Edwards-Wilkinson dynamics. Furthermore, when the non-linear KPZ coupling is strong, we discern a regime where the controlled dynamics shows scaling in accordance to the KPZ universality class. We perform a detailed numerical analysis to investigate the controlled dynamics subject to weak as well as strong non-linearity. A first-order perturbation theory calculation supports the simulation results in the weak non-linear regime. For strong non-linearity, we find a temporal crossover between KPZ and dispersive growth regimes, with the crossover time scaling with the number nc of controlled Fourier modes. We observe that the height distribution is positively skewed, indicating that as a consequence of the linear control, the surface morphology displays fewer and smaller hills than in the uncontrolled growth process, and that the inherent size-dependent stationary-state roughness provides an upper limit for the roughness of the controlled system.
Stabilizing spiral structures and population diversity in the asymmetric May-Leonard model through immigration
Serrao, Shannon R.; Täuber, Uwe C. (Springer, 2021-08-01)
We study the induction and stabilization of spiral structures for the cyclic three-species stochastic May-Leonard model with asymmetric predation rates on a spatially inhomogeneous two-dimensional toroidal lattice using Monte Carlo simulations. In an isolated setting, strongly asymmetric predation rates lead to rapid extinction from coexistence of all three species to a single surviving population. Even for weakly asymmetric predation rates, only a fraction of ecologies in a statistical ensemble manages to maintain full three-species coexistence. However, when the asymmetric competing system is coupled via diffusive proliferation to a fully symmetric May-Leonard patch, the stable spiral patterns from this region induce transient plane-wave fronts and ultimately quasi-stationary spiral patterns in the vulnerable asymmetric region. Thus the endangered ecological subsystem may effectively become stabilized through immigration from even a much smaller stable region. To describe the stabilization of spiral population structures in the asymmetric region, we compare the increase in the robustness of these topological defects at extreme values of the asymmetric predation rates in the spatially coupled system with the corresponding asymmetric May{Leonard model in isolation. We delineate the quasi-stationary nature of coexistence induced in the asymmetric subsystem by its diffusive coupling to a symmetric May{Leonard patch, and propose a (semi-)quantitative criterion for the spiral oscillations to be sustained in the asymmetric region.
A unified formulation of splitting-based implicit time integration schemes
Gonzalez-Pinto, Severiano; Hernandez-Abreu, Domingo; Perez-Rodriguez, Maria S.; Sarshar, Arash; Roberts, Steven; Sandu, Adrian (Academic Press – Elsevier, 2022-01-01)
Splitting-based time integration approaches such as fractional step, alternating direction implicit, operator splitting, and locally one dimensional methods partition the system of interest into components, and solve individual components implicitly in a cost-effective way. This work proposes a unified formulation of splitting time integration schemes in the framework of general-structure additive Runge–Kutta (GARK) methods. Specifically, we develop implicit-implicit (IMIM) GARK schemes, provide the order conditions for this class, and explain their application to partitioned systems of ordinary differential equations. We show that classical splitting methods belong to the IMIM GARK family, and therefore can be studied in this unified framework. New IMIM-GARK splitting methods are developed and tested using parabolic systems.

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