Coupled CFD-FEM Simulation of Steel Box Bridge Exposed to Fire

Increasing fire-induced bridge failures are demanding more precise behavior prediction for the bridges subjected to fires. However, current numerical methods are limited to temperature curves prescribed for building structures, which can misestimate the fire impact significantly. This paper developed a framework coupling the computational dynamics (CFD) method and finite element method (FEM) to predict the performance of fire-exposed bridges. The fire combustion was simulated in CFD software, Fire Dynamic Simulator, to calculate the thermal boundary required by the thermomechanical simulation. Then, the adiabatic surface temperatures and heat transfer coefficient were applied to the FEM model of the entire bridge girder. A sequential coupled thermomechanical FEM simulation was then carried out to evaluate the performance of the fire-exposed bridge, thermally and structurally. The methodology was then validated through a real fire experiment on a steel beam. The fire performance of a simply supported steel box bridge was simulated using the proposed coupled CFD-FEM methodology. Numerical results show that the presented method was able to replicate the inhomogeneous thermomechanical response of box bridges exposed to real fires. The girder failed due to the buckling of a central diaphragm after the ignition of the investigated tanker fire in no more than 10 min. The framework presented in this study is programmatic and friendly to researchers and can be applied for the estimation of bridges in different fire conditions.

A ROBUST COMPUTATIONAL DYNAMICS OF FRACTIONAL-ORDER SMOKING MODEL WITH RELAPSE HABIT

The social habit of smoking has affected the whole world in a social manner. It is the main cause of diseases like cancers, asthma, bad breath, etc., and a source of spreading of infectious diseases like COVID-19. This work is related to an existing smoking model with relapse habit converted in fractional order. First, formulation of fractional-order smoking model is presented and then the dynamics of proposed problem is analyzed. Fixed-point theory via Banach contraction and Schauder theorems is used to derive the existence and uniqueness of the model. At last, the adaptive predictor–corrector algorithm and Runge–Kutta fourth-order (RK4) strategy are used to perform simulation. To bolster the validity of the theoretical results, a set of numerical simulations are performed. A good agreement between hypothetical and numerical results is demonstrated via numerical simulations using MATLAB software.

Computational dynamics of a Lotka-Volterra Model with additive Allee effect based on Atangana-Baleanu fractional derivative

This paper studies an interaction between one prey and one predator following Lotka-Volterra model with additive Allee effect in predator. The Atangana-Baleanu fractional-order derivative is used for the operator. Since the theoretical ways to investigate the model using this operator are limited, the dynamical behaviors are identified numerically. By simulations, the influence of the order of the derivative on the dynamical behaviors is given. The numerical results show that the order of the derivative may impact the convergence rate, the occurrence of Hopf bifurcation, and the evolution of the diameter of the limit-cycle.

SIMULASI PENGARUH BUKAAN VALVE TERHADAP PRESSURE DROP DAN KAVITASI PADA CONTROL VALVE TIPE BALL VALVE DENGAN MENGGUNAKAN SOFTWARE AUTODESK CFD (COMPUTATIONAL DYNAMICS FLUID)

Control valve merupakan suatu instrumen yang digunakan dalam proses industri dan memiliki peran yang sangat penting. Sebagai final control element¸control valve digunakan untuk mengatur aliran fluida agar mampu mengimbangi adanya gangguan serta tetap menjaga variable proses tetap berada pada set point yang diinginkan. Simulasi pengaruh bukaan valve terhadap pressure drop dan kavitasi pada control valve menggunakan autodeks CFD (2019). Kavitasi adalah suatu keadaan yang disebabkan oleh berubahnya fase cairan yang sedang dialirkan dari fase cair menjadi fase uap sehingga menimbulkan gelembung-gelembung. Timbulnya gelembung tersebut disebabkan oleh menurunnya tekanan hingga berada di bawah tekanan uap jenuh cairan tersebut. Adapun variable tetap yang digunakan adalah tekanan 4 atm, 5 atm, 6 atm dan bukaan valve 50 %, 70 %, 90 % dan variable terikat Penurunan tekanan (∆P), Bilangan reynold (NRe), Kavitasi (CN). Di dalam penggambaran geometri valve menggunakan autodeks fusion 360. Untuk bukaan valve yang kecil yaitu 50% penurunan tekanannya sebesar 1.84 atm dengan tekanan awal 5 atm. Bilangan Reynold tertinggi pada bukaan valve 90% dengan bilangan reynold 78,352 dan aliran yang terbentuk adalah turbulen. Indeks kavitasi terendah adalah sebesar 9.47 dan yang tertinggi 36.7. Pada percobaan ini dapat dilihat antara variable terikat, penurunan tekanan dan kavitasi serta variabel bebas bukaan valve dan tekanan yang paling berpengaruh.

Categorical Semantics of Cyber-Physical Systems Theory

Cyber-physical systems require the construction and management of various models to assure their correct, safe, and secure operation. These various models are necessary because of the coupled physical and computational dynamics present in cyber-physical systems. However, to date the different model views of cyber-physical systems are largely related informally, which raises issues with the degree of formal consistency between those various models of requirements, system behavior, and system architecture. We present a category-theoretic framework to make different types of composition explicit in the modeling and analysis of cyber-physical systems, which could assist in verifying the system as a whole. This compositional framework for cyber-physical systems gives rise to unified system models, where system behavior is hierarchically decomposed and related to a system architecture using the systems-as-algebras paradigm. As part of this paradigm, we show that an algebra of (safety) contracts generalizes over the state of the art, providing more uniform mathematical tools for constraining the behavior over a richer set of composite cyber-physical system models, which has the potential of minimizing or eliminating hazardous behavior.

Attacking COVID-19 Progression Using Multi-Drug Therapy for Synergetic Target Engagement

COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 12 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 160 million (>20% located in United States) and killed more than 3.3 million people around the world (>20% deaths in USA). As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors—S/Ace2, Tmprss2, Cathepsins L and K, and Mpro—to prevent binding, membrane fusion and replication of the virus, respectively. All together, we generated an ensemble of structural conformations that increase high-quality docking outcomes to screen over >6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional >30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high-value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.

The phase space of meaning model of psychopathology: A computer simulation modelling study

Introduction The hypothesis of a general psychopathology factor that underpins all common forms of mental disorders has been gaining momentum in contemporary clinical research and is known as the p factor hypothesis. Recently, a semiotic, embodied, and psychoanalytic conceptualisation of the p factor has been proposed called the Harmonium Model, which provides a computational account of such a construct. This research tested the core tenet of the Harmonium model, which is the idea that psychopathology can be conceptualised as due to poorly-modulable cognitive processes, and modelled the concept of Phase Space of Meaning (PSM) at the computational level. Method Two studies were performed, both based on a simulation design implementing a deep learning model, simulating a cognitive process: a classification task. The level of performance of the task was considered the simulated equivalent to the normality-psychopathology continuum, the dimensionality of the neural network’s internal computational dynamics being the simulated equivalent of the PSM’s dimensionality. Results The neural networks’ level of performance was shown to be associated with the characteristics of the internal computational dynamics, assumed to be the simulated equivalent of poorly-modulable cognitive processes. Discussion Findings supported the hypothesis. They showed that the neural network’s low performance was a matter of the combination of predicted characteristics of the neural networks’ internal computational dynamics. Implications, limitations, and further research directions are discussed.

Parallel inference of hierarchical latent dynamics in two-photon calcium imaging of neuronal populations

Dynamic latent variable modelling has provided a powerful tool for understanding how populations of neurons compute. For spiking data, such latent variable modelling can treat the data as a set of point-processes, due to the fact that spiking dynamics occur on a much faster timescale than the computational dynamics being inferred. In contrast, for other experimental techniques, the slow dynamics governing the observed data are similar in timescale to the computational dynamics that researchers want to infer. An example of this is in calcium imaging data, where calcium dynamics can have timescales on the order of hundreds of milliseconds. As such, the successful application of dynamic latent variable modelling to modalities like calcium imaging data will rest on the ability to disentangle the deeper- and shallower-level dynamical systems’ contributions to the data. To-date, no techniques have been developed to directly achieve this. Here we solve this problem by extending recent advances using sequential variational autoencoders for dynamic latent variable modelling of neural data. Our system VaLPACa (Variational Ladders for Parallel Autoencoding of Calcium imaging data) solves the problem of disentangling deeper- and shallower-level dynamics by incorporating a ladder architecture that can infer a hierarchy of dynamical systems. Using some built-in inductive biases for calcium dynamics, we show that we can disentangle calcium flux from the underlying dynamics of neural computation. First, we demonstrate with synthetic calcium data that we can correctly disentangle an underlying Lorenz attractor from calcium dynamics. Next, we show that we can infer appropriate rotational dynamics in spiking data from macaque motor cortex after it has been converted into calcium fluorescence data via a calcium dynamics model. Finally, we show that our method applied to real calcium imaging data from primary visual cortex in mice allows us to infer latent factors that carry salient sensory information about unexpected stimuli. These results demonstrate that variational ladder autoencoders are a promising approach for inferring hierarchical dynamics in experimental settings where the measured variable has its own slow dynamics, such as calcium imaging data. Our new, open-source tool thereby provides the neuroscience community with the ability to apply dynamic latent variable modelling to a wider array of data modalities.

Retina-Inspired Organic Heterojunction-Based Optoelectronic Synapses for Artificial Visual Systems

For the realization of retina-inspired neuromorphic visual systems which simulate basic functions of human visual systems, optoelectronic synapses capable of combining perceiving, processing, and memorizing in a single device have attracted immense interests. Here, optoelectronic synaptic transistors based on tris(2-phenylpyridine) iridium (Ir(ppy)3) and poly(3,3-didodecylquarterthiophene) (PQT-12) heterojunction structure are presented. The organic heterojunction serves as a basis for distinctive synaptic characteristics under different wavelengths of light. Furthermore, synaptic transistor arrays are fabricated to demonstrate their optical perception efficiency and color recognition capability under multiple illuminating conditions. The wavelength-tunability of synaptic behaviors further enables the mimicry of mood-modulated visual learning and memorizing processes of humans. More significantly, the computational dynamics of neurons of synaptic outputs including associated learning and optical logic functions can be successfully demonstrated on the presented devices. This work may locate the stage for future studies on optoelectronic synaptic devices toward the implementation of artificial visual systems.

Q-cubed mutant cues clues to CLC antiport mechanism

A new structure of a CLC antiporter mutant, along with EPR spectroscopy and computational dynamics, now resolves several basic puzzles regarding how these transporters stoichiometrically move Cl− and H+ in opposite directions across biological membranes.