A Computational Technique to Generate Coupler Curve Equation of 6-bar Mechanism

ABSTRACT A new computational technique for the thermomechanical analysis of tires in stationary rolling contact is suggested. Different from the existing approaches, the proposed method uses the constitutive description of tire rubber components, such as large deformations, viscous hysteresis, dynamic stiffening, internal heating, and temperature dependency. A thermoviscoelastic constitutive model, which incorporates all the mentioned effects and their numerical aspects, is presented. An isentropic operator-split algorithm, which ensures numerical stability, was chosen for solving the coupled mechanical and energy balance equations. For the stationary rolling-contact analysis, the constitutive model presented and the operator-split algorithm are embedded into the Arbitrary Lagrangian Eulerian (ALE)–relative kinematic framework. The flow of material particles and their inelastic history within the spatially fixed mesh is described by using the recently developed numerical technique based on the Time Discontinuous Galerkin (TDG) method. For the efficient numerical solutions, a three-phase, staggered scheme is introduced. First, the nonlinear, mechanical subproblem is solved using inelastic constitutive equations. Next, deformations are transferred to the subsequent thermal phase for the solution of the heat equations concerning the internal dissipation as a source term. In the third step, the history of each material particle, i.e., each internal variable, is transported through the fixed mesh corresponding to the convective velocities. Finally, some numerical tests with an inelastic rubber wheel and a car tire model are presented.

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A Proposed Computational Technique for Obtaining Hypersonic Air Data On a Sharp-Nosed Vehicle

10.23919/acc.1990.4790838 ◽

1990 ◽

Author(s):

Jamie B. Tarasidis ◽

R.F. Hellbaum ◽

H. Douglas Garner

Keyword(s):

Computational Technique

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Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

10.26434/chemrxiv.7841270.v2 ◽

2019 ◽

Author(s):

Kyle Konze ◽

Pieter Bos ◽

Markus Dahlgren ◽

Karl Leswing ◽

Ivan Tubert-Brohman ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Active Learning ◽

Large Scale ◽

Chemical Space ◽

Population Based ◽

Free Energy Calculations ◽

Computational Technique ◽

Cyclin Dependent Kinase ◽

Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC50 < 100 nM, and four unique cores with a predicted IC50 < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

10.26434/chemrxiv.7841270 ◽

2019 ◽

Author(s):

Kyle Konze ◽

Pieter Bos ◽

Markus Dahlgren ◽

Karl Leswing ◽

Ivan Tubert-Brohman ◽

...

Keyword(s):

Free Energy ◽

Drug Discovery ◽

Active Learning ◽

Large Scale ◽

Chemical Space ◽

Population Based ◽

Free Energy Calculations ◽

Computational Technique ◽

Cyclin Dependent Kinase ◽

Energy Calculations

We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC50 < 100 nM, and four unique cores with a predicted IC50 < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

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Considering the impact of intermittent discharges when modelling overflows

Water Science & Technology ◽

10.2166/wst.1998.0370 ◽

1998 ◽

Vol 38 (10) ◽

pp. 23-30

Author(s):

Sarah Jubb ◽

Philip Hulme ◽

Ian Guymer ◽

John Martin

Keyword(s):

Water Quality ◽

Dissolved Oxygen ◽

Preliminary Investigation ◽

Oxygen Demand ◽

Sediment Oxygen Demand ◽

Combined Sewer Overflows ◽

River Hydraulics ◽

Combined Sewer ◽

The Impact ◽

Curve Equation

This paper describes a preliminary investigation that identified factors important in the prediction of river water quality, especially regarding dissolved oxygen (DO) concentration. Intermittent discharges from combined sewer overflows (CSOs) within the sewerage, and overflows at water reclamation works (WRW) cause dynamic conditions with respect to both river hydraulics and water quality. The impact of such discharges has been investigated under both wet and dry weather flow conditions. Data collected from the River Maun, UK, has shown that an immediate, transient oxygen demand exists downstream of an outfall during storm conditions. The presence of a delayed oxygen demand has also been identified. With regard to modelling, initial investigations used a simplified channel and the Streeter-Phelps (1925) dissolved oxygen sag curve equation. Later, a model taking into account hydrodynamic, transport and dispersion processes was used. This suggested that processes other than water phase degradation of organic matter significantly affect the dissolved oxygen concentration downstream of the location of an intermittent discharge. It is proposed that the dynamic rate of reaeration and the sediment oxygen demand should be the focus of further investigation.

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The Budapest Amyloid Predictor and Its Applications

Biomolecules ◽

10.3390/biom11040500 ◽

2021 ◽

Vol 11 (4) ◽

pp. 500

Author(s):

László Keresztes ◽

Evelin Szögi ◽

Bálint Varga ◽

Viktor Farkas ◽

András Perczel ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Support Vector ◽

User Needs ◽

Computational Technique ◽

Linear Support Vector Machine ◽

Β Sheets ◽

Artificial Neural ◽

Amorphous Aggregation

The amyloid state of proteins is widely studied with relevance to neurology, biochemistry, and biotechnology. In contrast with nearly amorphous aggregation, the amyloid state has a well-defined structure, consisting of parallel and antiparallel β-sheets in a periodically repeated formation. The understanding of the amyloid state is growing with the development of novel molecular imaging tools, like cryogenic electron microscopy. Sequence-based amyloid predictors were developed, mainly using artificial neural networks (ANNs) as the underlying computational technique. From a good neural-network-based predictor, it is a very difficult task to identify the attributes of the input amino acid sequence, which imply the decision of the network. Here, we present a linear Support Vector Machine (SVM)-based predictor for hexapeptides with correctness higher than 84%, i.e., it is at least as good as the best published ANN-based tools. Unlike artificial neural networks, the decisions of the linear SVMs are much easier to analyze and, from a good predictor, we can infer rich biochemical knowledge. In the Budapest Amyloid Predictor webserver the user needs to input a hexapeptide, and the server outputs a prediction for the input plus the 6 × 19 = 114 distance-1 neighbors of the input hexapeptide.

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A computational technique for thermal analysis in coaxial cylinder of one-dimensional flow of fractional Oldroyd-B nanofluid

International Journal of Ambient Energy ◽

10.1080/01430750.2021.1939157 ◽

2021 ◽

pp. 1-17

Author(s):

K. A. Abro ◽

A. Atangana

Keyword(s):

Thermal Analysis ◽

Coaxial Cylinder ◽

Computational Technique ◽

One Dimensional ◽

Dimensional Flow

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EEG Source Imaging (ESI) utility in clinical practice

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2019-0128 ◽

2020 ◽

Vol 65 (6) ◽

pp. 673-682

Author(s):

Pegah Khosropanah ◽

Eric Tatt-Wei Ho ◽

Kheng-Seang Lim ◽

Si-Lei Fong ◽

Minh-An Thuy Le ◽

...

Keyword(s):

Focal Epilepsy ◽

Cost Effective ◽

Head Model ◽

Computational Technique ◽

Epileptogenic Zone ◽

Clinical Tool ◽

Source Imaging ◽

Localization Accuracy ◽

Eeg Source Imaging ◽

Surgical Evaluation

AbstractEpilepsy surgery is an important treatment modality for medically refractory focal epilepsy. The outcome of surgery usually depends on the localization accuracy of the epileptogenic zone (EZ) during pre-surgical evaluation. Good localization can be achieved with various electrophysiological and neuroimaging approaches. However, each approach has its own merits and limitations. Electroencephalography (EEG) Source Imaging (ESI) is an emerging model-based computational technique to localize cortical sources of electrical activity within the brain volume, three-dimensionally. ESI based pre-surgical evaluation gives an overall clinical yield of 73–91%, depending on choice of head model, inverse solution and EEG electrode density. It is a cost effective, non-invasive method which provides valuable additional information in presurgical evaluation due to its high localizing value specifically in MRI-negative cases, extra or basal temporal lobe epilepsy, multifocal lesions such as tuberous sclerosis or cases with multiple hypotheses. Unfortunately, less than 1% of surgical centers in developing countries use this method as a part of pre-surgical evaluation. This review promotes ESI as a useful clinical tool especially for patients with lesion-negative MRI to determine EZ cost-effectively with high accuracy under the optimized conditions.

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Uniform Energy Decay Rates for the Fuzzy Viscoelastic Model with a Nonlinear Source

Journal of Mathematics ◽

10.1155/2020/8615149 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Fengyun Zhang

Keyword(s):

Viscoelastic Model ◽

Energy Decay ◽

Decay Rates ◽

Polynomial Form ◽

Computational Technique ◽

Exponential Form ◽

Nonlinear Source ◽

Priori Estimates ◽

Energy Decay Rates ◽

Mathematica Software

This paper considers the fuzzy viscoelastic model with a nonlinear source u t t + L u + ∫ 0 t g t − ζ Δ u ζ d ζ − u γ u − η Δ u t = 0 in a bounded field Ω. Under weak assumptions of the function g t , with the aid of Mathematica software, the computational technique is used to construct the auxiliary functionals and precise priori estimates. As time goes to infinity, we prove that the solution is global and energy decays to zero in two different ways: the exponential form and the polynomial form.

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