Relevant bibliographies by topics / Mechanistic Computational Model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Mechanistic Computational Model'

Author: Grafiati
Published: 7 September 2023

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Journal articles on the topic "Mechanistic Computational Model"

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Martina Perez, Simon, Heba Sailem, and Ruth E. Baker. "Efficient Bayesian inference for mechanistic modelling with high-throughput data." PLOS Computational Biology 18, no. 6 (June 21, 2022): e1010191. http://dx.doi.org/10.1371/journal.pcbi.1010191.

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Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a minibatch approach to approximate Bayesian computation. Through a case study of a high-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of single cell motility, proliferation and death to a data set of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and -independent motility and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the process of wound healing.
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Hearle, J. W. S., and A. H. Wilkins. "Mechanistic modelling of pilling. Part II: Individual-fibre computational model." Journal of the Textile Institute 97, no. 4 (July 2006): 369–76. http://dx.doi.org/10.1533/joti.2005.0164.

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Zabihi, Azam, John Tello, Sebastien Incerti, Ziad Francis, Ghasem Forozani, Farid Semsarha, Amir Moslehi, and Mario A. Bernal. "Determination of fast neutron RBE using a fully mechanistic computational model." Applied Radiation and Isotopes 156 (February 2020): 108952. http://dx.doi.org/10.1016/j.apradiso.2019.108952.

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Kraikivski, Pavel. "A Dynamic Mechanistic Model of Perceptual Binding." Mathematics 10, no. 7 (April 1, 2022): 1135. http://dx.doi.org/10.3390/math10071135.

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The brain’s ability to create a unified conscious representation of an object by integrating information from multiple perception pathways is called perceptual binding. Binding is crucial for normal cognitive function. Some perceptual binding errors and disorders have been linked to certain neurological conditions, brain lesions, and conditions that give rise to illusory conjunctions. However, the mechanism of perceptual binding remains elusive. Here, I present a computational model of binding using two sets of coupled oscillatory processes that are assumed to occur in response to two different percepts. I use the model to study the dynamic behavior of coupled processes to characterize how these processes can modulate each other and reach a temporal synchrony. I identify different oscillatory dynamic regimes that depend on coupling mechanisms and parameter values. The model can also discriminate different combinations of initial inputs that are set by initial states of coupled processes. Decoding brain signals that are formed through perceptual binding is a challenging task, but my modeling results demonstrate how crosstalk between two systems of processes can possibly modulate their outputs. Therefore, my mechanistic model can help one gain a better understanding of how crosstalk between perception pathways can affect the dynamic behavior of the systems that involve perceptual binding.
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Das, Shubhajit, and Swapan K. Pati. "Unravelling the mechanism of tin-based frustrated Lewis pair catalysed hydrogenation of carbonyl compounds." Catalysis Science & Technology 8, no. 20 (2018): 5178–89. http://dx.doi.org/10.1039/c8cy01227j.

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Zhao, Chen, Adam C. Mirando, Richard J. Sové, Thalyta X. Medeiros, Brian H. Annex, and Aleksander S. Popel. "A mechanistic integrative computational model of macrophage polarization: Implications in human pathophysiology." PLOS Computational Biology 15, no. 11 (November 18, 2019): e1007468. http://dx.doi.org/10.1371/journal.pcbi.1007468.

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Wright, A. Armean, Ghassan N. Fayad, James F. Selgrade, and Mette S. Olufsen. "Mechanistic model of hormonal contraception." PLOS Computational Biology 16, no. 6 (June 29, 2020): e1007848. http://dx.doi.org/10.1371/journal.pcbi.1007848.

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Sharifi, Soroosh, and Arash Massoudieh. "A novel hybrid mechanistic-data-driven model identification framework using NSGA-II." Journal of Hydroinformatics 14, no. 3 (March 6, 2012): 697–715. http://dx.doi.org/10.2166/hydro.2012.026.

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This paper describes a novel evolutionary data-driven model (DDM) identification framework using the NSGA-II multi-objective genetic algorithm. The central concept of this paper is the employment of evolutionary computation to search for model structures among a catalog of models, while honoring the physical principles and the constitutive theories commonly used to represent the system/processes being modeled. The presented framework provides high computational efficiency through connecting a series of NSGA-II runs which share results. Furthermore, the employment of a multi-objective optimization algorithm enables a unique way of incorporating different aspects of model goodness in the model selection process, and also, at the end of the search procedure, provides a number of potential optimal model structures, making it possible for the modeler to make a choice based on the goal of the modeling. As an illustration, the framework is used for modeling wash-off and build-up of suspended solids (TSS) in highway runoff. The performance of the discovered model confirms the potential of the proposed evolutionary DDM framework for modeling environmental processes.
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Ramatsoma, Mafeni S., and Evans M. N. Chirwa. "Computational simulation of flocculent sedimentation based on experimental results." Water Science and Technology 65, no. 6 (March 1, 2012): 1007–13. http://dx.doi.org/10.2166/wst.2012.923.

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Computerised interpolation algorithms as well as the empirical model for analysing the flocculent settling data were developed. A mechanistic semi-empirical model developed from fundamental physical principles of a falling particle in a viscous fluid was tested against actual flocculation column data. The accuracy of the mechanistic model was evaluated using the sum of the squared errors between the interpolated values (real values) and the model predictions. Its fitting capabilities were compared with Özer's model using nine flocculent data sets of which four were obtained from literature and the rest were actual data from the performed experiments. The developed model consistently simulated the flocculation behaviour of particles in settling columns better than Özer's model in eight of the nine data sets considered. It is recommended that the model's performance be further compared with other models like the Rule based and San's model. The errors due to the use of interpolated values when determining the performance of the empirical models need to be investigated. Furthermore, a three-way rather than two-way interpolation should now be achievable using the interpolation algorithm developed in this study thereby reducing the effects of interpolation bias. The above work opens the way to full automation of design of flocculation sedimentation basins and other gravitational particle separation systems which at present are designed manually and are susceptible to a wide range of human and random errors.
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Peterson, D. E., R. V. Lalla, R. Srivastava, and L. M. Loew. "Mucositis in cancer patients: Prototypic semi-mechanistic kinetic model." Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 19617. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.19617.

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19617 Background: Recent research advances have helped (i) define pathobiology of alimentary tract mucosal injury secondary to cancer therapy and (ii) link molecular mechanisms with clinically important outcomes. Recently-developed computational biology modeling may further enhance these advances. Semi-mechanistic (SM) modeling allows one to approach quantitative analysis of a biochemical system that is incompletely determined. In this study, data from sequential oral mucosal biopsies in 3 patients developing oral mucositis secondary to hematopoietic stem cell transplantation (HSCT) conditioning were utilized to establish a prototypic computational model for this toxicity. Methods: Plasma and oral mucosal biopsy specimens were obtained from 3 autologous HSCT patients before and after administration of conditioning chemotherapy: Day -10, +10, +28 and +100; Day 0 was day of transplant. Full-thickness tissue samples were measured by RT- PCR for COX-1, COX-2, IL-1β and TNF-a. Plasma samples were measured by ELISA for PGE2 and PGI2, markers of COX-2 activity. The SM model was implemented as a system of 6 ordinary differential equations with 15 parameters. Parameter estimation and simulations were conducted based on experimental results, using a combination of Mathematica, Berkeley Madonna and Virtual Cell software packages. Results: The SM model captured the behavior of COX-1, IL-1β and PGE2 dynamics, predicting an exponential decay for each of these species. Half-lives relative to average steady-state values were found to be 9.7 days, 8.7 days and 9.3 days for COX-1, IL-1β and PGE2 respectively. Correlation ratios for each of these species were calculated to be 0.62, 0.61 and 0.90 respectively. Conclusions: This prototypic model provides a basis for development of a detailed mathematical model for quantifying relevant components of the mucositis pathway. This combination of modeling and experiment could also identify gaps in the pathway that would be important targets for new hypotheses, including possible feedback mechanisms relative to inflammatory cytokines. No significant financial relationships to disclose.
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Dissertations / Theses on the topic "Mechanistic Computational Model"

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(8098121), Akshay Parag Biniwale. "Mechanistic Analysis of Sodiation in Electrodes." Thesis, 2019.

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The single particle model was extended to include electrode and particle volume expansion effects observed in high capacity alloying electrodes. The model was used to predict voltage profiles in sodium ion batteries with tin and tin-phosphide negative electrodes. It was seen that the profiles predicted by the modified model were significantly better than the classical model. A parametric study was done to understand the impact of properties such as particle radius, diffusivity, reaction rate etc on the performance of the electrode. The model was also modified for incorporating particles having a cylindrical morphology. For the same material properties, it was seen that cylindrical particles outperform spherical particles for large L/R values in the cylinder due to the diffusion limitations at low L/R ratios. A lattice spring-based degradation model was used to observe crack formation and creep relaxation within the particle. It was observed that the fraction of broken bonds increases with an increase in strain rate. At low strain rates, it was seen that there was a significant expansion in particle volumes due to creep deformation. This expansion helped release particle stresses subsequently reducing the amount of fracture.

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Yeh, Chung-Heng. "Mechanistic Models of Neural Computation in the Fruit Fly Brain." Thesis, 2019. https://doi.org/10.7916/d8-fp7c-a661.

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Understanding the operating principles of the brain functions is the key to building novel computing architectures for mimicking human intelligence. Neural activities at different scales lead to different levels of brain functions. For example, cellular functions, such as sensory transduction, occur in the molecular reactions, and cognitive functions, such as recognition, emerge in neural systems across multiple brain regions. To bridge the gap between neuroscience and artificial computation, we need systematic development of mechanistic models for neural computation across multiple scales. Existing models of neural computation are often independently developed for a specific scale and hence not compatible with others. In this thesis, we investigate the neural computations in the fruit fly brain and devise mechanistic models at different scales in a systematic manner so that models at one scale constitute functional building blocks for the next scale. Our study spans from the molecular and circuit computations in the olfactory system to the system-level computation of the central complex in the fruit fly. First, we study how the two key aspects of odorant, identity and concentration, are encoded by the odorant transduction process at the molecular scale. We mathematically quantify the odorant space and propose a biophysical model of the olfactory sensory neuron (OSN). To validate our modeling approaches, we examine the OSN model with a multitude of odorant waveforms and demonstrate that the model output reproduces the temporal responses of OSNs obtained from in vivo electrophysiology recordings. In addition, we evaluate the model at the OSN population level and quantify the combinatorial complexity of the transformation taking place between the odorant space and the OSNs. The resulting concentration-dependent combinatorial code determines the complexity of the input space driving olfactory processing in the downstream neuropil, the antennal lobe. Second, we investigate the neural information processing in the antennal lobe across the molecule scale and the circuit scale. The antennal lobe encodes the output of the OSN population from a concentration-dependent code into a concentration-independent combinatorial code. To study the transformation of the combinatorial code, we construct a computational model of the antennal lobe that consists of two sub circuits, a predictive coding circuit and an on-off circuit, realized by two distinct local neuron networks, respectively. By examining the entire circuit model with both monomolecular odorant and odorant mixtures, we demonstrate that the predictive coding circuit encodes the odorant identity into concentration invariant code and the on-off circuit encodes the onset and the offset of a unique odorant identity. Third, we investigate the odorant representation inherent in the Kenyon cell activities in the mushroom body. The Kenyon cells encodes the output of the antennal lobe into a high-dimensional, sparse neural code that is immediately used for learning and memory formation. We model the Kenyon cell circuitry as a real-time feedback normalization circuit converting odorant information into a time-dependent hash codes. The resultant real-time hash code represents odorants, pure or mixture alike, in a way conducive to classifications, and suggests an intrinsic partition of the odorant space with similar hash codes. Forth, we study at the system scale the neural coding of the central complex. The central complex is a set of neuropils in the center of the fly brain that integrates multiple sensory information and play an important role in locomotor control. We create an application that enables simultaneous graphical querying and construction of executable model of the central complex neural circuitry. By reconfiguring the circuitry and generating different executable models, we compare the model response of the wild type and mutant fly strains. Finally, we show that the multi-scale study of the fruit fly brain is made possible by the Fruit Fly Brain Observatory (FFBO), an open-source platform to support open, collaborative fruit fly neuroscience research. The software architecture of the FFBO and its key application are highlighted along with several examples.
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Books on the topic "Mechanistic Computational Model"

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Yeh, Chung-Heng. Mechanistic Models of Neural Computation in the Fruit Fly Brain. [New York, N.Y.?]: [publisher not identified], 2019.

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Narancic, Sanja. Mechanistic studies of organometallic complexes: Part I : Development of a new model for ligand binding energy determinations : Computational study of the conversion of rhenium diolates to metallaoxetanes and carbenes. 2007.

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Piccinini, Gualtiero. Computationalism. Edited by Eric Margolis, Richard Samuels, and Stephen P. Stich. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195309799.013.0010.

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The introduction of the concept of computation in cognitive science is discussed in this article. Computationalism is usually introduced as an empirical hypothesis that can be disconfirmed. Processing information is surely an important aspect of cognition so if computation is information processing, then cognition involves computation. Computationalism becomes more significant when it has explanatory power. The most relevant and explanatory notion of computation is that associated with digital computers. Turing analyzed computation in terms of what are now called Turing machines that are the kind of simple processor operating on an unbounded tape. Turing stated that any function that can be computed by an algorithm could be computed by a Turing machine. McCulloch and Pitts's account of cognition contains three important aspects that include an analogy between neural processes and digital computations, the use of mathematically defined neural networks as models, and an appeal to neurophysiological evidence to support their neural network models. Computationalism involves three accounts of computation such as causal, semantic, and mechanistic. There are mappings between any physical system and at least some computational descriptions under the causal account. The semantic account may be formulated as a restricted causal account.
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Al-Safran, Eissa M., and James P. Brill. Applied Multiphase Flow in Pipes and Flow Assurance: Oil and Gas Production. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/9781613994924.

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Applied Multiphase Flow in Pipes and Flow Assurance - Oil and Gas Production delivers the most recent advancements in multiphase flow technology while remaining easy to read and appropriate for undergraduate and graduate petroleum engineering students. Responding to the need for a more up-to-the-minute resource, this highly anticipated new book represents applications on the fundamentals with new material on heat transfer in production systems, flow assurance, transient multiphase flow in pipes and the TUFFP unified model. The complex computation procedure of mechanistic models is simplified through solution flowcharts and several example problems. Containing over 50 solved example problems and 140 homework problems, this new book will equip engineers with the skills necessary to use the latest steady-state simulators available.
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Book chapters on the topic "Mechanistic Computational Model"

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Brito, Adriana, Nelson MacQuhae, Francisco García, Nelson Fernández, and José Colmenares. "Mechanistic Model for Eccentric Annular Gas-Liquid Flow in Horizontal Pipelines." In Computational and Experimental Fluid Mechanics with Applications to Physics, Engineering and the Environment, 431–41. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-00191-3_29.

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Antonietti, Alberto, Claudia Casellato, Egidio D’Angelo, and Alessandra Pedrocchi. "Computational Modelling of Cerebellar Magnetic Stimulation: The Effect of Washout." In Lecture Notes in Computer Science, 35–46. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_3.

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AbstractNowadays, clinicians have multiple tools that they can use to stimulate the brain, by means of electric or magnetic fields that can interfere with the bio-electrical behaviour of neurons. However, it is still unclear which are the neural mechanisms that are involved and how the external stimulation changes the neural responses at network-level. In this paper, we have exploited the simulations carried out using a spiking neural network model, which reconstructed the cerebellar system, to shed light on the underlying mechanisms of cerebellar Transcranial Magnetic Stimulation affecting specific task behaviour. Namely, two computational studies have been merged and compared. The two studies employed a very similar experimental protocol: a first session of Pavlovian associative conditioning, the administration of the TMS (effective or sham), a washout period, and a second session of Pavlovian associative conditioning. In one study, the washout period between the two sessions was long (1 week), while the other study foresaw a very short washout (15 min). Computational models suggested a mechanistic explanation for the TMS effect on the cerebellum. In this work, we have found that the duration of the washout strongly changes the modification of plasticity mechanisms in the cerebellar network, then reflected in the learning behaviour.
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Goldsmith, Michael R., Shane D. Peterson, Daniel T. Chang, Thomas R. Transue, Rogelio Tornero-Velez, Yu-Mei Tan, and Curtis C. Dary. "Informing Mechanistic Toxicology with Computational Molecular Models." In Methods in Molecular Biology, 139–65. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-050-2_7.

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Sbalzarini, Ivo F., and Urs F. Greber. "How Computational Models Enable Mechanistic Insights into Virus Infection." In Methods in Molecular Biology, 609–31. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8678-1_30.

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Lecca, Paola, and Michela Lecca. "Mechanistic Models of Astrocytic Glucose Metabolism Calibrated on PET Images." In Lecture Notes in Computational Vision and Biomechanics, 131–55. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5890-2_6.

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Axenie, Cristian, and Daria Kurz. "CHIMERA: Combining Mechanistic Models and Machine Learning for Personalized Chemotherapy and Surgery Sequencing in Breast Cancer." In Mathematical and Computational Oncology, 13–24. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64511-3_2.

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Lewis, Joanna, and Joseph F. Standing. "Mechanistic Models of CD4 T Cell Homeostasis and Reconstitution in Health and Disease." In Mathematical, Computational and Experimental T Cell Immunology, 65–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57204-4_4.

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Azhar, Nabil, Qi Mi, Cordelia Ziraldo, Marius Buliga, Gregory M. Constantine, and Yoram Vodovotz. "Integrating Data-Driven and Mechanistic Models of the Inflammatory Response in Sepsis and Trauma." In Complex Systems and Computational Biology Approaches to Acute Inflammation, 143–57. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8008-2_8.

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Vodovotz, Yoram. "Integrating Data-Driven and Mechanistic Models of the Inflammatory Response in Sepsis and Trauma." In Complex Systems and Computational Biology Approaches to Acute Inflammation, 53–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56510-7_4.

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Mahanama, Thilini V., Arpan Biswas, and Dong Wang. "Optimize and Strengthen Machine Learning Models Based on in Vitro Assays with Mechanistic Knowledge and Real-World Data." In Machine Learning and Deep Learning in Computational Toxicology, 183–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20730-3_7.

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Conference papers on the topic "Mechanistic Computational Model"

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Kim, Yong-Soo, and Chan-Bok Lee. "Mechanistic Two Stage Fission Gas Release Model." In 10th International Conference on Nuclear Engineering. ASMEDC, 2002. http://dx.doi.org/10.1115/icone10-22635.

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In this study, a mechanistic two stages model is developed which analytically simulates the two-step diffusion processes, grain lattice diffusion and grain boundary diffusion, coupled with the bubbles trap/resolution. Mathematical manipulation reveals that the release at high burn-up depend on the ratio of the diffusivities in the both processes, i.e., α ≅ Dveff/Dgbeff where Dveff and Dgbeff are effective volume and grain boundary diffusion coefficients, respectively. Thus, the ratio α is incorporated in the time-dependent third kind boundary condition at the equivalent grain surface. This model brings forth analytical solutions of the fractional release which are identical to that of either ANS5.4 or modified ANS5.4 model when α goes to the infinity. It turns out that this model describes the release behavior well in the high burn-up fuel and puts out a comparable prediction to the solution of FRAPCON-3 model under the same condition. It is also demonstrated that the new factor α not only ease the computational treatment for the high burn-up fuel performance evaluation, but also enables us to possibly separate the burn-up enhancement from the diffusion coefficients and to easily simulate the bubble-related phenomena in the grain boundary.
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Mbuguiro, Wangui, and Feilim Mac Gabhann. "Mechanistic model demonstrates importance of autocrine IL-8 secretion by neutrophils." In BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3459930.3469500.

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Neto, E. T. "A Mechanistic Computational Fluid Dynamic CFD Model to Predict Hydrate Formation in Offshore Pipelines." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2016. http://dx.doi.org/10.2118/184491-stu.

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Almajid, Muhammad Majid, Zuhair A. AlYousef, and Othman S. Swaie. "Local Equilibrium Mechanistic Simulation of CO2-Foam Flooding." In SPE Reservoir Simulation Conference. SPE, 2021. http://dx.doi.org/10.2118/203922-ms.

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Abstract Mechanistic modeling of the non-Newtonian CO2-foam flow in porous media is a challenging task that is computationally expensive due to abrupt gas mobility changes. The objective of this paper is to present a local equilibrium (LE) CO2-foam mechanistic model, which could alleviate some of the computational cost, and its implementation in the Matlab Reservoir Simulation Tool (MRST). Interweaving the LE-foam model into MRST enables users quick prototyping and testing of new ideas and/or mechanistic expressions. We use MRST, the open source tool available from SINTEF, to implement our LE-foam model. The model utilizes MRST automatic differentiation capability to compute the fluxes as well as the saturations of the aqueous and the gaseous phases at each Newton iteration. These computed variables and fluxes are then fed into the LE-foam model that estimates the bubble density (number of bubbles per unit volume of gas) in each grid block. Finally, the estimated bubble density at each grid block is used to readjust the gaseous phase mobility until convergence is achieved. Unlike the full-physics model, the LE-foam model does not add a population balance equation for the flowing bubbles. The developed LE-foam model, therefore, does not add much computational cost to solving a black oil system of equations as it uses the information from each Newton iteration to adjust the gas mobility. Our model is able to match experimental transient foam flooding results from the literature. The chosen flowing foam fraction (Xf) formula dictates to a large extent the behavior of the solution. An appropriate formula for Xf needs to be chosen such that our simulations are more predictive. The work described in this paper could help in prototyping various ideas about generation and coalescence of bubbles as well as any other correlations used in any population balance model. The chosen model can then be used to predict foam flow and estimate economic value of any foam pilot project.
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Gallaway, Tara, Steven P. Antal, and Michael Z. Podowski. "Multidimensional Model of Fluid Flow and Heat Transfer in Generation-IV Supercritical Water Reactors." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89897.

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This paper is concerned with the mechanistic modeling and theoretical/computational analysis of flow and heat transfer in future Generation-IV Supercritical Water Cooled Reactors (SCWR). The issues discussed in the paper include: the development of analytical models of the properties of supercritical water, and the application of full three-dimensional computational modeling framework to simulate fluid flow and heat transfer in SCWRs. Several results of calculations are shown, including the evaluation of water properties (density, specific heat, thermal conductivity, viscosity, and Prandtl number) near the pseudo-critical temperature for various supercritical pressures, and the CFD predictions using the NPHASE computer code. It is demonstrated that the proposed approach is very promising for future mechanistic analyses of SCWR thermal-hydraulics and safety.
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Lorenzo, Guillermo, Thomas J. Hughes, Alessandro Reali, Hector Gomez, and Thomas E. Yankeelov. "Abstract 5483: An image-based mechanistic computational model for early prediction of organ-confined untreated prostate cancer growth." In Proceedings: AACR Annual Meeting 2020; April 27-28, 2020 and June 22-24, 2020; Philadelphia, PA. American Association for Cancer Research, 2020. http://dx.doi.org/10.1158/1538-7445.am2020-5483.

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Ma, Chenhui, Alexandru Almasan, and Evren Gurkan-Cavusoglu. "Abstract 225: Computational analysis of 5-fluorouracil antitumor activity in colon cancer using a mechanistic pharmacokinetic/pharmacodynamic model." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-225.

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Kamhawi, Khalid, Yabin Zhao, and Liam Finch. "A Multiscale Mechanistic Model for Slow Transient Two-Phase Gas Condensate Flows in Pipelines." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-42040.

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Various technical, commercial and operational requirements and conditions warrant the modelling of gas condensate pipelines as two-phase flows. Although phenomenological descriptions of two-phase flows are commonly used in the Oil and Gas Industry, the thermal-hydraulic complexities of such systems mean that a number of mechanistic formulations are available, some emphasising accuracy at the expense of computational efficiency, others preferring a more simplified approach. This article proposes a fully mechanistic slow transient model of two-phase condensate gas flows in pipelines, where the slip relation is derived from first principles using a mutliscale expansion method. Representative steady state and transient case studies for different operational conditions are simulated and solved numerically. Results are analysed and validated against an industry standard Two-Fluid Model based software.
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Guillen, Donna Post, Jonathan K. Shelley, Steven P. Antal, Elena A. Tselishcheva, Michael Z. Podowski, Dirk Lucas, and Matthias Beyer. "Optimization of a Two-Fluid Hydrodynamic Model of Churn-Turbulent Flows." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75113.

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A hydrodynamic model of two-phase, churn-turbulent flows is being developed using the computational multiphase fluid dynamics (CMFD) code, NPHASE-CMFD. The numerical solutions obtained by this model are compared with experimental data obtained at the TOPFLOW facility of the Institute of Safety Research at the Forschungszentrum Dresden-Rossendorf. The TOPFLOW data is a high quality experimental database of upward, co-current air-water flows in a vertical pipe suitable for validation of computational fluid dynamics (CFD) codes. A five-field CMFD model was developed for the continuous liquid phase and four bubble size groups using mechanistic closure models for the ensemble-averaged Navier-Stokes equations. Mechanistic models for the drag and non-drag interfacial forces are implemented to include the governing physics to describe the hydrodynamic forces controlling the gas distribution. The closure models provide the functional form of the interfacial forces, with user defined coefficients to adjust the force magnitude. An optimization strategy was devised for these coefficients using commercial design optimization software. This paper demonstrates an approach to optimizing CMFD model parameters using a design optimization approach. Computed radial void fraction profiles predicted by the NPHASE-CMFD code are compared to experimental data for four bubble size groups.
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10

Berg, Lawrence D., Soroor Karimi, and Siamack A. Shirazi. "Application of a Mechanistic Erosion and Abrasion Model to Pulverized Coal (PC) Injections." In ASME 2021 Power Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/power2021-63620.

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Abstract Coal use for generation of electricity is used extensively world-wide accounting for 40% of total power generation. Even with reductions in use over the last 10 years, coal still accounts for 20% of total electrical generation in the United States. An often-overlooked aspect of Pulverized Coal (PC) combustion is the erosion and abrasion of the coal injection nozzles. Currently there are over 300 active PC boilers in the US and over 1000 worldwide, with each boiler having 20–40 high alloy cast injectors. Due to the high velocity of PC injection and associated elevated rates of metal loss, these nozzles require constant replacement. Replacement and costs associated with loss of revenue, required scaffolding and casting can be a significant part of Operation and Maintenance (O&M) of a PC boiler. In addition to the constant requirement for thousands of replacement injection nozzles every year, combustion performance, NOx reduction, carbon conversion and general boiler efficiency will be impacted by hardware that is out of specification, if not replaced in a “timely” manner. Significant research in the 1980’s [1] provided some insight into the loss-of-metal process during PC injection, but limitations of existing hardware and software prevented more than an empirical methodology to be developed. In parallel with the literature work and research specifically for PC coal erosion rates, generalized efforts were employed and reported [6–9]. Meng [4] summarized model development for solid particles transported by a liquid or gas as highly empirical with little commonality between the models developed by the various researchers. Meng also made specific recommendations for less empiricism in model development methodology. Although there are several state-of-the-art empirical models [6, 8 & 9] more recently, semi-mechanistic models have been developed to predict solid particle erosion (e.g. Arabnejad et al., [17]) and have been successfully applied to sand erosion and abrasion in pipelines. In the current study, this method is being applied to PC injection nozzles coupled to detailed computational fluid dynamics (CFD) simulations. The intent is to quantify nozzle material loss rates, due to impacting coal particles, as a function of geometry, local velocities, and coal properties. The method used is utilizing CFD to model flow of particles and their impingement velocity with the PC nozzles. Then erosion models that are a function of impingement speed, angle, frequency and materials properties to examine erosion rates. The insight gained from the modeling will allow improved nozzle design, increased duty life, more cost-effective supply, and elevated injection velocity. In particular, low NOx coal combustion can be critically dependent on utilization of elevated injection velocities, which previous empirical models discourage. This paper reports on the application of the erosion equations and methods developed at the Erosion/Corrosion Research Center of The University of Tulsa for predicting solid particle erosion of a PC injection nozzle that shows details of erosion patterns and parameters that are responsible for elevated erosion tendencies in the field. RJM-International is familiar with the nozzle from various applications that are associated with Low NOx operation. The advantages of utilizing semi-mechanistic erosion equations and models coupled with CFD simulations as compared to previous empirical methods are discussed. Shortcomings of applying the existing coal erosion model is also reported along with “next steps” required to successfully apply the method to PC injection nozzle designs for much higher combustion efficiencies than existing ones.
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Reports on the topic "Mechanistic Computational Model"

1

COLD FORMED STEEL SHEAR WALL RACKING ANALYSIS THROUGH A MECHANISTIC APPROACH: CFS-RAMA. The Hong Kong Institute of Steel Construction, September 2022. http://dx.doi.org/10.18057/ijasc.2022.18.3.2.

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Cold-formed steel shear wall panels are an effective lateral load resisting system in cold-formed steel or light gauge constructions. The behavior of these panels is governed by the interaction of the sheathing - frame fasteners and the sheathing itself. Therefore, analysis of these panels for an applied lateral load (monotonic/cyclic) is complex due to the inherent non-linearity that exists in the fastener-sheathing interaction. This paper presents a novel and efficient, fastener based mechanistic approach that can reliably predict the response of cold-formed steel wall panels for an applied monotonic lateral load. The approach is purely mechanistic, alleviating the modelling complexity, computational costs and convergence issues which is generally confronted in finite element models. The computational time savings are in the order of seven when compared to the finite element counterparts. Albeit its simplicity, it gives a good insight into the component level forces such as on studs, tracks and individual fasteners for post-processing and performance-based seismic design at large. The present approach is incorporated in a computational framework - CFS-RAMA. The approach is general and thereby making it easy to analyze a variety of configurations of wall panels with brittle sheathing materials and the results are validated using monotonic racking test data published from literature. The design parameters estimated using EEEP (Equivalent Energy Elastic Plastic) method are also compared against corresponding experimental values and found in good agreement. The method provides a good estimate of the wall panel behavior for a variety of configurations, dimensions and sheathing materials used, making it an effective design tool for practicing engineers.
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