scholarly journals Validation of Fuel-Coolant Interaction Model for Severe Accident Simulations

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Vladimir Melikhov ◽  
Oleg Melikhov ◽  
Sergey Yakush ◽  
Nikita Rtishchev
Keyword(s):  
Computational Analysis ◽  
Interaction Model ◽  
Measured Data ◽  
Severe Accident ◽  
Physical Parameters ◽  
Physical Processes ◽  
Severe Accidents ◽  
Wide Range ◽  
Fuel Fragmentation ◽  
The Mathematical Model

A specialized module VAPEX-M has been developed and implemented as a part of an integral code, SOCRAT, to enable the modeling of fuel-coolant interactions (FCIs) during severe accidents. The mathematical model and correlations for the main physical processes are described. Results of computational analysis of three experimental series reported in the literature are presented. The calculations were carried out by the combined SOCRAT/VAPEX code and were aimed at validation of the predictive capabilities of the code. The experiments chosen cover a wide range of physical parameters, which enables different aspects of the code to be verified, that is, drag correlations (MAGICO-2000), evaporation rate (QUEOS), fuel fragmentation, and interaction with the coolant in all complexity (FARO). Generally, reasonable agreement between the measured data and calculated results was obtained, which allows one to use the combined SOCRAT/VAPEX code for severe accidents analysis.

scholarly journals A Computational Study on the Role of Parameters for Identification of Thyroid Nodules by Infrared Images (and Comparison with Real Data)

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4459
Author(s):  
José R. González ◽  
Charbel Damião ◽  
Maira Moran ◽  
Cristina A. Pantaleão ◽  
Rubens A. Cruz ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Thyroid Nodules ◽  
Numerical Study ◽  
Computational Study ◽  
Internal Heat ◽  
Heat Transfer Process ◽  
The Body ◽  
Physical Parameters ◽  
Infrared Sensors ◽  
Wide Range

According to experts and medical literature, healthy thyroids and thyroids containing benign nodules tend to be less inflamed and less active than those with malignant nodules. It seems to be a consensus that malignant nodules have more blood veins and more blood circulation. This may be related to the maintenance of the nodule’s heat at a higher level compared with neighboring tissues. If the internal heat modifies the skin radiation, then it could be detected by infrared sensors. The goal of this work is the investigation of the factors that allow this detection, and the possible relation with any pattern referent to nodule malignancy. We aim to consider a wide range of factors, so a great number of numerical simulations of the heat transfer in the region under analysis, based on the Finite Element method, are performed to study the influence of each nodule and patient characteristics on the infrared sensor acquisition. To do so, the protocol for infrared thyroid examination used in our university’s hospital is simulated in the numerical study. This protocol presents two phases. In the first one, the body under observation is in steady state. In the second one, it is submitted to thermal stress (transient state). Both are simulated in order to verify if it is possible (by infrared sensors) to identify different behavior referent to malignant nodules. Moreover, when the simulation indicates possible important aspects, patients with and without similar characteristics are examined to confirm such influences. The results show that the tissues between skin and thyroid, as well as the nodule size, have an influence on superficial temperatures. Other thermal parameters of thyroid nodules show little influence on surface infrared emissions, for instance, those related to the vascularization of the nodule. All details of the physical parameters used in the simulations, characteristics of the real nodules and thermal examinations are publicly available, allowing these simulations to be compared with other types of heat transfer solutions and infrared examination protocols. Among the main contributions of this work, we highlight the simulation of the possible range of parameters, and definition of the simulation approach for mapping the used infrared protocol, promoting the investigation of a possible relation between the heat transfer process and the data obtained by infrared acquisitions.

2D and 3D CFD Investigations of Seabed Shear Stresses Around Subsea Pipelines

2013 ◽  
Author(s):  
Wenwen Shen ◽  
Terry Griffiths ◽  
Mengmeng Xu ◽  
Jeremy Leggoe
Keyword(s):  
Sediment Transport ◽  
Suspended Sediment ◽  
Interaction Model ◽  
Suspended Sediment Transport ◽  
Shear Stresses ◽  
Parametric Function ◽  
Ample Evidence ◽  
North West ◽  
Wide Range ◽  
Subsea Pipelines

For well over a decade it has been widely recognised that existing models and tools for subsea pipeline stability design fail to account for the fact that seabed soils tend to become mobile well before the onset of pipeline instability. Despite ample evidence obtained from both laboratory and field observations that sediment mobility has a key role to play in understanding pipeline/soil interaction, no models have been presented previously which account for the tripartite interaction between the fluid and the pipe, the fluid and the soil, and the pipe and the soil. There are numerous well developed and widely used theories available to model pipe-fluid and pipe-soil interactions. A challenge lies in the way to develop a satisfactory fluid-soil interaction algorithm that has the potential for broad implementation under both ambient and extreme sea conditions due to the complexity of flow in the vicinity of a seabed pipeline or cable. A widely used relationship by Shields [1] links the bedload and suspended sediment transport to the seabed shear stresses. This paper presents details of computational fluid dynamics (CFD) research which has been undertaken to investigate the variation of seabed shear stresses around subsea pipelines as a parametric function of pipeline spanning/embedment, trench configuration and wave/current properties using the commercial RANS-based software ANSYS Fluent. The modelling work has been undertaken for a wide range of seabed geometries, including cases in 3D to evaluate the effects of finite span length, span depth and flow attack angle on shear stresses. These seabed shear stresses have been analysed and used as the basis for predicting sediment transport within the Pipe-Soil-Fluid (PSF) Interaction Model [2] in determining the suspended sediment concentration and the advection velocity in the vicinity of pipelines. The model has significant potential to be of use to operators who struggle with conventional stabilisation techniques for the pipelines, such as those which cross Australia’s North West Shelf, where shallow water depths, highly variable calcareous soils and extreme metocean conditions driven by frequent tropical cyclones result in the requirement for expensive and logistically challenging secondary stabilisation measures.

scholarly journals In Vitro Synergistic Interactions of Isavuconazole and Echinocandins against Candida auris

Antibiotics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 355
Author(s):  
Unai Caballero ◽  
Sarah Kim ◽  
Elena Eraso ◽  
Guillermo Quindós ◽  
Valvanera Vozmediano ◽  
...  
Keyword(s):  
Interaction Model ◽  
Antifungal Drugs ◽  
Health Concern ◽  
Candida Auris ◽  
Fungistatic Activity ◽  
Drug Concentrations ◽  
Low Concentrations ◽  
Wide Range ◽  
Time Kill

Candida auris is an emergent fungal pathogen that causes severe infectious outbreaks globally. The public health concern when dealing with this pathogen is mainly due to reduced susceptibility to current antifungal drugs. A valuable alternative to overcome this problem is to investigate the efficacy of combination therapy. The aim of this study was to determine the in vitro interactions of isavuconazole with echinocandins against C. auris. Interactions were determined using a checkerboard method, and absorbance data were analyzed with different approaches: the fractional inhibitory concentration index (FICI), Greco universal response surface approach, and Bliss interaction model. All models were in accordance and showed that combinations of isavuconazole with echinocandins resulted in an overall synergistic interaction. A wide range of concentrations within the therapeutic range were selected to perform time-kill curves. These confirmed that isavuconazole–echinocandin combinations were more effective than monotherapy regimens. Synergism and fungistatic activity were achieved with combinations that included isavuconazole in low concentrations (≥0.125 mg/L) and ≥1 mg/L of echinocandin. Time-kill curves revealed that once synergy was achieved, combinations of higher drug concentrations did not improve the antifungal activity. This work launches promising results regarding the combination of isavuconazole with echinocandins for the treatment of C. auris infections.

scholarly journals Modelling H2 and its effects on star formation using a joint implementation of gadget-3 and KROME

2021 ◽  
Vol 504 (2) ◽  
pp. 2325-2345
Author(s):  
Emanuel Sillero ◽  
Patricia B Tissera ◽  
Diego G Lambas ◽  
Stefano Bovino ◽  
Dominik R Schleicher ◽  
...  
Keyword(s):  
Star Formation ◽  
Formation Rate ◽  
Physical Parameters ◽  
Density Profiles ◽  
Star Forming ◽  
Chemical Enrichment ◽  
Numerical Resolution ◽  
Stellar Radiation ◽  
Wide Range ◽  
The Impact

ABSTRACT We present p-gadget3-k, an updated version of gadget-3, that incorporates the chemistry package krome. p-gadget3-k follows the hydrodynamical and chemical evolution of cosmic structures, incorporating the chemistry and cooling of H2 and metal cooling in non-equilibrium. We performed different runs of the same ICs to assess the impact of various physical parameters and prescriptions, namely gas metallicity, molecular hydrogen formation on dust, star formation recipes including or not H2 dependence, and the effects of numerical resolution. We find that the characteristics of the simulated systems, both globally and at kpc-scales, are in good agreement with several observable properties of molecular gas in star-forming galaxies. The surface density profiles of star formation rate (SFR) and H2 are found to vary with the clumping factor and resolution. In agreement with previous results, the chemical enrichment of the gas component is found to be a key ingredient to model the formation and distribution of H2 as a function of gas density and temperature. A star formation algorithm that takes into account the H2 fraction together with a treatment for the local stellar radiation field improves the agreement with observed H2 abundances over a wide range of gas densities and with the molecular Kennicutt–Schmidt law, implying a more realistic modelling of the star formation process.

Calibration of Mobility and Interface Trap Parameters for High Temperature TCAD Simulation of 4H-SiC VDMOSFETs

2012 ◽  
Vol 717-720 ◽  
pp. 1101-1104 ◽  
Author(s):  
M.G. Jaikumar ◽  
Shreepad Karmalkar
Keyword(s):  
Surface Roughness ◽  
Measured Data ◽  
Interface Trap Density ◽  
Interface Trap ◽  
Material Parameters ◽  
Velocity Saturation ◽  
Physical Mechanisms ◽  
Surface Roughness Scattering ◽  
Wide Range ◽  
Tcad Simulation

4H-Silicon Carbide VDMOSFET is simulated using the Sentaurus TCAD package of Synopsys. The simulator is calibrated against measured data for a wide range of bias conditions and temperature. Material parameters of 4H-SiC are taken from literature and used in the available silicon models of the simulator. The empirical parameters are adjusted to get a good fit between the simulated curves and measured data. The simulation incorporates the bias and temperature dependence of important physical mechanisms like interface trap density, coulombic interface trap scattering, surface roughness scattering and velocity saturation.

scholarly journals Multiwavelength Modeling the SED of Strongly Interacting Binaries

2011 ◽  
Vol 7 (S282) ◽  
pp. 65-66
Author(s):  
Augustin Skopal
Keyword(s):  
Physical Parameters ◽  
X Rays ◽  
Physical Processes ◽  
Short Contribution ◽  
Composite Spectrum ◽  
X Ray ◽  
Strongly Interacting ◽  
Low Mass ◽  
X Ray Binaries

AbstractThe spectrum of strongly interacting binaries, as for example, high and low mass X-ray binaries, symbiotic (X-ray) binaries and/or classical and recurrent novae, consists of more components of radiation contributing from hard X-rays to radio wavelengths. To understand the basic physical processes responsible for the observed spectrum we have to disentangle the composite spectrum into its individual components, i.e. to determine their physical parameters. In this short contribution I demonstrate the method of modeling the multiwavelength SED on the example of the extragalactic super-soft X-ray source RX J0059.1-7505 (LIN 358).

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

Uncertainty Estimation for Magnetic Maps

2021 ◽  
Author(s):  
Richard Saltus ◽  
Arnaud Chulliat ◽  
Brian Meyer ◽  
Christopher Amante
Keyword(s):  
Local Level ◽  
Uncertainty Estimation ◽  
Measured Data ◽  
Magnetic Minerals ◽  
Geochemical Environment ◽  
The Earth ◽  
Uncertainty Estimates ◽  
Wide Range ◽  
Machine Learning Applications ◽  
Map Data

<p>Magnetic maps depict spatial variations in the Earth’s magnetic field.  These variations occur at a wide range of scales and are produced via a variety of physical processes related to factors including structure and evolution of the Earth’s core field and the geologic distribution of magnetic minerals in the lithosphere.  Mankind has produced magnetic maps for 100’s of years with increasing fidelity and accuracy and there is a general understanding (particularly among the geophysicists who produce and use these maps) of the approximate level of resolution and accuracy of these maps.  However, few magnetic maps, or the digital grids that typically underpin these maps, have been produced with accompanying uncertainty quantification.  When uncertainty is addressed, it is typically a statistical representation at the grid or survey level (e.g., +- 10 nT overall uncertainty based on line crossings for a modern airborne survey) and not at the cell by cell local level.</p><p>As magnetic map data are increasingly used in complex inversions and in combination with other data or constraints (including in machine learning applications), it is increasingly important to have a handle on the uncertainties in these data.  An example of an application with need for detailed uncertainty estimation is the use of magnetic map information for alternative navigation.  In this application data from an onboard magnetometer is compared with previously mapped (or modeled) magnetic variations.  The uncertainty of this previously mapped information has immediate implications for the potential accuracy of navigation.</p><p>We are exploring the factors contributing to magnetic map uncertainty and producing uncertainty estimates for testing using new data collection in previously mapped (or modeled) map areas.  These factors include (but are likely not limited to) vintage and type of measured data, spatial distribution of measured data, expectation of magnetic variability (e.g., geologic or geochemical environment), statistics of redundant measurement, and spatial scale/resolution of the magnetic map or model.  The purpose of this talk is to discuss the overall issue and our initial results and solicit feedback and ideas from the interpretation community.</p>

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

2021 ◽  
Author(s):  
Tim Brandes ◽  
Stefano Scarso ◽  
Christian Koch ◽  
Stephan Staudacher
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Principal Component ◽  
General Term ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Model ◽  
Precision Error ◽  
Operational Conditions ◽  
Wide Range

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

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