Mesoscale Numerical Simulation of Cirrus Clouds—FIRE Case Study and Sensitivity Analysis

1993 ◽  
Vol 121 (8) ◽  
pp. 2264-2284 ◽  
Author(s):  
Scot T. Heckman ◽  
William R. Cotton
Keyword(s):  
Numerical Simulation ◽  
Sensitivity Analysis ◽  
Cirrus Clouds

scholarly journals ANALISIS PENGARUH NILAI KONDUKTIVITAS HIDRAULIK DAN DISPERSIVITAS DINAMIK TERHADAP REMEDIASI AIR TANAH MENGGUNAKAN SIMULASI NUMERIK

2021 ◽  
Vol 12 (2) ◽  
pp. 107-118
Author(s):  
Agus Mochamad Ramdhan ◽  
Arifin Arifin ◽  
Erik Hermawan ◽  
Lambok M. Hutasoit
Keyword(s):  
Numerical Simulation ◽  
Sensitivity Analysis ◽  
Hydraulic Conductivity ◽  
Groundwater Remediation ◽  
Flow Simulation ◽  
Pumping Wells ◽  
Time Required ◽  
Close Distance ◽  
High Hydraulic Conductivity

Groundwater remediation is one of the solutions to restore the contaminated groundwater. This study was conducted to determine the effect of hydraulic conductivity and dynamic dispersivity on the groundwater remediation effectiveness. As a case study, in 2020, in an area located in Balikpapan, groundwater remediation will be carried out by injecting water containing NaOH through five wells and pumping it back through five wells to form a cycle. The method used is a numerical simulation consisting of groundwater flow simulation, mass transport, and sensitivity analysis. The results show that it takes 124 to 300 days for the injected NaOH to arrive at the pumping wells. The sensitivity analysis results show that when the hydraulic conductivity value is ten times greater, the time required is reduced to 84 to 172 days. Meanwhile, when the dynamic dispersivity is twice larger, the time required is reduced to 75 to 189 days. These results indicate that the groundwater remediation method will be effective for aquifers with high hydraulic conductivity and dynamic dispersivity values. For the study area, the groundwater remediation is suggested to be carried out by increasing the number of injection and pumping wells with a relatively close distance, i.e., around 10 meters, so that NaOH arrives at the pumping wells more quickly.Keywords: groundwater, remediation, hydraulic conductivity, dynamic dispersivity, numerical simulation

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

scholarly journals Transient Analysis of Heat Transfer Across the Residential Building Roof with Pcm and Wood Wool- A Case Study by Numerical Simulation Approach

2013 ◽  
Vol 59 (4) ◽  
pp. 483-497 ◽  
Author(s):  
D. Prakash ◽  
P. Ravikumar
Keyword(s):  
Heat Transfer ◽  
Numerical Simulation ◽  
Phase Change ◽  
Phase Change Material ◽  
Transient Analysis ◽  
Residential Building ◽  
Building Roof ◽  
Type 3 ◽  
Change Material

Abstract In this paper, transient analysis on heat transfer across the residential building roof having various materials like wood wool, phase change material and weathering tile is performed by numerical simulation technique. 2-dimensional roof model is created, checked for grid independency and validated with the experimental results. Three different roof structures are included in this study namely roof with (i). Concrete and weathering tile, (ii). Concrete, phase change material and weathering tile and (iii). Concrete, phase change material, wood wool and weathering tile. Roof type 3 restricts 13% of heat entering the room in comparison with roof having only concrete and weathering tile. Also the effect of various roof layers’ thickness in the roof type 3 is investigated and identified that the wood wool plays the major role in arresting the entry of heat in to the room. The average reduction of heat is about 10 % for an increase of a unit thickness of wood wool layer.

scholarly journals Sensitivity Analysis of Fuzzy Activity-Based Life Cycle Costing

2018 ◽  
Vol 225 ◽  
pp. 05002
Author(s):  
Freselam Mulubrhan ◽  
Ainul Akmar Mokhtar ◽  
Masdi Muhammad
Keyword(s):  
Sensitivity Analysis ◽  
Life Cycle ◽  
Interest Rate ◽  
Life Cycle Costing ◽  
High Impact ◽  
Final Output ◽  
Left And Right ◽  
The Interest Rate ◽  
Operation Cost

A sensitivity analysis is typically conducted to identify how sensitive the output is to changes in the input. In this paper, the use of sensitivity analysis in the fuzzy activity based life cycle costing (LCC) is shown. LCC is the most frequently used economic model for decision making that considers all costs in the life of a system or equipment. The sensitivity analysis is done by varying the interest rate and time 15% and 45%, respectively, to the left and right, and varying 25% of the maintenance and operation cost. It is found that the operation cost and the interest rate give a high impact on the final output of the LCC. A case study of pumps is used in this study.

scholarly journals Procedure for the Selection of Rubber Compound in Rubber-Metal Springs for Vibration Isolation

Polymers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1737
Author(s):  
Milan Banić ◽  
Dušan Stamenković ◽  
Aleksandar Miltenović ◽  
Dragan Jovanović ◽  
Milan Tica
Keyword(s):  
Numerical Simulation ◽  
Constitutive Model ◽  
Vibration Isolation ◽  
Correct Selection ◽  
Virtual Experiment ◽  
Rubber Compound ◽  
Key Factor ◽  
Railway Engineering ◽  
Selection Of

The selection of a rubber compound has a determining influence on the final characteristics of rubber-metal springs. Therefore, the correct selection of a rubber compound is a key factor for development of rubber-metal vibration isolation springs with required characteristics. The procedure for the selection of the rubber compound for vibration isolation of rubber-metal springs has been proposed, so that the rubber-metal elements have the necessary characteristics, especially in terms of deflection. The procedure is based on numerical simulation of spring deflection with Bergström-Boyce constitutive model in virtual experiment, with a goal to determine which parameters of the constitutive model will lead to spring required deflection. The procedure was verified by case study defined to select rubber compound for a rubber–metal spring used in railway engineering.

Improving Casthouse Throughput Using Discrete-Event Modelling

2011 ◽  
Vol 693 ◽  
pp. 3-9 ◽  
Author(s):  
Bruce Gunn ◽  
Yakov Frayman
Keyword(s):  
Sensitivity Analysis ◽  
Discrete Event ◽  
Casting Machine ◽  
Production Data ◽  
Model Output ◽  
Arrival Times ◽  
Event Simulation ◽  
Modelling System ◽  
Key Parameter

The scheduling of metal to different casters in a casthouse is a complicated problem, attempting to find the balance between pot-line, crucible carrier, furnace and casting machine capacity. In this paper, a description will be given of a casthouse modelling system designed to test different scenarios for casthouse design and operation. Using discrete-event simulation, the casthouse model incorporates variable arrival times of metal carriers, crucible movements, caster operation and furnace conditions. Each part of the system is individually modelled and synchronised using a series of signals or semaphores. In addition, an easy to operate user interface allows for the modification of key parameters, and analysis of model output. Results from the model will be presented for a case study, which highlights the effect different parameters have on overall casthouse performance. The case study uses past production data from a casthouse to validate the model outputs, with the aim to perform a sensitivity analysis on the overall system. Along with metal preparation times and caster strip-down/setup, the temperature evolution within the furnaces is one key parameter in determining casthouse performance.

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