scholarly journals Cavitation Model Calibration Using Machine Learning Assisted Workflow

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2107
Author(s):  
Ante Sikirica ◽  
Zoran Čarija ◽  
Ivana Lučin ◽  
Luka Grbčić ◽  
Lado Kranjčević
Keyword(s):  
Machine Learning ◽  
Parameter Estimation ◽  
Model Calibration ◽  
Three Dimensional ◽  
Test Case ◽  
Assessment Methodology ◽  
Cavitation Model ◽  
Universal Solution ◽  
Thrust And Torque ◽  
Empirical Constants

Conventional cavitation assessment methodology in industrial and scientific applications generally depends on cavitation models utilizing homogeneous mixture assumption. These models have been extensively assessed, modified and expanded to account for deficiencies of their predecessors. Unfortunately, none of the proposed models can be classified as the universal solution for all engineering applications, with usage mainly directed by experience or general availability of the models. In this study we propose a workflow through which the empirical constants governing the phase change of the Kunz mixture cavitation model can be calibrated for a given application or a series of problems, with machine learning as a tool for parameter estimation. The proposed approach was validated on a three-dimensional propeller test case with results in excellent agreement for the case in question. Results for thrust and torque were within 2% with cavity extents differing by up to 20%. This is a significant improvement when compared to previously proposed parameters. Despite the lack of generalization due to the limited nature of the dataset on which the model was trained, the proposed parameters entail acceptable results for similar cases as well. The overall methodology is applicable to other problems as well and should lead to more accurate cavitation predictions.

Towards opensource LOD2 modelling of urban spaces using an optimised machine learning and rules-based approach.

2020 ◽  
Author(s):  
Tom Rowan ◽  
Adrian Butler
Keyword(s):  
Water Management ◽  
Machine Learning ◽  
Water Conservation ◽  
Three Dimensional ◽  
Flood Management ◽  
Local Scale ◽  
Small Scale ◽  
Test Case ◽  
Local Planning ◽  
Scale Modelling

<p><span>In order to enable community groups and other interested parties to evaluate the effects of flood management, water conservation and other hydrological issues, better localised mapping is required.  Although some maps are publicly available many are behind paywalls, especially those with three dimensional features.  In this study London is used as a test case to evaluate, machine learning and rules-based approaches with opensource maps and LiDAR data to create more accurate representations (LOD2) of small-scale areas.  Machine learning is particularly well suited to the recognition of local repetitive features like building roofs and trees, while roads can be identified and mapped best using a faster rules-based approach. </span></p><p><span>In order to create a useful LOD2 representation, a user interface, processing rules manipulation and assumption editor have all been incorporated. Features like randomly assigning sub terrain features (basements) - using Monte-Carlo methods - and artificial sewage representation enable the user to grow these models from opensource data into useful model inputs. This project is aimed at local scale hydrological modelling, rainfall runoff analysis and other local planning applications. </span></p><p><span> </span></p><p><span>The goal is to provide turn-key data processing for small scale modelling, which should help advance the installation of SuDs and other water management solutions, as well as having broader uses. The method is designed to enable fast and accurate representations of small-scale features (1 hectare to 1km<sup>2</sup>), with larger scale applications planned for future work.  This work forms part of the CAMELLIA project (Community Water Management for a Liveable London) and aims to provide useful tools for local scale modeller and possibly the larger scale industry/scientific user. </span></p>

Interactional Aerodynamic Analysis of an Asymmetric Lift-Offset Compound Helicopter in Forward Flight

2021 ◽  
Author(s):  
Jan-Arun Faust ◽  
Yong Su Jung ◽  
James Baeder ◽  
André Bauknecht ◽  
Jürgen Rauleder
Keyword(s):  
High Speed ◽  
Stokes Equations ◽  
Reverse Flow ◽  
Three Dimensional ◽  
Particle Image Velocimetry Data ◽  
Operating Conditions ◽  
Test Case ◽  
Forward Flight ◽  
Compound Helicopter ◽  
Thrust And Torque

Recently, an asymmetric lift-offset compound helicopter has been conceptualized at the University of Maryland with the objective of improving the overall performance of a medium-lift utility helicopter. The investigated form of lift-compounding incorporates an additional stubbed wing attached to the fuselage on the retreating side. This design alleviates rotor lift requirements and generates a roll moment that enables increased thrust potential on the advancing side in high-speed forward flight. In this study, a numerical model was developed based on the corresponding experimental test case. Three-dimensional unsteady Reynolds-averaged Navier–Stokes equations were solved on overset grids with computational fluid dynamics–computational structural dynamics (CFD–CSD) coupling using the in-house CPU–GPU heterogeneous Mercury CFD framework. Simulations were performed at high-speed, high-thrust operating conditions and showed satisfactory agreement with the experimental measurements in terms of the cyclic control angles, rotor thrust, and torque values. CFD results indicated that for an advance ratio of 0.5 with a collective pitch of 10.6°, a vehicle lift-to-equivalent-drag ratio improvement of 47% was attainable using 11% wing-lift offset. The CFD-computed flow fields provide insights into the origin of a reverse flow entry vortex that was observed in particle image velocimetry data, and they characterize the wing–rotor interactional aerodynamics.

scholarly journals Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment

2021 ◽  
Vol 11 (13) ◽  
pp. 5956
Author(s):  
Elena Parra ◽  
Irene Alice Chicchi Giglioli ◽  
Jestine Philip ◽  
Lucia Amalia Carrasco-Ribelles ◽  
Javier Marín-Morales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Ecological Validity ◽  
Objective Assessment ◽  
Leadership Skills ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Promising Tool ◽  
Future Research Agenda

In this article, we introduce three-dimensional Serious Games (3DSGs) under an evidence-centered design (ECD) framework and use an organizational neuroscience-based eye-tracking measure to capture implicit behavioral signals associated with leadership skills. While ECD is a well-established framework used in the design and development of assessments, it has rarely been utilized in organizational research. The study proposes a novel 3DSG combined with organizational neuroscience methods as a promising tool to assess and recognize leadership-related behavioral patterns that manifest during complex and realistic social situations. We offer a research protocol for assessing task- and relationship-oriented leadership skills that uses ECD, eye-tracking measures, and machine learning. Seamlessly embedding biological measures into 3DSGs enables objective assessment methods that are based on machine learning techniques to achieve high ecological validity. We conclude by describing a future research agenda for the combined use of 3DSGs and organizational neuroscience methods for leadership and human resources.

scholarly journals Virtual Archaeology of Death and Burial: A Procedure for Integrating 3D Visualization and Analysis in Archaeothanatology

2021 ◽  
Vol 7 (1) ◽  
pp. 540-555
Author(s):  
Hayley L. Mickleburgh ◽  
Liv Nilsson Stutz ◽  
Harry Fokkens
Keyword(s):  
Spatial Information ◽  
3D Visualization ◽  
Rapid Development ◽  
Three Dimensional ◽  
3D Models ◽  
The Body ◽  
Test Case ◽  
Archaeology Of Death ◽  
Human Burials ◽  
3D Information

Abstract The reconstruction of past mortuary rituals and practices increasingly incorporates analysis of the taphonomic history of the grave and buried body, using the framework provided by archaeothanatology. Archaeothanatological analysis relies on interpretation of the three-dimensional (3D) relationship of bones within the grave and traditionally depends on elaborate written descriptions and two-dimensional (2D) images of the remains during excavation to capture this spatial information. With the rapid development of inexpensive 3D tools, digital replicas (3D models) are now commonly available to preserve 3D information on human burials during excavation. A procedure developed using a test case to enhance archaeothanatological analysis and improve post-excavation analysis of human burials is described. Beyond preservation of static spatial information, 3D visualization techniques can be used in archaeothanatology to reconstruct the spatial displacement of bones over time, from deposition of the body to excavation of the skeletonized remains. The purpose of the procedure is to produce 3D simulations to visualize and test archaeothanatological hypotheses, thereby augmenting traditional archaeothanatological analysis. We illustrate our approach with the reconstruction of mortuary practices and burial taphonomy of a Bell Beaker burial from the site of Oostwoud-Tuithoorn, West-Frisia, the Netherlands. This case study was selected as the test case because of its relatively complete context information. The test case shows the potential for application of the procedure to older 2D field documentation, even when the amount and detail of documentation is less than ideal.

scholarly journals Three-dimensional modelling using spatial regression machine learning and hydrogeological basement VES

2021 ◽  
Vol 156 ◽  
pp. 104907
Author(s):  
Gastón M. Mendoza Veirana ◽  
Santiago Perdomo ◽  
Jerónimo Ainchil
Keyword(s):  
Machine Learning ◽  
Spatial Regression ◽  
Three Dimensional

scholarly journals Assessing biases, relaxing moralism: On ground-truthing practices in machine learning design and application

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110135
Author(s):  
Florian Jaton
Keyword(s):  
Machine Learning ◽  
William James ◽  
A Priori ◽  
Learning Algorithms ◽  
Three Dimensional ◽  
Ground Truth ◽  
Machine Learning Algorithms ◽  
Ground Truthing ◽  
Set Up ◽  
The Moment

This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here defined as the more or less accounted experience of hesitation when faced with what pragmatist philosopher William James called “genuine options”—that is, choices to be made in the heat of the moment that engage different possible futures. I then stress three constitutive dimensions of this pragmatist morality, as far as ground-truthing practices are concerned: (I) the definition of the problem to be solved (problematization), (II) the identification of the data to be collected and set up (databasing), and (III) the qualification of the targets to be learned (labeling). I finally suggest that this three-dimensional conceptual space can be used to map machine learning algorithmic projects in terms of the morality of their respective and constitutive ground-truthing practices. Such techno-moral graphs may, in turn, serve as equipment for greater governance of machine learning algorithms and systems.

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