Develop a Better Simulation Strategy for Army Aviation

2000 ◽  
Author(s):  
Gregory A. Adams
Keyword(s):  
Simulation Strategy

Implementation of tunable resonators in planar groove gap waveguide technology

2021 ◽  
pp. 1-7
Author(s):  
Titus Oyedokun ◽  
Riana H. Geschke ◽  
Tinus Stander
Keyword(s):  
Printed Circuit Board ◽  
Tuning Range ◽  
Frequency Tuning ◽  
Resonant Cavity ◽  
Circuit Board ◽  
Resonant Cavities ◽  
Tunable Resonators ◽  
Simulation Strategy ◽  
Dc Bias ◽  
Continuous Frequency

Abstract We present a tunable planar groove gap waveguide (PGGWG) resonant cavity at Ka-band. The cavity demonstrates varactor loading and biasing without bridging wires or annular rings, as commonly is required in conventional substrate-integrated waveguide (SIW) resonant cavities. A detailed co-simulation strategy is also presented, with indicative parametric tuning data. Measured results indicate a 4.48% continuous frequency tuning range of 32.52–33.98 GHz and a Qu tuning range of 63–85, corresponding to the DC bias voltages of 0–16 V. Discrepancies between simulated and measured results are analyzed, and traced to process variation in the multi-layer printed circuit board stack, as well as unaccounted varactor parasitics and surface roughness.

System Level Dynamic Modeling Framework Being Developed at Clemson University’s Wind Turbine Drivetrain Testing Facility

2013 ◽  
Author(s):  
Ryan Schkoda ◽  
Konstantin Bulgakov ◽  
Kalyan Chakravarthy Addepalli ◽  
Imtiaz Haque
Keyword(s):  
Wind Turbine ◽  
Dynamic Modeling ◽  
Preliminary Analysis ◽  
Collaborative Work ◽  
Model Development ◽  
System Level ◽  
Modeling Framework ◽  
Testing Facility ◽  
Simulation Strategy ◽  
North Charleston

This paper describes the system level, dynamic modeling and simulation strategy being developed at the Wind Turbine Drivetrain Testing Facility (WTDTF) at Clemson University’s Restoration Institute in North Charleston, SC, USA. An extensible framework that allows various workflows has been constructed and used to conduct preliminary analysis of one of the facility’s test benches. The framework dictates that component and subsystem models be developed according to a list of identified needs and modeled in software best suited for the particular task. Models are then integrated according to the desired execution target. This approach allows for compartmentalized model development which is well suited for collaborative work. The framework has been applied to one of the test benches and has allowed researches to begin characterizing its behavior in the time and frequency domain.

A Fully Integrated Approach to Component Zooming Using Computational Fluid Dynamics

2004 ◽  
Vol 128 (3) ◽  
pp. 579-584 ◽  
Author(s):  
Vassilios Pachidis ◽  
Pericles Pilidis ◽  
Fabien Talhouarn ◽  
Anestis Kalfas ◽  
Ioannis Templalexis
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Gas Turbine ◽  
Engine Performance ◽  
Integrated Approach ◽  
High Fidelity ◽  
Performance Simulation ◽  
Fully Integrated ◽  
Engine Component ◽  
Simulation Strategy

Background . This study focuses on a simulation strategy that will allow the performance characteristics of an isolated gas turbine engine component, resolved from a detailed, high-fidelity analysis, to be transferred to an engine system analysis carried out at a lower level of resolution. This work will enable component-level, complex physical processes to be captured and analyzed in the context of the whole engine performance, at an affordable computing resource and time. Approach. The technique described in this paper utilizes an object-oriented, zero-dimensional (0D) gas turbine modeling and performance simulation system and a high-fidelity, three-dimensional (3D) computational fluid dynamics (CFD) component model. The work investigates relative changes in the simulated engine performance after coupling the 3D CFD component to the 0D engine analysis system. For the purposes of this preliminary investigation, the high-fidelity component communicates with the lower fidelity cycle via an iterative, semi-manual process for the determination of the correct operating point. This technique has the potential to become fully automated, can be applied to all engine components, and does not involve the generation of a component characteristic map. Results. This paper demonstrates the potentials of the “fully integrated” approach to component zooming by using a 3D CFD intake model of a high bypass ratio turbofan as a case study. The CFD model is based on the geometry of the intake of the CFM56-5B2 engine. The high-fidelity model can fully define the characteristic of the intake at several operating condition and is subsequently used in the 0D cycle analysis to provide a more accurate, physics-based estimate of intake performance (i.e., pressure recovery) and hence, engine performance, replacing the default, empirical values. A detailed comparison between the baseline engine performance (empirical pressure recovery) and the engine performance obtained after using the coupled, high-fidelity component is presented in this paper. The analysis carried out by this study demonstrates relative changes in the simulated engine performance larger than 1%. Conclusions. This investigation proves the value of the simulation strategy followed in this paper and completely justifies (i) the extra computational effort required for a more automatic link between the high-fidelity component and the 0D cycle, and (ii) the extra time and effort that is usually required to create and run a 3D CFD engine component, especially in those cases where more accurate, high-fidelity engine performance simulation is required.

scholarly journals Accurate Stochastic Simulation Methods for Homogeneous Biochemical Networks

2021 ◽  
Author(s):  
Farida Ansari
Keyword(s):  
Master Equation ◽  
Stochastic Simulation ◽  
Growth Factor Receptor ◽  
Stochastic Simulation Algorithm ◽  
Chemical Master Equation ◽  
Biochemical Networks ◽  
Random Time Change ◽  
Simulation Strategy ◽  
Epidermal Growth ◽  
Exact Stochastic Simulation

Stochastic models of intracellular processes are subject of intense research today. For homogeneous systems, these models are based on the Chemical Master Equation, which is a discrete stochastic model. The Chemical Master Equation is often solved numerically using Gillespie’s exact stochastic simulation algorithm. This thesis studies the performance of another exact stochastic simulation strategy, which is based on the Random Time Change representation, and is more efficient for sensitivity analysis, compared to Gillespie’s algorithm. This method is tested on several models of biological interest, including an epidermal growth factor receptor model.

scholarly journals Opportunities and limits of combining microbiome and genome data for complex trait prediction

2020 ◽  
Author(s):  
Miguel Pérez-Enciso ◽  
Laura M. Zingaretti ◽  
Yuliaxis Ramayo-Caldas ◽  
Gustavo de los Campos
Keyword(s):  
Complex Traits ◽  
Variance Components ◽  
Complex Trait ◽  
Parameter Estimates ◽  
Phenotypic Variance ◽  
Genome Data ◽  
Individual Snps ◽  
Simulation Strategy ◽  
Microbiome Data ◽  
Trait Prediction

AbstractThe analysis and prediction of complex traits using microbiome data combined with host genomic information is a topic of utmost interest. However, numerous questions remain to be answered: How useful can the microbiome be for complex trait prediction? Are microbiability estimates reliable? Can the underlying biological links between the host’s genome, microbiome, and the phenome be recovered? Here, we address these issues by (i) developing a novel simulation strategy that uses real microbiome and genotype data as input, and (ii) proposing a variance-component approach which, in the spirit of mediation analyses, quantifies the proportion of phenotypic variance explained by genome and microbiome, and dissects it into direct and indirect effects. The proposed simulation approach can mimic a genetic link between the microbiome and SNP data via a permutation procedure that retains the distributional properties of the data. Results suggest that microbiome data could significantly improve phenotype prediction accuracy, irrespective of whether some abundances are under direct genetic control by the host or not. Overall, random-effects linear methods appear robust for variance components estimation, despite the highly leptokurtic distribution of microbiota abundances. Nevertheless, we observed that accuracy depends in part on the number of microorganisms’ taxa influencing the trait of interest. While we conclude that overall genome-microbiome-links can be characterized via variance components, we are less optimistic about the possibility of identifying the causative effects, i.e., individual SNPs affecting abundances; power at this level would require much larger sample sizes than the ones typically available for genome-microbiome-phenome data.Author summaryThe microbiome consists of the microorganisms that live in a particular environment, including those in our organism. There is consistent evidence that these communities play an important role in numerous traits of relevance, including disease susceptibility or feed efficiency. Moreover, it has been shown that the microbiome can be relatively stable throughout an individual’s life and that is affected by the host genome. These reasons have prompted numerous studies to determine whether and how the microbiome can be used for prediction of complex phenotypes, either using microbiome alone or in combination with host’s genome data. However, numerous questions remain to be answered such as the reliability of parameter estimates, or which is the underlying relationship between microbiome, genome, and phenotype. The few available empirical studies do not provide a clear answer to these problems. Here we address these issues by developing a novel simulation strategy and we show that, although the microbiome can significantly help in prediction, it will be difficult to retrieve the actual biological basis of interactions between the microbiome and the trait.

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