Appendix 9: Thermal, quantum and numerical noise

2011 ◽  
pp. 372-385
Keyword(s):  
Numerical Noise

Numerical noise-stability modeling of ukrainian regional satellite communucation networks

1998 ◽  
Vol 4 (1) ◽  
pp. 92-101
Author(s):  
A.S. Mazmanishvili ◽  
◽  
O.Ya. Rafalovich ◽  
◽  
Keyword(s):  
Noise Stability ◽  
Numerical Noise ◽  
Stability Modeling

An Implicit to Explicit FEA Solving of Tire F&M with Detailed Tread Blocks

2012 ◽  
Vol 40 (2) ◽  
pp. 83-107 ◽  
Author(s):  
Zhao Li ◽  
Ziran R. Li ◽  
Yuanming M. Xia
Keyword(s):  
Test System ◽  
Mapping Method ◽  
Friction Model ◽  
Experimental Results ◽  
Slip Angle ◽  
Slip Ratio ◽  
The Road ◽  
Numerical Noise ◽  
Nonlinear Stress ◽  
Solving Strategy

ABSTRACT A detailed tire-rolling model (185/75R14), using the implicit to explicit FEA solving strategy, was constructed to provide a reliable, dynamic simulation with several modeling features, including mesh, material modeling, and a solving strategy that could contribute to the consideration of the serious numerical noises. High-quality hexahedral meshes of tread blocks were obtained with a combined mapping method. The actual rubber distributing and nonlinear, stress-strain relationship of the rubber and bilinear elastic reinforcement were modeled for realism. In addition, a tread-rubber friction model obtained from the Laboratory Abrasion and Skid Tester (LAT 100) was applied to simulate the interaction of the tire with the road. The force and moment (F&) behaviors of tire cornering when subjected to a slip-angle sweep of −10 to 10° were studied with that model. To demonstrate the efficiency of the proposed simulation, the computed F&M were compared with experimental results from an MTS Flat-Trac Tire Test System. The computed cornering F&M agreed well with the experimental results, so the footprint shape and contact pressure distribution of several cornering conditions were investigated. Furthermore, the longitudinal forces in response to braking/driving torque application in a slip-ratio range of −100% to 100% were computed. The proposed FEA solution confines the numerical noise within a smaller range and can serve as a valid tool in tire design.

scholarly journals Structural Response of a Single-Stage Centrifugal Compressor to Fluid-Induced Excitations at Low-Flow Operating Condition: Experimental and Numerical Study

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4292
Author(s):  
Kirill Kabalyk ◽  
Andrzej Jaeschke ◽  
Grzegorz Liśkiewicz ◽  
Michał Kulak ◽  
Tomasz Szydłowski ◽  
...  
Keyword(s):  
Centrifugal Compressor ◽  
Numerical Study ◽  
Structural Response ◽  
Flow Coefficient ◽  
Low Flow ◽  
Dynamic Strain ◽  
Duct Acoustics ◽  
Numerical Noise ◽  
Partial Load ◽  
Compressor Impeller

The article describes an assessment of possible changes in constant fatigue life of a medium flow-coefficient centrifugal compressor impeller subject to operation at close-to-surge point. Some aspects of duct acoustics are additionally analyzed. The experimental measurements at partial load are presented and are primarily used for validation of unidirectionally coupled fluid-structural numerical model. The model is based on unsteady finite-volume fluid-flow simulations and on finite-element transient structural analysis. The validation is followed by the model implementation to replicate the industry-scale loads with reasonably higher rotational speed and suction pressure. The approach demonstrates satisfactory accuracy in prediction of stage performance and unsteady flow field in vaneless diffuser. The latter is deduced from signal analysis relying on continuous wavelet transformations. On the other hand, it is found that the aerodynamic incidence losses at close-to-surge point are underpredicted. The structural simulation generates considerable amounts of numerical noise, which has to be separated prior to evaluation of fluid-induced dynamic strain. The main source of disturbance is defined as a stationary region of static pressure drop caused by flow contraction at volute tongue and leading to first engine-order excitation in rotating frame of reference. Eventually, it is concluded that the amplitude of excitation is too low to lead to any additional fatigue.

Linear and nonlinear behaviour of two-stream instabilities in collisionless plasmas

2015 ◽  
Vol 81 (6) ◽  
Author(s):  
Y. W. Hou ◽  
M. X. Chen ◽  
M. Y. Yu ◽  
B. Wu
Keyword(s):  
Normal Mode Analysis ◽  
System Size ◽  
Point Of View ◽  
Mode Analysis ◽  
Nonlinear Behaviour ◽  
Initial Growth ◽  
Small Magnitude ◽  
Plasma Oscillations ◽  
Numerical Noise ◽  
Growth Properties

The transient, growth and nonlinear saturation stages in the evolution of the electrostatic two-stream instabilities as described by the Vlasov–Poisson system are reconsidered by numerically following the evolution of the total wave energy of the plasma oscillations excited from (numerical) noise. Except for peculiarities related to the necessarily finite (even though very small) magnitude of the perturbations in the numerical simulation, the existence and initial growth properties of the instabilities from the numerical results are found to be consistent with those from linear normal mode analysis and the Penrose criteria. However, contradictory to the traditional point of view, the growth of instability before saturation is not always linear. The initial stage of the growth can exhibit fine structures that can be attributed to the harmonics of the excited plasma oscillations, whose wavelengths are determined by the system size and the numerical noise. As expected, saturation of the unstable oscillations is due to electron trapping when they reach sufficiently large amplitudes.

scholarly journals Effect of Numerical Noise on Beam Emittance Growth in PIC Code

2001 ◽  
Author(s):  
Yuri K Batygin
Keyword(s):  
Beam Emittance ◽  
Numerical Noise ◽  
Emittance Growth ◽  
Pic Code

scholarly journals Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics

2020 ◽  
Author(s):  
Gregory Kiar ◽  
Yohan Chatelain ◽  
Ali Salari ◽  
Alan C. Evans ◽  
Tristan Glatard
Keyword(s):  
Monte Carlo ◽  
Data Augmentation ◽  
Learning Models ◽  
Reduction Techniques ◽  
Numerical Noise ◽  
Numerical Instabilities ◽  
Dimensionality Reduction Techniques ◽  
Improved Performance ◽  
Low Dimensional ◽  
Machine Learning Models

AbstractMachine learning models are commonly applied to human brain imaging datasets in an effort to associate function or structure with behaviour, health, or other individual phenotypes. Such models often rely on low-dimensional maps generated by complex processing pipelines. However, the numerical instabilities inherent to pipelines limit the fidelity of these maps and introduce computational bias. Monte Carlo Arithmetic, a technique for introducing controlled amounts of numerical noise, was used to perturb a structural connectome estimation pipeline, ultimately producing a range of plausible networks for each sample. The variability in the perturbed networks was captured in an augmented dataset, which was then used for an age classification task. We found that resampling brain networks across a series of such numerically perturbed outcomes led to improved performance in all tested classifiers, preprocessing strategies, and dimensionality reduction techniques. Importantly, we find that this benefit does not hinge on a large number of perturbations, suggesting that even minimally perturbing a dataset adds meaningful variance which can be captured in the subsequently designed models.

Optimizing the Numerical Noise of Parallel Second-Order Filters in Fixed-Point Arithmetic

2019 ◽  
Vol 67 (10) ◽  
pp. 763-771
Author(s):  
Kristóf Horváth ◽  
Balázs Bank
Keyword(s):  
Fixed Point ◽  
Second Order ◽  
Fixed Point Arithmetic ◽  
Numerical Noise ◽  
Point Arithmetic

scholarly journals (GO)<sup>2</sup>-SIM: A GCM-Oriented Ground-Observation Forward-Simulator Framework for Objective Evaluation of Cloud and Precipitation Phase

2018 ◽  
Author(s):  
Katia Lamer ◽  
Ann M. Fridlind ◽  
Andrew S. Ackerman ◽  
Pavlos Kollias ◽  
Eugene E. Clothiaux ◽  
...  
Keyword(s):  
General Circulation ◽  
Doppler Radar ◽  
Circulation Model ◽  
Objective Evaluation ◽  
Grid Cells ◽  
Mixing Ratios ◽  
The North ◽  
Numerical Noise ◽  
Phase Classification ◽  
North Slope Of Alaska

Abstract. General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward-simulator designed for objective hydrometeor phase evaluation, and assess its performance over the North Slope of Alaska using a one-year GCM simulation. For uncertainty quantification, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise. Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidar-radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase classification technique leads to misclassification in fewer than 8 % of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40 %. So, while hydrometeor phase maps determined in forward space are very different from model "reality" they capture the information sensors can provide and thereby enable objective model evaluation.

scholarly journals (GO)<sup>2</sup>-SIM: a GCM-oriented ground-observation forward-simulator framework for objective evaluation of cloud and precipitation phase

2018 ◽  
Vol 11 (10) ◽  
pp. 4195-4214 ◽  
Author(s):  
Katia Lamer ◽  
Ann M. Fridlind ◽  
Andrew S. Ackerman ◽  
Pavlos Kollias ◽  
Eugene E. Clothiaux ◽  
...  
Keyword(s):  
General Circulation ◽  
Doppler Radar ◽  
Circulation Model ◽  
Objective Evaluation ◽  
Uncertainty Assessment ◽  
Grid Cells ◽  
Mixing Ratios ◽  
The North ◽  
Numerical Noise ◽  
Phase Classification

Abstract. General circulation model (GCM) evaluation using ground-based observations is complicated by inconsistencies in hydrometeor and phase definitions. Here we describe (GO)2-SIM, a forward simulator designed for objective hydrometeor-phase evaluation, and assess its performance over the North Slope of Alaska using a 1-year GCM simulation. For uncertainty assessment, 18 empirical relationships are used to convert model grid-average hydrometeor (liquid and ice, cloud, and precipitation) water contents to zenith polarimetric micropulse lidar and Ka-band Doppler radar measurements, producing an ensemble of 576 forward-simulation realizations. Sensor limitations are represented in forward space to objectively remove from consideration model grid cells with undetectable hydrometeor mixing ratios, some of which may correspond to numerical noise. Phase classification in forward space is complicated by the inability of sensors to measure ice and liquid signals distinctly. However, signatures exist in lidar–radar space such that thresholds on observables can be objectively estimated and related to hydrometeor phase. The proposed phase-classification technique leads to misclassification in fewer than 8 % of hydrometeor-containing grid cells. Such misclassifications arise because, while the radar is capable of detecting mixed-phase conditions, it can mistake water- for ice-dominated layers. However, applying the same classification algorithm to forward-simulated and observed fields should generate hydrometeor-phase statistics with similar uncertainty. Alternatively, choosing to disregard how sensors define hydrometeor phase leads to frequency of occurrence discrepancies of up to 40 %. So, while hydrometeor-phase maps determined in forward space are very different from model “reality” they capture the information sensors can provide and thereby enable objective model evaluation.

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