Dynamic Slicing for Deep Neural Networks

Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constructed with instructions and variables, rather than neural networks that are composed of neurons and synapses. In this paper, we propose NNSlicer, the first approach for slicing deep neural networks based on data flow analysis. Our method understands the reaction of each neuron to an input based on the difference between its behavior activated by the input and the average behavior over the whole dataset. Then we quantify the neuron contributions to the slicing criterion by recursively backtracking from the output neurons, and calculate the slice as the neurons and the synapses with larger contributions. We demonstrate the usefulness and effectiveness of NNSlicer with three applications, including adversarial input detection, model pruning, and selective model protection. In all applications, NNSlicer significantly outperforms other baselines that do not rely on data flow analysis.Comment: 11 pages, ESEC/FSE '2

Unstructured mesh methods for stratified turbulent flows

Developments are reported of unstructured-mesh methods for simulating stratified, turbulent and shear flows. The numerical model employs nonoscillatory forward in-time integrators for anelastic and incompressible flow PDEs, built on Multidimensional Positive Definite Advection Transport Algorithm (MPDATA) and a preconditioned conjugate residual elliptic solver. Finite-volume spatial discretisation adopts an edge-based data structure. Tetrahedral-based and hybrid-based median-dual options for unstructured meshes are developed, enabling flexible spatial resolution. Viscous laminar and detached eddy simulation (DES) flow solvers are developed based on the edge-based NFT MPDATA scheme. The built-in implicit large eddy simulation (ILES) capability of the NFT scheme is also employed and extended to fully unstructured tetrahedral and hybrid meshes. Challenging atmospheric and engineering problems are solved numerically to validate the model and to demonstrate its applications. The numerical problems include simulations of stratified, turbulent and shear flows past obstacles involving complex gravity-wave phenomena in the lee, critical-level laminar-turbulence transitioning and various vortex structures in the wake. Qualitative flow patterns and quantitative data analysis are both presented in the current study

Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity

Development of Methods for Uncertainty Quantification in CFD Applied to Wind Turbine Wake Prediction

The CFD 2030 vision aims to improve computer simulations of fluid dynamics in fields like aerospace and energy. They focus on managing uncertainties in these simulations. This study presents two methods:1. Intrusive Polynomial Chaos (IPC) Stochastic Solver: This method employs Polynomial Chaos expansion to tackle uncertainties linked to fluid flow simulations. It characterizes parametric uncertainties, studying their nonlinear effects. The solver is tested on various scenarios, showing its promise for reliable Uncertainty Quantification (UQ) analysis in CFD without being overly intrusive or costly.2. Surrogate Based Uncertainty Quantification (SBUQ) using Deep Learning: A novel approach involves constructing a surrogate model using a neural network, capable of predicting wind flow within a wind farm based on single wind turbine data. This model is used to assess uncertainty in wind farm predictions, accounting for parameter and model form uncertainties.These techniques were tested on different scenarios and demonstrated their capability to analyze complex CFD simulations under various uncertainties. They contribute to the potential of enhancing accuracy and efficiency in UQ analysis. The IPC-based stochastic solver integrates efficiently with existing code, while the SBUQ approach utilizes data from individual wind turbine simulations to predict flow patterns in wind farms.Both methods enhance the accuracy of fluid simulations under different uncertainties. This research contributes to more dependable simulations for aerospace, energy, and environmental engineering applications

Design and development of a slot-less permanent magnet linear motor using permeance analysis method for spray application

Mostly in industrial spray applications pneumatic systems are utilized for operating the automatic spray gun. Linear motor can be one of the alternatives for triggering the spray gun instead of pneumatic systems due to its accuracy in valve positioning according to the required flow rate. From this point of view a tubular linear permanent magnet motor has been designed using Permeance Analysis Method (PAM) and developed. Three permeance models have been developed for PAM analysis. Among these three models, only one model is selected as a PAM model which can be produced the required amount of thrust for triggering the spray gun. After selecting the PAM thrust model, the size of the motor has been optimized by analyzing the effect of thrust constant, electrical and mechanical time constant. Finally based on the optimized data, the motor has been fabricated and tested that shows the good argument with the analysis result

First Wholly-Analytical Gas Volume Fraction Model for Virtual Multiphase Flow Metering Petroleum Industry Applications

In this seminal contribution, the world’s first wholly-analytical gas volume fraction multiphase flow model is formulated and demonstrated in virtual flow meter and production allocation field applications for its differentiated ability to achieve improved reliability of phase flow rate calculations given pressure and temperature measurements at two different locations along multiphase production systems. The presented simple gas volume fraction equation is explicit in form and is validated against both lab data and oilfield flowline data. A crucial requirement for differential pressure flow meters for multiphase production systems, particularly wet gas systems in annular and annular-mist flows, is the calculation of the averaged gas volume fraction. Additional calculations include multidirectional entrainment calculations, which strongly affect the simultaneous entrainment of liquids in the gas phase and the gas in the liquid phases. Historically, prior published gas volume fraction two-phase flow models had closure relations and artificial adjustment (fitting) factors linked to controlled lab-scale conditions involving immiscible fluids that bear no resemblance to the complex petroleum mixtures undergoing phase change in uncontrolled long wellbore and flowline environments. Thus, ambiguous extrapolations were necessary leading to increased uncertainties. Using an asymptotic approximation analysis approach, an analytical gas volume fraction equation is derived that overcomes this empirical-based restriction. In terms of comprehensive validation, the presented analytical gas volume fraction equation is demonstrated first for its ability to reliably reproduce over 2600 two-phase annular and annular-mist flow experimental datasets inclusive of circular and non-circular conduits. Secondly, readily available published experimental data of both constant-diameter as well as variable-diameter sub-critical to critical choke two-phase flows are used for model validation in scenarios involving different flow obstructions. Lastly, an offshore subsea flowline dataset is used to demonstrate the improved reliability of the new equation at field-scale operational conditions