A physics-informed neural network for sound propagation in the atmospheric boundary layer

2020 ◽  
Vol 148 (4) ◽  
pp. 2739-2739
Author(s):  
Chris L. Pettit ◽  
D. Keith Wilson
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Sound Propagation

scholarly journals A physics-informed neural network for sound propagation in the atmospheric boundary layer

2021 ◽  
Author(s):  
Chris Pettit ◽  
D. Wilson
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Loss Function ◽  
Sound Speed ◽  
Network Architecture ◽  
Sound Propagation ◽  
Transmission Loss ◽  
Training Data ◽  
Data Driven

We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.

scholarly journals Optical Turbulence Profile Forecasting and Verification in the Offshore Atmospheric Boundary Layer

2021 ◽  
Vol 11 (18) ◽  
pp. 8523
Author(s):  
Manman Xu ◽  
Shiyong Shao ◽  
Qing Liu ◽  
Gang Sun ◽  
Yong Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Single Parameter ◽  
Optical Turbulence ◽  
Outer Scale ◽  
Backpropagation Neural Network ◽  
Statistical Results

A backpropagation neural network (BPNN) approach is proposed for the forecasting and verification of optical turbulence profiles in the offshore atmospheric boundary layer. To better evaluate the performance of the BPNN approach, the Holloman Spring 1999 thermosonde campaigns (HMNSP99) model for outer scale, and the Hufnagel/Andrew/Phillips (HAP) model for a single parameter are selected here to estimate profiles. The results have shown that the agreement between the BPNN approach and the measurement is very close. Additionally, statistical operators are used to quantify the performance of the BPNN approach, and the statistical results also show that the BPNN approach and measured profiles are consistent. Furthermore, we focus our attention on the ability of the BPNN approach to rebuild integrated parameters, and calculations show that the BPNN approach is reliable. Therefore, the BPNN approach is reasonable and remarkable for reconstructing the strength of optical turbulence of the offshore atmospheric boundary layer.

scholarly journals Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks Applied to Lidar Measurements

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3659
Author(s):  
Adrián García-Gutiérrez ◽  
Diego Domínguez ◽  
Deibi López ◽  
Jesús Gonzalo
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Wind Profile ◽  
Single Point ◽  
Near Surface ◽  
Cfd Models ◽  
The Neural Network ◽  
Ground Measurement ◽  
Wind Resource

This paper introduces a new methodology for estimating the wind profile within the ABL (Atmospheric Boundary Layer) using a neural network and a single-point near-ground measurement. An important advantage of this solution when compared with others available in the literature is that it only requires near surface measurements for the prognosis once the neural network is trained. Another advantage is that it can be used to study the wind profile temporal evolution. This work uses data collected by a lidar sensor located at the Universidad de León (Spain). The neural network best configuration was determined using sensibility analyses. The result is a multilayer perceptron with three layers for each altitude: the input layer has six nodes for the last three measurements, the second has 128 nodes and the third consists of two nodes that provide u and v. The proposed method has better performance than traditional methods. The obtained wind profile information obtained is useful for multiple applications, such as preliminary calculations of the wind resource or CFD models.

scholarly journals Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model

2019 ◽  
Author(s):  
Jiali Wang ◽  
Prasanna Balaprakash ◽  
Rao Kotamarthi
Keyword(s):  
Neural Network ◽  
Boundary Layer ◽  
Neural Networks ◽  
Water Vapor ◽  
Atmospheric Boundary Layer ◽  
Planetary Boundary Layer ◽  
Diurnal Cycle ◽  
Deep Neural Networks ◽  
Wrf Model ◽  
Physical Processes

Abstract. Parameterizations for physical processes in weather and climate models are computationally expensive. We use model output from a set of simulations performed using the Weather Research Forecast (WRF) model to train deep neural networks and evaluate whether trained models can provide an accurate alternative to the physics-based parameterizations. Specifically, we develop an emulator using deep neural networks for a planetary boundary layer (PBL) parameterization in the WRF model. PBL parameterizations are commonly used in atmospheric models to represent the diurnal variation of the formation and collapse of the atmospheric boundary layer – the lowest part of the atmosphere. The dynamics of the atmospheric boundary layer, mixing and turbulence within the boundary layer, velocity, temperature, and humidity profiles are all critical for determining many of the physical processes in the atmosphere. PBL parameterizations are used to represent these processes that are usually unresolved in a typical numerical weather model that operates at horizontal spatial scales in the tens of kilometers. We demonstrate that a domain-aware deep neural network, which takes account of underlying domain structure that are locality specific (e.g., terrain, spatial dependence vertically), can successfully simulate the vertical profiles within the boundary layer of velocities, temperature, and water vapor over the entire diurnal cycle. We then assess the spatial transferability of the domain-aware neural networks by using a trained model from one location to nearby locations. Results show that a single trained model from a location over the midwestern United States produces predictions of wind components, temperature, and water vapor profiles over the entire diurnal cycle and all nearby locations with errors less than a few percent when compared with the WRF simulations used as the training dataset.

Downwind sound propagation in an atmospheric boundary layer

AIAA Journal ◽  
1989 ◽  
Vol 27 (5) ◽  
pp. 515-523 ◽  
Author(s):  
William E. Zorumski ◽  
William L. Willshire
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Sound Propagation

Characteristics of the Night and Day Time Atmospheric Boundary Layer at Dome C, Antarctica

2007 ◽  
Vol 25 ◽  
pp. 49-55 ◽  
Author(s):  
S. Argentini ◽  
I. Pietroni ◽  
G. Mastrantonio ◽  
A. Viola ◽  
S. Zilitinchevich
Keyword(s):  
Boundary Layer ◽  
Atmospheric Boundary Layer ◽  
Night And Day
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