Long-Range Hydroacoustic Propagation Modelling Schemes on Distributed Memory Parallel Computers

Solving an acoustic wave equation using a parabolic approximation is a popular approach for many existing ocean acoustic models. Commonly used parabolic equation (PE) model programs, such as the range-dependent acoustic model (RAM), are discretized by the finite difference method (FDM). Considering the idea and theory of the wide-angle rational approximation, a discrete PE model using the Chebyshev spectral method (CSM) is derived, and the code is developed. This method is currently suitable only for range-independent waveguides. Taking three ideal fluid waveguides as examples, the correctness of using the CSM discrete PE model in solving the underwater acoustic propagation problem is verified. The test results show that compared with the RAM, the method proposed in this paper can achieve higher accuracy in computational underwater acoustics and requires fewer discrete grid points. After optimization, this method is more advantageous than the FDM in terms of speed. Thus, the CSM provides high-precision reference standards for benchmark examples of the range-independent PE model.

Proper characterization of underwater acoustic transmission loss (TL) relies on proper treatment of the air/water and water/sediment interfaces. This paper will focus on how the air/water interface is treated in parabolic equation (PE) models such as the range dependent acoustic model (RAM) [Collins, M. D., ¿Applications and time-domain solution of higher-order parabolic equations in underwater acoustics,¿ J. Acoust. Soc. Am., 86 (3), 1097-1102, 1989] and the finite element parabolic equation (FEPE) model [Collins, M.D., "A higher-order parabolic equation for wave propagation in an ocean overlying an elastic bottom", J. Acoust. Soc. Am. 86, 1459-1464, 1989]. Two surface loss methods have been implemented in RAM, a surface loss versus angle (LVA) and a conformal mapping (CM) method. Additionally, the CM method has been implemented in FEPE. These two methods are discussed and compared for realistic test cases and a third option, which has accuracy consistent with the CM but speed more aligned with the LVA approach is discussed.

Sound propagation of wind farms is typically simulated by the use of engineering tools that are neglecting some atmospheric conditions and terrain effects. Wind and temperature profiles, however, can affect the propagation of sound and thus the perceived sound in the far field. A better understanding and application of those effects would allow a more optimized farm operation towards meeting noise regulations and optimizing energy yield. This paper presents the parabolic equation (PE) model development for accurate wind turbine noise propagation. The model is validated against analytic solutions for a uniform sound speed profile, benchmark problems for nonuniform sound speed profiles, and field sound test data for real environmental acoustics. It is shown that PE provides good agreement with the measured data, except upwind propagation cases in which turbulence scattering is important. Finally, the PE model uses computational fluid dynamics results as input to accurately predict sound propagation for complex flows such as wake flows. It is demonstrated that wake flows significantly modify the sound propagation characteristics.

Broadband acoustic propagation models vary greatly in their computation times. To define the speed-versus-accuracy tradeoffs, two questions are addressed: (1) For ducted environments, how does the parabolic equation (PE) model compare with the faster Labianca virtual-mode model (VM) and the FAME eigenray part of the generic sonar model (GSM)? (2) In nonducted environments, must a GSM complex transmission loss (TL) be computed for all the signal frequencies, or is a single frequency of computation sufficient (i.e., is it fair to assume that the only frequency dependence in the complex-TL phase is in the ray time delay)? To answer these questions, the effects of the computed complex-TL on sine-wave-burst and HFM-burst signals are presented. The multipath arrival structures of the environments enforced a 4-s received-signal duration, and the transmit signals enforced a bandwidth of 200 Hz. The results are as follows: (1) A weak duct shows favorable comparison between GSM, VM, and PE models; however, a strong duct does not, and in fact reveals numerical instability in the PE computations. (2) Single-frequency GSM captures the basic arrival structure of multiple-frequency GSM, but the multipath interference is different and sensitive to the dominant signal frequency. It is concluded that GSM (but not single-frequency GSM) may in most practical cases yield adequate predictions for the acoustic filtering of broadband signals.

We present fast and highly scalable parallel computations for a number of important and fundamental matrix problems on distributed memory systems (DMS). These problems include matrix multiplication, matrix chain product, and computing the powers, the inverse, the characteristic polynomial, the determinant, the rank, the Krylov matrix, and an LU- and a QR-factorization of a matrix, and solving linear systems of equations. Our highly scalable parallel computations for these problems are based on a highly scalable implementation of the fastest sequential matrix multiplication algorithm on DMS. We show that compared with the best known parallel time complexities on parallel random access machines (PRAM), the most powerful but unrealistic shared memory model of parallel computing, our parallel matrix computations achieve the same speeds on distributed memory parallel computers (DMPC), and have an extra polylog factor in the time complexities on DMS with hypercubic networks. Furthermore, our parallel matrix computations are fully scalable on DMPC and highly scalable over a wide range of system size on DMS with hypercubic networks. Such fast (in terms of parallel time complexity) and highly scalable (in terms of our definition of scalability) parallel matrix computations were rarely seen before on any distributed memory systems.

We present fast and scalable parallel computations for a number of important and fundamental matrix problems on distributed memory systems (DMS). These problems include computing the powers, the inverse, the characteristic polynomial, the determinant, the rank, the Krylov matrix, and an LU- and a QR-factorization of a matrix, and solving linear systems of equations. These parallel computations are based on efficient implementations of the fastest sequential matrix multiplication algorithm on DMS. We show that compared with the best known time complexities on PRAM, our parallel matrix computations achieve the same speeds on distributed memory parallel computers (DMPC), and have an extra polylog factor in the time complexities on DMS with hypercubic networks. Furthermore, our parallel matrix computations are fully scalable on DMPC and highly scalable over a wide range of system size on DMS with hypercubic networks. Such fast and scalable parallel matrix computations were not seen before on any distributed memory systems.

Metamaterials composed of artificial subwavelength structures exhibit extraordinary properties that cannot be found in nature. Designing artificial structures having exceptional properties plays a pivotal role in current metamaterial research. We present a new numerical simulation scheme for metamaterial research. The scheme is based on a graphic processing unit (GPU)-accelerated finite-difference time-domain (FDTD) method. The FDTD computation can be significantly accelerated when GPUs are used instead of only central processing units (CPUs). We explain how the fast FDTD simulation of large-scale metamaterials can be achieved through communication optimization in a heterogeneous CPU/GPU-based computer cluster. Our method also includes various advanced FDTD techniques: the non-uniform grid technique, the total-field/scattered-field (TFSF) technique, the auxiliary field technique for dispersive materials, the running discrete Fourier transform, and the complex structure setting. We demonstrate the power of our new FDTD simulation scheme by simulating the negative refraction of light in a coaxial waveguide metamaterial.

The present paper examines the temporal evolution of acoustic fields by modeling forward propagation subject to sea surface dynamics with time scales of less than a second to tens of seconds. A time-evolving rough sea surface model is combined with a rough surface formulation of a parabolic equation model for predicting time-varying acoustic fields. Surface waves are generated from surface wave spectra, and stepped in time using a Runge-Kutta integration technique applied to linear evolution equations. This evolving, range-dependent surface information is combined with other environmental parameters and input to the acoustic model, giving an approximation of the time-varying acoustic field. The wide-angle parabolic equation model manages the rough sea surfaces by molding them into the boundary conditions for calculations of the near-surface acoustic field. This merged acoustic model is validated using concurrently-collected acoustic and environmental information, including surface wave spectra. Data to model comparisons demonstrate that the model is able to approximate the ensemble-averaged acoustic intensity at ranges of about a kilometer for acoustic signals of around 15 kHz. Furthermore, the model is shown to capture variations due to surface fluctuations occurring over time scales of less than a second to tens of seconds.

MSA Probs is a parallel algorithm developed to align multiple sequence alignment using a central processing unit (CPU). Whereas the CPU has some limitations, such as the inability to parallelize tasks in the processor (latency-oriented). To overcome these limitations, this paper proposes an improved version of MSA Probs that is compatible with a graphical Processing Unit (GPU). This idea helps in enhancing the performance of our algorithm (MSAprobs). To parallelize the sequential algorithm, Compute Unified Device Architecture (CUDA) or OpenCL is commonly used on GPUs. The NIVIDIA API is used to investigate the GPU's computing power. The results of using CPU only in MSAprobs versus the CPU and GPU are compared using two data sets from the Bali Base and OX Bench. The evaluation of the CPU and GPU is done using Threads 1,2 and 4. The results showed that by combining CPU and GPU, performance is improved and execution time is reduced.

Oceanographic and geo-acoustic variability in the shallow water can dominate acoustic propagation. Often the environment is critically undersampled, compared to the sensitivity of the full-field propagation model used [such as the Parabolic Equation (PE) model]. For regions with environmental uncertainty (which is known from measurements or models), there will be a Minimum Achievable Uncertainty (MAU) in acoustic propagation modeling (Transmission Loss). Given this MAU, it is proposed that field interpolation techniques be applied to speed up the acoustic propagation computation, consistent with keeping the interpolation errors below the MAU. Techniques for estimating the uncertainty in TL and speeding up the broadband range-dependent computation using the Navy Standard PE model will be presented. For fully 3-D field computations in a range-dependent environment, the speed up is expected to be several orders of magnitude.

An efficient parallel-computing method for modeling nonisothermal multiphase flow and multicomponent transport in porous and fractured media

The finite-difference time-domain (FDTD) algorithm has become a tool of choice in many areas of RF and microwave engineering and optics. However, FDTD runs too slow for some simulations to be practical, even when carried out on supercomputers. The development of dedicated hardware to accelerate FDTD computations has been investigated. In this paper, we demonstrate that off-the-shelf graphics processor units (GPUs) can be successfully used to accelerate FDTD simulations. Using C++, OpenGL, and several OpenGL extensions, a modern GPU has been programmed to solve a simple two dimensional electromagnetic scattering problem. The GPU outperforms a central processing unit (CPU) of comparable technology generation.

The prediction of underwater acoustic transmission loss in the sea plays a key role in generating situational awareness in complex naval battles and assisting underwater operations. However, the traditional classical underwater acoustic transmission loss models do not consider the regional hydrological elements, and the performance of underwater acoustic transmission loss prediction under complex environmental conditions in a wide range of sea areas is limited. In order to solve this problem, we propose a deep learning-based underwater acoustic transmission loss prediction method. First, we studied the application domains of typical underwater acoustic transmission loss models (ray model, normal model, fast field program model, parabolic equation model), analyzed the constraint rules of its characteristic parameters, and constructed a dataset according to the rules. Then, according to the characteristics of the dataset, we built a DBN (deep belief net) neural network model and used DBN to train and learn the dataset. Through the DBN method, the adaptation and calculation of the underwater acoustic transmission loss model under different regional hydrological elements were carried out in a simulation environment. Finally, the new method was verified with the measured transmission loss data of acoustic sea trials in a certain sea area. The results show that the RMSE error between the underwater acoustic transmission loss calculated by the new method and the measured data was less than 6.5 dB, the accuracy was higher than that of the traditional method, and the prediction speed was faster, the result was more accurate, and had a wide range of adaptability in complex seas.

The Shallow Water Acoustic Modeling (SWAM'99) Workshop was organized to examine the ability of various acoustic propagation models to accurately predict sound transmission in a variety of shallow water environments designed with realistic perturbations. In order to quantify this, tests of reciprocity, convergence, and stability must be considered. This paper presents the results of an established parabolic equation model based on the split-step Fourier algorithm. The test cases examined in this paper include a simple isospeed water column over a flat bottom with geoacoustic parameter variations, a randomly sloping bottom with geoacoustic parameter variations, and a canonical shallow water profile perturbed by internal waves over a flat, homogeneous bottom. Source configurations were generally held constant but numerous single frequency and broadband runs were performed. Model testing is emphasized with specific criteria for accurate solutions being specified. Random perturbations are added to one test case to examine the influence of environmental uncertainty on the details of the propagation. The results indicate that point-wise accurate solutions to the acoustic field in shallow water cannot be achieved beyond a few kilometers. This is partly due to the inaccuracies of the split-step Fourier algorithm employed in these shallow water scenarios and the treatment of the bottom interface boundary conditions, but also due to the inherent variability caused by uncertain environmental specification. Thus, more general features of the acoustic field should be emphasized at longer ranges.

Implementation of Image Enhancement Algorithms and Recursive Ray Tracing using CUDA