Transmission Loss Function Research Articles

Fiber metal laminates for high strain rate applications with layerwise shock impedance tuning

Novel materials such as fiber-metal laminates (FMLs) have demonstrated significant potential in a variety of applications. They must contend with problems such fatigue, creep, high-speed projectile impact, and deformation at high strain rates while in use. When employed as structural materials in aircraft, especially when exposed to shock wave impact and high velocity impact, fiber-metal laminates’ high strain rate characteristics become crucial. Shock impedance matching is a revolutionary approach used for shock-tuning the separate layers. The novelty of the current work is in developing custom shielding laminates, with in-depth analysis on the response of the shock impedance tuning of individual layers on the laminate behaviour at high strain rates. In the current study, five stackups of FMLs comprising metallic (AA 6061-T6) and fiber-reinforced polymer (FRP) plies, were formulated, incorporating shock impedance matching. The fiber-polymer plies used in the FMLs include ultra-high molecular weight polyethylene (UHMWPE), p-aramid for supplementing the impact resistance. Transmission loss functions (TL) estimated from the impedance tube experiments were used to indicate the shock tuning of the various laminates. The laminates underwent testing using a Split Hopkinson Pressure Bar (SHPB) apparatus to determine their properties at high strain rates (350, textrm{s}^{-1} to 460, textrm{s}^{-1}). The variation in the Shock Energy (SE) absorbed by the laminates at various strain rates was analyzed as a function of the corresponding Transmission Loss employing regression. The dynamic stress-strain curves showed an increase in shock energy absorption at higher strain rates. The sequence SSP-IV and SSP-II showed the highest values of energy absorption as well as Transmission Loss.

Development of channel transmission loss function for WRF_HYDRO modeling of semi-arid regions: The case of wadi Faria, Palestine

In arid and semi-arid regions, the quantitative understanding of the processes of runoff generation and prediction of the streamflow hydrographs and their transmission to the catchment outlet are among the most basic challenges of hydrology. To address these challenges, hydrological modeling has been used to gain understanding of the hydro-logical systems and to provide the required inputs needed for sustainable water resources management. Further-more, coupling hydrological models with climatic models provides even more understanding of the influence of the climatic parameters on hydrological processes and systems, and hence on runoff generation mechanism. In this research, WRF-Hydro (the Weather Research Forecasting model coupled with the Distributed Hydrologic Modeling System) was used for streamflow forecasting in the Faria catchment. Faria (320 km2), located in the northeastern part of the West Bank, Palestine, is a gauged catchment where rainfall and streamflow data are available for more than ten years. This study aims to assess the functionality of the WRF-Hydro to predict reliable streamflow hydro-graphs at the upper part of the catchment. Additionally, a new channel loss function was incorporated into WRF-Hydro to model the amount of water lost in the ephemeral (loosing) streams of the catchment. Hence, the rainfall and streamflow data for the rainy season; 2017-2018.were utilized. Results indicate that WRF-Hydo model is well functioning, but model calibration and validation is still required to further improve the model performance.

Optimal Current Allocation Strategy for Hybrid Hierarchical HVDC System with Parallel Operation of High-Voltage and Low-Voltage DC Lines

For long-distance and bulk-power delivery of new energy, high-voltage direct current (HVDC) is a more effective way than high-voltage alternative current (HVAC). In view of the current capacity disparity between line commutated converter (LCC) and voltage source converter (VSC), a hybrid hierarchical HVDC topology with parallel operation of 800 kV and 400 kV DC lines is investigated. The optimal current allocation method for hybrid hierarchical HVDC is proposed distinct from the same rated current command configuration method of high-voltage and low-voltage converters in traditional topology. Considering the transmission loss reduction of the HVDC system, a multi-order fitting function of transmission loss including LCC converter stations, VSC converter stations and DC lines is established. To minimize the transmission loss and the voltage deviation of key DC nodes comprehensively, a multi-objective genetic algorithm and maximum satisfaction method are utilized to obtain the optimal allocation value of rated current command for high-voltage and low-voltage converters. Through the optimization model, an improved constant current controller based on the current allocation strategy is designed. The hybrid hierarchical HVDC system model is built in PSCAD software, and simulation results verify the effectiveness of the proposed topology and optimal current allocation strategy.

Using neural networks to estimate the probability density function of transmission loss in ocean environments with a database-driven model of uncertainty

The prediction of transmission loss (TL) in the ocean depends on the relevant environmental characteristics, such as sound speed field, bathymetry, and bottom properties. These characteristics are often uncertain as they are commonly not all measured in situ, and are instead estimated from available databases. Thus, the resulting predictions of TL are uncertain, and this uncertainty can be quantified via the probability density function (PDF) of TL. Given a model for the environmental uncertainty, Monte Carlo techniques and thousands of realizations of the environment can be used to estimate the PDF of TL, but this method is computationally costly. A variety of alternative uncertainty-estimation techniques have been proposed, including a machine learning approach which uses neural networks trained on large Monte Carlo-produced TL datasets. The resulting neural network inherits the limitations of its training datasets and their underlying model of environmental uncertainty. A new environmental uncertainty model is described here which uses higher-resolution databases. A revised neural network approach to predicting PDFs of TL is developed and tested on ∼4.5 × 106 examples across 300 environments with the new, more realistic model of uncertainty at ranges up to 100 km for source frequencies between 50 and 600 Hz. [Sponsored by an NDSEG Fellowship.]

The scope of acoustic impedance matching of hybrid fiber metal laminates for shielding applications

In a multi-layered shielding material, the sequence of the arrangement of the layers affects the extent of insulation to acoustic waves. In the current work, hybrid composite laminates have been taken up comprising 10 sequences, employing metallic faceplate (AA6061), paperboard, ballistic-grade aramid, and ultra-high molecular weight polyethylene (UHMWPE) fabrics with an epoxy binder. In the theoretical studies, an analytical model for the transmission loss function has been developed by incorporating the multiple wave reflection principle in combination with interface-wise acoustic impedance grading. The analytical model has been validated using the transmission loss functions from numerical and experimental studies on the different sequences. The numerical simulation has been carried out using the harmonic acoustic analysis module, on Ansys R19.0. The experimentation has been carried out on an impedance tube. The results from the analytical model are in good agreement with the experimental and numerical simulation results, the analytical model can be used for predicting the transmission losses of composite laminates.

Reliability assessment of multi-state phased mission systems with common bus performance sharing considering transmission loss and performance storage

Multi-state phased mission systems (MS-PMSs) with common bus performance sharing widely exist in practical engineering, such as intelligent transmission systems and power supply systems. The challenges in assessing the reliability of these systems come from the dependence among phases and the complexity of multi-state behaviors of components. Besides, the performance transfer between different phases and the performance transfer with transmission loss have never been studied in such systems. To effectively address these problems, this paper proposes a reliability model for MS-PMSs with common bus performance sharing considering transmission loss and performance storage. First, a novel Markov model for multi-state components is established to solve the dependence problem among phases. Then, considering the performance sharing among subsystems within each phase through the common bus and the performance transferring between phases by an energy storage device, an iteration method combined with transmission loss function is adopted to establish the system reliability model, where the performance transmission loss is proportional to transmission distance and transmission performance. Furthermore, a universal generating function (UGF) based method is used for evaluating the system reliability. Finally, an analytical example and a case study of the IEEE-24 bus system are utilized to verify the availability of the proposed method.

Estimating the probability density function of transmission loss in an uncertain ocean using machine learning

Predicted values of transmission loss (TL) in ocean environments are sensitive to environmental uncertainties. The resulting predicted-TL uncertainty can be quantified via the probability density function (PDF) of TL. Monte Carlo methods can determine the PDF of TL but typically require thousands of field calculations, making them inappropriate for real-time applications. Thus, a variety of alternative techniques based on polynomial chaos, field shifting, modal propagation in ocean waveguides, and spatial variations of TL near the point(s) of interest have been proposed. This presentation describes an innovative approach to estimating the PDF of TL based on nominal TL, ocean environmental parameters, and machine learning. This approach has two main challenges. First, appropriate representations must be found for ground-truth PDFs of TL generated from Monte Carlo calculations so that a neural network can be constructed to predict each parameter of the estimated PDF of TL. Four such representations are considered here. And second, a framework must be developed to generate training data for the neural networks. A proposed framework that predicts candidate environments’ training utility without computing any PDFs of TL is described. The performance of this approach is analyzed and compared to that of prior techniques. [Sponsored by ONR.]

Topology optimization of reactive acoustic mufflers using a bi-directional evolutionary optimization method

This article proposes an acoustic muffler design procedure based on finite element models and a Bi-directional Evolutionary Acoustic Topology Optimization. The main goal is to find the best configuration of barriers inside acoustic mufflers used in the automotive industry that reduces sound pressure level in the outlet of the muffler. The acoustic medium is governed by Helmholtz equation and rigid wall boundary conditions are introduced to represent acoustic barriers. The continuum problem is written in the frequency domain and it is discretized using the finite element method. The adopted objective function is Transmission Loss (TL). Increasing TL guarantees that the sound pressure level ratio between outlet and inlet of the muffler is reduced. To find the configuration of acoustic barriers that increases the Transmission Loss function of the muffler an adaptation of the Bi-directional Evolutionary Structural Optimization (BESO) method is used. Applying the proposed design procedure topologies in 2D models are reached, which raises the Transmission Loss function for one or multiple frequencies. Three examples are presented to show the efficiency of the proposed procedure.

Estimation of the probability density function of transmission loss in the ocean using area statistics

Predictions of acoustic transmission loss (TL) in the ocean and its uncertainty are useful for a variety of ocean and naval engineering applications. Previously, an ad-hoc technique, area statistics (AS), was developed as a computationally efficient method to estimate the probability density function (PDF) of TL in any uncertain ocean environment from a single range-depth TL-field calculation in that environment. Here, the performance of AS is reported for ten ocean environments having uncertain sound speed profile, bathymetry, and seabed properties. In each environment, a parabolic-equation-based prediction of TL from a point source was generated as a function of range and depth at frequencies of 100, 200, and 300 Hz using the most probable values of the uncertain environmental parameters. From these calculations, AS was used to estimate the PDF of TL at more than 3000 locations within the ten environments. These estimated PDFs are then quantitatively compared, via an L1-error norm, to PDFs generated from traditional Monte Carlo techniques for the same locations and frequencies. Current results show that the L1-error of the AS PDF is less than 0.5, indicating 75% or greater overlap between the AS and Monte Carlo PDFs, at 90% of the test locations. [Sponsored by ONR.]

The acoustical features analysis of Indo-Pacific humpback dolphins’ whistle and modeling research

Depending on species-specific characteristics of echolocation and recognition,dolphin could travel in groups without collision, and they could also prey and defend mutually. As a multi-harmonics, frequency-modulated signal, the variety of whistles revealed the rich group identification and location information of free-ranging Indo-Pacific humpback dolphins (Sousa chinensis). This paper introduces the features of whistle on the basis of Joint time-frequency distribution, dealing with recording data collected in the area of Guangxi and Zhanjiang seacoast, in China. Then the ability of individual identification is discussed from the correlation of whistles, we will get the knowledge of dolphins how to cognize the communication information in the complex environment. The transmission loss function will be depicted using the BELLHOP at last. By extracting the acoustical features, the modeling of whistle signal is implemented based on the Sinusoids Model (Buck, 2000). Specifically, the validity of mathematical model is proved according to comparison with the recording data.

Efficient estimation of the probability density function of transmission loss in uncertain ocean environments

Predictions of acoustic transmission loss (TL) in the ocean are useful in a variety of naval and ocean engineering applications. However, real ocean environments are imperfectly known and have many uncertainties. Consequently, TL calculations performed for these environments are also uncertain. Traditional Monte-Carlo methods used to obtain probability density functions (PDF) of TL in such circumstances require many field calculations, and are impractical for real-time applications. This presentation describes how the PDF of TL at a point of interest may be estimated using a single TL-field calculation and the statistics of TL within a range-depth area containing that point. To document the performance of this technique, RAM was used to perform TL field calculations for 100 Hz and 300 Hz sound sources in realistic ocean sound channels with eight uncertain environmental parameters describing the bathymetry, sound speed profile, and bottom properties. Preliminary results indicate that TL PDFs generated with area statistics have an L1-error ≤ 0.5 when compared with 1000-calculation Monte-Carlo PDF results throughout most of the sound channel. However, greater errors were observed in the near field as well as in areas where the sound channel depth was less than 100 m, for 100 Hz sound. [Sponsored by ONR.]

Oppositional biogeography-based optimisation for optimal power flow

This paper describes optimal power flow based on oppositional biogeography-based optimisation (OBBO) in which fuel cost, voltage deviation and power transmission loss functions are individually used as the problem objectives. The feasibility of the proposed method is exhibited for standard IEEE 30-bus and IEEE 118-bus systems and the results obtained by using the proposed approach are compared with other optimisation methods. The proposed OBBO provides results better than other heuristic techniques. The results also show that OBBO has the effectiveness and potential to solve both small and large scale OPF problems.

Solution behaviors of multistatic transmission loss equations

A sonar arrangement is one of the most important problems in the multistatic sonar. Although it is very useful to proceed numerical simulations for arrangements, they usually consume much computing resources and their results are sometimes difficult to grasp physical meanings. On the other hand, it is beneficial to analyze the acoustical field further from the classical sonar equation view point as a quick looking. One of the most important element is the transmission loss in the sonar equation. In the multistatic configurations, the transmission loss is dependent on the direction from a receiver. The transmission loss is usually notified as a function using transmitter parameters. New descriptions are introduced without transmitter parameters in this presentation. Spreading dominant cases produce quartic transmission loss functions of the target distance form the receiver. They are solved analytically; however, the solutions do not constitute Cassini oval in spite of some preceding researches. Absorption dominant cases are also solved analytically. Mixtures of spreading dominant and absorption dominant are not able to be solved analytically. These solution behaviors are easily surveyed by contour maps of the transmission loss. These maps are useful for sonar arrangements.

Loss Minimization Using Optimal Power Flow Based on Swarm Intelligences

This paper describes optimal power flow based on swarm intelligences in which the power transmission loss function is used as the problem objective. Although most of optimal power flow problems involve the total production cost of the entire power system, in some cases some different objective may be chosen. In this paper, to minimize the overall power transmission losses, four decision variables are participated. They are i) power generated from generating plants, ii) specified voltage magnitude at control substations, iii) tap position of tap changing transformers and iv) reactive power injection from reactive power compensators. Swarm intelligences are wellknown and widely accepted as potential intelligent search methods for solving such a problem. In this paper, Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Differential Evolution (DE) are employed to solve optimal power flow problems. A 6-bus and 30-bus IEEE power systems are used for test. As a result, all swarm intelligences search algorithms can solve optimal power flow problems efficiently. The artificial bee colony and the differential evolution provide results better than other swarm intelligent techniques.

Exploiting correlation in passive sonar Bayesian localization techniques.

Low frequency acoustic signals in shallow water are strongly affected by interference between multiple paths resulting from boundary interactions. As the acoustic source moves through this interference pattern, the spatial variation in transmission loss can result in strong temporal modulation of the received signal, which can be used to localize the source. Acoustic propagation models can produce accurate transmission loss field predictions, but are sensitive to ocean environmental parameters such as bottom composition, bathymetry, and sound speed profile. If the uncertainty in the undersea environment can be described by probability density functions of these parameters, Monte Carlo forward models can be used to produce an ensemble of possible transmission loss realizations. A probabilistic model representing this ensemble must include a density function of transmission loss at each location, as well as correlation of transmission loss between locations. In addition, the choice of probabilistic model has a large impact on the form of the resulting Bayesian localization algorithm. Previous results have shown that including spatial correlation of transmission loss can result in improved localization. This talk will introduce a non‐Gaussian probabilistic model for representing uncertainty in transmission loss predictions that includes correlation, and discuss the resulting recursive Bayesian localization algorithm.

Towards flash-flood prediction in the dry Dead Sea region utilizing radar rainfall information

Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Validation was performed for a subsequent 5-year period for the same catchment and then for an entire 10-year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.

Predicting sound-pressure levels in the near field and far field from radiating partitions and building elements

One of the most common tasks of the noise control engineer is predicting sound transmission and noise reduction through structures and the sound radiation from the structures to the near and far fields. Concerning transmission loss, a large database of test data (at least from 100 to 5000 Hz), verified transmission loss theory, and practical equations for relatively simple constructions are all readily available to the noise control engineer. On the other hand, the topics of noise reduction from space to space as a function of transmission loss, and radiation patterns from partitions and building elements are rarely found in the literature or acoustical texts. A large variety of noise reduction situations arise in practice. Sound may transmit from diffuse spaces, semireverberant, or highly absorbent spaces to similar or dissimilar spaces including the outdoors. The transmission path is likely to be through the primary surface as well as windows, ventilation openings, or a combination of different wall and roof constructions. At times, the transmission from outdoors to interior spaces is the primary consideration. Often, in industrial situations, the level close to the structure is of interest for hearing conservation considerations, as is the level in the far field for community impact assessments. This paper reviews noise reduction equations as a function of transmission loss drawn from many sources for predicting sound-pressure levels in the situations discussed above. The radiation pattern from a plane wall or opening to the near and far fields is discussed. The material is presented in an orderly fashion to serve as a guide for the noise control engineer. Recommendations are given for situations in which current analytical prediction methods appear inadequate.

Array beam noise characterization

The formulation of the stochastic process for beam noise of a horizontal array operating at low frequency is analyzed based on the array's response to the distant ship-induced noise field. The general form of beam noise characteristic function obtained with nonhomogeneous compound Poisson process assumption for shipping distribution is related to three major operation environment parameters: the mean number of shipping density, the array aperture length and beam pattern, and random fluctuation and range dependence in the transmission loss function. This characteristic function can be used to derive the probability of observing low noise region in terms of frequency-azimuth-time representation of noise surface for some specific operation scenarios. Computer modeling for ship lane dominant noise field and uniform shipping distributed noise field are performed for investigating the sensitivity of the relevant parameters to the array's beam noise statistical property.

The occurrence of low beam noise level in ship induced noise field

The formulation of the stochastic process for beam noise of a horizontal array operating at low frequency is analyzed based on the array's response to the distant ship-induced noise field. The general form of beam noise characteristic function obtained with nonhomogeneous compound Poisson process assumption for shipping distribution is related to three major environmental parameters: the mean number of shipping density, the array aperture length and beam pattern, and random fluctuation and range dependence in the transmission loss function. This characteristic function can be used to derive the probability of observing low noise regions in terms of frequency-azimuth-time representation of noise surface for certain acoustic environments. Computer modeling for ship lane dominant noise field and uniform shipping distributed noise field were performed for investigating the relationship among the relevant parameters in the array's beamforming output. The results illustrate the sensitivity of beam noise statistical characteristics to the angular ship density.

Complex Image Theory of Low-Frequency Sound Propagation in Shallow Water

The problem of propagation of low-frequency sound in shallow water over several layers and a basement is approached through an image theory in which the first thin layers are lumped together with the water layer. This effective structure is assumed on the basis of expected good transmission through a thin layer (less than one wavelength thick having acoustic properties not very different from those of its bounding media. The required reflection coefficient at the lower boundary of the lumped layer, expressed as a function of range and the order of the image source of a ray, is taken in the form given by Abelés for reflection of plane waves from a layered system. The theory yields calculated transmission loss functions which show reasonable agreement with experimental results. Comparisons of theoretical loss and data obtained at Panama City, Florida, are made over the frequency range 6–244 cps.