Adam Trahan – ajt6261@louisiana.edu
Andi Petculescu – andi@louisiana.edu

University of Louisiana at Lafayette
Physics Department
240 Hebrard Blvd., Broussard Hall
Lafayette, LA 70503-2067

Popular version of paper 3aPA8
Presented Wednesday morning, May 9, 2018
175^th ASA Meeting, Minneapolis, MN

Artist’s rendition of the envisioned HAVOC mission. (Credit: NASA Systems Analysis and Concepts Directorate, sacd.larc.nasa.gov/smab/havoc)

The motivation for this research stems from NASA’s proposed High Altitude Venus Operational Concept (HAVOC), which, if successful, would lead to a possible month-long human presence above the cloud layer of Venus.

The atmosphere of Venus is composed of primarily carbon dioxide with small amounts of Nitrogen and other trace molecules in the parts-per-million. With surface temperatures exceeding that of Earth’s by about 2.5 times and pressures roughly 100 times, the Venusian surface is quite a hostile environment. Higher into the atmosphere, however, the environment becomes relatively benign, with temperatures and pressures similar to those at Earth’s surface. In the 40-70 km region, condensational sulfuric acid clouds prevail, which contribute to the so-called “runaway greenhouse” effect.

The main condensable species on Venus is a binary mixture of sulfuric acid dissolved in water. The existence of aqueous sulfuric acid droplets is restricted to a thin region in Venus’ atmosphere, namely40-70 km from the surface. Nothing more than a light haze can exist in liquid form above and below this main cloud layer due to evaporation below and above. Inside the cloud layer, there exist three further sublayers; the upper cloud layer is produced using energy from the sun, while the lower and middle cloud layers are produced via condensation. The goal of this research is to determine how the lower and middle condensational cloud layers, affect the propagation of a sound waves, as they travel through the atmosphere.

It is true that for most waves to travel there must be a medium present, except for the case of electromagnetic waves (light), which are able to travel through the vacuum of space. But for sound waves, a fluid (gas or liquid) is necessary to support the wave. The presence of tiny particles affects the propagation of acoustic waves via energy loss processes; these effects have been well studied in Earth’s atmosphere. Using theoretical and numerical techniques, we are able to predict how much an acoustic wave would be weakened (attenuated) for every kilometer traveled in Venus’ clouds.

Figure 2. The frequency dependence of the wave attenuation coefficient. The attenuation is stronger at high frequencies, with a large transition region between 1 and 100 Hz.

Figure 2 shows how the attenuation parameter changes with frequency. At higher frequencies (greater than 100 Hz), the attenuation is larger than at lower frequencies, due primarily to the motion of the liquid cloud droplets as they react to the passing acoustic wave. In the lower frequency region, the attenuation is lower and is due primarily to evaporation and condensation processes, which require energy from the acoustic wave.

For the present study, the cloud environment was treated like a perfect (ideal) gas, which assumes the gas molecules behave like billiard balls, simply bouncing off one another. This assumption is valid for low-frequency sound waves. To complete the model, real-gas effects are added, to obtain the background attenuation in the surrounding atmosphere. This will enable us to predict the net amount of losses an acoustic wave is likely to experience at the projected HAVOC altitudes.

The results of this study could prove valuable for guiding the development of acoustic sensors designed to investigate atmospheric properties on Venus.

This research was sponsored by a grant from the Louisiana Space Consortium (LaSPACE).

Menachem Rafaelof
National Institute of Aerospace (NIA)

Andrew Schroeder
NASA Langley Research Center (NIFS intern, summer 2017)

175th Meeting of the
Acoustical Society of America
Minneapolis Minnesota
7-11 May 2018
1pPA, Novel Methods in Computational Acoustics II

Sound and its Perception
Sound waves are basically fluctuations of air pressure at points in a space. While this simple physical description of sound captures what sound is, its perception is much more complicated involving physiological and psychological processes.

Physiological processes involve a number of functions during transmission of sound through the outer, middle and inner ear before transduction into neural signals. Examples of these processes include amplification due to resonance within the outer ear, substantial attenuation at low frequencies within the inner ear and frequency component separation within the inner ear. Central processing of sound is based on neural impulses (counts of electrical signals) transferred to the auditory center of the brain. This transformation occurs at different levels in the brain. A major component in this processing is the auditory cortex, where sound is consciously perceived as being, for example, loud, soft, pleasing, or annoying.

Motivation
Currently an effort is underway to develop and put to use “air taxis”, vehicles for on-demand passenger transport. A major concern with these plans is operation of air vehicles close to the public and the potential negative impact of their noise. This concern motivates the need for the development of an approach to predict human perception of sound. Such capability will enable the designers to compare different vehicle configurations and their sounds, and address design factors that are important to noise perception.

Approach
Supervised learning algorithms are a class of machine learning algorithms capable of learning from examples. During the learning stage samples of input and matching response data are used to construct a predictive model. This work compared the performance of four supervised learning algorithms (Linear Regression (LR), Support Vector Machines (SVM), Decision Trees (DTs) and Random Forests (RFs)) to predict human annoyance from sounds. Construction of predictive models included three stages: 1) sample sounds for training are analyzed in term of loudness (N), roughness (R) , sharpness (S) , tone prominence ratio (PR) and fluctuation strength (FS). These parameters quantify various subjective attributes of sound and serve as predictors within the model. 2) Each training sound is presented to a group of test subjects and their annoyance response (Y in Figure 1) to each sound is gathered. 3) A predictive model (H-hat) is constructed using a machine learning algorithm and is used to predict the annoyance of new sample sounds (Y-hat).

Figure 1: Construction of a model (H-hat) to predict the annoyance of sound. Path a: training sounds are presented to subjects and their annoyance rating (Y) is gathered. Subject rating of training samples and matching predictors are used to construct the model, H-hat. Path b: annoyance of a new sound is estimated using H-hat.

Findings
In this work the performance of four models, or learning algorithms, was examined. Construction of these models relied on the annoyance response of 38 subjects to 103 sounds from 10 different sound sources grouped in four categories: road vehicles, unmanned aerial vehicles for package delivery, distributed electric propulsion aircraft and a simulated quadcopter. Comparison of these algorithms in terms of prediction accuracy (see Figure 2), model interpretability, versatility and computation time points to Random Forests as the best algorithms for the task. These results are encouraging considering the precision demonstrated using a low-dimension model (five predictors only) and the variety of sounds used.

Future Work
• Account for variance in human response data and establish a target error tolerace.
• Explore the use one or two additional predictors (i.e., impulsiveness and audibility)
• Develop an inexpensive, standard, process to gather human response data
• Collect additional human response data
• Establish an annoyance scale for air taxi vehicles

Figure 2: Prediction accuracy for the algorithms examined. Accuracy here is expressed as the fraction of points predicted within error tolerance (in terms of Mean Absolute Error (MAE)) vs. error tolerance or absolute deviation. For each case, Area Over the Curve (AOC) represents the total MAE.