Next Article in Journal
Water Demand and Photosynthetic Performance of Tomatoes Grown Hydroponically Under Increasing Nitrogen Concentrations
Previous Article in Journal
Quantitative Characterization of Carbonate Mineralogy in Lake Yangzong Sediments Using XRF-Derived Calcium Signatures and Inorganic Carbon Measurements
Previous Article in Special Issue
Prediction of Sluice Seepage Based on Impact Factor Screening and the IKOA-BiGRU Model
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Project Report

Auditory Representation of Transient Hydraulic Phenomena: A Novel Approach to Sonification of Pressure Waves in Hydraulic Systems

by
Muhammad Khizer Zaman
School of Engineering, University of Dayton, Dayton, OH 45469, USA
Water 2025, 17(13), 1950; https://doi.org/10.3390/w17131950
Submission received: 19 January 2025 / Revised: 15 March 2025 / Accepted: 21 March 2025 / Published: 29 June 2025

Abstract

This study explores the novel integration of data sonification into hydraulic engineering by translating transient pressure fluctuations in a hydraulic system into sound. Using a simple hydraulic model built in KYPipe, a pump connected to a reservoir and a tank was simulated to trip, causing transient pressure changes. These pressure variations were mapped onto the C-major scale using Microsoft Excel, creating an auditory representation. The methodology included generating a sound library using recorded piano samples and applying VBA code to link pressure values with musical notes. The results demonstrated that sonification provides an innovative means of presenting transient hydraulic phenomena, enabling users to identify critical events such as pressure spikes audibly. While the study highlights challenges, such as computational limitations and resolution trade-offs in mapping, it opens pathways for employing auditory data representation in engineering contexts. Future work could focus on expanding audio sample libraries and optimizing computational methods to improve resolution and usability.

1. Introduction

Data sonification is the production, study, and application of non-speech auditory representation of data for informational purposes. Data sonification is a new, interdisciplinary, and developing area of modern inquiry and practice that coexists with data visualization. Sonification and auralization are used interchangeably; the latter specifically refers to presenting data in audio format. There are several reasons why sound might, in some situations, be the ideal means of information representation and communication.
For instance, most of us can easily attest from personal experience that performing a purely visual side-by-side comparison of two sets of textual data demands a high level of focus and is prone to error, especially over longer periods of time. In contrast, it is relatively simpler to listen to an audio form of such representations.
For thousands of years, sound has also been a crucial component of both theoretical and empirical research. The idea of the harmony of the spheres, which integrates the philosophical theory that mathematical relationships represent qualities or “tones” of energy ratios, played a unifying role in the development of the arts and sciences at least as far back as Pythagoras (born around 569 BCE). The use of ratios and sound has been fundamental to the conceptualization and realization of Western music throughout all eras [1].
The philosophical idea known as the “harmony of the spheres” views the proportions in the motions of the Sun, Moon, and planets as a form of music. The idea of the “harmony of the spheres” incorporates the metaphysical idea that mathematical relationships communicate qualities or “tones” of energy that appear in numbers, visual angles, shapes, and sounds, all of which are interconnected within a system of ratios. Pythagoras was the first person to discover that intervals between harmonious sound frequencies form simple numerical ratios and that the pitch of a musical note is inversely proportional to the length of the string that produces it. The Pythagorean theory, which had its roots in ancient Greece, was later developed by astronomer Johannes Kepler in the 16th century. Kepler thought that even though this “music” was not audible, the soul could still hear it. Up until the end of the Renaissance, the concept was still appealing to academics and had an impact on many schools of thought [1].
This study combines the fields of sonification and hydraulic engineering. The engineering discipline that explains and predicts the behavior of liquids and gases at rest or in motion is known as fluid mechanics. Many commonplace occurrences, such as why a ship floats, why pumps are required to convey water, why an inflated balloon shrinks in cold air, etc., may be explained using the principles covered in fluid mechanics [2]. This study implements data sonification of transient pressure in a hydraulic system.

Purpose of Study

The objective of this study is to present a novel method of presenting transient pressure in a hydraulic model. The objective of this study is accomplished by creating a simple hydraulic model in KYPipe where a pump is connected to a reservoir and a tank. The pump is simulated to trip which creates transient pressure in the pipe downstream of the pump. The transient pressure data are sonified by mapping the pressure values to frequencies of the C-major scale using Microsoft Excel by referencing a sound sample library created for this study. This study presents the unique methodology used for the sound-mapping process of transient pressure of a hydraulic network, as well as its limitations and future recommendations. After conducting a rigorous literature review, no methodology of sonification of transient pressure in a hydraulic network was found.

2. Literature Review

2.1. Transient Pressure and the Wave Plan Method

Hydraulic transients, often referred to as water hammer, are rapid changes in pressure and flow conditions within pressurized conduit systems [3,4]. These transients arise primarily from abrupt velocity changes in the working fluid, such as those caused by sudden valve closures or openings, pump starts or stops, rapid variations in flow demand, or equipment malfunctions. Mass oscillation can further contribute to transient events when liquid columns move at different velocities within the pipeline [5].
Below are some of the common causes of transient events in hydraulic networks:
Vale operations (closure/opening): Rapid closure of valves can reduce flow velocity to zero almost instantly, creating a pressure wave that travels at high speed through the pipeline. Conversely, quickly opened valves also initiate shock waves due to the sudden acceleration of fluid [6].
Pump starts and stops: Pump trips (loss of power) alter flow velocity abruptly. Pump start-ups also induce pressure surges when flow is accelerated from a stationary to an operating condition [2,6].
Sudden changes in flow demand: Rapid changes in system demand (e.g., large industrial consumers opening/closing flow) can generate transients that propagate through the entire pipeline network.
Water hammer: This term is often used interchangeably with hydraulic transients and is characterized by a distinctive pressure wave or “hammer blow” effect.
Mass oscillations: In systems with large storage tanks, surge vessels, or extended pipe segments, fluid columns can oscillate back and forth, creating cyclical pressure fluctuations [5,7].
When closed conduits were first used to convey water, the search for pressure protection for water conduits may have begun. Beginning in the early 20th century, significant scientific research was conducted to determine what causes abrupt increases in pressure in closed conduits [6]. Abrupt pressure increases in pipelines could damage pipes and other equipment such as pumps and valves.
Joukowski’s equation is commonly used to estimate the pressure increase associated with a change in pipeline velocity. Joukowski’s equation, shown below, can be derived by applying momentum concepts.
Δ P = ( C ) ( Δ V ) γ g
where ΔP—pressure increase due to velocity change; ΔV—velocity change; C—wave speed.
Notice from Joukowski’s equation that the magnitude of the pressure increase is directly proportional to the magnitude of the velocity change. For example, when a valve is suddenly closed, the velocity in a pipeline may go from 0.91 to 1.22 m/s to zero almost instantly. Depending upon the wave speed, the resulting pressure increase could several times higher than the initial pressure in the pipeline, which could potentially cause damage to the pipeline or connected equipment. It is important for engineers to consider the potential pressure increase when designing and operating pipelines to ensure its safety and reliability.
In a pipeline system, pressure waves travel at very high speeds—as much as 1220 m/s—through all hydraulically linked locations. The speed at which a pressure wave travels through a pipeline is dependent upon the pipeline material and the pipe wall thickness. When pressure waves reach pipe junctions, that is, locations where two or more pipes come together, they are both reflected and transmitted. Additionally, they completely reflect off reservoirs, dead ends, tanks, and totally closed valves. Pumps, turbines, and other loss components, such as completely or partially open valves, modify pressure waves [6].
A quick change in flowrate will be accompanied by a rapid change in pressure. The sudden shift in pressure that results is thought of as a pressure wave that travels through the pipeline system. As the pressure wave moves through the pipe segment, frictional loss occurs in the pipe which helps to dampen the magnitude of the pressure wave. At junction nodes, particularly basic junction nodes made of merely two pipes with differing elastic properties, pressure waves are also reflected and transmitted.
Equation (2) below can be used to calculate the pressure head at a discontinuity following the arrival of a pressure wave. This is depicted visually in Figure 1.
H J t = H J t + Δ H ( T i )
where HJ(t′)—pressure head at discontinuity j after the pressure wave arrives; HJ(t)—pressure head at discontinuity j the instant before the pressure wave arrives; ΔH—magnitude of the arriving pressure wave; Ti—transmission coefficient for the pipe containing the pressure wave ΔH.
An abrupt change or disturbance in the pressure or flow characteristics of a fluid within a system is referred to as a discontinuity. Flow path obstructions, sudden valve closures, and changes in pipe diameter, etc. can all result in discontinuities. When a discontinuity occurs, it can give rise to pressure waves, also known as water hammer or hydraulic transients, which can propagate through the fluid system, causing fluctuations in pressure and velocity.
The head at a discontinuity following the arrival of a pressure wave is equal to the head before the arrival of the pressure wave plus the portion of head contributed by transmission as illustrated in the figure below [2].
Wood et al. give a thorough explanation and the derivation of a full set of equations for modeling surge protection devices, such as pressure relief valves, surge tanks, and air valves. This technique is known as the wave plan method [7].

2.2. Effects and Consequences

Hydraulic transients can exert significant impacts on pipeline systems, often leading to pipe failures, leakage, equipment damage, system instability, and operational disruptions. Excessive pressures or vacuum conditions may result in pipe bursts, joint failures, or structural deterioration [3]. Pumps, turbines, valves, and other mechanical components are particularly vulnerable to fatigue or catastrophic failure when subjected to repeated high-pressure surges. Furthermore, persistent oscillations or resonance caused by transient events can undermine normal operations, making it difficult to maintain stable flow or pressure at critical points in the system [5]. In many cases, frequent or severe transients require shutdowns for inspection and repairs, reducing overall system availability and increasing maintenance costs [6].

2.3. Mitigation Strategies

Various surge control and protective devices are employed to minimize the magnitude and duration of transient pressures in pipeline systems. Strategically placed surge tanks can absorb excessive pressure rises and provide additional fluid when pressure drops occur, thereby smoothing out rapid fluctuations [4]. Air chambers, which store compressed air, and air valves, such as air release/vacuum breaker valves, help counteract sudden positive or negative pressure waves while preventing sub-atmospheric conditions that could lead to column separation [3]. Control valves, including surge anticipation valves and pressure relief valves, automatically release pressure spikes to a safe discharge path, therefore limiting peak overpressure. Surge suppressors and accumulators, such as bladder-type or piston-type accumulators, temporarily store hydraulic energy to reduce the amplitude of pressure transients. Additionally, following operational best practices such as gradual valve operations and careful pump ramp-up or ramp-down procedures can further mitigate transients by minimizing abrupt changes in flow [8].

2.4. Literature Review on Sonification

In 1981, Sara Bly conducted research to present information using sound. The study examined the possibility of using sound to share information, especially when it was difficult or impossible to use visuals. The author examined various sonification approaches, including parameter mapping such as pitch, volume, timbre, and event-based triggers, highlighting the importance of clear, intuitive mappings and the need to account for human auditory perception limits. Through examples such as monitoring complex systems and aiding individuals with visual impairments, the author demonstrated the practical potential of auditory displays. Overall, this study laid critical groundwork for modern sonification techniques and emphasized the value of further research in this emerging field [9].
In 2008, Zhao et al. looked into the possibility of data sonification as a way to make information easier for people with visual impairments. The authors examined several auditory display methods most notably parameter mapping, earcons, and auditory icons. Earcons are short, distinctive sounds (for instance, a beep to indicate an error) commonly used in computer operating systems to convey specific information. The authors highlighted the importance of balancing aesthetics with functionality, avoiding information overload, and tailoring displays to user needs. The author found parameter mapping particularly effective for complex data, while simpler cues like earcons worked best for basic events. Proper training and customization further enhanced user comprehension. The study concluded that sonification holds significant promise for improving data accessibility, calling for more research to refine and expand these techniques [10].
In 2009, Forssén et al. investigated sonification techniques for traffic noise within the LISTEN project, aiming to develop tools for urban planning and noise control. The authors highlighted the challenges of realistically simulating environmental noise and described a methodology for capturing, processing, and rendering passenger car pass-by sound data. The study also examined how factors such as vehicle speed, road surface, and microphone placement affect perceived sonification quality by comparing simulated outputs with real-world recordings. Overall, the authors demonstrated that sonification can be a powerful tool for analyzing and mitigating traffic noise problems. The authors concluded by advocating further research to refine these methods and enhance their applicability in addressing environmental noise challenges [11].
In 2019, Lenzi et al. made a sonification system that would help operators at water distribution plants find cyber threats and anomalies in water distribution systems. The authors suggested trying out a method that combined sonification techniques with cybersecurity measures to make it easier to monitor and protect important infrastructure. Recognizing that such infrastructure is highly vulnerable to cyberattacks, the authors emphasized the importance of robust monitoring methods capable of detecting threats in real time. Their solution involved transforming operational data into sound using different mapping techniques to help operators quickly identify anomalous events and cyber intrusions. To evaluate their method, they simulated a series of cyberattacks on a water distribution system and examined how effectively operators could detect these attacks when guided by auditory cues. Results showed that with thoughtfully designed mappings, sonification substantially improved the operator’s ability to recognize irregularities and potential breaches. Consequently, the study highlighted that merging data sonification with strategic cybersecurity measures can provide an additional layer of real-time protection for critical water infrastructure. This research offers a valuable contribution by demonstrating how auditory displays can enhance situational awareness and safeguard vital resources in the water sector [12].
In 2020, Axon et al. investigated how transforming computer network traffic data into auditory signals could help operators detect cyber threats more quickly and accurately than with traditional visual methods. The authors noted sonification’s potential to provide an additional data dimension, enable real-time monitoring, and reduce cognitive load, yet also recognized key challenges like selecting appropriate sound mappings and avoiding information overload. Experimental results showed participants identifying anomalies more efficiently when guided by sonified data. In the end, the researchers urged further interdisciplinary work to refine sonification approaches and enhance cybersecurity strategies across complex networked systems [13].
In 2021, Muralidharan et al. introduced “Sonify”, a tool that leverages machine learning and computer vision to convert visual graphs into audio cues, thereby enhancing data accessibility for visually impaired (VIP) individuals. By mapping elements like axes and data points to distinct sounds, the system allows users to interpret complex information using auditory cues. User studies showed that both VIP and sighted participants benefited from this approach, demonstrating how sonification can broaden data accessibility and engagement [14].
In summary, sonification is the process of representing data or information through sound. It serves as an alternative to visual representation, particularly for visually impaired individuals who face challenges interpreting complex visual data. Sonification has various applications, such as detecting cyber threats in water distribution systems, monitoring computer networks, and enhancing accessibility for visually impaired users. Despite its benefits, sonification has some limitations. One of the main challenges is the need for an appropriate mapping strategy to make sure that sonified data accurately convey the intended information. Finding a balance between auditory aesthetics with functionality and managing information overload are some additional challenges. Sonification techniques must consider human auditory perception limitations when designing sound displays to be considered effective. Research has shown that sonification techniques can effectively help visually impaired individuals perceive and understand data.
Sonification has the potential to transform the way data are represented and interpreted especially for visually impaired people. It offers new possibilities for enhancing accessibility, detecting anomalies, and improving cybersecurity as well. However, to fully realize its potential researchers must discuss the limitations and challenges associated with sonification techniques and continue to develop innovative solutions that can be applied across different fields.

3. Methodology

3.1. Hydraulic Model

A hydraulic model was built in KYPipe’s Surge software, part of Pipe2022, a computer simulation model that has the ability to examine pressure changes associated with rapid changes in flow. KYPipe was originally developed by Dr. Don J. Wood and colleagues at the University of Kentucky in the 1970s [15]. Over time, the software evolved through further research and collaboration, eventually being commercialized by KYPipe LLC, Lexington, KY, USA. Today, KYPipe offers both steady-state and transient (surge) analysis capabilities, making it particularly well suited for designing and optimizing water distribution networks, wastewater systems, and various industrial pipeline applications. The robust transient module, based in part on the wave plan method pioneered by Wood and coauthors, enables comprehensive modeling of rapid flow changes and pressure surges. The hydraulic model developed for this study included a pump connected to a reservoir and a tank as shown in Figure 2 below. The pump was simulated to trip (suddenly lose power) after 5 s of operation to create transient pressure (or water hammer) in the pipe downstream of the pump.
The following are elevations and other relevant parameters of the model.
Reservoir elevation: 433.6 m
Reservoir grade: 468.6 m
Pump type: Grundfos SP 9–29 pump
Pipe length from reservoir to pump: 5 m
Pipe length from pump to junction: 5 m
Pipe length from junction to tank: 900 m
Tank elevation: 580.5 m
Tank volume: 500 m3
Pipe type: 150 mm HDPE
Dimension ratio: 11
Hydrostatic design stress, σs: 6890 kPa
Fluid density: 1000 kg/m3
Bulk modulus of water: 2,200,000 kPa [15]

3.2. Pump File Number and Inertia

When a pump suddenly loses power, called a pump trip, the pump speed begins to decrease. The duration and manner over which the pump speed goes from full operational speed to zero speed is dependent upon the mass of the rotating components of the pump (pump inertia). KYPipe has the functionality to model the full rotational dynamics of a pump trip using what are called pump files.
In KYPipe, the selection of pump file number depends upon the specific speed of the pump. The inertia and specific speed calculator were utilized to select the corresponding pump file number. Figure 3 shows the input parameters and results obtained for the Grundfos SP 9–29 pump. The pump inertia was found to be 0.19 Nm2, specific speed equaled 3.33, and the suggested pump file number was 1.
Figure 4 shows pressure history of the pump from time t = 4.8 s to t = 6.4 s where the pump trip occurs at t = 5 s. The x-axis (time step) of the chart was truncated for visual clarity and to make the pump trip waveform from steady-state to transient easier to see.

3.3. The Major Scale

In music theory, one of the most common scales in Western music is the major scale [16]. A scale is a set of notes within an octave. An octave is the interval between two notes (or frequencies) where the higher note is twice that of the lower note. An interval is the distance in frequency between two notes in music theory. For example, the frequency 440 Hz, which is the “A” note in music, will have an octave at 880 Hz.
Western music divides the octave into twelve equal intervals represented by 12 notes. This is known as the 12-tone equal temperament (12-TET) system. The 12-TET system occurred sometime in the 16th century [17]. It divides the octave into 12 equal parts that are all evenly spaced on a logarithmic scale with a ratio equal to the 12th root of 2 ( 2 12 = 1.05946). Prior to the 12-TET system, Pythagorean tuning was used throughout the world. Music composed before the 16th century would sound different or even sometimes, be perceived as “out of tune” because our ears have been trained to get accustomed to the 12-TET system. The set of 12 notes from its starting frequency to an octave higher is known as the chromatic scale.
Scales in music theory are made from the chromatic scale in Western music. Different scales contain different number of notes. The major scale contains seven notes and is derived by a specific set of ratios. Figure 5 below shows piano keys with the C-major scale labeled. Looking at a piano, to play the C-major scale, one would play all the white keys starting at C note and ending at the B note. The notes would be C, D, E, F, G, and A.

3.4. Sound Mapping and Sound Library

In this study, the first step was to simulate the hydraulic model to obtain pressure values in a pipe caused by a pump trip. The maximum and minimum pressure values were found, and an octave range was selected to be mapped onto the pressure ranging from 208.29 kPa to 274.34 kPa onto notes spanning from C0 to B7. The note C0 was assigned to a pressure of 206.84 kPa and the note B7 was assigned to a pressure of 282.67 kPa. Each successive note was mapped to a pressure increase of 1.39 kPa. Table 1 shows the musical note assigned to pressure ranging from 206.84 kPa to 282.67 kPa.
A sound library was created by using sound samples recorded in Logic Pro X which is a music production software by Apple Inc., Cupertino, CA, USA. Figure 6 below shows samples recorded using the sound of a Steinway grand piano. The sounds were exported as wave files.
A VBA code (Appendix A) was written in Microsoft Excel where the time step and pressure values were read in two columns, and a musical note was assigned to each pressure. The macro would then play the corresponding frequency by referencing the note from the sound library.

4. Results

The following video file is the output of the Microsoft Excel macro where, at each time step, the pressure value is sonified by assigning a frequency and a musical note from the C-major scale. The macro then plays the wave file that corresponds to the referenced note in the sound library. Microsoft Excel executed the macro at a slow speed without allowing the user to change the speed at which audio files were played; therefore, Logic Pro software was used to record and speed up the audio produced by Microsoft Excel. Furthermore, the audio output of Excel macro was routed through a virtual synthesizer in Logic Pro to produce a continuous sound. The video is available on YouTube and can be viewed at the following link “https://www.youtube.com/watch?v=HYqgvy5XawI (accessed on 26 April 2023)” [18].
The start of the video clip corresponds to time t = 4.8 s of the simulation. The pump trip occurs at t = 5 s. The simulation runs at a steady-state until t = 5 s where a pump trip occurs and the pressure goes into transient state. At time t = 5.36 s, the pressure fluctuates. Table A1 in Appendix A provides pressure values at each time step for the 10 s simulation. When playing the sound clip, one notices the steady-state conditions before the pump trip as a series of sounds having the same pitch. When the pump trip is initiated, the sound is very high pitched, thus indicating the pressure spike. Specifically, the results of the transient analysis indicate a pressure spike of 274.34 kPa. Immediately the pitch lowers, thus indicating a pressure drop to 208.29 kPa. The pressures and sounds then fluctuate as friction dampen out the pressure wave with an eventual return to steady-state conditions.
Figure 7 is a screenshot of the Microsoft Excel file where the VBA code was written to sonify pressures. As the macro runs, it prints the pitch associated with the pressure in the third column and plays the sound simultaneously.

5. Limitation and Practical Considerations

A key consideration in applying sonification to hydraulic transients is specifying which transient phenomena are being targeted. In the present study, the focus is on water hammer events and rapid flow changes associated with pump trip scenarios. However, other important transient phenomena, such as cavitation and column separation, may arise in pipelines lacking surge suppression devices [3]. These events can cause significant pressure drops or vapor pocket formation, potentially leading to pipe collapse, implosion, or severe oscillations once the vapor pocket collapses [5]. Although such effects were not directly modeled in this study, the principles of mapping pressure data to acoustic signals can be extended to identify unique acoustic signatures corresponding to both cavitation and column separation.
Moreover, practical implementation of acoustic monitoring is inherently challenging when pipelines and distribution networks are buried or otherwise inaccessible. Direct placement of acoustic sensors on the pipe may be infeasible in such scenarios. One potential workaround is indirect acoustic sensing, where sensors are installed on the ground surface or within accessible chambers (e.g., valve vaults, pump houses) to detect vibrations or pressure waves transmitted through the ground or pipe supports [19]. Advanced data-driven analysis involving machine learning or signal-processing techniques can further isolate characteristic acoustic patterns from background noise, allowing researchers and operators to identify transient events more accurately [20]. Such methods often rely on extracting features (e.g., amplitude, frequency shifts) that correlate with specific transient phenomena and comparing these features against known baseline conditions or training datasets.
Another key recommendation is integration with other monitoring systems, including real-time pressure transducers and flow meters [21]. Correlating acoustic signals with direct measurements of pressure or flow changes can enhance both the accuracy and reliability of transient detection. By combining acoustic data with conventional sensor data, operators can develop a more robust diagnostic system. This multi-sensor approach can be programmed to raise alarms whenever transient-driven anomalies arise, thus forming a holistic pipeline monitoring framework.
Overall, while the proposed sonification method demonstrates the feasibility of translating transient pressure data into audible cues, field deployments must account for the practical challenges of buried pipelines, the complexity of various transient modes (including cavitation and column separation), and the importance of coupling acoustic sensing with other monitoring strategies for comprehensive pipeline management.

6. Conclusions and Recommendations

A hydraulic network model was created in KYPipe with a pump connected to a reservoir and a tank. The pump was simulated to be tripped at 5 s of operation to create water hammer effects in the pipeline. The pressure values were sonified by utilizing music theory. The maximum and minimum pressures were identified, and a range of octaves were selected to map the pressure to notes of the C-major scale. The range of octaves was from C0 to B7. A sound library was created in Apple Logic Pro using sound samples of the Steinway grand piano and each sample was exported as a wave file into a folder. VBA code was written where pressure values were assigned a musical pitch and as the macro was executed, the corresponding musical note was referenced from the sound library and played simultaneously. This innovative integration of hydraulic simulation with auditory display not only provides an intuitive means of visualizing transient events but also opens new avenues for real-time monitoring and analysis of hydraulic phenomena. Furthermore, the sonification approach can serve as an effective training tool for engineers and operators, facilitating rapid recognition of abnormal pressure transients without constant reliance on visual data. This method has the potential to enhance operational safety in water distribution and industrial systems by providing immediate auditory feedback during critical events.
One of the limitations of the study was the computation power. A large dataset was produced by running the surge analysis simulation in KYPipe because the time step of simulation was a very small value (t = 0.008 s). Running the macro on the entire dataset would freeze the computer. Therefore, for the sonified output, pressure history from time t = 4.8 s to t = 6 s was used. Another limitation was controlling the speed at which the Microsoft Excel macro runs. For example, if the audio file of the sample was 3 s, then Microsoft Excel would play the next sample after 3 s which resulted in some silence or a pause in audio. To overcome this, the sample sizes were trimmed to 1 s and the audio output was sped up in Logic Pro; however, this resulted in producing sonification that sounded highly discretized instead of a smooth transitioning sound. Furthermore, using the notes of music scales omits many frequencies that fall in between two consecutive notes of a scale. For example, the note C1 in the C-major scale has a frequency of 32.7 Hz and the next note in the scale, D1, has a frequency of 36.71 Hz. The frequencies that lie between 32.7 Hz and 36.71 Hz get omitted in the sound-mapping process. The pressure values were assigned the closest matching frequencies of the C-major scale in the sound-mapping process. This resulted in losing resolution in terms of translating pressure into sound. However, mapping smaller increments of pressure change would require creating a large number of sound samples. This may result in a sound mapping method that is not perceived well by the human ear and information overload, which are issues discussed in several research studies conducted in other fields of sonification. Furthermore, this may require a lot of processing power of a computer, especially, if a Microsoft Excel macro is used for sonification. This compromise reduced the resolution of the sonification, potentially overlooking subtle transient fluctuations. However, these approximations resulted in a more musical output, which was a deliberate design choice.
A future recommendation for this study would be the creation of a larger collection of audio samples, where pressure can be discretized into smaller values in order to map pressure to sound more accurately, if computation power is not an issue. Additionally, exploring adaptive mapping algorithms that dynamically adjust the pitch-to-pressure relationship could capture subtle variations more effectively while minimizing auditory overload. Integrating alternative sonification techniques may also enhance the overall user experience and broaden the application of this methodology in both engineering and monitoring contexts. Overall, the unique methodology used in this study to sonify transient pressure of a hydraulic network was successful using a sound mapping technique that is easy to understand and perceive by the human ear.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to file size limitations and format considerations.

Conflicts of Interest

The author declares no conflict of interest.

Appendix A

Appendix A.1. VBA CODE

Sub Play_Data_Freq()
  Dim rng_Data As Range
  Dim i As Integer, iRow As Integer, iFreq As Integer
  Dim sFolderPath As String, sAudioFile As String
  Set rng_Data = Sh_Data.Range(“B2”, Range(“B2”).End(xlDown).Address)
  ‘Change the path below for the music library
  sFolderPath = “C:\Users\Khizer\Documents\UdaytonNew\Independent Study\SoundLibrary”
  ‘Traverse rows in the data sheet
  For i = 1 To rng_Data.Rows.Count
    iFreq = rng_Data.Cells(i, 1)
    On Error Resume Next
    ‘Lookup the filename in Frequency sheet for the data and get the closest (same or next biggest) match
    iRow = WorksheetFunction.Match(Round(iFreq, 0), Sh_Freq.Range(“A:A”), 2)
    If iRow > 0 Then
      rng_Data.Cells(i, 1).Offset(0, 1) = Sh_Freq.Range(“B” & iRow)
      ‘Play the file in Syncronous mode
      ‘The filename is derived based on frequency vs Filename data in the Frequency sheet
      SoundMe (sFolderPath & “\” & Sh_Freq.Range(“B” & iRow) & “.wav”)
    End If
  Next i
End Sub

Appendix A.2. KYPipe Simulation Results

Table A1. KYPipe model results.
Table A1. KYPipe model results.
Time Step (s)Pressure (kPa)Time Step (s)Pressure (kPa)
0241.254.992241.25
0.008241.255241.25
0.016241.255.008252.83
0.024241.255.016263.59
0.032241.255.024256.14
0.04241.255.032249.52
0.048241.255.04251.31
0.056241.255.048252.76
0.064241.255.056251.59
0.072241.255.064250.76
0.08241.255.072250.76
0.088241.255.08250.56
0.096241.255.088250.14
0.104241.255.096249.87
0.112241.255.104249.52
0.12241.255.112249.38
0.128241.255.12249.11
0.136241.255.128248.69
0.144241.255.136248.76
0.152241.255.144248.35
0.16241.255.152248
0.168241.255.16248
0.176241.255.168247.38
0.184241.255.176247.04
0.192241.255.184246.63
0.2241.255.192246.56
0.208241.255.2246.63
0.216241.255.208246.14
0.224241.255.216246.35
0.232241.255.224246.21
0.24241.255.232245.8
0.248241.255.24245.8
0.256241.255.248245.18
0.264241.255.256245.32
0.272241.255.264245.32
0.28241.255.272244.49
0.288241.255.28244.63
0.296241.255.288244.35
0.304241.255.296244.14
0.312241.255.304244.21
0.32241.255.312243.73
0.328241.255.32243.52
0.336241.255.328243.39
0.344241.255.336243.32
0.352241.255.344243.32
0.36241.255.352243.18
0.368241.255.36243.11
0.376241.255.368210.01
0.384241.255.376208.29
0.392241.255.384272.62
0.4241.255.392274.34
0.408241.255.4210.01
0.416241.255.408208.29
0.424241.255.416272.62
0.432241.255.424274.34
0.44241.255.432210.01
0.448241.255.44208.29
0.456241.255.448272.62
0.464241.255.456274.34
0.472241.255.464210.08
0.48241.255.472208.29
0.488241.255.48272.55
0.496241.255.488274.34
0.504241.255.496210.08
0.512241.255.504208.36
0.52241.255.512272.55
0.528241.255.52274.27
0.536241.255.528210.08
0.544241.255.536208.36
0.552241.255.544272.55
0.56241.255.552274.27
0.568241.255.56210.08
0.576241.255.568208.36
0.584241.255.576272.55
0.592241.255.584274.27
0.6241.255.592210.08
0.608241.255.6208.36
0.616241.255.608272.55
0.624241.255.616274.27
0.632241.255.624210.08
0.64241.255.632208.36
0.648241.255.64272.55
0.656241.255.648274.27
0.664241.255.656210.08
0.672241.255.664208.36
0.68241.255.672272.55
0.688241.255.68274.27
0.696241.255.688210.08
0.704241.255.696208.36
0.712241.255.704272.55
0.72241.255.712274.27
0.728241.255.72210.08
0.736241.255.728208.36
0.744241.255.736272.55
0.752241.255.744274.27
0.76241.255.752210.08
0.768241.255.76208.36
0.776241.255.768272.55
0.784241.255.776274.27
0.792241.255.784210.08
0.8241.255.792208.36
0.808241.255.8272.55
0.816241.255.808274.27
0.824241.255.816210.08
0.832241.255.824208.36
0.84241.255.832272.55
0.848241.255.84274.27
0.856241.255.848210.08
0.864241.255.856208.36
0.872241.255.864272.55
0.88241.255.872274.27
0.888241.255.88210.08
0.896241.255.888208.36
0.904241.255.896272.55
0.912241.255.904274.27
0.92241.255.912210.08
0.928241.255.92208.36
0.936241.255.928272.55
0.944241.255.936274.27
0.952241.255.944210.08
0.96241.255.952208.36
0.968241.255.96272.55
0.976241.255.968274.27
0.984241.255.976210.08
0.992241.255.984208.36
1241.255.992272.55
1.008241.256274.27
1.016241.256.008210.08
1.024241.256.016208.36
1.032241.256.024272.55
1.04241.256.032274.27
1.048241.256.04210.08
1.056241.256.048208.36
1.064241.256.056272.55
1.072241.256.064274.27
1.08241.256.072210.08
1.088241.256.08208.36
1.096241.256.088272.55
1.104241.256.096274.27
1.112241.256.104210.08
1.12241.256.112208.36
1.128241.256.12272.55
1.136241.256.128274.27
1.144241.256.136210.08
1.152241.256.144208.36
1.16241.256.152272.55
1.168241.256.16274.27
1.176241.256.168210.08
1.184241.256.176208.36
1.192241.256.184272.55
1.2241.256.192274.27
1.208241.256.2210.08
1.216241.256.208208.36
1.224241.256.216272.55
1.232241.256.224274.27
1.24241.256.232210.08
1.248241.256.24208.36
1.256241.256.248272.55
1.264241.256.256274.27
1.272241.256.264210.08
1.28241.256.272208.36
1.288241.256.28272.55
1.296241.256.288274.27
1.304241.256.296210.15
1.312241.256.304208.36
1.32241.256.312272.48
1.328241.256.32274.27
1.336241.256.328210.15
1.344241.256.336208.43
1.352241.256.344272.48
1.36241.256.352274.2
1.368241.256.36210.15
1.376241.256.368208.43
1.384241.256.376272.48
1.392241.256.384274.2
1.4241.256.392210.15
1.408241.256.4208.43
1.416241.256.408272.48
1.424241.256.416274.2
1.432241.256.424210.15
1.44241.256.432208.43
1.448241.256.44272.48
1.456241.256.448274.2
1.464241.256.456210.15
1.472241.256.464208.43
1.48241.256.472272.48
1.488241.256.48274.2
1.496241.256.488210.15
1.504241.256.496208.43
1.512241.256.504272.48
1.52241.256.512274.2
1.528241.256.52210.15
1.536241.256.528208.43
1.544241.256.536272.48
1.552241.256.544274.2
1.56241.256.552210.15
1.568241.256.56208.43
1.576241.256.568272.48
1.584241.256.576274.2
1.592241.256.584210.15
1.6241.256.592208.43
1.608241.256.6272.48
1.616241.256.608274.2
1.624241.186.616210.15
1.632241.256.624208.43
1.64241.256.632272.48
1.648241.256.64274.2
1.656241.256.648210.15
1.664241.256.656208.43
1.672241.256.664272.48
1.68241.256.672274.2
1.688241.256.68210.15
1.696241.256.688208.43
1.704241.256.696272.48
1.712241.256.704274.2
1.72241.256.712210.15
1.728241.256.72208.43
1.736241.256.728272.48
1.744241.256.736274.2
1.752241.256.744210.15
1.76241.256.752208.43
1.768241.256.76272.48
1.776241.256.768274.2
1.784241.256.776210.15
1.792241.256.784208.43
1.8241.256.792272.48
1.808241.256.8274.2
1.816241.256.808210.15
1.824241.256.816208.43
1.832241.256.824272.48
1.84241.256.832274.2
1.848241.256.84210.15
1.856241.256.848208.43
1.864241.256.856272.48
1.872241.256.864274.2
1.88241.256.872210.15
1.888241.256.88208.43
1.896241.256.888272.48
1.904241.256.896274.2
1.912241.256.904210.15
1.92241.256.912208.43
1.928241.256.92272.48
1.936241.256.928274.2
1.944241.256.936210.15
1.952241.256.944208.43
1.96241.256.952272.48
1.968241.256.96274.2
1.976241.256.968210.15
1.984241.256.976208.43
1.992241.256.984272.48
2241.256.992274.2
2.008241.257210.15
2.016241.257.008208.43
2.024241.257.016272.48
2.032241.257.024274.2
2.04241.257.032210.15
2.048241.257.04208.43
2.056241.257.048272.48
2.064241.257.056274.2
2.072241.257.064210.15
2.08241.257.072208.43
2.088241.257.08272.48
2.096241.257.088274.2
2.104241.257.096210.15
2.112241.257.104208.43
2.12241.257.112272.48
2.128241.257.12274.2
2.136241.257.128210.22
2.144241.257.136208.43
2.152241.257.144272.41
2.16241.257.152274.14
2.168241.257.16210.22
2.176241.257.168208.5
2.184241.257.176272.41
2.192241.257.184274.14
2.2241.257.192210.22
2.208241.257.2208.5
2.216241.257.208272.41
2.224241.257.216274.14
2.232241.257.224210.22
2.24241.257.232208.5
2.248241.257.24272.41
2.256241.257.248274.14
2.264241.257.256210.22
2.272241.257.264208.5
2.28241.257.272272.41
2.288241.257.28274.14
2.296241.257.288210.22
2.304241.257.296208.5
2.312241.257.304272.41
2.32241.257.312274.14
2.328241.257.32210.22
2.336241.257.328208.5
2.344241.257.336272.41
2.352241.257.344274.14
2.36241.257.352210.22
2.368241.257.36208.5
2.376241.257.368272.41
2.384241.257.376274.14
2.392241.257.384210.22
2.4241.257.392208.5
2.408241.257.4272.41
2.416241.257.408274.14
2.424241.257.416210.22
2.432241.257.424208.5
2.44241.257.432272.41
2.448241.257.44274.14
2.456241.257.448210.22
2.464241.257.456208.5
2.472241.257.464272.41
2.48241.257.472274.14
2.488241.257.48210.22
2.496241.257.488208.5
2.504241.257.496272.41
2.512241.257.504274.14
2.52241.257.512210.22
2.528241.257.52208.5
2.536241.257.528272.41
2.544241.257.536274.14
2.552241.257.544210.22
2.56241.257.552208.5
2.568241.257.56272.41
2.576241.257.568274.14
2.584241.257.576210.22
2.592241.257.584208.5
2.6241.257.592272.41
2.608241.257.6274.14
2.616241.257.608210.22
2.624241.257.616208.5
2.632241.257.624272.41
2.64241.257.632274.14
2.648241.257.64210.22
2.656241.257.648208.5
2.664241.257.656272.41
2.672241.257.664274.14
2.68241.257.672210.22
2.688241.257.68208.5
2.696241.257.688272.41
2.704241.257.696274.14
2.712241.257.704210.22
2.72241.257.712208.5
2.728241.257.72272.41
2.736241.257.728274.14
2.744241.257.736210.22
2.752241.257.744208.5
2.76241.257.752272.41
2.768241.257.76274.14
2.776241.257.768210.22
2.784241.257.776208.5
2.792241.257.784272.41
2.8241.257.792274.14
2.808241.257.8210.22
2.816241.257.808208.5
2.824241.257.816272.41
2.832241.257.824274.14
2.84241.257.832210.22
2.848241.257.84208.5
2.856241.257.848272.41
2.864241.257.856274.14
2.872241.257.864210.22
2.88241.257.872208.5
2.888241.257.88272.41
2.896241.257.888274.14
2.904241.257.896210.22
2.912241.257.904208.5
2.92241.257.912272.41
2.928241.257.92274.14
2.936241.257.928210.22
2.944241.257.936208.5
2.952241.257.944272.41
2.96241.257.952274.14
2.968241.257.96210.22
2.976241.257.968208.57
2.984241.257.976272.41
2.992241.257.984274.07
3241.257.992210.29
3.008241.258208.57
3.016241.258.008272.34
3.024241.258.016274.07
3.032241.258.024210.29
3.04241.258.032208.57
3.048241.258.04272.34
3.056241.258.048274.07
3.064241.258.056210.29
3.072241.258.064208.57
3.08241.258.072272.34
3.088241.258.08274.07
3.096241.258.088210.29
3.104241.258.096208.57
3.112241.258.104272.34
3.12241.258.112274.07
3.128241.258.12210.29
3.136241.258.128208.57
3.144241.258.136272.34
3.152241.258.144274.07
3.16241.258.152210.29
3.168241.258.16208.57
3.176241.258.168272.34
3.184241.258.176274.07
3.192241.258.184210.29
3.2241.258.192208.57
3.208241.258.2272.34
3.216241.258.208274.07
3.224241.258.216210.29
3.232241.258.224208.57
3.24241.258.232272.34
3.248241.258.24274.07
3.256241.258.248210.29
3.264241.258.256208.57
3.272241.258.264272.34
3.28241.258.272274.07
3.288241.258.28210.29
3.296241.258.288208.57
3.304241.258.296272.34
3.312241.258.304274.07
3.32241.258.312210.29
3.328241.258.32208.57
3.336241.258.328272.34
3.344241.258.336274.07
3.352241.258.344210.29
3.36241.258.352208.57
3.368241.258.36272.34
3.376241.258.368274.07
3.384241.258.376210.29
3.392241.258.384208.57
3.4241.258.392272.34
3.408241.258.4274.07
3.416241.258.408210.29
3.424241.258.416208.57
3.432241.258.424272.34
3.44241.258.432274.07
3.448241.258.44210.29
3.456241.258.448208.57
3.464241.258.456272.34
3.472241.258.464274.07
3.48241.258.472210.29
3.488241.258.48208.57
3.496241.258.488272.34
3.504241.258.496274.07
3.512241.258.504210.29
3.52241.258.512208.57
3.528241.258.52272.34
3.536241.258.528274.07
3.544241.258.536210.29
3.552241.258.544208.57
3.56241.258.552272.34
3.568241.258.56274.07
3.576241.258.568210.29
3.584241.258.576208.57
3.592241.258.584272.34
3.6241.258.592274.07
3.608241.258.6210.29
3.616241.258.608208.57
3.624241.258.616272.34
3.632241.258.624274.07
3.64241.258.632210.29
3.648241.258.64208.57
3.656241.258.648272.34
3.664241.258.656274.07
3.672241.258.664210.29
3.68241.258.672208.57
3.688241.258.68272.34
3.696241.258.688274.07
3.704241.258.696210.29
3.712241.258.704208.57
3.72241.258.712272.34
3.728241.258.72274.07
3.736241.258.728210.29
3.744241.258.736208.57
3.752241.258.744272.34
3.76241.258.752274.07
3.768241.258.76210.29
3.776241.258.768208.57
3.784241.258.776272.34
3.792241.258.784274.07
3.8241.258.792210.29
3.808241.258.8208.64
3.816241.258.808272.34
3.824241.258.816274
3.832241.258.824210.29
3.84241.258.832208.64
3.848241.258.84272.27
3.856241.258.848274
3.864241.258.856210.36
3.872241.258.864208.64
3.88241.258.872272.27
3.888241.258.88274
3.896241.258.888210.36
3.904241.258.896208.64
3.912241.258.904272.27
3.92241.258.912274
3.928241.258.92210.36
3.936241.258.928208.64
3.944241.258.936272.27
3.952241.258.944274
3.96241.258.952210.36
3.968241.258.96208.64
3.976241.258.968272.27
3.984241.258.976274
3.992241.258.984210.36
4241.258.992208.64
4.008241.259272.27
4.016241.259.008274
4.024241.259.016210.36
4.032241.259.024208.64
4.04241.259.032272.27
4.048241.259.04274
4.056241.259.048210.36
4.064241.259.056208.64
4.072241.259.064272.27
4.08241.259.072274
4.088241.259.08210.36
4.096241.259.088208.64
4.104241.259.096272.27
4.112241.259.104274
4.12241.259.112210.36
4.128241.259.12208.64
4.136241.259.128272.27
4.144241.259.136274
4.152241.259.144210.36
4.16241.259.152208.64
4.168241.259.16272.27
4.176241.259.168274
4.184241.259.176210.36
4.192241.259.184208.64
4.2241.259.192272.27
4.208241.259.2274
4.216241.259.208210.36
4.224241.259.216208.64
4.232241.259.224272.27
4.24241.259.232274
4.248241.259.24210.36
4.256241.259.248208.64
4.264241.259.256272.27
4.272241.259.264274
4.28241.259.272210.36
4.288241.259.28208.64
4.296241.259.288272.27
4.304241.259.296274
4.312241.259.304210.36
4.32241.259.312208.64
4.328241.259.32272.27
4.336241.259.328274
4.344241.259.336210.36
4.352241.259.344208.64
4.36241.259.352272.27
4.368241.259.36274
4.376241.259.368210.36
4.384241.259.376208.64
4.392241.259.384272.27
4.4241.259.392274
4.408241.259.4210.36
4.416241.259.408208.64
4.424241.259.416272.27
4.432241.259.424274
4.44241.259.432210.36
4.448241.259.44208.64
4.456241.259.448272.27
4.464241.259.456274
4.472241.259.464210.36
4.48241.259.472208.64
4.488241.259.48272.27
4.496241.259.488274
4.504241.259.496210.36
4.512241.259.504208.64
4.52241.259.512272.27
4.528241.259.52274
4.536241.259.528210.36
4.544241.259.536208.64
4.552241.259.544272.27
4.56241.259.552274
4.568241.259.56210.36
4.576241.259.568208.64
4.584241.259.576272.27
4.592241.259.584274
4.6241.259.592210.36
4.608241.259.6208.64
4.616241.259.608272.27
4.624241.259.616274
4.632241.259.624210.36
4.64241.259.632208.64
4.648241.259.64272.27
4.656241.259.648273.93
4.664241.259.656210.36
4.672241.259.664208.7
4.68241.259.672272.27
4.688241.259.68273.93
4.696241.259.688210.43
4.704241.259.696208.7
4.712241.259.704272.21
4.72241.259.712273.93
4.728241.259.72210.43
4.736241.259.728208.7
4.744241.259.736272.21
4.752241.259.744273.93
4.76241.259.752210.43
4.768241.259.76208.7
4.776241.259.768272.21
4.784241.259.776273.93
4.792241.259.784210.43
4.8241.259.792208.7
4.808241.259.8272.21
4.816241.259.808273.93
4.824241.259.816210.43
4.832241.259.824208.7
4.84241.259.832272.21
4.848241.259.84273.93
4.856241.259.848210.43
4.864241.259.856208.7
4.872241.259.864272.21
4.88241.259.872273.93
4.888241.259.88210.43
4.896241.259.888208.7
4.904241.259.896272.21
4.912241.259.904273.93
4.92241.259.912210.43
4.928241.259.92208.7
4.936241.259.928272.21
4.944241.259.936273.93
4.952241.259.944210.43
4.96241.259.952208.7
4.968241.259.96272.21
4.976241.259.968273.93
4.984241.259.976210.43
9.984208.7
9.992272.21
10273.93

References

  1. Weiss, P.; Taruskin, R. Music in the Western World; Cengage Learning: Farmington Hills, MI, USA, 2007. [Google Scholar]
  2. Chase, D. Transient Analysis in Hydraulic Networks; University of Dayton: Dayton, OH, USA, 2020. [Google Scholar]
  3. Chaudhry, M.H. Applied Hydraulic Transients, 3rd ed.; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar]
  4. Wylie, E.B.; Streeter, V.L. Fluid Transients in Systems; Prentice Hall: Englewood Cliffs, NJ, USA, 1993. [Google Scholar]
  5. Thorley, A.R.D. Fluid Transients in Pipeline Systems, 2nd ed.; Professional Engineering Publishing: London, UK, 2004. [Google Scholar]
  6. Lingireddy, S.; Wood, D. Surge Analysis and the Wave Plan Method: A Powerful, Accurate, and Stable Method for Water Hammer Studies; KYPipe: Cary, NC, USA, 2021. [Google Scholar]
  7. Wood, D.J.; Funk, J.E. Hydraulic transients in systems with significant pipe elasticity. J. Basic Eng. 1966, 88, 185–196. [Google Scholar]
  8. Tullis, J.P. Hydraulics of Pipelines: Pumps, Valves, Cavitation, Transients; John Wiley & Sons: Hoboken, NJ, USA, 1989. [Google Scholar]
  9. Bly, S. Presenting Information in Sound. In Proceedings of the 1982 Conference on Human Factors in Computing Systems, Gaithersburg, MD, USA, 15–17 March 1982. [Google Scholar]
  10. Zhao, H.; Plaisant, C.; Shneiderman, B.; Lazar, J. Data Sonification for Users with Visual Impairment. ACM Trans. Comput.-Hum. Interact. 2008, 15, 4. [Google Scholar] [CrossRef]
  11. Forssén, J.; Kaczmarek, T.; Alvarsson, J.; Lundén, P.; Nilsson, M.E. Auralization of traffic noise within the LISTEN project—Preliminary results for passenger car pass-by. In Proceedings of Euronoise 2009: Action on Noise in Europe; Kang, J., Ed.; Institute of Acoustics: Milton Keynes, UK, 2009; pp. 1–11. [Google Scholar]
  12. Lenzi, S.; Terenghi, G.; Taormina, R.; Galelli, S.; Ciuccarelli, P. Disclosing Cyber Attacks on Water Distribution Systems. An Experimental Approach to the Sonification of Threats and Anomalous Data. In Proceedings of the 25th International Conference on Auditory Display (ICAD 2019), Newcastle upon Tyne, UK, 23–27 June 2019. [Google Scholar]
  13. Axon, L.; Creese, S.; Goldsmith, M.; Nurse, J.R.C. Reflecting on the Use of Sonification for Network Monitoring. In ThinkMind; IARIA: Wilmington, DE, USA, 2016; pp. 254–261. [Google Scholar]
  14. Muralidharan, L.; Ali, S.; Alfieri, F.; Jorgensen, J.; Agrawal, M. Sonify: Making Visual Graphs Accessible. In Proceedings of the 1st International Conference on Human Interaction and Emerging Technologies (IHIET 2019), Nice, France, 22–24 August 2019. [Google Scholar]
  15. Wood, D.J.; Lingireddy, S.; Boulos, P.F. Numerical Methods for Modeling Transient Flow in Distribution Systems; MWH Soft Press: Broomfield, CO, USA, 2005. [Google Scholar]
  16. Suits, B.H. Physics of Music—Notes. Retrieved from Michigan Technological University. 2023. Available online: https://web.archive.org/web/20230129030505/https://pages.mtu.edu/~suits/Physicsofmusic.html (accessed on 10 January 2023).
  17. Hermann von Helmholtz, A.J. On the Sensations of Tone as a Physiological Basis for the Theory of Music; Green Longmans: London, UK, 1885. [Google Scholar]
  18. Zaman, K. Sonification of Transient Pressure (Water Hammer) in a Hydraulic Network [Video]. YouTube. Available online: https://www.youtube.com/watch?v=HYqgvy5XawI (accessed on 26 April 2023).
  19. Muggleton, J.M.; Yan, J.; Brennan, M.J. Pipeline leak detection using acoustic methods. J. Pipeline Syst. Eng. Pract. 2011, 2, 145–153. [Google Scholar]
  20. Zhou, C.; Zhang, Y.; Zhao, X. Machine learning-based anomaly detection for pipeline acoustic monitoring. Sensors 2020, 20, 724. [Google Scholar]
  21. Meniconi, S.; Brunone, B.; Ferrante, M. Water pipeline integrity assessment by means of unsteady-state tests. J. Hydraul. Eng. 2012, 138, 715–725. [Google Scholar]
Figure 1. Head change at a discontinuity due to pressure wave [2].
Figure 1. Head change at a discontinuity due to pressure wave [2].
Water 17 01950 g001
Figure 2. KYPipe model schematic.
Figure 2. KYPipe model schematic.
Water 17 01950 g002
Figure 3. Inertia and specific speed calculator.
Figure 3. Inertia and specific speed calculator.
Water 17 01950 g003
Figure 4. Pressure history of KYPipe hydraulic model from 4.8 s to 6.4 s.
Figure 4. Pressure history of KYPipe hydraulic model from 4.8 s to 6.4 s.
Water 17 01950 g004
Figure 5. The C-major scale on a piano and staff (source: Google images).
Figure 5. The C-major scale on a piano and staff (source: Google images).
Water 17 01950 g005
Figure 6. Sound samples recorded in Logic Pro X Digital Audtio Workstation.
Figure 6. Sound samples recorded in Logic Pro X Digital Audtio Workstation.
Water 17 01950 g006
Figure 7. Microsoft Excel spreadsheet.
Figure 7. Microsoft Excel spreadsheet.
Water 17 01950 g007
Table 1. Pressure values and the corresponding musical note.
Table 1. Pressure values and the corresponding musical note.
Pressure (kPa)Musical NotePressure (kPa)Musical Note
206.84C0245.51C4
208.35D0246.89D4
209.79E0248.21E4
211.22F0249.59F4
212.36G0250.97G4
213.74A0252.35A4
215.12B0253.73B4
216.50C1255.11C5
217.87D1256.47D5
219.25E1257.86E5
220.63F1259.24F5
222.01G1260.62G5
223.39A1261.99A5
224.77B1263.37B5
226.15C2264.75C6
227.53D2266.13D6
228.91E2267.51E6
230.28F2268.91F6
231.66G2270.28G6
233.04A2271.66A6
234.42B2273.04B6
235.80C3274.42C7
237.18D3275.79D7
238.56E3277.17E7
239.94F3278.55F7
241.32G3279.93G7
242.70A3281.31A7
244.07B3282.69B7
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zaman, M.K. Auditory Representation of Transient Hydraulic Phenomena: A Novel Approach to Sonification of Pressure Waves in Hydraulic Systems. Water 2025, 17, 1950. https://doi.org/10.3390/w17131950

AMA Style

Zaman MK. Auditory Representation of Transient Hydraulic Phenomena: A Novel Approach to Sonification of Pressure Waves in Hydraulic Systems. Water. 2025; 17(13):1950. https://doi.org/10.3390/w17131950

Chicago/Turabian Style

Zaman, Muhammad Khizer. 2025. "Auditory Representation of Transient Hydraulic Phenomena: A Novel Approach to Sonification of Pressure Waves in Hydraulic Systems" Water 17, no. 13: 1950. https://doi.org/10.3390/w17131950

APA Style

Zaman, M. K. (2025). Auditory Representation of Transient Hydraulic Phenomena: A Novel Approach to Sonification of Pressure Waves in Hydraulic Systems. Water, 17(13), 1950. https://doi.org/10.3390/w17131950

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop