Acoustic Simulation Applications

Air is invisible… so why can we hear it?” 💨👂 When air flows around objects like cars, or airplane blades it forms tiny swirls called vortices creating rapid pressure changes that we hear as #AerodynamicNoise. Here’s how engineers study it: 1️⃣ CFD (Computational Fluid Dynamics) → calculates how air moves, where it swirls, and how pressure changes around the object. 2️⃣ Aerodynamic Noise Simulation → takes the CFD results and predicts how loud the noise will be, its frequency, and where it will be strongest. 💡 Why this matters: • Design quieter and smoother vehicles • Reduce energy and fuel consumption • Detect problems before building anything • Optimize comfort, safety, and efficiency By combining CFD and Noise Simulation, engineers can “hear” invisible air and improve designs before anything exists physically. #Aerodynamics #CFD #AcousticSimulation #AerodynamicNoise #Engineering #AerospaceEngineering #VehicleDesign #DroneDesign #AircraftDesign #Innovation #Efficiency #STEM #EngineeringInsights

AI can now simulate a room's acoustics before a single wall is built. Ray tracing and image source methods predict reverberation times, early reflection maps, and speech intelligibility indices — faster and more cheaply than any physical scale model. These are powerful tools. I use them. Most serious acoustic consultants do. But there are two layers of limitation worth naming honestly. The first is technical. Room Acoustics (Kuttruff) notes that current simulation algorithms haven't found a practical way to model wave diffraction — behaviour at edges, corners, and openings. The tools are geometric approximations. Good approximations. Not complete physics. The second is harder to simulate. The algorithm cannot read the client. It cannot weigh the cultural significance of reverberance in a prayer hall for a particular congregation. It cannot resolve a conflict between what the brief says and what the client actually needs. It cannot make the call when two options both meet spec but feel different. @Prof. Mahavir Singh, PhD IITD, has articulated this boundary clearly: simulation optimises. The consultant decides. The consultant who uses these tools well has a significant advantage. The one who mistakes the output for the answer has a different problem. Where do you see AI shifting — or not shifting — acoustic design practice? #KevinMarioDSouza #SoundAndAbout #Acoustics #ArchitecturalAcoustics

A few days ago I mentioned that I was working on a small experimental platform for underwater acoustic and digital signal processing. Today I would like to introduce it: ❇️ PASLAB : Programmable Acoustic Signal Laboratory. ▶️ PASLAB is a multidisciplinary bench-top development platform designed for hardware-validated acoustic and signal-processing experiments. ▶️ It combines low-noise analog front-end design, controlled waveform and noise generation, measurement of transducer characteristics, analog self-noise observation, embedded configurability, DSP-oriented experimentation, and Qt-based scenario control within a single practical system. ▶️ The system was designed with a strong focus on real-world behavior, where analog noise, gain stages, and hardware limitations are treated as first-class design constraints. ▶️ Controlled Signal and Noise Injection : PASLAB also supports controlled noise injection, allowing programmable signal-to-noise ratio(SNR) experiments to evaluate detection algorithms under repeatable laboratory conditions. ▶️ The platform can emulate a programmable acoustic channel in a Qt-based scenario by combining acoustic signal generation, attenuation, and controlled noise injection (SL, TL, NL, TVR, RVS, TS, etc.) ❇️ PASLAB – What can be done with it? ▶️ Arbitrary waveform generation (rectangular, hann, hamming, blackman, burst, chirp(FM), coded, noise, ping) ▶️ It includes a controllable noise injection and attenuation stage that allows experiments under different SNR conditions.. ▶️ Scenario based Underwater Acoustic Communication ▶️ Scenario based Acoustic Target Detection ▶️ Detecting weak signals buried in noise ▶️ Testing DSP algorithms under controlled conditions (FFT, THD, windowing, energy det., matched filtering, FIR, IIR, SNR , processing gain etc.) ▶️ Transducer Behavior and Matching (BVD Model) ▶️ Transducer Matching & Power Transfer & Estimates Preamplifier Input Impedance ▶️ Measuring analog chain noise floor and dynamic range ▶️ Recording and Replaying real signals under different conditions using SRAM/FRAM ▶️ Detection Algorithms   • Matched Filter   • Energy Detection   • Envelope Detection   • FFT-based Detection   • Adaptive Threshold (CFAR concepts) The images provide the system architecture, signal chain overview, capability summary, and HW design criteria. Note :  In the design of this platform, engineering is not about being 10 out of 10 in one field, but about being 8 out of 10 across several fields at the same time. BTW Passive filters, low noise buffers.. are not shown in the block diagram. The system is currently under development. More results and experiments will be shared once the first hardware tests are completed. #signalprocessing #acoustics #embedded #electronics #instrumentation

New paper out! “Acoustic Propagation/Refraction Through Diffuse Interface Models” Fantastic work of Abbas Ballout, Oscar Andrés Mariño Sánchez, Gonzalo Rubio Calzado and myself, where we present a new method to simulate acoustic wave propagation across diffuse interfaces, capturing transmission and refraction with spectral accuracy. The approach combines our entropy-stable DG framework with a weak compressibility formulation, enabling different sound speeds in each phase. We accurately predict Snell’s law, transmission coefficients, and air–water wave behaviour — paving the way for advanced multiphase acoustic modelling and marine aero/hydro-acoustics applications, such as offshore wind turbines. Full paper here: https://lnkd.in/dcUmUUW8 European Research Council (ERC) Universidad Politécnica de MadridETSIAE - UPM

Assessing Aeroacoustics of Fan Noise in CFD by ENGYS 🚗 Reducing automotive cooling fan noise is critical - with levels reaching up to 85dBA, manufacturers seek efficient CFD solutions. Automotive cooling fans are a major noise source, reaching up to 85dBA at certain frequencies. To tackle this, Johnson Electric partnered with Engys to simulate and reduce fan noise using advanced CFD techniques. The project combined two computational approaches: an unsteady RANS (uRANS) simulation to analyze tonal noise within a 12-hour CPU time and a detached eddy simulation (DES) to assess broadband noise, validated against experimental results. Simulating turbulent flows is challenging, requiring accurate modeling of both the noise source and acoustic wave propagation. Engys leveraged its Helxy software, using an acoustic analogy approach to balance accuracy and computational efficiency. A CAD model of the fan, including the anechoic chamber, was analyzed to optimize mesh, time steps, and numerical schemes. Their extrude meshing algorithm improved boundary layer resolution while maintaining smooth transitions, cutting turnaround times by 20-30%. More on tackling CFD aeroacoustic challenges here: https://lnkd.in/eJxNhuAX #CFD #Aeroacoustics #NoiseReduction #AutomotiveEngineering #Simulation

Aeroacoustic Simulation in Aerospace --> Flow Physics to Noise Control In aerospace engineering, noise is not just something to reduce at the end, it is a design parameter from the very beginning. Working with simulations makes it clear how complex aeroacoustic problems really are. Noise is generated through the interaction of turbulent flow, rotating components, and pressure fluctuations, and these effects are strongly coupled. This is where aeroacoustic simulation becomes essential. By combining CFD with acoustic propagation models, it becomes possible to: • Identify where noise is actually generated • Understand how it propagates around the aircraft • Evaluate design changes early, before physical testing One interesting example is the propagation of sound from monopole sources near propellers, where the interaction between flow and geometry shapes the overall noise radiation pattern. The attached animation (Siemens-Simcenter https://lnkd.in/dhfA3wMn) illustrates how acoustic waves travel and interact with the aircraft structure, something that is not easy to capture without simulation. It matters: • Noise directly impacts certification and environmental constraints • It affects passenger comfort and community acceptance • Early prediction reduces costly redesign cycles Challenges in practice: • Strong coupling between flow physics and acoustics • Different scales between turbulence and sound waves • High computational demand for time-resolved simulations • Need for accurate source-to-far-field modeling From a CAE perspective, aeroacoustics is not only about predicting noise levels, it is about understanding the physics well enough to design quieter systems from the start. As aircraft concepts continue to evolve, especially with new propulsion architectures, the role of simulation in controlling noise will only become more critical. #Aeroacoustics #CFD #CAE #AerospaceEngineering #Simulation #Noise #Multiphysics

𝗔 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗽𝗮𝘁𝗵 𝘁𝗼 𝘃𝗶𝗯𝗿𝗼𝗮𝗰𝗼𝘂𝘀𝘁𝗶𝗰𝘀 𝗶𝗻 𝗙𝗘𝗔. • We all know FEA for linear static stress. • Some of us run modal/natural-frequency analysis. • Now, level up: 𝗰𝗼𝘂𝗽𝗹𝗲 𝘃𝗶𝗯𝗿𝗮𝘁𝗶𝗻𝗴 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝘀𝘂𝗿𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗳𝗹𝘂𝗶𝗱 to simulate the 𝘀𝗼𝘂𝗻𝗱 𝘄𝗲 𝗵𝗲𝗮𝗿—and the 𝗳𝗲𝗲𝗱𝗯𝗮𝗰𝗸 that sound exerts on the structure. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 (𝘁𝗼𝗱𝗮𝘆): • How the 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴 𝗺𝗮𝘁𝗿𝗶𝗰𝗲𝘀 form: C_sf = −C_fs^T • Implementation for 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 vs 𝗻𝗼𝗻-𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 fluid–structure meshes • Practical notes 𝗣𝗿𝗲𝘃𝗶𝗼𝘂𝘀𝗹𝘆 (𝗿𝗲𝗰𝗮𝗽): • Weak-form foundations for coupled solid–acoustic FEM • Discrete 𝗯𝗹𝗼𝗰𝗸 𝗺𝗮𝘁𝗿𝗶𝗰𝗲𝘀 & partitioned systems • 𝗦𝗼𝗹𝘃𝗲𝗿 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 and when to use them Structural accelerations 𝗿𝗮𝗱𝗶𝗮𝘁𝗲 𝘀𝗼𝘂𝗻𝗱; acoustic pressure 𝗹𝗼𝗮𝗱𝘀 𝘁𝗵𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲. Capturing this two-way path makes your predictions credible for NVH, marine, and consumer acoustics. 𝗙𝗼𝗿 𝗰𝘂𝗿𝗶𝗼𝘂𝘀 𝗙𝗘𝗔 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀: See the 𝗔𝗽𝗽𝗲𝗻𝗱𝗶𝘅 for a big-picture, variational roadmap (principle of virtual work) that anchors the implementation. 𝗥𝗲𝗮𝗱𝗲𝗿-𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗲𝗱 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀: • 📚 Cremer — 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦-𝘣𝘰𝘳𝘯𝘦 𝘚𝘰𝘶𝘯𝘥 • 📚 Beranek — 𝘕𝘰𝘪𝘴𝘦 𝘢𝘯𝘥 𝘝𝘪𝘣𝘳𝘢𝘵𝘪𝘰𝘯 𝘊𝘰𝘯𝘵𝘳𝘰𝘭 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 • 📚 Fahy — 𝘍𝘰𝘶𝘯𝘥𝘢𝘵𝘪𝘰𝘯𝘴 𝘰𝘧 𝘌𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨 𝘈𝘤𝘰𝘶𝘴𝘵𝘪𝘤𝘴; 𝘚𝘰𝘶𝘯𝘥 𝘢𝘯𝘥 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘢𝘭 𝘝𝘪𝘣𝘳𝘢𝘵𝘪𝘰𝘯 • 📚 Sigrist — 𝘍𝘭𝘶𝘪𝘥-𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦 𝘐𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘰𝘯: 𝘈𝘯 𝘐𝘯𝘵𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 𝘵𝘰 𝘍𝘪𝘯𝘪𝘵𝘦 𝘌𝘭𝘦𝘮𝘦𝘯𝘵 𝘊𝘰𝘶𝘱𝘭𝘪𝘯𝘨 • 📚 Anselmet — 𝘈𝘤𝘰𝘶𝘴𝘵𝘪𝘤𝘴, 𝘈𝘦𝘳𝘰𝘢𝘤𝘰𝘶𝘴𝘵𝘪𝘤𝘴 𝘢𝘯𝘥 𝘝𝘪𝘣𝘳𝘢𝘵𝘪𝘰𝘯𝘴 • 📚 Blevins — 𝘍𝘭𝘰𝘸-𝘐𝘯𝘥𝘶𝘤𝘦𝘥 𝘝𝘪𝘣𝘳𝘢𝘵𝘪𝘰𝘯 • 📚 Morse & Ingard — 𝘛𝘩𝘦𝘰𝘳𝘦𝘵𝘪𝘤𝘢𝘭 𝘈𝘤𝘰𝘶𝘴𝘵𝘪𝘤𝘴 • 📚 Junger & Feit — 𝘚𝘰𝘶𝘯𝘥, 𝘚𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘴, 𝘢𝘯𝘥 𝘛𝘩𝘦𝘪𝘳 𝘐𝘯𝘵𝘦𝘳𝘢𝘤𝘵𝘪𝘰𝘯 𝗣.𝗦. What other classic or modern books are your go-to—and 𝘄𝗵𝘆? Drop your list (with use-cases) in the comments. #FEA #Vibroacoustics #Acoustics #FSI #NVH #Simulation #CAE