Professor in Acoustics and Biomedical Technology
Anurag is the Head of Fluids group and the Acoustics lab in the Engineering Department at the University of Cambridge. He is a Professor at the University of Cambridge, a Fellow of Emmanuel College and the Cambridge Philosophical Society, and the CEO of BioPhonics Limited. His research interests are in the field of acoustics and aerodynamics of aerospace, domestic appliances and biomedical applications. His collaborators include Rolls-Royce, General Electric, Boeing, Mitsubishi Heavy Industries, JCB, Dyson, Addenbrookes, Queen Elizabeth, King’s, John Radcliffe and Papworth Hospitals.
Andrew is developing machine learning techniques to diagnose cardiovascular disease from heart sounds. He is developing an intelligent stethoscope, which is capable of reliable detection of valvular heart disease. The device will help clinicians detect heart problems earlier and more accurately, to improve patient prognoses and reduce unnecessary referrals to cardiologists.
Francesca De Domenico
Junior Research Fellow
Francesca is currently a Junior Research Fellow at Gonville and Caius College, Cambridge. She holds a BSc and a MSc from the University of Padua (Italy) and she obtained her PhD in 2019 from the University of Cambridge. Her PhD research focused on understanding and reducing the noise emitted by aero-engines and gas turbines, and on developing laser diagnostic techniques suitable for high-pressure, high temperature environments. Recently, her research has shifted from flames to human body, and now she is working on understanding the physics behind osculatory blood pressure measurements.
Kate is analysing cuff-based measurement of blood pressure. This involves investigating both the origin of Korotkoff sounds, which are used in the manual measurement of blood pressure, and the inaccuracy of this measurement technique due to biological factors such as artery and tissue stiffness. Kate’s goal is to develop an improved understanding of the causes – and scale – of error in this technique, which can then be used to increase the accuracy of non-invasive blood pressure measurement.
Nirmani completed her BEng in Electronic Engineering at the University of Hong Kong. She is currently looking at the use of sensors to detect bodily sounds. She is looking at cardiorespiratory sound detection sensors, and their application in the development of wearable technology, to allow for the early detection of diseases. Her goal is to develop a novel wearable device to detect abnormal bodily sounds indicative of cardiorespiratory conditions such as valvular heart disease.
Amélie studied brain aneurysms by trying to understand the fundamental mechanisms behind the generation of their noise. The final aim of her project was to develop a novel non-invasive device for early detection of aneurysms. As ruptured aneurysms carry a high mortality rate, an early diagnosis permits better management of the lesion which at a larger scale could save several lives.
Junior Research Fellow
Alastair was the Neville Junior Research Fellow at Magdalene College, and worked in the acoustics lab from 2014 to 2021. His early work developed a new kind of transformation to allow better understanding of how sound propagates in the presence of background flow, using analogies between aeroacoustics and relativity. He then moved into biomedical applications. His PhD developed a model for the mechanism behind wheezing sounds, producing a simple relationship between wheezing frequency and the tube material properties and geometry. This can be used by clinicians to learn about changes in stiffness of lung tissue and the position of blockages and stenoses. He then expanded understanding of other sounds made in the lung, such as crackles, as well as more general bodily sounds that can be used for diagnosis. In his spare time he built a wooden sailing dingy in the Dyson Centre at the Engineering Department.
Benjamin worked on a numerical approach aiming to model both the acoustic and hydrodynamic fields in a turbulent jet flow. An input-output method was used, involving a singular value decomposition of the resolvent operator. From this, a reduced basis was built to efficiently describe the flow dynamics and to shed light on the physical mechanisms at play. The second-order statistics of the fluctuation quantities were embedded through cross-spectral density matrices. In doing so, the jittering of wave packets – an essential ingredient of jet noise – was accounted for. This work was part of the European project “Daretomodel”, in collaboration with LadHyx (Paris) and Pprime (Poitiers).
Ed worked on understanding and classifying heart sounds. He produced physical models to understand the causes of the heart murmurs associated with aortic stenosis and mitral regurgitation. He also used machine learning techniques to classify heart sounds as either normal or abnormal.
Oscar developed noise prediction methods for turbomachinery operating at low Reynolds number. Noise reduction of air-moving devices such as axial compressors is becoming increasingly important for the industrial engineer, as stringent regulations are placing acoustic design on near equal terms with aerodynamic efficiency. Consequently, noise can no longer be accepted as an undesirable by-product, but rather must be accounted for at an earlier stage, ideally in tandem with aerodynamic design. Oscar worked to implement low order models that can be used to assess noise levels early in the design process. This work involved using analytical models together with computationally demanding fluid dynamics simulations to devise quick but accurate methods for noise production. Oscar was also interested in utilising machine learning algorithms in optimisation for low noise design.