Två exjobb inom området ”Signaler och System” utannonseras (kan utföras i Tyskland)
Water networks are an important part of our infrastructure as they deliver drinking water to households, public spaces and businesses. Providing clean and save drinking water is an important responsibility and requires considerable resources. Unfortunately, considerable amounts of drinking water are lost due to leakage ranking from small holes, which may only be detected after a long time and may hence still cause considerable losses, to complete breaking of large pipes leading to a loss of large quantities of water within a short time.
In any case, losing drinking water due to leakage also means that the energy required for cleaning and transporting the water is essentially lost. Since it is estimated that usually between 15% and 40% of drinking water is lost due to leakage, this also entails a large loss
2. Thesis 1: Model based detection of leakage in drinking water networks
One major obstacle in avoiding water loss due to leakage is the need to detect and localise leakages before they can be repaired. Especially in the case of small leakages the consequence changes in the flow data, used to monitor the system might be small and gradual.
In this project, we seek to investigate an automated, model-driven approach to detect and localise water leakage in a drinking water system. It hence seeks to minimise the time required to detect and localise leaks such that the amount of water lost due to leakage can be
2.2 Required or desired prior knowledge and skills:
modelling of dynamical systems, signal processing, statistics, programming skills
3. Thesis 2: Model based optimisation of flow data sensor locations in drinking water networks
One major obstacle in avoiding water loss due to leakage is the need to detect and localise leakages before they can be repaired. For this purpose, flow sensors are placed into the water network, which can be used to detect changes in the flow and hence aid the detection and localisation of leaks. However, flow sensors and their installation is costly such that typically far less measurements are available than would be desired.
In this project, we seek to investigate a model-driven approach to find optimal locations where sensors should be placed in the
water network such that the overall, estimated time required to detect and localise leakages can be minimised throughout the
3.2 Required or desired prior knowledge and skills:
modelling of dynamical systems, optimisation, signal processing, statistics, programming skills
4. Time plan:
The master thesis is planned to be conducted during the spring semester 2020.
Steffi Knorn, firstname.lastname@example.org
Bengt Carlsson, email@example.com