Fremtidens forskere inden for ingeniørvidenskab støbes hos os. Vores ph.d.-studerende har høje akademiske ambitioner og leverer resultater af høj kvalitet til både den private og den offentlige sektor. Vores hovedfokus er anvendt forskning, og vi har et stærkt samarbejde med branchen for byggeri og bygningsdesign, fordi vi forstår deres kerneudfordringer og bidrager til at udvikle løsningerne.
Her på siden kan du møde nogle af vores ph.d.-studerende og læse om deres projekter.
Et team af forskere fra Aarhus Universitet har for første gang nogensinde koblet 40 års produktivitetsdata i byggeriet med det faktisk udførte arbejde. Resultaterne viser, at produktiviteten i byggeriet har været faldende siden 70’erne, og giver samtidig svar på, hvorfor den falder, samt hvordan vi kan få langt mere effektivt byggeri i Nordamerika og Europa.
”Dem, der har størst indflydelse på effektiviteten, og dermed på hvor mange penge der tjenes, er byggeledelsen. Og måden, man øger effektiviteten, er ved at bruge metoder, værktøjer og viden, der allerede eksisterer," siger ph.d. Hasse Neve, der sammen med bl.a. professor Søren Wandahl står bag den nye forskning, der viser, hvordan man kan ændre produktivitetsudviklingen i byggeriet.
Mød flere af vores ph.d.-studerende ved Institut for Byggeri og Bygningsdesign og læs om deres projekter her:
Wind, solar, wave, and tidal energy play a central role in achieving the decarbonization of our energy system. As a consequence, a large portion of future power grids will be installed offshore in the form of floating structures interconnected by a shared mooring system in a scalable and cost-optimal way.
Optimizing such systems requires accurate prediction of hydrodynamic loading exerted on floating structures. Despite the tremendous development of computational modeling tools, hydrodynamic loading models still require extensive experimental validation to provide accurate predictions. Such experiments are time-consuming and, therefore, limited in duration and number.
This project aims at developing and implementing machine learning algorithms for the design of optimal hydrodynamic experiments. The goal of the algorithms is to provide information about a floating model such that the cost of calibration of hydrodynamic loading models is minimized, and uncertainty on responses of interest can be quantified.
Large concrete structures such as bridges, dams and tunnels are often exposed to water flowing at high speeds and carrying a substantial amount of debris that causes surface damage due to mechanical erosion. The damage, which is also known as abrasion, leads to a premature end of the service life if the structures are not designed properly. The economic, societal and environmental costs of poorly design infrastructure is colossal and should therefore be avoided.
This project aims to develop a practical design guidance regarding concrete abrasion for hydraulic structures from a long-term durability perspective. With this being said, a central task is to establish the relation between the actual abrasion rate and the relevant parameters, including hydraulic parameters and concrete properties. Once the abrasion rate is known, the service life of the structure can be designed with high confidence. More specifically, the main objectives include:
An improper thermal environment may result in a negative spiral of development for animals, especially domestic animals raised in a relatively closed environment. Some regions of high latitude, i.e. Northeast China, are main areas for dairy production. However, the climate there in winter is especially cold and the average temperature can be as low as minus 20 degrees. Additionally, high-humidity air and high concentration of harmful gases, i.e. Carbon dioxide, methane, ammonia, and nitrous oxide, appeared in dairy cattle barns contribute to a passive impact on the production and reproduction of cows. Hence, it is always a challenge to achieve a balance between the construction economy and good indoor climate towards dairy cattle barns in these regions.
The aims of the project are: 1) to introduce an innovative ventilation design for optimizing the thermal and airflow conditions in these different types of cattle barns; 2) to set up a dynamic predictive model to provide a precision environment control strategy at individual animal or defined zone level; 3) to improve animal welfare and to reduce environmental impact in cold region; 4) to generate a design standard for ventilation and construction of cattle barn with considerations of energy saving and animal welfare.
The initial design phase is a stage in the construction process that is often neglected because it is highly time consuming. This is an issue because the initial design phase holds the most influence on the success of the final building design. Today the current procedure of finding the overall structural layout is based on a trial-and-error approach with very few iterations. This PhD project aims to redeem this untapped potential by creating a tool that can automate the process of creating optimized design suggestions in the initial design phase based on architectural drawings and models.
Designing optimum structural solutions involves a holistic approach because of the many design-variables, objectives and constraints. The project will apply reinforcement learning algorithms coupled with surrogate models and expert systems to handle the high complexity. Additionally, the tool will be constructed with nested loops where each level represents an increasing fidelity level of the calculations. The development of the tool will also extend to areas of interaction, automation and visualization to improve the mediation of the results.
The strategy of the Danish government is for the Danish energy production to be free from fossil fuels by 2050. This requires renewable energy production, which is typically controlled by weather conditions and do not follow the energy demand. A challenge arise in aligning the energy demand to the energy production, either by storing energy or shifting the demand in time. Previous studies in and outside Aarhus university have found a great potential of using model predictive control (MPC) of residential space heating to shift the energy demand in time by exploiting the heat storage potential of thermal mass. It is however yet to be investigated how the MPC reacts towards disturbances from occupant behavior.
Occupants affect the MPC in two ways; firstly, the MPC is set to follow a set of temperature conditions, which change with the occupancy, secondly, the unpredictable behavior of occupants will likely create disturbances in the system and affect the potential of the MPC.
The object of this project comes down to the overall question; do we need to account for occupant behavior in the MPC model? A sub-question related to this is how occupants are affected by MPC of space heating.
The Danish society has set the ambitious goal to be independent of fossil fuels by 2050, i.e. a transition to an energy system relying solely on renewable energy sources such as wind and sun. Innovative utilisation of existing district heating systems plays an essential role in this transition. This PhD project is part of a larger project called HEAT 4.0 financed by Danish companies, universities and the Innovation Fund Denmark. The overall aim of HEAT 4.0 is to create a technology and service platform for district heating companies to enable them to meet consumer and societal demands in costs and the forthcoming post-fossil fuel era.
A main challenge in using renewable energy for district heating is the large fluctuations in production. To face these challenges, previous studies have investigated the potential of residential buildings as thermal “batteries” to change the temporal heating patterns, e.g. to even out the heating demand profile of the district heating systems. The idea is to preheat the thermal mass of the building when the energy price or CO2 emission is low, and discharge the stored energy during the subsequent period with high prices or CO2 emission. This way of controlling the building heating system can reduce cost, CO2 emissions and help district heating companies to solve a range of operational challenges.
This PhD project focuses on testing whether the theoretical potentials can be realised “in real life” using IoT and innovative control algorithms based on Model Predictive Control.
The number of piglets per litter per sow has increased drastically, which means that the heat production from the sow has also increased greatly. This presents a challenge for sows in hot weather, and cooling the highly productive sows is imperative. In order to cool the sow efficiently, the mechanism of the physiological reaction of sows in a hot environment and the heat release to the environment should be investigated.
The PhD project is conducted through a combination of the numerical simulations and experimental investigations to: (1) develop mathematical models of heat transfer coefficients based on the conditions that the sow’s body is cooled wholly or partly; (2) to model the heat transfer from sows to the ambient and combine the heat release process with the physiological reactions based on reasonable assumptions; (3) to investigate and evaluate the chill effects by using different strategies to cool the sows.
According to the Danish National Annex to Eurocode 7, part I, the shaft resistance for a bored cast-in-place pile should not be assumed to be greater than 30 per cent of the shaft resistance of the corresponding driven pile, and the toe resistance is maximised to 1000 kPa. Since 1977 this principle has been enforced (code requirement) in Denmark, allegedly due to execution problems encountered in one or two un-documented case histories.
Hence, it is widely recognised that this reduction in bearing capacity is believed to be overly conservative. If the bored cast-in-place pile is established correct, the reduction of the shaft resistance is still applicable due to limited understanding of the governing mechanism and limited knowledge of the complex soil-pile interaction.
A consequence of this lack of understanding is that bored cast-in-place piles are often designed too conservative, and the bored cast-in-place piles are built more expensive than what is required.
This Industrial PhD project will investigate the shaft and toe resistance of bored cast-in-place piles based on full-scale field tests, model field tests, geotechnical and structural monitoring, and develop a first order analytical method for determination of the shaft (and toe) resistance for bored cast-in-place piles.
Project title: Soil-pile interaction for bored cast-in-place piles in stiff clays and soft rocks
PhD student: Jannie Knudsen
Project start: June 2018
Main supervisor: Kenny Kataoka Sørensen
Co-supervisors: Jørgen S. Steenfelt (COWI A/S) and Helle Trankjær (COWI A/S)
Interest has been growing recently in floating offshore wind turbines (FOWTs), along with a rapid growth in wind energy more generally. Although FOWT is considered the most promising candidate for future offshore wind energy, its mass application cannot be realised before solving the vibration and stability problems, since the floating structure is subjected to stochastic wind and wave loads together with mooring loads.
The main aim of the project is to develop rigorous theoretical and numerical models for carrying out stochastic dynamic analysis and reliability-based design and maintenance of FOWTs subjected to extreme wave loads. Therefore, different models need to be developed during the project, including a mechanical model of the FOWT and a stochastic model of the extreme wave loads. All the proposed theoretical models will be verified and validated by existing codes, experimental results or measurements.
The project also aims at developing novel structural control techniques for FOWT under extreme wave loads.