This page has only limited features, please log in for full access.

Mr. Libor Kudela
Brno University of Technology, Faculty of Mechanical Engineering

Basic Info


Research Keywords & Expertise

0 District Heating
0 Energy
0 Machine Learning
0 Modelling
0 Optimization

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 02 December 2020 in Energies
Reads 0
Downloads 0

Modern control strategies for district-level heating and cooling supply systems pose a difficult challenge. In order to integrate a wide range of hot and cold sources, these new systems will rely heavily on accumulation and much lower operating temperatures. This means that predictive models advising the control strategy must take into account long-lasting thermal effects but must not be computationally too expensive, because the control would not be possible in practice. This paper presents a simple but powerful systematic approach to reducing the complexity of individual components of such models. It makes it possible to combine human engineering intuition with machine learning and arrive at comprehensive and accurate models. As an example, a simple steady-state heat loss of buried pipes is extended with dynamics observed in a much more complex model. The results show that the process converges quickly toward reasonable solutions. The new auto-generated model performs 5 × 104 times faster than its complex equivalent while preserving essentially the same accuracy. This approach has great potential to enhance the development of fast predictive models not just for district heating. Only open-source software was used, while OpenModelica, Python, and FEniCS were predominantly used.

ACS Style

Libor Kudela; Radomír Chýlek; Jiří Pospíšil. Efficient Integration of Machine Learning into District Heating Predictive Models. Energies 2020, 13, 6381 .

AMA Style

Libor Kudela, Radomír Chýlek, Jiří Pospíšil. Efficient Integration of Machine Learning into District Heating Predictive Models. Energies. 2020; 13 (23):6381.

Chicago/Turabian Style

Libor Kudela; Radomír Chýlek; Jiří Pospíšil. 2020. "Efficient Integration of Machine Learning into District Heating Predictive Models." Energies 13, no. 23: 6381.

Journal article
Published: 16 February 2019 in Energies
Reads 0
Downloads 0

This paper compares approaches for accurate numerical modeling of transients in the pipe element of district heating systems. The distribution grid itself affects the heat flow dynamics of a district heating network, which subsequently governs the heat delays and entire efficiency of the distribution. For an efficient control of the network, a control system must be able to predict how “temperature waves” move through the network. This prediction must be sufficiently accurate for real-time computations of operational parameters. Future control systems may also benefit from the accumulation capabilities of pipes. In this article, the key physical phenomena affecting the transients in pipes were identified, and an efficient numerical model of aboveground district heating pipe with heat accumulation was developed. The model used analytical methods for the evaluation of source terms. Physics of heat transfer in the pipe shells was captured by one-dimensional finite element method that is based on the steady-state solution. Simple advection scheme was used for discretization of the fluid region. Method of lines and time integration was used for marching. The complexity of simulated physical phenomena was highly flexible and allowed to trade accuracy for computational time. In comparison with the very finely discretized model, highly comparable transients were obtained even for the thick accumulation wall.

ACS Style

Libor Kudela; Radomir Chylek; Jiri Pospisil. Performant and Simple Numerical Modeling of District Heating Pipes with Heat Accumulation. Energies 2019, 12, 633 .

AMA Style

Libor Kudela, Radomir Chylek, Jiri Pospisil. Performant and Simple Numerical Modeling of District Heating Pipes with Heat Accumulation. Energies. 2019; 12 (4):633.

Chicago/Turabian Style

Libor Kudela; Radomir Chylek; Jiri Pospisil. 2019. "Performant and Simple Numerical Modeling of District Heating Pipes with Heat Accumulation." Energies 12, no. 4: 633.

Journal article
Published: 01 July 2018 in Energy
Reads 0
Downloads 0
ACS Style

Jiří Pospíšil; Michal Špiláček; Libor Kudela. Potential of predictive control for improvement of seasonal coefficient of performance of air source heat pump in Central European climate zone. Energy 2018, 154, 415 -423.

AMA Style

Jiří Pospíšil, Michal Špiláček, Libor Kudela. Potential of predictive control for improvement of seasonal coefficient of performance of air source heat pump in Central European climate zone. Energy. 2018; 154 ():415-423.

Chicago/Turabian Style

Jiří Pospíšil; Michal Špiláček; Libor Kudela. 2018. "Potential of predictive control for improvement of seasonal coefficient of performance of air source heat pump in Central European climate zone." Energy 154, no. : 415-423.