6 research outputs found
A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation
In this paper, we introduce an efficient sparse Gaussian process (E-SGP) for
the surrogate modelling of fluid mechanics. This novel Bayesian machine
learning algorithm allows efficient model training using databases of different
structures. It is a further development of the approximated sparse GP
algorithm, combining the concept of efficient GP (E-GP) and variational energy
free sparse Gaussian process (VEF-SGP). The developed E-SGP approach exploits
the arbitrariness of inducing points and the monotonically increasing nature of
the objective function with respect to the number of inducing points in
VEF-SGP. By specifying the inducing points on the orthogonal grid/input
subspace and using the Kronecker product, E-SGP significantly improves
computational efficiency without imposing any constraints on the covariance
matrix or increasing the number of parameters that need to be optimised during
training.
The E-SGP algorithm developed in this paper outperforms E-GP not only in
scalability but also in model quality in terms of mean standardized logarithmic
loss (MSLL). The computational complexity of E-GP suffers from the cubic growth
regarding the growing structured training database. However, E-SGP maintains
computational efficiency whilst the resolution of the model, (i.e., the number
of inducing points) remains fixed. The examples show that E-SGP produces more
accurate predictions in comparison with E-GP when the model resolutions are
similar in both. E-GP benefits from more training data but comes with higher
computational demands, while E-SGP achieves a comparable level of accuracy but
is more computationally efficient, making E-SGP a potentially preferable choice
for fluid mechanic problems. Furthermore, E-SGP can produce more reasonable
estimates of model uncertainty, whilst E-GP is more likely to produce
over-confident predictions
Flexible and robust control of heavy duty diesel engine airpath using data driven disturbance observers and GPR models
Diesel engine airpath control is crucial for modern engine development due to increasingly stringent emission regulations. This thesis aims to develop and validate a exible and robust control approach to this problem for speci cally heavy-duty engines. It focuses on estimation and control algorithms that are implementable to the current and next generation commercial electronic control units (ECU). To this end, targeting the control units in service, a data driven disturbance observer (DOB) is developed and applied for mass air ow (MAF) and manifold absolute pressure (MAP) tracking control via exhaust gas recirculation (EGR) valve and variable geometry turbine (VGT) vane. Its performance bene ts are demonstrated on the physical engine model for concept evaluation. The proposed DOB integrated with a discrete-time sliding mode controller is applied to the serial level engine control unit. Real engine performance is validated with the legal emission test cycle (WHTC - World Harmonized Transient Cycle) for heavy-duty engines and comparison with a commercially available controller is performed, and far better tracking results are obtained. Further studies are conducted in order to utilize capabilities of the next generation control units. Gaussian process regression (GPR) models are popular in automotive industry especially for emissions modeling but have not found widespread applications in airpath control yet. This thesis presents a GPR modeling of diesel engine airpath components as well as controller designs and their applications based on the developed models. Proposed GPR based feedforward and feedback controllers are validated with available physical engine models and the results have been very promisin
THIESEL 2020.Thermo-and Fluid Dynamic Processes in Direct Injection Engines.8th-11th September
'The THIESEL 2020 Conference on Thermo-and Fluid Dynamic Processes in Direct Injection Engines planned in Valencia (Spain) for 8th to 11th September 2020 has been successfully held in a virtual format, due to the COVID19 pandemic. In spite of the very tough environmental demands, combustion engines will probably remain the main propulsion system in transport for the next 20 to 50 years, at least for as long as alternative solutions cannot provide the flexibility expected by customers of the 21st century. But it needs to adapt to the new times, and so research in combustion engines is nowadays mostly focused on the new challenges posed by hybridization and downsizing. The topics presented in the papers of the conference include traditional ones, such as Injection & Sprays, Combustion, but also Alternative Fuels, as well as papers dedicated specifically to CO2 Reduction and Emissions Abatement.Papers stem from the Academic Research sector as well as from the IndustryXandra Marcelle, M.; Desantes Fernández, JM. (2020). THIESEL 2020.Thermo-and Fluid Dynamic Processes in Direct Injection Engines.8th-11th September. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/150759EDITORIA