Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier–Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles

TREFF: Reflectometer and instrument component test beamline at MLZ

TREFF is a high resolution polarized neutron reflectometer and instrument component test beamline resulting in a highly modular instrument providing a flexible beam line for various applications

Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

An optimal control of the combustion process of an engine ensures lower emissions and fuel consumption plus high efficiencies. Combustion parameters such as the peak firing pressure (PFP) and the crank angle (CA) corresponding to 50% of mass fraction burned (MFB50) are essential for a closed-loop control strategy. These parameters are based on the measured in-cylinder pressure that is typically gained by intrusive pressure sensors (PSs). These are costly and their durability is uncertain. To overcome these issues, the potential of using a virtual sensor based on the vibration signals acquired by a knock sensor (KS) for control of the combustion process is investigated. The present work introduces a data-driven approach where a signal-processing technique, designated as discrete wavelet transform (DWT), will be used as the preprocessing step for extracting informative features to perform regression tasks of the selected combustion parameters with extreme gradient boosting (XGBoost) regression models. The presented methodology will be applied to data from two different spark-ignited, single cylinder gas engines. Finally, an analysis is obtained where the important features based on the model’s decisions are identified

Time domain classification of grasp and hold tasks

Brain-Computer Interfaces (BCIs) enable its users to interact with their environment only by thought. Earlier studies indicated [1, 2] that BCI might be a suitable method for controlling a neuroprostheses, which could assist people with spinal cord injuries (SCI) in their daily life. One drawback for the end user is that only simple motor imaginations (MI) are available for control e.g. MI of both feet to control ones arm is abstract and in contradiction to an associated natural movement. Therefore we are looking for means to design a more natural control modality. One promising scenario would be to use MI of different grasps to actually control different grasps of the neuroprosthesis. In this study we attempt to classify the execution of different grasp types in low-frequency time-domain EEG signals

Movements of the same upper limb can be classified from low-frequency time-domain EEG signals

Brain-computer interfaces (BCIs) can be used to control neuroprostheses of spinal cord injured (SCI) persons. A neuroprosthesis can restore different movement functions (e.g., hand open/close, supination/pronation etc.), and requires a BCI with a sufficiently high number of classes. However, sensorimotor rhythm-based BCIs can often only provide less than 3 classes, and new types of BCIs need to be developed. Since a couple of years, a new EEG feature has evolved: low-frequency time-domain signals. For example movement trajectories [1] and movement directions [2] were decoded using this feature. In the present study, we investigated whether low-frequency time-domain signals can also be used to classify several (executed) hand/arm movements of the same limb. A BCI relying on the imagination of such movements may be used to control a neuroprosthesis more naturally and provide a higher number of classes

Discriminating goal-directed from nongoal-directed movements and its potential impact for BCI control

Differences in the electroencephalographic (EEG) recordings between the execution of goal-directed and nongoal-directed movements have been recently shown in [1]. Such differences can be of interest for brain-computer interfaces (BCIs) control, when combined with information on the kinematic level (e.g. velocity decoding), since this combination mirrors the hierarchic way one plans a movement. In this study, we show that the time-domain differences between these movements are discriminable in a single-trial classification

Comment on "On the Extraction of Purely Motor EEG Neural Correlates during an Upper Limb Visuomotor Task"

Bibian et al. show in their recent paper (Bibi\'an et al. 2021) that eye and head movements can affect the EEG-based classification in a reaching motor task. These movements can generate artefacts that can cause an overoptimistic estimation of the classification accuracy. They speculate that such artefacts jeopardise the interpretation of the results from several motor decoding studies including our study (Ofner et al. 2017). While we endorse their warning about artefacts in general, we do have doubts whether their work supports such a statement with respect to our study. We provide in this commentary a more nuanced contextualization of our work presented in Ofner et al. and the type of artefacts investigated in Bibian et al

Manual Khalifa Therapy Improves Functional and Morphological Outcome of Patients with Anterior Cruciate Ligament Rupture in the Knee: A Randomized Controlled Trial

Rupture of the anterior cruciate ligament (ACL) is a high incidence injury usually treated surgically. According to common knowledge, it does not heal spontaneously, although some claim the opposite. Regeneration therapy by Khalifa was developed for injuries of the musculoskeletal system by using specific pressure to the skin. This randomized, controlled, observer-blinded, multicentre study was performed to validate this assumption. Thirty patients with complete ACL rupture, magnetic resonance imaging (MRI) verified, were included. Study examinations (e.g., international knee documentation committee (IKDC) score) were performed at inclusion (t0). Patients were randomized to receive either standardised physiotherapy (ST) or additionally 1 hour of Khalifa therapy at the first session (STK). Twenty-four hours later, study examinations were performed again (t1). Three months later control MRI and follow-up examinations were performed (t2). Initial status was comparable between both groups. There was a highly significant difference of mean IKDC score results at t1 and t2. After 3 months, 47% of the STK patients, but no ST patient, demonstrated an end-to-end homogeneous ACL in MRI. Clinical and physical examinations were significantly different in t1 and t2. ACL healing can be improved with manual therapy. Physical activity can be performed without pain and nearly normal range of motion after one treatment of specific pressure