Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Pressure-induced dissociation of casein micelles: size distribution and effect of temperature

Pressure-induced dissociation of a turbid solution of casein micelles was studied in situ in static and dynamic light scattering experiments. We show that at high pressure casein micelles decompose into small fragments comparable in size to casein monomers. At intermediate pressure we observe particles measuring 15 to 20 nm in diameter. The stability against pressure dissociation increased with temperature, suggesting enhanced hydrophobic contacts. The pressure transition curves are biphasic, compatible with a temperature (but not pressure)-dependent conformational equilibrium of two micelle species. Our thermodynamic model predicts an increase in structural entropy with temperature.

Radar studies of the vertical distribution of insects migrating over southern Britain: the influence of temperature inversions on nocturnal layer concentrations

Insects migrating over two sites in southern UK (Malvern in Worcestershire, and Harpenden in Hertfordshire) have been monitored continuously with nutating vertical-looking radars (VLRs) equipped with powerful control and analysis software. These observations make possible, for the first time, a systematic investigation of the vertical distribution of insect aerial density in the atmosphere, over temporal scales ranging from the short (instantaneous vertical profiles updated every 15 min) to the very long (profiles aggregated over whole seasons or even years). In the present paper, an outline is given of some general features of insect stratification as revealed by the radars, followed by a description of occasions during warm nights in the summer months when intense insect layers developed. Some of these nocturnal layers were due to the insects flying preferentially at the top of strong surface temperature inversions, and in other cases, layering was associated with higher-altitude temperature maxima, such as those due to subsidence inversions. The layers were formed from insects of a great variety of sizes, but peaks in the mass distributions pointed to a preponderance of medium-sized noctuid moths on certain occasions.

Distribution related to temperature and salinity of the shrimps Acetes americanus and Peisos petrunkevitchi (Crustacea: Sergestoidea) in the south-eastern Brazilian littoral zone

The abundance and ecological distribution of Acetes americanus and Peisos petrunkevitchi were investigated from July 2006 to June 2007, in Ubatuba, Brazil. Eight transects were identified and sampled monthly: six of these transects were located in Ubatuba bay, with depths reaching 21 m, and the other two transects were in estuarine environments. A total of 33,888 A. americanus shrimp were captured, with the majority coming from the shallower transects (up to 10 m). Conversely, 6,173 of the P. petrunkevitchi shrimps were captured in deeper areas (from 9 to 21 m). No individuals from either species were found in the estuary. The highest abundances obtained for both species were sampled during the summer. Canonical correlation analysis resulted in a coefficient value of 0.68 (P = 0.00). The abundance of both species was strongly correlated with depth. Variations in temperature and salinity values were also informative in predicting the seasonal presence of P. petrunkevitchi in deeper areas and A. americanus in the shallower areas of the bay. It is conceivable that the shrimp adjust their ecological distribution according to their intrinsic physiological limitations. © 2012 Marine Biological Association of the United Kingdom.

Proliferative kidney disease in rainbow trout: time- and temperature-related renal pathology and parasite distribution

Proliferative kidney disease is a parasitic infection of salmonid fishes caused by Tetracapsuloides bryosalmonae. The main target organ of the parasite in the fish is the kidney. To investigate the influence of water temperature on the disease in fish, rainbow trout Oncorhynchus mykiss infected with T bryosalmonae were kept at 12 degrees C and 18 degrees C. The number of parasites, the type and degree of lesions in the kidney and the mortality rate was evaluated from infection until full development of disease. While mortality stayed low at 12 degrees C, it reached 77% at 18 degrees C. At 12 degrees C, pathological lesions were dominated by a multifocal proliferative and granulomatous interstitial nephritis. This was accompanied by low numbers of T. bryosalmonae, mainly located in the interstitial lesions. With progression of the disease, small numbers of parasites appeared in the excretory tubuli, and parasite DNA was detected in the urine. Parasite degeneration in the interstitium was observed at late stages of the disease. At 18 degrees C, pathological lesions in kidneys were more severe and more widely distributed, and accompanied by significantly higher parasite numbers. Distribution of parasites in the renal compartments, onset of parasite degeneration and time course of appearance of parasite DNA in urine were not clearly different from the 12 degrees C group. These findings indicate that higher mortality at 18 degrees C compared to 12 degrees C is associated with an enhanced severity of renal pathology and increased parasite numbers.

Temperature Prediction Model for Bone Drilling Based on Density Distribution and In Vivo Experiments for Minimally Invasive Robotic Cochlear Implantation

Surgical robots have been proposed ex vivo to drill precise holes in the temporal bone for minimally invasive cochlear implantation. The main risk of the procedure is damage of the facial nerve due to mechanical interaction or due to temperature elevation during the drilling process. To evaluate the thermal risk of the drilling process, a simplified model is proposed which aims to enable an assessment of risk posed to the facial nerve for a given set of constant process parameters for different mastoid bone densities. The model uses the bone density distribution along the drilling trajectory in the mastoid bone to calculate a time dependent heat production function at the tip of the drill bit. Using a time dependent moving point source Green's function, the heat equation can be solved at a certain point in space so that the resulting temperatures can be calculated over time. The model was calibrated and initially verified with in vivo temperature data. The data was collected in minimally invasive robotic drilling of 12 holes in four different sheep. The sheep were anesthetized and the temperature elevations were measured with a thermocouple which was inserted in a previously drilled hole next to the planned drilling trajectory. Bone density distributions were extracted from pre-operative CT data by averaging Hounsfield values over the drill bit diameter. Post-operative [Formula: see text]CT data was used to verify the drilling accuracy of the trajectories. The comparison of measured and calculated temperatures shows a very good match for both heating and cooling phases. The average prediction error of the maximum temperature was less than 0.7 °C and the average root mean square error was approximately 0.5 °C. To analyze potential thermal damage, the model was used to calculate temperature profiles and cumulative equivalent minutes at 43 °C at a minimal distance to the facial nerve. For the selected drilling parameters, temperature elevation profiles and cumulative equivalent minutes suggest that thermal elevation of this minimally invasive cochlear implantation surgery may pose a risk to the facial nerve, especially in sclerotic or high density mastoid bones. Optimized drilling parameters need to be evaluated and the model could be used for future risk evaluation.