Nested Topology Optimization Methodology for Designing Two-Wheel Chassis

Weight reduction has always been a challenge for the automotive industry, mainly to reduce consumption but also improve handling. In electric vehicle design, the battery packs, their shape and positioning are critical aspects that determine the overall weight, weight distribution and, as a consequence, the efficiency, dynamics and stability of the vehicle. This presented a new challenge, to manage this necessary and inflexible weight and volume, developing the vehicle chassis around it and in the best possible way, without compromising the overall efficiency and behaviour. In this work, a methodology for nested topology optimization has been developed which combines structural topology optimization and battery pack shaping and positioning. The new methodology is implemented, without limiting its applicability, into the framework of the commercial software Hyperstudy by Altair. Document type: Articl

Nested Topology Optimization Methodology for Designing Two-Wheel Chassis

Weight reduction has always been a challenge for the automotive industry, mainly to reduce consumption but also improve handling. In electric vehicle design, the battery packs, their shape and positioning are critical aspects that determine the overall weight, weight distribution and, as a consequence, the efficiency, dynamics and stability of the vehicle. This presented a new challenge, to manage this necessary and inflexible weight and volume, developing the vehicle chassis around it and in the best possible way, without compromising the overall efficiency and behavior. In this work, a methodology for nested topology optimization has been developed which combines structural topology optimization and battery pack shaping and positioning. The new methodology is implemented without limiting its applicability, into the framework of the commercial software Hyperstudy by Altair

Βελτιστοποίηση τοπολογίας πλαισίου δίκυκλου ηλεκτρικού οχήματος

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Υπολογιστική Μηχανική

Predicting VO2max in Children and Adolescents Aged between 6 and 17 Using Physiological Characteristics and Participation in Sport Activities: A Cross-Sectional Study Comparing Different Regression Models Stratified by Gender

Background: The aim of this study is to use different regression models to capture the association between cardiorespiratory fitness VO2max (measured in mL/kg/min) and somatometric characteristics and sports activities and making better predictions. Methods: multiple linear regression (MLR), quantile regression (QR), ridge regression (RR), support vector regression (SVR) with three different kernels, artificial neural networks (ANNs), and boosted regression trees (RTs) were compared to explain and predict VO2max and to choose the best performance model. The sample consisted of 4908 children (2314 males and 2594 females) aged between 6 and 17. Cardiorespiratory fitness was assessed by the 20 m maximal multistage shuttle run test and maximal oxygen uptake (VO2max) was calculated. Welch t-tests, Mann–Whitney-U tests, X2 tests, and ANOVA tests were performed. The performance measures were root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). All analyses were stratified by gender. Results: A comparison of the statistical indices for both the predicted and actual data indicated that in boys, the MLR model outperformed all other models in all indices, followed by the linear SVR model. In girls, the MLR model performed better than the other models in R2 but was outperformed by SVR-RBF in terms of RMSE and MAE. The overweight and obesity categories in both sexes (p < 0.001) and maternal prepregnancy obesity in girls had a significant negative effect on VO2max. Age, weekly football training, track and field, basketball, and swimming had different positive effects based on gender. Conclusion: The MLR model showed remarkable performance against all other models and was competitive with the SVR models. In addition, this study’s data showed that changes in cardiorespiratory fitness were dependent, to a different extent based on gender, on BMI category, weight, height, age, and participation in some organized sports activities. Predictors that are not considered modifiable, such as gender, can be used to guide targeted interventions and policies

Assessment of the Effectiveness of a Computerised Decision-Support Tool for Health Professionals for the Prevention and Treatment of Childhood Obesity. Results from a Randomised Controlled Trial

We examined the effectiveness of a computerised decision-support tool (DST), designed for paediatric healthcare professionals, as a means to tackle childhood obesity. A randomised controlled trial was conducted with 65 families of 6–12-year old overweight or obese children. Paediatricians, paediatric endocrinologists and a dietitian in two children’s hospitals implemented the intervention. The intervention group (IG) received personalised meal plans and lifestyle optimisation recommendations via the DST, while families in the control group (CG) received general recommendations. After three months of intervention, the IG had a significant change in dietary fibre and sucrose intake by 4.1 and −4.6 g/day, respectively. In addition, the IG significantly reduced consumption of sweets (i.e., chocolates and cakes) and salty snacks (i.e., potato chips) by −0.1 and −0.3 portions/day, respectively. Furthermore, the CG had a significant increase of body weight and waist circumference by 1.4 kg and 2.1 cm, respectively, while Body Mass Index (BMI) decreased only in the IG by −0.4 kg/m2. However, the aforementioned findings did not differ significantly between study groups. In conclusion, these findings indicate the dynamics of the DST in supporting paediatric healthcare professionals to improve the effectiveness of care in modifying obesity-related behaviours. Further research is needed to confirm these findings

Assessment of the Effectiveness of a Computerised Decision-Support Tool for Health Professionals for the Prevention and Treatment of Childhood Obesity. Results from a Randomised Controlled Trial

We examined the effectiveness of a computerised decision-support tool (DST), designed for paediatric healthcare professionals, as a means to tackle childhood obesity. A randomised controlled trial was conducted with 65 families of 6-12-year old overweight or obese children. Paediatricians, paediatric endocrinologists and a dietitian in two children's hospitals implemented the intervention. The intervention group (IG) received personalised meal plans and lifestyle optimisation recommendations via the DST, while families in the control group (CG) received general recommendations. After three months of intervention, the IG had a significant change in dietary fibre and sucrose intake by 4.1 and -4.6 g/day, respectively. In addition, the IG significantly reduced consumption of sweets (i.e., chocolates and cakes) and salty snacks (i.e., potato chips) by -0.1 and -0.3 portions/day, respectively. Furthermore, the CG had a significant increase of body weight and waist circumference by 1.4 kg and 2.1 cm, respectively, while Body Mass Index (BMI) decreased only in the IG by -0.4 kg/m(2). However, the aforementioned findings did not differ significantly between study groups. In conclusion, these findings indicate the dynamics of the DST in supporting paediatric healthcare professionals to improve the effectiveness of care in modifying obesity-related behaviours. Further research is needed to confirm these findings

Effects of Nutrition, and Physical Activity Habits and Perceptions on Body Mass Index (BMI) in Children Aged 12–15 Years: A Cross-Sectional Study Comparing Boys and Girls

Background: The aim of the present study was to examine the effects of socioeconomic status, nutrition and physical activity lifestyle habits and perceptions on Body Mass Index (BMI) in children aged 12–15 years in Greece. Furthermore, to compare the difference between the two sexes. Methods: This is a cross-sectional study conducted on a representative secondary school cohort that included 5144 subjects, aged 12 to 15 years. Students and their parents filled in validated questionnaires evaluating socioeconomic status, nutrition and physical activity. International Obesity Task Force cut offs were used to classify the children. Factor analysis of mixed data and partial proportional ordered logistic models were used to analyze ΒMΙ distributions. All analyses were stratified by gender. Results: Boys were 2.9 (95%CI: 2.592–3.328) times more likely to be overweight/obese than girls. Partial proportional ordinal models indicate significant associations between nutritional and physical habits and perceptions variables but also significant gender differences in socio-demographic, nutritional risk factors as well as physical activity habits and perceptions. Conclusions: A clear understanding of the factors that contribute to the sex differences in nutrition and physical activity habits and perceptions may guide intervention efforts