A “Big Data” approach to measurement for real-world, real-time automotive aerodynamics

A “typical” sedan vehicle was instrumented with pressure, inertial (IMU), video, and GPS sensors to fully categorize the on-road conditions during an extended, multi-hour tests at the Allan Hancock EVOC track. The static pressure data sampled over the trunk lid of the test vehicle was processed along with all other pressure and IMU data gathered at the front and center of the test vehicle to build categorical and continuous models of the data using techniques borrowed from computer science and machine learning. These techniques highlighted both expected and unexpected trends in the aerodynamic data as well as indicating it is notionally possible to build a continuous model of rear-vehicle aerodynamic response to on-road conditions. Front-vehicle aerodynamic data showed to be the most important dataset in the categorical models (Bayesian-Gaussian Mixture Model and Random Forest Classifiers), predicting 74% of the variation in rear-vehicle aerodynamics with only modest improvements in predictive capability coming from IMU data (2%) for a maximum prediction rate of 76%. When the models were trained only on discerning between direction of corner (IMU data indicated the occurrence of a cornering event), model performance improved to 81%. Continuous models (multivariate linear regression) showed significant predictive capability over the categorical models with an averaged R2 values on the order of 0.95 (95% of variance in rear-vehicle aerodynamics captured by model). However, these models fall short in predicting asymmetric flow over the trunk lid (R2 = 0.40 for this feature). Overall, categorical models predict a more complete breadth of the aerodynamic variation over the trunk lid but suffer from generalized conclusions resultant from data categorization. Continuous models numerically capture more of variation of the rear-vehicle aerodynamics but with a key blind spot relating to asymmetric flow patterns

Additive Manufacturing for Post-Processing

Additive Manufacturing for Post Processing (AMPP) is a team comprised of two Cal Poly Mechanical Engineering students: Nathan Goodwin and Andrew Furmidge. The project is focused in the area of metal additive manufacturing (AM) machines, which are still a developing technology. Improvements have been made to the quality of the machines in the past years, but many limitations still exist. One of these is the inability to print parts that are larger than the build volume. In an effort to solve this problem, whole parts are divided into pieces that are printed individually. This team’s senior project is to create a joining method for Lawrence Livermore National Laboratory’s (LLNL) AM department. An employee at LLNL, Stephen Knaus, is providing the requirements of the joining method, and Professor Peter Schuster is advising the team through the design process

Graduate Prospects: Expectation, Disillusion and Precarity

Natasha Furmidge&nbsp

Thyroglobulin as a functional biomarker of iodine status in a cohort study of pregnant women in the United Kingdom

Background Though iodine deficiency in pregnancy is a matter of public-health concern, a functional measure of iodine status is lacking. The thyroid-specific protein, thyroglobulin (Tg), which reflects thyroid size, has shown promise as a functional measure in studies of children and adults, but data in pregnancy are sparse. In a cohort of mildly-to-moderately iodine-deficient pregnant women, we aimed to explore whether serum Tg is a sensitive functional biomarker of iodine status and to examine longitudinal change in Tg with gestational age. Method 230 pregnant women were recruited at an ante-natal clinic at 12 weeks of gestation to the Selenium in PRegnancy INTervention (SPRINT) study, in Oxford, UK. Repeated measures of urinary iodine-to-creatinine ratio, serum TSH and Tg at 12, 20, and 35 weeks of gestation were collected. Women were dichotomised by their iodine-to-creatinine ratio, (<150 or ≥150 μg/g) to group them broadly as iodine-deficient or iodine-sufficient. Women with thyroid antibodies were excluded; data and samples were available for 191 women. Results Median Tg concentration was 21, 19, and 23 μg/L in the first, second, and third trimesters, respectively. In a linear mixed model, controlling for confounders, Tg was higher in the <150 μg/g than in the ≥150 μg/g group (p<0.001) but there was no difference in TSH (p=0.27). Gestational week modified the effect of iodine status on TSH (p=0.01) and Tg (p=0.012); Tg did not increase with gestational week in the ≥150 μg/g group but did in the <150 μg/g group, and TSH increased more steeply in the <150 μg/g group. Conclusions Low iodine status (<150 μg/g) in pregnancy is associated with higher serum Tg, suggesting that the thyroid is hyper-stimulated by iodine deficiency, which causes it to enlarge. Tg is a more sensitive biomarker of iodine status in pregnancy than is TSH

Effect of solvent type on porous structure of emulsion templated poly(glycerol sebacate)-methacrylate

Polymerised emulsion templating is a common method for the fabrication of biomaterials with interconnected porous structures. Here, we present the fabrication of poly(glycerol sebacate)-methacrylate (PGSM) porous structures via emulsion templating. The mixing speed and photoinitiator concentration for emulsions were optimised (350 rpm, 16 wt%, respectively). The resulting emulsion separation before/after mixing and pore morphology of PGSM emulsions was then assessed by altering the emulsion formulation using four different types of diluting solvent (chloroform, dichloromethane, dichloroethane, toluene) for the first time. By altering the type and volume of solvents, the overall pore morphology of polymerised emulsions was tuned

Surfactant-free gelatin-stabilised biodegradable polymerised high internal phase emulsions with macroporous structures

High internal phase emulsion (HIPE) templating is a well-established method for the generation of polymeric materials with high porosity (>74%) and degree of interconnectivity. The porosity and pore size can be altered by adjusting parameters during emulsification, which affects the properties of the resulting porous structure. However, there remain challenges for the fabrication of polyHIPEs, including typically small pore sizes (∼20–50 μm) and the use of surfactants, which can limit their use in biological applications. Here, we present the use of gelatin, a natural polymer, during the formation of polyHIPE structures, through the use of two biodegradable polymers, polycaprolactone-methacrylate (PCL-M) and polyglycerol sebacate-methacrylate (PGS-M). When gelatin is used as the internal phase, it is capable of stabilising emulsions without the need for an additional surfactant. Furthermore, by changing the concentration of gelatin within the internal phase, the pore size of the resulting polyHIPE can be tuned. 5% gelatin solution resulted in the largest mean pore size, increasing from 53 μm to 80 μm and 28 μm to 94 µm for PCL-M and PGS-M respectively. In addition, the inclusion of gelatin further increased the mechanical properties of the polyHIPEs and increased the period an emulsion could be stored before polymerisation. Our results demonstrate the potential to use gelatin for the fabrication of surfactant-free polyHIPEs with macroporous structures, with potential applications in tissue engineering, environmental and agricultural industries