Automotive starting systems require substantial amounts of mechanical energy in a short period of time. Lead-acid batteries have historically provided that energy through a starter motor. Springs have been identified as an alternative energy storage medium and are well suited to engine-starting applications due to their ability to rapidly deliver substantial mechanical power and their long service life. This paper presents the development of a conceptual, spring-based starter. The focus of the study was to determine whether a spring of acceptable size could provide the required torque and rotational speed to start an automotive engine. Engine testing was performed on a representative 600 cc, inline 4-cylinder internal combustion engine to determine the required torque and engine speed during the starting cycle. An optimization was performed to identify an appropriate spring design, minimizing its size. Results predict that the test engine could be started by a torsional steel spring with a diameter and length of approximately 150 mm, similar in size, but lower weight than an electrical starting system of the engine. A proof-of-concept prototype has been constructed and evaluated.
Incomplete data, whether realized from nonresponse in survey data or counterfactual outcomes in observational studies, may lead to biased estimation of study variables. Nonresponse and selection bias may be mitigated with techniques that weight the incomplete data to match characteristics of the partially unobserved complete data. Inverse probability weighting is a widely used method in causal inference that relies on a propensity model to construct adjusted weights; whereas calibration is a common method used by survey statisticians to use constrained optimization to construct adjusted weights. This article reviews inverse probability weighting and entropy balancing calibration by distinguishing them in the statistical sense of variable balancing, extending propensity score construction to include generalized boosting models, and demonstrating the use of inverse probability weighting and calibration separately and together through a widely cited simulation study evaluation.This article is categorized under:
Statistical and Graphical Methods of Data Analysis > Sampling
Data: Types and Structure > Categorical Data
Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods
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