Multiobjective gas turbine engine controller design using genetic algorithms

This paper describes the use of multiobjective genetic algorithms (MOGAs) in the design of a multivariable control system for a gas turbine engine. The mechanisms employed to facilitate multiobjective search with the genetic algorithm are described with the aid of an example. It is shown that the MOGA confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This allows the engineer to examine the trade-offs between the different design objectives and configurations during the course of an optimization. In addition, the paper demonstrates how the genetic algorithm can be used to search in both controller structure and parameter space thereby offering a potentially more general approach to optimization in controller design than traditional numerical methods. While the example in the paper deals with control system design, the approach described can be expected to be applicable to more general problems in the fields of computer aided design (CAD) and computer aided engineering (CAE

Inagawa Cemetery chapel and visitor centre

https://openscholarship.wustl.edu/bcs/1354/thumbnail.jp

“I hear the music and my spirits lift!” Pleasure and ballroom dancing for community-dwelling older adults.

© Human Kinetics, Inc.Physical activity for older adults is recommended to encourage the maintenance of functional autonomy and improve mental health. Ballroom dancing involves aerobic, strength and balance work and is an inherently social activity. This 12-month qualitative study considered the influence of ballroom dancing on health and well-being in community-dwelling older adults. It explores an under-reported aspect of physical activity, which may incentivise older people to participate, that is, pleasure. Qualitative data were managed and analysed using the Framework Analysis approach. Semi-structured interviews were conducted with 26 older-adult ballroom dancers. Five typologies of pleasure were identified. In addition to ‘sensual pleasure’, ‘pleasure of habitual action’ and ‘pleasure of immersion’, as suggested by Phoenix and Orr (2014), the ‘pleasure of practice’ and ‘pleasure of community’ were also identified. Ballroom dancing produces a strong sense of embodied pleasure for older adults and should be promoted by health and exercise professionals for community-dwelling older adults

“I Hear the Music and My Spirits Lift!” Pleasure and Ballroom Dancing for Community-Dwelling Older Adults.

Physical activity for older adults is recommended to encourage the maintenance of functional autonomy and improve mental health. Ballroom dancing involves aerobic, strength and balance work and is an inherently social activity. This 12-month qualitative study considered the influence of ballroom dancing on health and well-being in community-dwelling older adults. It explores an under-reported aspect of physical activity, which may incentivise older people to participate, that is, pleasure. Qualitative data were managed and analysed using the Framework Analysis approach. Semi-structured interviews were conducted with 26 older-adult ballroom dancers. Five typologies of pleasure were identified. In addition to ‘sensual pleasure’, ‘pleasure of habitual action’ and ‘pleasure of immersion’, as suggested by Phoenix and Orr (2014), the ‘pleasure of practice’ and ‘pleasure of community’ were also identified. Ballroom dancing produces a strong sense of embodied pleasure for older adults and should be promoted by health and exercise professionals for community-dwelling older adults

Virtual Reality Interactive Design Utilizing Meshless Stress Re-Analysis

Interactive design gives engineers the ability to modify the shape of a part and immediately see the changes in the part’s stress state. Virtual reality techniques are utilized to make the process more intuitive and collaborative. The results of a meshless stress analysis are superimposed on the original design. As the engineer modifies the design using subdivision volume free-form deformation, the stress state for the modified design is computed using a Taylor series approximation. When the designer requests a more accurate analysis, a stress re-analysis technique based on the pre-conditioned conjugate gradient method is used with parallel processing to quickly compute an accurate approximation of the stresses for the new design

Modeling of Hydraulic Hose Paths

Hydraulic hoses are key components used to transfer power in heavy industrial machinery. The routing of these hoses is currently performed late in the product design process because no accurate physical models of the hoses exist that allow designers to predict the path the hoses will follow when installed in the machine. Designers must either guess the path the hose will take based on prior experience or wait until the first product prototype is built in order to experiment with the hose routes. This paper describes the use of ADAMS, a commercially available dynamic modeling package, to predict hose paths. The hose path model was verified by comparing the predicted paths to the paths of real hoses

Using machine learning to construct TOMCAT model and occultation measurement-based stratospheric methane (TCOM-CH4) and nitrous oxide (TCOM-N2O) profile data sets

oai:publications.copernicus.org:essd109417Monitoring the atmospheric concentrations of greenhouse gases (GHGs) is crucial to improve our understanding of their climate impact. However, there are no long-term profile data sets of important GHGs that can be used to gain a better insight into the processes controlling their variations in the atmosphere. In this study, we apply corrections to chemical transport model (CTM) output based on profile measurements from two solar occultation instruments: the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS). The goal is to construct long-term (1991–2021), gap-free stratospheric profile data sets, hereafter referred to as TCOM, for two important GHGs. To estimate the corrections that need to be applied to the CTM profiles, we use the extreme gradient boosting (XGBoost) regression model. For methane (TCOM-CH4), we utilize both HALOE and ACE satellite profile measurements from 1992 to 2018 to train the XGBoost model, while profiles from 2019 to 2021 serve as an independent evaluation data set. As there are no nitrous oxide (N2O) profile measurements for earlier years, we derive XGBoost-derived correction terms to construct TCOM-N2O profiles using only ACE-FTS profiles from the 2004–2018 time period, with profiles from 2019–2021 used for the independent evaluation. Overall, both TCOM-CH4 and TCOM-N2O profiles exhibit excellent agreement with the available satellite-measurement-based data sets. We find that compared to evaluation profiles, biases in TCOM-CH4 and TCOM-N2O are generally less than 10 % and 50 %, respectively, throughout the stratosphere. The daily zonal mean profile data sets, covering altitude (15–60 km) and pressure (300–0.1 hPa) levels, are publicly available via the following links: https://doi.org/10.5281/zenodo.7293740 for TCOM-CH4 (Dhomse, 2022a) and https://doi.org/10.5281/zenodo.7386001 for TCOM-N2O (Dhomse, 2022b).</p