We present a generalized contact computation model for arbitrarily shaped polyhedra to simplify the contact analysis in discontinuous deformation analysis. A list of generalized contact constraints can be established for contacting polyhedra during contact detection. Each contact constraint contains information for 2 contact points, unique contact plane, and related contact modes (open, locked, or sliding). Computational aspects of the generalized contact model include identification of contact positions and contact modes, uniform penalty formulation of generalized contact constraint, and uniform updating of contact modes and contact planes in the open-close iteration. Compared with previous strategies, the generalized contact computation model has a simpler data structure and fewer memory requirements. Meanwhile, it simplifies the penalty formulation and facilitates the open-close iteration check while producing enough accuracy. Illustrative examples show the ability of the method to handle the full range of polyhedral shapes.

A fast direct search (FDS) algorithm is presented to increase the efficiency of contact detection for convex polygonal and polyhedral particles. All contact types are detected using only a small subset of these contact types: vertex-to-edge for polygons while vertex-to-face and edge-to-edge for polyhedra. First, an initial contact list is generated. Then in subsequent steps the contact list is updated by checking only local boundaries of the blocks and their separation. An exclusion algorithm is applied to avoid unnecessary examination for particles that are near but not-in-contact. The benchmark tests show that the FDS produces significant speed-up in various cases.

Older people are very likely to experience transitions among spaces with different temperatures in daily life. But little has been known about their thermal comfort and physiological responses to these temperature steps. This study investigated 18 healthy older people's thermal perceptions and physiological parameters under cold and warm exposures with 3/5/6 °C temperature steps. The results showed that subjects' thermal sensation was sensitive to all moderate temperature steps, but their thermal comfort perception could only distinguish temperature changes greater than 5 °C. Thermal unacceptability was only observed when subjects' tympanic temperature reached at 37.08 °C. Also, we found older people need more than 50 min time to get their mean skin temperature steady after cold stimuli, while they only need <24 min after warm ones. Cold stimuli could significantly boost subjects' blood pressure, respiratory rate, blood oxygen saturation, and depress heart rate. To predict older people's transient thermal sensation after temperature steps, we proposed two regression models for cold and warm exposures respectively. Based on the above observations, we suggest older people should try their best to avoid large step temperature changes, especially for cold side steps.

There is insufficient knowledge on how environmental and physiological factors affect older people's thermal perceptions. In this paper, we present two data-driven models (a field study model and a lab study model) using the algorithm of random forests to predict older people's thermal sensation. These two models were developed from a field study dataset and a lab study dataset separately. The field study dataset was collected from 1040 old subjects (70 + years) who lived in 19 aged-care homes, which contains multi-dimension factors such as environmental parameters, subjects' demographic information, health condition, acclimatization degrees, living habits and thermal perceptions' votes. The lab study dataset was collected from a lab study and contains 18 old subjects' (65 + years) eight local skin temperatures and thermal perceptions' votes under five thermal environments (21/23/26/29/32 °C). After the procedure of feature selection, the field study model was developed with four environmental variables (air temperature, velocity, CO2 concentration, illuminance) plus two human-related variables (health condition and living time in aged-care homes) as inputs. It produced an overall accuracy of 56.6%, which was 24.9% higher than that of the PMV model. The lab study model was built on five local skin temperatures including head, lower arm, upper leg, chest and back temperatures, which demonstrated an overall accuracy of 76.7%, 30.1% higher than UC Berkeley thermal sensation model's accuracy. We then interpreted how these inputs distinguish thermal sensations by applying a partial dependence analysis. Finally, we proposed two applications of the above models and present older people's seasonally neutral indoor temperature zones.