Subzone control method of stratum ventilation for thermal comfort improvement

The conventional control method of a collective ventilation (e.g., stratum ventilation) controls the averaged thermal environment in the occupied zone to satisfy the averaged thermal preference of a group of occupants. However, the averaged thermal environment in the occupied zone is not the same as the microclimates of the occupants, because the thermal environment in the occupied zone is not absolutely uniform. Moreover, the averaged thermal preference of the occupants could deviate from the individual thermal preferences, because the occupants could have different individual thermal preferences. This study proposes a subzone control method for stratum ventilation to improve thermal comfort. The proposed method divides the occupied zone into subzones, and controls the microclimates of the subzones to satisfy the thermal preferences of the respective subzones. Experiments in a stratum-ventilated classroom are conducted to model and validate the Predicted Mean Votes (PMVs) of the subzones, with a mean absolute error between 0.05 scale and 0.14 scale. Using the PMV models, the supply air parameters are optimized to minimize the deviation between the PMVs of the subzones and the respective thermal preferences. Case studies show that the proposed method can fulfill the thermal constraints of all subzones for thermal comfort, while the conventional method fails. The proposed method further improves thermal comfort by reducing the deviation of the achieved PMVs of subzones from the preferred ones by 17.6%–41.5% as compared with the conventional method. The proposed method is also promising for other collective ventilations (e.g., mixing ventilation and displacement ventilation)

Indoor mould growth prediction using coupled computational fluid dynamics and mould growth model

This study investigates, using in-situ and numerical simulation experiments, airflow and hygrothermal distribution in a mechanically ventilated academic research facility with known cases of microbial proliferations. Microclimate parameters were obtained from in-situ experiments and used as boundary conditions and validation of the numerical experiments with a commercial computational fluid dynamics (CFD) analysis tool using the standard k–ε model. Good agreements were obtained with less than 10% deviations between the measured and simulated results. Subsequent upon successful validation, the model was used to investigate hygrothermal and airflow profile within the shelves holding stored components in the facility. The predicted in-shelf hygrothermal profile was superimposed on mould growth limiting curve earlier documented in the literature. Results revealed the growth of xerophilic species in most parts of the shelves. The mould growth prediction was found in correlation with the microbial investigation in the case-studied room reported by the authors elsewhere. Satisfactory prediction of mould growth in the room successfully proved that the CFD simulation can be used to investigate the conditions that lead to microbial growth in the indoor environment

Optimization on fresh outdoor air ratio of air conditioning system with stratum ventilation for both targeted indoor air quality and maximal energy saving

Stratum ventilation can energy efficiently provide good inhaled indoor air quality with a proper operation (e.g., fresh outdoor air ratio). However, the non-uniform CO2 distribution in a stratum-ventilated room challenges the provision of targeted indoor air quality. This study proposes an optimization on the fresh outdoor air ratio of stratum ventilation for both the targeted indoor air quality and maximal energy saving. A model of CO2 concentration in the breathing zone is developed by coupling CO2 removal efficiency in the breathing zone and mass conservation laws. With the developed model, the ventilation parameters corresponding to different fresh outdoor air ratios are quantified to achieve the targeted indoor air quality (i.e., targeted CO2 concentration in the breathing zone). Using the fresh outdoor air ratios and corresponding ventilation parameters as inputs, energy performance evaluations of the air conditioning system are conducted by building energy simulations. The fresh outdoor air ratio with the minimal energy consumption is determined as the optimal one. Experiments show that the mean absolute error of the developed model of CO2 concentration in the breathing zone is 1.9%. The effectiveness of the proposed optimization is demonstrated using TRNSYS that the energy consumption of the air conditioning system with stratum ventilation is reduced by 6.4% while achieving the targeted indoor air quality. The proposed optimization is also promising for other ventilation modes for targeted indoor air quality and improved energy efficiency

Field study on adaptive thermal comfort in typical air conditioned classrooms

This study investigates adaptive thermal comfort in air conditioned classrooms in Hong Kong. A field survey was conducted in several typical classrooms at the City University of Hong Kong. This survey covered objective measurement of thermal environment parameters and subjective human thermal responses. A total of 982 student volunteers participated in the investigation. The results indicate that students in light clothing (0.42 clo) have adapted to the cooler classroom environments. The neutral temperature is very close to the preferred temperature of approximately 24 °C. Based on the MTSV ranging between −0.5 and + 0.5, the comfort range is between 21.56 °C and 26.75 °C. The lower limit is below that of the ASHRAE standard. Of the predicted mean vote (PMV) and the University of California, Berkeley (UCB) model, the UCB model predictions agree better with the mean thermal sensation vote (MTSV). Also, the respective fit regression models of the MTSV versus each of the following: operative temperature (Top), PMV, and UCB were obtained. This study provides a better understanding of acceptable classroom temperatures

Heat removal efficiency of stratum ventilation for air-side modulation

Stratum ventilation has significant thermal non-uniformity between the occupied and upper zones. Although the non-uniformity benefits indoor air quality and energy efficiency, it increases complexities and difficulties in the air-side modulation. In this study, a heat removal efficiency (HRE) model is first established and validated, and then used for the air-side modulation. The HRE model proposed is a function of supply air temperature, supply airflow rate and cooling load. The HRE model proposed has been proven to be applicable to stratum ventilation and displacement ventilation for different room geometries and air terminal configurations, with errors generally within ±5% and a mean absolute error less than 4% for thirty-three experimental cases and five simulated cases. Investigations into the air-side modulation with the proposed HRE model reveal that for both the typical stratum-ventilated classroom and office, the variable-air-volume system can serve a wider range of cooling load than the constant-air-volume system. The assumption of a constant HRE used in the conventional method could lead to errors in the room temperature prediction up to ±1.3 °C, thus the proposed HRE model is important to the air-side modulation for thermal comfort. An air-side modulation method is proposed based on the HRE model to maximize the HRE for improving energy efficiency while maintaining thermal comfort. Results show that the HRE model based air-side modulation can improve the energy efficiency of stratum ventilation up to 67.3%. The HRE model based air-side modulation is also promising for displacement ventilation

Response-surface-model-based system sizing for nearly/net zero energy buildings under uncertainty

Properly treating uncertainty is critical for robust system sizing of nearly/net zero energy buildings (ZEBs). To treat uncertainty, the conventional method conducts Monte Carlo simulations for thousands of possible design options, which inevitably leads to computation load that is heavy or even impossible to handle. In order to reduce the number of Monte Carlo simulations, this study proposes a response-surface-model-based system sizing method. The response surface models of design criteria (i.e., the annual energy match ratio, self-consumption ratio and initial investment) are established based on Monte Carlo simulations for 29 specific design points which are determined by Box-Behnken design. With the response surface models, the overall performances (i.e., the weighted performance of the design criteria) of all design options (i.e., sizing combinations of photovoltaic, wind turbine and electric storage) are evaluated, and the design option with the maximal overall performance is finally selected. Cases studies with 1331 design options have validated the proposed method for 10,000 randomly produced decision scenarios (i.e., users’ preferences to the design criteria). The results show that the established response surface models reasonably predict the design criteria with errors no greater than 3.5% at a cumulative probability of 95%. The proposed method reduces the number of Monte Carlos simulations by 97.8%, and robustly sorts out top 1.1% design options in expectation. With the largely reduced Monte Carlo simulations and high overall performance of the selected design option, the proposed method provides a practical and efficient means for system sizing of nearly/net ZEBs under uncertainty

A novel approach in development of dynamic muscle model for paraplegic with functional electrical stimulation

This paper presents the development of paraplegic muscle model with Adaptive Neuro-Fuzzy Inference System (ANFIS). A series of experiments using Functional Electrical Stimulation (FES) with different stimulation frequencies, pulse width and pulse duration to investigate the impact on muscle output torque are conducted. The data that is obtained is used to develop the paraplegic muscle model. 500 training data and 300 testing data set are used in the development of muscle model. The muscle model thus developed is validated with clinical data from one paraplegic subject and in comparison with two other muscle models from previous researchers. The ANFIS muscle model is found to be the most accurate muscle model representing paraplegic muscle model. The established model is then used to predict the behaviour of the underlying system and will be used in the future for the design and evaluation of various control strategies

Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture

Needle insertion is common during minimally invasive interventions such as biopsy or brachytherapy. During soft tissue needle insertion, forces acting at the needle tip cause tissue deformation and needle deflection. Accurate needle tip force measurement provides information on needle-tissue interaction and helps detecting and compensating potential misplacement. For this purpose we introduce an image-based needle tip force estimation method using an optical fiber imaging the deformation of an epoxy layer below the needle tip over time. For calibration and force estimation, we introduce a novel deep learning-based fused convolutional GRU-CNN model which effectively exploits the spatio-temporal data structure. The needle is easy to manufacture and our model achieves a mean absolute error of 1.76 +- 1.5 mN with a cross-correlation coefficient of 0.9996, clearly outperforming other methods. We test needles with different materials to demonstrate that the approach can be adapted for different sensitivities and force ranges. Furthermore, we validate our approach in an ex-vivo prostate needle insertion scenario.Comment: Accepted for Publication at MICCAI 201

Hidden itinerant-spin phase in heavily-overdoped La2-xSrxCuO4 revealed by dilute Fe doping: A combined neutron scattering and angle-resolved photoemission study

We demonstrated experimentally a direct way to probe a hidden propensity to the formation of spin density wave (SDW) in a non-magnetic metal with strong Fermi surface nesting. Substituting Fe for a tiny amount of Cu (1%) induced an incommensurate magnetic order below 20 K in heavily-overdoped La2-xSrxCuO4 (LSCO). Elastic neutron scattering suggested that this order cannot be ascribed to the localized spins on Cu or doped Fe. Angle-resolved photoemission spectroscopy (ARPES), combined with numerical calculations, revealed a strong Fermi surface nesting inherent in the pristine LSCO that likely drives this order. The heavily-overdoped Fe-doped LSCO thus represents the first plausible example of the long-sought "itinerant-spin extreme" of cuprates, where the spins of itinerant doped holes define the magnetic ordering ground state. This finding complements the current picture of cuprate spin physics that highlights the predominant role of localized spins at lower dopings. The demonstrated set of methods could potentially apply to studying hidden density-wave instabilities of other "nested" materials on the verge of density wave ordering.Comment: Abstract and discussion revised; to appear in Phys. Rev. Let