Environmental Dependence of Cold Dark Matter Halo Formation

We use a high-resolution $N$-body simulation to study how the formation of cold dark matter (CDM) halos is affected by their environments, and how such environmental effects produce the age-dependence of halo clustering observed in recent $N$-body simulations. We estimate, for each halo selected at redshift $z=0$, an `initial' mass $M_{\rm i}$ defined to be the mass enclosed by the largest sphere which contains the initial barycenter of the halo particles and within which the mean linear density is equal to the critical value for spherical collapse at $z=0$. For halos of a given final mass, $M_{\rm h}$, the ratio $M_{\rm i}/M_{\rm h}$ has large scatter, and the scatter is larger for halos of lower final masses. Halos that form earlier on average have larger $M_{\rm i}/M_{\rm h}$, and so correspond to higher peaks in the initial density field than their final masses imply. Old halos are more strongly clustered than younger ones of the same mass because their initial masses are larger. The age-dependence of clustering for low-mass halos is entirely due to the difference in the initial/final mass ratio. Low-mass old halos are almost always located in the vicinity of big structures, and their old ages are largely due to the fact that their mass accretions are suppressed by the hot environments produced by the tidal fields of the larger structure. The age-dependence of clustering is weaker for more massive halos because the heating by large-scale tidal fields is less important.Comment: 18 pages,19 figures, accepted by MNRA

On-Body Channel Measurement Using Wireless Sensors

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10.1109/TAP.2012.219693

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

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)

Accurate determination of the Lagrangian bias for the dark matter halos

We use a new method, the cross power spectrum between the linear density field and the halo number density field, to measure the Lagrangian bias for dark matter halos. The method has several important advantages over the conventional correlation function analysis. By applying this method to a set of high-resolution simulations of 256^3 particles, we have accurately determined the Lagrangian bias, over 4 magnitudes in halo mass, for four scale-free models with the index n=-0.5, -1.0, -1.5 and -2.0 and three typical CDM models. Our result for massive halos with $M \ge M_*$ ($M_*$ is a characteristic non-linear mass) is in very good agreement with the analytical formula of Mo & White for the Lagrangian bias, but the analytical formula significantly underestimates the Lagrangian clustering for the less massive halos $M < M_*. Our simulation result however can be satisfactorily described, with an accuracy better than 15%, by the fitting formula of Jing for Eulerian bias under the assumption that the Lagrangian clustering and the Eulerian clustering are related with a linear mapping. It implies that it is the failure of the Press-Schechter theories for describing the formation of small halos that leads to the inaccuracy of the Mo & White formula for the Eulerian bias. The non-linear mapping between the Lagrangian clustering and the Eulerian clustering, which was speculated as another possible cause for the inaccuracy of the Mo & White formula, must at most have a second-order effect. Our result indicates that the halo formation model adopted by the Press-Schechter theories must be improved.Comment: Minor changes; accepted for publication in ApJ (Letters) ; 11 pages with 2 figures include