Crystal size induced reduction in thermal hysteresis of Ni-Ti-Nb shape memory thin films

Ni41.7Ti38.8Nb19.5 shape memory alloy films were sputter-deposited onto silicon substrates and annealed at various temperatures. A narrow thermal hysteresis was obtained in the Ni-Ti-Nb films with a grain size of less than 50 nm. The small grain size, or large amount of grain boundaries, facilitates the phase transformation, thus reduces the hysteresis. The corresponding less transformation friction and heat transfer during the shear process, as well as reduced spontaneous lattice distortion, are also responsible for this reduction of the thermal hysteresis

Dynamic behavior investigations and disturbance rejection predictive control of solvent-based post-combustion CO2 capture process

Increasing demand for flexible operation has posed significant challenges to the control system design of solvent-based post-combustion CO2 capture (PCC) process: 1) the capture system itself has very slow dynamics; 2) in the case of wide range of operation, dynamic behavior of the PCC process will change significantly at different operating points; and 3) the frequent variation of upstream flue gas flowrate will bring in strong disturbances to the capture system. For these reasons, this paper provides a comprehensive study on the dynamic characteristics of the PCC process. The system dynamics under different CO2 capture rates, re-boiler temperatures, and flue gas flow rates are analyzed and compared through step-response tests. Based on the in-depth understanding of the system behavior, a disturbance rejection predictive controller (DRPC) is proposed for the PCC process. The predictive controller can track the desired CO2 capture rate quickly and smoothly in a wide operating range while tightly maintaining the re-boiler temperature around the optimal value. Active disturbance rejection approach is used in the predictive control design to improve the control property in the presence of dynamic variations or disturbances. The measured disturbances, such as the flue gas flow rate, is considered as an additional input in the predictive model development, so that accurate model prediction and timely control adjustment can be made once the disturbance is detected. For unmeasured disturbances, including model mismatches, plant behavior variations, etc., a disturbance observer is designed to estimate the value of disturbances. The estimated signal is then used as a compensation to the predictive control signal to remove the influence of disturbances. Simulations on a monoethanolamine (MEA) based PCC system developed on gCCS demonstrates the excellent effect of the proposed controller

Reinforced coordinated control of coal-fired power plant retrofitted with solvent based CO2 capture using model predictive controls

Solvent-based post-combustion CO2 capture (PCC) provides a promising technology for the CO2 removal of coal-fired power plant (CFPP). However, there are strong interactions between the CFPP and the PCC system, which makes it challenging to attain a good control for the integrated plant. The PCC system requires extraction of large amounts of steam from the intermediate/low pressure steam turbine to provide heat for solvent regeneration, which will reduce power generation. Wide-range load variation of power plant will cause strong fluctuation of the flue gas flow and brings in a significant impact on the PCC system. To overcome these issues, this paper presents a reinforced coordinated control scheme for the integrated CFPP-PCC system based on the investigation of the overall plant dynamic behavior. Two model predictive controllers are developed for the CFPP and PCC plants respectively, in which the steam flow rate to re-boiler and the flue-gas flow rate are considered as feed-forward signals to link the two systems together. Three operating modes are considered for designing the coordinated control system, which are: (1) normal operating mode; (2) rapid power load change mode; and (3) strict carbon capture mode. The proposed coordinated controller can enhance the overall performance of the CFPP-PCC plant and achieve a flexible trade-off between power generation and CO2 reduction. Simulation results on a small-scale subcritical CFPP-PCC plant developed on gCCS demonstrates the effectiveness of the proposed controller

Regulatory networks and connected components of the neutral space

The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality - typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into 4.7 * 10^8 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.Comment: 6 pages, 5 figure

Variational approximation for mixtures of linear mixed models

Mixtures of linear mixed models (MLMMs) are useful for clustering grouped data and can be estimated by likelihood maximization through the EM algorithm. The conventional approach to determining a suitable number of components is to compare different mixture models using penalized log-likelihood criteria such as BIC.We propose fitting MLMMs with variational methods which can perform parameter estimation and model selection simultaneously. A variational approximation is described where the variational lower bound and parameter updates are in closed form, allowing fast evaluation. A new variational greedy algorithm is developed for model selection and learning of the mixture components. This approach allows an automatic initialization of the algorithm and returns a plausible number of mixture components automatically. In cases of weak identifiability of certain model parameters, we use hierarchical centering to reparametrize the model and show empirically that there is a gain in efficiency by variational algorithms similar to that in MCMC algorithms. Related to this, we prove that the approximate rate of convergence of variational algorithms by Gaussian approximation is equal to that of the corresponding Gibbs sampler which suggests that reparametrizations can lead to improved convergence in variational algorithms as well.Comment: 36 pages, 5 figures, 2 tables, submitted to JCG

Reconstructing interacting new agegraphic polytropic gas model in non-flat FRW universe

We study the correspondence between the interacting new agegraphic dark energy and the polytropic gas model of dark energy in the non-flat FRW universe. This correspondence allows to reconstruct the potential and the dynamics for the scalar field of the polytropic model, which describe accelerated expansion of the universe.Comment: 9 page

Interacting New Agegraphic Dark Energy in a Cyclic Universe

The main goal of this work is investigation of NADE in the cyclic universe scenario. Since, cyclic universe is explained by a phantom phase ($\omega<-1$), it is shown when there is no interaction between matter and dark energy, ADE and NADE do not produce a phantom phase, then can not describe cyclic universe. Therefore, we study interacting models of ADE and NADE in the modified Friedmann equation. We find out that, in the high energy regime, which it is a necessary part of cyclic universe evolution, only NADE can describe this phantom phase era for cyclic universe. Considering deceleration parameter tells us that the universe has a deceleration phase after an acceleration phase, and NADE is able to produce a cyclic universe. Also it is found valuable to study generalized second law of thermodynamics. Since the loop quantum correction is taken account in high energy regime, it may not be suitable to use standard treatment of thermodynamics, so we turn our attention to the result of \citep{29}, which the authors have studied thermodynamics in loop quantum gravity, and we show that which condition can satisfy generalized second law of thermodynamics.Comment: 8 pages, 3 figure

Testing the Nature of Kaluza-Klein Excitations at Future Lepton Colliders

With one extra dimension, current high precision electroweak data constrain the masses of the first Kaluza-Klein excitations of the Standard Model gauge fields to lie above $\simeq 4$ TeV. States with masses not much larger than this should be observable at the LHC. However, even for first excitation masses close to this lower bound, the second set of excitations will be too heavy to be produced thus eliminating the possibility of realizing the cleanest signature for KK scenarios. Previous studies of heavy $Z'$ and $W'$ production in this mass range at the LHC have demonstrated that very little information can be obtained about their couplings to the conventional fermions given the limited available statistics and imply that the LHC cannot distinguish an ordinary $Z'$ from the degenerate pair of the first KK excitations of the $\gamma$ and $Z$. In this paper we discuss the capability of lepton colliders with center of mass energies significantly below the excitation mass to resolve this ambiguity. In addition, we examine how direct measurements obtained on and near the top of the first excitation peak at lepton colliders can confirm these results. For more than one extra dimension we demonstrate that it is likely that the first KK excitation is too massive to be produced at the LHC.Comment: 38 pages, 10 Figs, LaTex, comments adde

Factors driving the seasonal and hourly variability of sea-spray aerosol number in the North Atlantic

Four North Atlantic Aerosol and Marine Ecosystems Study (NAAMES) field campaigns from winter 2015 through spring 2018 sampled an extensive set of oceanographic and atmospheric parameters during the annual phytoplankton bloom cycle. This unique dataset provides four seasons of open-ocean observations of wind speed, sea surface temperature (SST), seawater particle attenuation at 660 nm (cp,660, a measure of ocean particulate organic carbon), bacterial production rates, and sea-spray aerosol size distributions and number concentrations (NSSA). The NAAMES measurements show moderate to strong correlations (0.56 \u3c R \u3c 0.70) between NSSA and local wind speeds in the marine boundary layer on hourly timescales, but this relationship weakens in the campaign averages that represent each season, in part because of the reduction in range of wind speed by multiday averaging. NSSA correlates weakly with seawater cp,660 (R = 0.36, P \u3c\u3c 0.01), but the correlation with cp,660, is improved (R = 0.51, P \u3c 0.05) for periods of low wind speeds. In addition, NAAMES measurements provide observational dependence of SSA mode diameter (dm) on SST, with dm increasing to larger sizes at higher SST (R = 0.60, P \u3c\u3c 0.01) on hourly timescales. These results imply that climate models using bimodal SSA parameterizations to wind speed rather than a single SSA mode that varies with SST may overestimate SSA number concentrations (hence cloud condensation nuclei) by a factor of 4 to 7 and may underestimate SSA scattering (hence direct radiative effects) by a factor of 2 to 5, in addition to overpredicting variability in SSA scattering from wind speed by a factor of 5

Follow-up analyses to the O3 LIGO-Virgo-KAGRA lensing searches

Along their path from source to observer, gravitational waves may be gravitationally lensed by massive objects leading to distortion in the signals. Searches for these distortions amongst the observed signals from the current detector network have already been carried out, though there have as yet been no confident detections. However, predictions of the observation rate of lensing suggest detection in the future is a realistic possibility. Therefore, preparations need to be made to thoroughly investigate the candidate lensed signals. In this work, we present some follow-up analyses that could be applied to assess the significance of such events and ascertain what information may be extracted about the lens-source system by applying these analyses to a number of O3 candidate events, even if these signals did not yield a high significance for any of the lensing hypotheses. These analyses cover the strong lensing, millilensing, and microlensing regimes. Applying these additional analyses does not lead to any additional evidence for lensing in the candidates that have been examined. However, it does provide important insight into potential avenues to deal with high-significance candidates in future observations