Transient non-isothermal model of a polymer electrolyte fuel cell

In this paper we present a one-dimensional transient model for the membrane electrode assembly of a polymer-electrolyte fuel cell. In earlier work we established a framework to describe the water balance in a steady-state, non-isothermal cathode model that explicitly included an agglomerate catalyst layer component. This paper extends that work in several directions, explicitly incorporating components of the anode, including a micro-porous layer, and accounting for electronic potential variations, gas convection and time dependence. The inclusion of temperature effects, which are vital to the correct description of condensation and evaporation, is new to transient modelling. Several examples of the modelling results are given in the form of potentiostatic sweeps and compared to experimental results. Excellent qualitative agreement is demonstrated, particularly in regard to the phenomenon of hysteresis, a manifestation of the sensitive response of the system to the presence of water. Results pertaining to pore size, contact angle and the presence of a micro-porous layer are presented and future work is discussed

Relative Stability of Network States in Boolean Network Models of Gene Regulation in Development

Progress in cell type reprogramming has revived the interest in Waddington's concept of the epigenetic landscape. Recently researchers developed the quasi-potential theory to represent the Waddington's landscape. The Quasi-potential U(x), derived from interactions in the gene regulatory network (GRN) of a cell, quantifies the relative stability of network states, which determine the effort required for state transitions in a multi-stable dynamical system. However, quasi-potential landscapes, originally developed for continuous systems, are not suitable for discrete-valued networks which are important tools to study complex systems. In this paper, we provide a framework to quantify the landscape for discrete Boolean networks (BNs). We apply our framework to study pancreas cell differentiation where an ensemble of BN models is considered based on the structure of a minimal GRN for pancreas development. We impose biologically motivated structural constraints (corresponding to specific type of Boolean functions) and dynamical constraints (corresponding to stable attractor states) to limit the space of BN models for pancreas development. In addition, we enforce a novel functional constraint corresponding to the relative ordering of attractor states in BN models to restrict the space of BN models to the biological relevant class. We find that BNs with canalyzing/sign-compatible Boolean functions best capture the dynamics of pancreas cell differentiation. This framework can also determine the genes' influence on cell state transitions, and thus can facilitate the rational design of cell reprogramming protocols.Comment: 24 pages, 6 figures, 1 tabl

Inertial coalescence of droplets on a partially wetting substrate

We consider the growth rate of the height of the connecting bridge in rapid surface-tension-driven coalescence of two identical droplets attached on a partially wetting substrate. For a wide range of contact angle values, the height of the bridge grows with time following a power law with a universal exponent of 2/3, up to a threshold time, beyond which a 1/2 exponent results, that is known for coalescence of freely-suspended droplets. In a narrow range of contact angle values close to 90°, this threshold time rapidly vanishes and a 1/2 exponent results for a 90° contact angle. The argument is confirmed by three-dimensional numerical simulations based on a diffuse interface method with adaptive mesh refinement and a volume-of-fluid method

Preference-Based Learning for Exoskeleton Gait Optimization

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users

Iterative CZT-based frequency offset estimation for frequency-selective channels

In this paper, we present an accurate frequency offset estimation method for frequency-selective channels. Through the iterative use of the chirp z-transform (CZT) algorithm, an accurate frequency offset estimator is proposed, approaching the Cramer-Rao Bound (CRB) even at low signal-to-noise ratio (SNR). Further, the estimation can be achieved within one block of training sequence, thus avoiding the transmission of repetitive known blocks as is usually required in many conventional methods. Meanwhile, the overall complexity is acceptable. More importantly, the CZT operation can utilize the fast Fourier transform (FFT) structure that is favourable for digital signal processor (DSP) implementation. Simulation results show that two or at most three iterations of the CZT computation are sufficient for an accurate frequency offset estimation in the SNR range from 0 dB to 30 dB. © 2005 IEEE.published_or_final_versio

Electron Acceleration by Multi-Island Coalescence

Energetic electrons of up to tens of MeV are created during explosive phenomena in the solar corona. While many theoretical models consider magnetic reconnection as a possible way of generating energetic electrons, the precise roles of magnetic reconnection during acceleration and heating of electrons still remain unclear. Here we show from 2D particle-in-cell simulations that coalescence of magnetic islands that naturally form as a consequence of tearing mode instability and associated magnetic reconnection leads to efficient energization of electrons. The key process is the secondary magnetic reconnection at the merging points, or the `anti-reconnection', which is, in a sense, driven by the converging outflows from the initial magnetic reconnection regions. By following the trajectories of the most energetic electrons, we found a variety of different acceleration mechanisms but the energization at the anti-reconnection is found to be the most important process. We discuss possible applications to the energetic electrons observed in the solar flares. We anticipate our results to be a starting point for more sophisticated models of particle acceleration during the explosive energy release phenomena.Comment: 14 pages, 12 figures (degraded figure quality), 1 table. Accepted for publication in ApJ