Electrochemical evaluation of the de-/re-activation of oxygen evolving Ir oxide

Understanding the influence of dynamic and stationary polarization on the deactivation of state-of-the-art IrOx catalysts is imperative for the design and operation of robust and efficient proton exchange membrane water electrolyzers. In this work, the deactivation and activity regeneration of a commercial IrOx catalyst investigated under potentiodynamic and potentiostatic conditions in acidic media by means of rotating disk electrode and electrogravimetry. Systematic electrochemical protocols were designed to decouple reversible from irreversible activity losses. Cyclic voltammetry provided a metric of the active surface area and traced the charge growth under different oxygen evolution reaction conditions. A direct logt dependent charge growth is observed, accompanied by the same fractional kinetic activity decay under potentiodynamic conditions. The loss is essentially recoverable after electrochemical reductive treatment, however at the expense of mild material dissolution. In contrast, extended potentiostatic operation induced irreversible intrinsic degradation after a critical time (0.5-1 h), accompanied by stability enhancement. This irreversible deactivation attributed to a gradual transformation of the hydrated IrOx to a dehydrated condensed oxide. Our results suggest that Ir dissolution during the regenerative treatment is not prohibitive, as long as the low potential modulations are not frequent

Multi-agent deep reinforcement learning with centralized training and decentralized execution for transportation infrastructure management

We present a multi-agent Deep Reinforcement Learning (DRL) framework for managing large transportation infrastructure systems over their life-cycle. Life-cycle management of such engineering systems is a computationally intensive task, requiring appropriate sequential inspection and maintenance decisions able to reduce long-term risks and costs, while dealing with different uncertainties and constraints that lie in high-dimensional spaces. To date, static age- or condition-based maintenance methods and risk-based or periodic inspection plans have mostly addressed this class of optimization problems. However, optimality, scalability, and uncertainty limitations are often manifested under such approaches. The optimization problem in this work is cast in the framework of constrained Partially Observable Markov Decision Processes (POMDPs), which provides a comprehensive mathematical basis for stochastic sequential decision settings with observation uncertainties, risk considerations, and limited resources. To address significantly large state and action spaces, a Deep Decentralized Multi-agent Actor-Critic (DDMAC) DRL method with Centralized Training and Decentralized Execution (CTDE), termed as DDMAC-CTDE is developed. The performance strengths of the DDMAC-CTDE method are demonstrated in a generally representative and realistic example application of an existing transportation network in Virginia, USA. The network includes several bridge and pavement components with nonstationary degradation, agency-imposed constraints, and traffic delay and risk considerations. Compared to traditional management policies for transportation networks, the proposed DDMAC-CTDE method vastly outperforms its counterparts. Overall, the proposed algorithmic framework provides near optimal solutions for transportation infrastructure management under real-world constraints and complexities

Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes

Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the solution of a complex sequential decision-making problem under uncertainty, with the main objective of efficiently controlling the risk associated with structural failures. Addressing this complexity, risk-based inspection planning methodologies, supported often by dynamic Bayesian networks, evaluate a set of pre-defined heuristic decision rules to reasonably simplify the decision problem. However, the resulting policies may be compromised by the limited space considered in the definition of the decision rules. Avoiding this limitation, Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical methodology for stochastic optimal control under uncertain action outcomes and observations, in which the optimal actions are prescribed as a function of the entire, dynamically updated, state probability distribution. In this paper, we combine dynamic Bayesian networks with POMDPs in a joint framework for optimal inspection and maintenance planning, and we provide the formulation for developing both infinite and finite horizon POMDPs in a structural reliability context. The proposed methodology is implemented and tested for the case of a structural component subject to fatigue deterioration, demonstrating the capability of state-of-the-art point-based POMDP solvers for solving the underlying planning optimization problem. Within the numerical experiments, POMDP and heuristic-based policies are thoroughly compared, and results showcase that POMDPs achieve substantially lower costs as compared to their counterparts, even for traditional problem settings

Multicritical Points and Crossover Mediating the Strong Violation of Universality: Wang-Landau Determinations in the Random-Bond d=2d=2 Blume-Capel model

The effects of bond randomness on the phase diagram and critical behavior of the square lattice ferromagnetic Blume-Capel model are discussed. The system is studied in both the pure and disordered versions by the same efficient two-stage Wang-Landau method for many values of the crystal field, restricted here in the second-order phase transition regime of the pure model. For the random-bond version several disorder strengths are considered. We present phase diagram points of both pure and random versions and for a particular disorder strength we locate the emergence of the enhancement of ferromagnetic order observed in an earlier study in the ex-first-order regime. The critical properties of the pure model are contrasted and compared to those of the random model. Accepting, for the weak random version, the assumption of the double logarithmic scenario for the specific heat we attempt to estimate the range of universality between the pure and random-bond models. The behavior of the strong disorder regime is also discussed and a rather complex and yet not fully understood behavior is observed. It is pointed out that this complexity is related to the ground-state structure of the random-bond version.Comment: 12 pages, 11 figures, submitted for publicatio