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1. Accelerated Degradation Testing of Rigid Wet Cooling Media to Analyse the Impact of Calcium Scaling

   https://doi.org/10.1115/IMECE2018-88508  

   Dakshinamurthy, Hemanth N.; Siddarth, Ashwin; Guhe, Abhishek; Kasukurthy, Rajesh; Hoverson, James; Agonafer, Dereje (November 2018, Proceedings of the ASME 2018 International Mechanical Engineering Congress and Exposition)

   Rigid wet cooling media is a key component of direct and indirect evaporative cooling systems. Evaporation is the process of a substance in a liquid state changing to a gaseous state. When water evaporates only water molecules get evaporated and the other chemicals in the water are left behind on the surface as residue. Many studies have been conducted on how the change in air flow velocity, media depth, porosity and water distribution affect performance of the cooling system. The operational efficiency of the cooling media varies over its life cycle and depends primarily on temperature and speed of inlet air, water distribution system, type of pad and dimension of the pad.Although evaporative cooling when implemented with air-side economization enables efficiency gains, a trade-off between the system maintenance and its operational efficiency exists. In this study, the primary objective is to determine how calcium scale affects the overall performance of the cooling pad and the water system. Areas of the pad that are not wetted effectively allow air to pass through without being cooled and the edges between wetted and dry surface establish sites for scale formation. An Accelerated Degradation Testing (ADT) by rapid wetting and drying on the media pads at elevated levels of calcium is designed and conducted on the cellulose wet cooling media pad. This research focuses on monitoring the degradation that occurs over its usage and establish a key maintenance parameter for water used in media pad.As a novel study, preliminary tests were mandatory because there were no established standards for media pad degradation testing. Sump water conductivity is identified as the key maintenance parameter for monitoring sump replenishing and draining cycles which will result in reduced water usage. The average water conductivity in the sump during wetting cycles increases monotonically when ADT was performed on a new media pad. An empirical relationship between sump water conductivity and number of wetting cycles is proposed. This information will be very helpful for the manufacturers to guide their customers for maintenance of the media pad and sump water drain cycles. 

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2. A General Accelerated Destructive Degradation Testing Model for Reliability Analysis

   https://doi.org/10.1109/TR.2018.2883983  

   Ding, Yu; Yang, Qingyu; King, Caleb B.; Hong, Yili (January 2019, IEEE Transactions on Reliability)

   In recent years, accelerated destructive degradation testing (ADDT) has been applied to obtain the reliability information of an asset (component) at use conditions when the component is highly reliable. In ADDT, degradation data are measured under stress levels more severe than usual so that more component failures can be observed in a short period. In the literature, most application-specific ADDT models assume a parametric degradation process under different accelerating conditions. Models without strong parametric assumptions are desirable to describe the complex ADDT processes. This paper proposes a general ADDT model that consists of a nonparametric part to describe the degradation path and a parametric part to describe the accelerating-variable effect. The proposed model not only provides more model flexibility with few assumptions, but also retains the physical mechanisms of degradation. Due to the complexity of parameter estimation, an efficient method based on self-adaptive differential evolution is developed to estimate model parameters. A simulation study is implemented to verify the developed methods. Two real-world case studies are conducted, and the results show the superior performance of the developed model compared with the existing methods. 

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3. Bounded Cost Path Planning for Underwater Vehicles Assisted by a Time-Invariant Partitioned Flow Field Model

   https://doi.org/10.3389/frobt.2021.575267  

   Hou, Mengxue; Cho, Sungjin; Zhou, Haomin; Edwards, Catherine R.; Zhang, Fumin (July 2021, Frontiers in Robotics and AI)

   A bounded cost path planning method is developed for underwater vehicles assisted by a data-driven flow modeling method. The modeled flow field is partitioned as a set of cells of piece-wise constant flow speed. A flow partition algorithm and a parameter estimation algorithm are proposed to learn the flow field structure and parameters with justified convergence. A bounded cost path planning algorithm is developed taking advantage of the partitioned flow model. An extended potential search method is proposed to determine the sequence of partitions that the optimal path crosses. The optimal path within each partition is then determined by solving a constrained optimization problem. Theoretical justification is provided for the proposed extended potential search method generating the optimal solution. The path planned has the highest probability to satisfy the bounded cost constraint. The performance of the algorithms is demonstrated with experimental and simulation results, which show that the proposed method is more computationally efficient than some of the existing methods. 

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4. Ti3C2Tx MXene-Based Hybrid Photocatalysts in Organic Dye Degradation: A Review

   https://doi.org/10.3390/molecules30071463  

   Seling, Tank R; Songsart-Power, Mackenzie; Shringi, Amit Kumar; Paudyal, Janak; Yan, Fei; Limbu, Tej B (April 2025, Molecules)

   This review provides an overview of the fabrication methods for Ti3C2Tx MXene-based hybrid photocatalysts and evaluates their role in degrading organic dye pollutants. Ti3C2Tx MXene has emerged as a promising material for hybrid photocatalysts due to its high metallic conductivity, excellent hydrophilicity, strong molecular adsorption, and efficient charge transfer. These properties facilitate faster charge separation and minimize electron–hole recombination, leading to exceptional photodegradation performance, long-term stability, and significant attention in dye degradation applications. Ti3C2Tx MXene-based hybrid photocatalysts significantly improve dye degradation efficiency, as evidenced by higher percentage degradation and reduced degradation time compared to conventional semiconducting materials. This review also highlights computational techniques employed to assess and enhance the performance of Ti3C2Tx MXene-based hybrid photocatalysts for dye degradation. It identifies the challenges associated with Ti3C2Tx MXene-based hybrid photocatalyst research and proposes potential solutions, outlining future research directions to address these obstacles effectively. 

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5. Physics-Informed Machine Learning for Degradation Diagnostics of Lithium-Ion Batteries

   https://doi.org/10.1115/DETC2021-71407  

   Thelen, Adam; Lui, Yu Hui; Shen, Sheng; Laflamme, Simon; Hu, Shan; Hu, Chao (November 2021, 47th Design Automation Conference (DAC))

   State of health (SOH) estimation of lithium-ion batteries has typically been focused on estimating present cell capacity relative to initial cell capacity. While many successes have been achieved in this area, it is generally more advantageous to not only estimate cell capacity, but also the underlying degradation modes which cause capacity fade because these modes give further insight into maximizing cell usage. There have been some successes in estimating cell degradation modes, however, these methods either require long-term degradation data, are demonstrated solely on artificially constructed cells, or exhibit high error in estimating late-life degradation. To address these shortfalls and alleviate the need for long-term cycling data, we propose a method for estimating the capacity of a battery cell and diagnosing its primary degradation mechanisms using limited early-life degradation data. The proposed method uses simulation data from a physics-based half-cell model and early-life degradation data from 16 cells cycled under two temperatures and C rates to train a machine learning model. Results obtained from a four-fold cross validation study indicate that the proposed physics-informed machine learning method trained with only 60 early life data (five data from each of the 12 training cells) and 30 high-degradation simulated data can decrease estimation error by up to a total of 9.77 root mean square error % when compared to models which were trained only on the early-life experimental data. 

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