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Results for “reliablity”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A reliability-aware multi-armed bandit approach to learn and select users in demand response
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Improving the reliability of Fe- and S-XANES measurements in silicate glasses: Correcting beam damag
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Finding a common ground for RCM experiments. Part A: On the influences of facility effects regarding the reliability of experimental validations
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Intelligent industrial demand response to increase grid flexibility and reliability: A review
The rapid transition toward renewable energy has introduced challenges in grid stability due to the intermittency of non-dispatchable sources like solar and wind. Industrial Demand Response (IDR) offers a promising, cost-effective solution that adjusts energy consumption patterns to align with supply, increases renewable utilization, and reduces costs. This review provides an updated analysis of IDR, sorting technologies into five categories: energy storage, scheduled energy usage, operational flexibility, on-site generation, and intelligent operations. Energy storage solutions, while requiring little flexibility, often have the longest payback periods. While slightly better, on-site generation also has longer payback periods, ranging from 5 to 20 years or more. Scheduled energy usage, operational flexibility, and intelligent operations allow significant peak reduction at lower capital costs but require greater flexibility. While 15–20 % peak reduction is within the range of all five categories, scheduled energy use and on-site energy generation are shown to have reductions of up to 70–80 % in select scenarios. Combining multiple IDR strategies from these five categories maximizes both financial and operational benefits. Synergistic approaches are shown to enhance grid stability while reducing costs. As the grid evolves, IDR will enable a more flexible, renewable-powered future that will benefit industrial facilities and the broader energy system.
How reliable is distribution of relaxation times (DRT) analysis? A dual regression-classification perspective on DRT estimation, interpretation, and accuracy
The distribution of relaxation times (DRT) has gained increasing attention and adoption in recent years as a versatile method for analyzing electrochemical impedance spectroscopy (EIS) data obtained from complex devices like fuel cells, electrolyzers, and batteries. The DRT deconvolutes the impedance without a priori specification of a generative model, which is especially useful for interpretation and model selection when the governing principles of the system under study are not fully understood. However, DRT estimation is an ill-posed inversion problem that must be addressed with a subjective choice of regularization and tuning, which leaves substantial risk of misleading interpretations of EIS data. In this work, we suggest a new classification view of the DRT inversion to clarify DRT estimation and interpretation. We introduce a dual regression-classification framework that unifies the classification and regression views of the DRT inversion with wide-reaching implications for DRT analysis. The dual framework is employed to demonstrate a new kind of DRT inversion algorithm and develop novel evaluation metrics that capture previously ignored aspects of DRT accuracy. These approaches are applied to both synthetic data and experimental spectra collected from a protonic ceramic fuel cell and a lithium-ion battery to illustrate their broad utility. The dual inversion algorithm shows promising performance for accurate DRT estimation and autonomous model identification, while the dual evaluation approach produces metrics that meaningfully assess the strengths and risks of DRT algorithms. Here this work provides valuable insight for both practical application of the DRT to experimental data and further development of EIS analysis methods.
Application of Thévenin equivalent sensitivity equations for reliable voltage stability assessment
In this paper, sensitivity equations for the Thévenin equivalent parameters, voltage and reactance, are derived using the duality between the Thévenin parameters from two consecutive PMU measurements. Using the sensitivity equation for the Thévenin reactance, a robust adaptive X-Th algorithm is developed aiming to extend a well-known adaptive E-Th approach by being able to accurately estimate the parameters for a wider range of loading conditions. In the performed case studies the resulting accuracy is evaluated when estimating the Thévenin parameters for large disturbances, for two sides with respect to a boundary bus, accommodating sensitivity to topology changes and nonlinearities in power systems. In conclusion, the results can be used for accurate voltage stability assessment using the Thévenin parameters.
Sensitivity and reliability of key electrochemical markers for detecting lithium plating during extreme fast charging
Lithium plating is one of the key challenges for enabling extreme fast charging (XFC, ≤10 to 15 min charging at ≥6C) in graphite-based lithium-ion batteries. Significant R&D effort has been focused on how to mitigate Li plating. Parallel effort is also being devoted to developing methods to detect Li plating when and if it happens during fast charging. In that regard, electrochemical (EC) signature-based detection techniques are less resource intensive, more convenient, and more practical from an end-user application perspective. However, a comprehensive understanding of key plating related EC signatures for extreme fast charging is presently unavailable. In particular, there exist distinct issues of unreliability with key plating-related EC signatures—e.g., incremental capacity (dQ.dV -1 ), differential OCV (dOCV.dt -1 ), end of lithiation (EOL) rest voltage—at XFC conditions, and the underlying reasons have not been explored and identified methodically. Using a comprehensive test matrix and XFC conditions with Li/graphite half cells, this article highlights the unreliability issues associated with the EC Li plating diagnostics and explains the underlying root cause. This study finds distinct sensitivity and unreliability issues with plating related dQ.dV -1 , dOCV.dt -1 , and EOL rest voltage signatures with charging rates. Furthermore, the complex interaction between graphite and plated Li that happens through multiple competing mechanisms —Li stripping and chemical intercalation— at different charging rates is at the core of the sensitivity and unreliability issue.
Enhanced thermal reliability and performance of calcium chloride hexahydrate phase change material using cellulose nanofibril and graphene nanoplatelet
In recent years, thermal energy storage (TES) has gained attention for its role in enhancing renewable energy solutions and sustainable energy consumption. The usage of strontium chloride hexahydrate (SCH), graphene nanoplatelet (GNP), and cellulose nanofibril (CNF) additives were investigated to enhance the performance of calcium chloride hexahydrate (CCH) based on the melting/solidification behavior for TES applications. In this work, we develop a promising phase-change-material (PCM) formulation by introducing these additives that reduce supercooling, improve the thermal conductivity and stabilizing the energy storage capacity of CCH. Rheological characterizations demonstrated that the addition of 1 wt% of CNF into CCH produced the required improvement in viscosity and boosted solid-like rheological behavior. Structural characterizations show a physical mixing of the materials within the PCM composites. Our observations show that the amphiphilicity of CNF enables the surface attachment to GNP via hydrophobic interactions providing effective dispersion of GNP throughout the PCM composite. The addition of a nucleating agent, SCH decreased the degree of supercooling of ~20 g of CCH from >20 °C to 3 °C at a cooling rate of 5 °C/min. Thermal characterization showed the resulting PCM composite has a latent heat of melting of 186 Jg -1 , phase change temperature of 32 °C, and stable thermal properties after being subjected to 70 melt-freeze cycles. Adding CNF and GNP to pure CCH increased its thermal conductivity by 76 %. The high thermal conductivity of GNP and its effective dispersion by CNF is responsible for this enhancement. The study highlights the use of biodegradable nanocellulose for the preparation of sustainable PCM composites with improved performance. In conclusion, these PCM composites are scalable, they have potential to increase energy efficiency and revolutionalize the heating/cooling applications in buildings and other TES systems.
100 years of data is not enough to establish reliable drought thresholds
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Structural reliability analysis of contact fatigue design of gears in wind turbine drivetrains
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