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An extreme thermal cycling reliability test of ATLAS ITk Strips barrel modules
At the end of Run 3 of the Large Hadron Collider (LHC), the accelerator complex will be upgraded to the High-Luminosity LHC (HL-LHC) in order to increase the total amount of data provided to its experiments. To cope with the increased rates of data, radiation, and pileup, the ATLAS detector will undergo a substantial upgrade, including a replacement of the Inner Detector with a future Inner Tracker, called the ITk. The ITk will be composed of pixel and strip sub-detectors, where the strips portion will be composed of 17,888 silicon strip detector modules. During the HL-LHC running period, the ITk will be cooled and warmed a number of times from about -35°C to room temperature as part of the operational cycle, including warm-ups during yearly shutdowns. To ensure ITk Strips modules are functional after these expected temperature changes, and to ensure modules are mechanically robust, each module must undergo ten thermal cycles and pass a set of electrical and mechanical criteria before it is placed on a local support structure. This paper describes the thermal cycling Quality Control (QC) procedure, and results from the barrel pre-production phase (about 5% of the production volume). Additionally, in order to assess the headroom of the nominal QC procedure of 10 cycles and to ensure modules don't begin failing soon after, four representative ITk Strips barrel modules were thermally cycled 100 times — this study is also described.
Toward reliability in the NISQ era: Robust interval guarantee for quantum measurements on approximate states
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A Hybrid Reliability Model Using Generalized Renewal Processes for Predictive Maintenance in Nuclear Power Plant Circulating Water Systems
Not Available
Co-Optimization of Boost Converter Reliability and Volumetric Power Density Using Genetic Algorithm.
Abstract not provided.
Eris : fault injection and tracking framework for reliability analysis of open-source hardware.
Abstract not provided.
Mobile Energy Storage Systems: A Grid-Edge Technology to Enhance Reliability and Resilience
Increase in the number and frequency of widespread outages in recent years has been directly linked to drastic climate change necessitating better preparedness for outage mitigation. Severe weather conditions are experienced more frequently and on larger scales, challenging system operation and recovery time after an outage. In conclusion, the impact is more evident and concerning than before, considering the increased dependency on electricity in all aspects of our lives.
How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements
Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.
Reliability Implications of Solder in Multiwire Modules under Dynamic Mechanical Loading
Two generations of multiwire modules were studied under dynamic mechanical loading (DML) with in-situ differential conductance (dG) and electroluminescence (EL) imaging. Energy-dispersive x-ray spectroscopy (EDS) was used to identity the solder alloys. The earlier generation module was found to use an In-based solder alloy, and the current generation a Bi-based alloy. The earlier generation module degraded significantly under DML with increasing resistance, while the current generation module did not demonstrate degradation under DML. Atomic force microscopy scratch testing was used to probe the wear resistance of each solder alloy. These results indicate that current multiwire designs may have higher mechanical durability than earlier generations.
A Mission Profile-Based Reliability Modeling Framework for Fault-Tolerant Electric Propulsion
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Charge Trap Layer Supercharging for Improved Bit Reliability in 3-D NAND Flash Under Proton Irradiation
Single-event upset (SEU) cross sections are reduced in 176-layer charge trap (CT) 3-D nand devices under proton irradiation when multiple write operations are applied sequentially without the typical erase-before-write. Here, this effect is observed for multiple data patterns and in both single-level cell (SLC) and triple-level cell (TLC) operating modes. SEU cross section calculation methodologies are discussed for highly scaled 3-D devices both with and without the application of rewrites, and potential implications for long-term endurance effects are proposed.
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
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Robust Distribution State Estimation for Reliable Locational Marginal Pricing under Cyber-Attacks
Here this paper examines the impact of false data injection (FDI) cyber-attacks on distribution system state estimation (DSSE) and the resulting distribution locational marginal price (DLMP) in power markets. Two robust high-breakdown regression estimators, namely S- and MM- estimators, are implemented to provide resistance against FDI attacks targeting measurements and grid topology, creating leverage points. The introduced estimators are compared to the weighted least squares (WLS) with a bad data detection and rejection module (BDD) and the robust Huber M-estimator. The proposed estimators are shown to be effective and compare favorably to both existing Huber M- and the WLS with BDD in the presence of topology FDI attacks. Both the S- and MM-estimators provide good performance in the case of clean and corrupted measurements. Their performance is comparable in this case to the Huber M- and the WLS, followed by a BDD module. The simulation considered a modified distribution IEEE 13 and 34-bus systems where the impact of FDI attack scenarios is shown on the state and the DLMP pricing in the presence of distributed Generation.
Reliability-Aware Offloading and Allocation in Multilevel Edge Computing System
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Slow Replica and Shared Protection: Energy-Efficient and Reliable Task Assignment in Cloud Data Centers
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Reliability Assessment Framework for the Distribution System Including Distributed Energy Resources
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