Engaging Communities and Other Stakeholders to Develop and Implement a Socially Acceptable Consent-Based Siting Process.
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This presentation compares the performance of reinforcement learning controller with model predictive control for a real-world New York high rise building with 40 floors.
Waste chemistry controls for the Hanford double-shell tanks (DSTs) were established in the 1980's in response to tank failures caused by stress corrosion cracking (SCC) at the Savannah River Site (SRS). The controls were established based on a combination of SRS chemistry limits and corrosion testing results. Conditions in the Hanford DSTs have changed over time, with the temperature in the majority of the DSTs dropping below 50 deg. C. Testing indicated that the original chemistry control specification and SCC testing performed in 2010 did not protect against pitting corrosion mechanisms - what is believed to have caused the failure of tank 241-AY-102. New waste chemistry controls are necessary to protect the DSTs from both SCC and pitting corrosion. Savannah River National Laboratory (SRNL), with input from the Tank Integrity Expert Panel (TIEP) Corrosion Subgroup (CSG), performed a statistically based investigation of nitrate and halide ion induced pitting corrosion. The investigation was intended to develop a comprehensive waste chemistry envelope that minimizes the risk of both SCC and pitting caused by halide and nitrate ions. The waste chemistry envelope needed to be robust enough to address future waste retrieval and process changes which could significantly change the waste composition of the DSTs. The experimental design allowed the statistically significant ions and their effect on pitting corrosion to be determined. SRNL developed the 'pitting factor' which is an empirical relationship between the statistically significant inhibitive species (hydroxide, and nitrite), and pitting inducing species (nitrate, chloride, and fluoride). The coefficients are weighted factors that show the degree of influence the species have on the propensity for pitting. New waste chemistry control limits were recommended by SRNL, and concurred with by the TIEP CSG, that incorporated the pitting factor along with other limits. The controls developed to reduce the risk of pitting corrosion were evaluated to determine if they also protected against SCC. Hundreds of test results were reviewed and compared against the proposed limits. With the exception of one test result, instances where cracking was observed corresponded to chemistry that was outside of the proposed limits - indicating that the chemistry controls developed for pitting also adequately addressed SCC concerns. Existing tank composition estimates were evaluated against the new chemistry controls - three current tank chemistries are suspect, and may not meet the new chemistry control requirements. These included the interstitial liquid (liquid associated with solids) of DSTs 241-AN-102, 241-AN-106, 241-AN-107, and 241-AY-101. Measures are ongoing to evaluate the tanks with the suspect chemistry. These include varying combinations of: performing additional evaluations (history of use, modeling); DST specific laboratory testing; and, pursuing core samples of the DSTs. (authors)
MIS M441 Presentation will be presented at the Materials, Identification and Surveillance conference at Savanah River. Foreign Visitors will be present for the meetings.
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Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.
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Quantifying the effects of radiation on the operation of qubits both as quantum information systems as well as particle detectors has emerged as a pressing issue in quantum science in recent years. We present an overview of the design, operation, and deployment of a 90-$\mathrm{in}^2$ three-panel muon detector for use in the NEXUS experimental facility at Fermilab to temporally isolate correlated errors in qubits and determine if they possess an astrophysical origin. Constructed with three scintillator-attached PMTs read out with NIM modules in a triply-coincident logic scheme, we measure a surface-level muon luminosity of 9.7425 muons per second---consistent with an average surface-level cosmic-ray flux of approximately one muon per square centimeter per minute. Integrating the NIM modules with an MCC 128 DAQ HAT and Raspberry Pi, a Python script records exactly when a muon struck the detector and writes a timestamp to a log file for follow-up cross referencing. This experiment will broadly contribute to further studies aimed at understanding the source and mitigation of information loss in qubits.
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