Accuracy in temperature sensor response time estimation for new nuclear reactor designs

2011 ◽  
Vol 6 (1) ◽  
pp. 17 ◽  
Author(s):  
H.M. Hashemian ◽  
Jin Jiang
Keyword(s):  
Response Time ◽  
Temperature Sensor ◽  
Nuclear Reactor ◽  
Time Estimation ◽  
Sensor Response

Temperature Sensor Response Time Testing in Transient Steam Environments of Small Modular Reactors

2020 ◽  
Author(s):  
A. Hashemian ◽  
B. Shumaker ◽  
B. Arnholt ◽  
S. Tyler ◽  
E. Riggsbee
Keyword(s):  
Response Time ◽  
Temperature Sensor ◽  
Sensor Response ◽  
Small Modular Reactors

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Assessment of UV Sensors for Flameout Detection

2017 ◽  
Author(s):  
Edouard Bahous ◽  
Ram Srinivasan ◽  
Priyank Saxena ◽  
John Bowen
Keyword(s):  
Response Time ◽  
Detection System ◽  
Signal Strength ◽  
Sensor Response ◽  
System Level ◽  
Fuel Composition ◽  
Engine Load ◽  
Uv Sensor ◽  
Uv Sensors ◽  
Load Conditions

UV sensors were tested to evaluate the response and reliability as a flameout detection system to reduce system level risks. In this study, UV sensors from two manufacturers were tested on high pressure experimental rigs and on a 15MW gas turbine engine with annular diffusion flame combustion system. Tests were run to investigate the effect of fuel composition, engine load, and sensor circumferential position. The effect of each variable on sensor signal strength and response time is presented in this paper. The response time of the sensor is evaluated against the rate of change of combustor pressure and the time for fuel-air mixture to reach lean extinction limit in the primary zone. Results show that the UV sensor response is not affected by engine load, circumferential location of the sensors, or fuel composition down to Wobbe index of 18.7 MJ/Sm3. At lower Wobbe indices, the signal strength decreased significantly. This result has been attributed to the movement of flame location away from the line of sight of the sensor. Furthermore, it was found that the UV sensor responded before the bulk average reactant mixture reached lean blow out fuel-air ratios. When compared to the baseline detection system the UV sensor performs faster at low load conditions (800 milliseconds) but slower at full load conditions (400 milliseconds). Experimental rig testing led to similar conclusions for sensor response time and signal strength. Future testing of UV sensors on hydrogen blends is planned.

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