Static sensitivity of a spaceplane Ramjet’s thrust and impulse

2021 ◽  
Author(s):  
V. V. Svyatushenko ◽  
D. A. Yagodnikov
Keyword(s):  
Static Sensitivity

Meteorological Measurement Systems

2001 ◽  
Author(s):  
Fred V. Brock ◽  
Scott J. Richardson
Keyword(s):  
Dynamic Performance ◽  
Measurement Systems ◽  
Sensor Performance ◽  
Performance Specifications ◽  
Meteorological Measurement ◽  
Design Type ◽  
Static Sensitivity ◽  
And Function ◽  
Logical Functions ◽  
Source Of Error

This book treats instrumentation used in meteorological surface systems, both on the synoptic scale and the mesoscale, and the instrumentation used in upper air soundings. The text includes material on first- and second-order differential equations as applied to instrument dynamic performance, and required solutions are developed. Sensor physics are emphasized in order to explain how sensors work and to explore the strengths and weaknesses of each design type. The book is organized according to sensor type and function (temperature, humidity, and wind sensors, for example), though several unifying themes are developed for each sensor. Functional diagrams are used to portray sensors as a set of logical functions, and static sensitivity is derived from a sensor's transfer equation, focusing attention on sensor physics and on ways in which particular designs might be improved. Sensor performance specifications are explored, helping to compare various instruments and to tell users what to expect as a reasonable level of performance. Finally, the text examines the critical area of environmental exposure of instruments. In a well-designed, properly installed, and well-maintained meteorological measurement system, exposure problems are usually the largest source of error, making this chapter one of the most useful sections of the book.

Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

2021 ◽  
Author(s):  
Rodric Mérimé Nonki ◽  
André Lenouo ◽  
Christopher J. Lennard ◽  
Raphael M. Tshimanga ◽  
Clément Tchawoua
Keyword(s):  
Sensitivity Analysis ◽  
Impact Assessment ◽  
Hydrological Modeling ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Sensitivity Approach ◽  
Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

Overview

2001 ◽  
Author(s):  
Fred V. Brock ◽  
Scott J. Richardson
Keyword(s):  
Data Storage ◽  
Dynamic Performance ◽  
Quantitative Information ◽  
Essential Elements ◽  
Static Characteristic ◽  
Data Display ◽  
Analog To Digital ◽  
Storage Devices ◽  
High Quality Information ◽  
Static Sensitivity

Measurements are required to obtain quantitative information about the atmosphere. Elements of a good measurement system, one that produces high-quality information, are briefly described in the following sections. All of these items are, or should be, of concern to everyone who uses data. None may be safely delegated, in their entirety, to those who have little or no interest in the ultimate use of the data. An instrument is a device containing at least a sensor, a signal conditioning device, and a data display. In addition, the instrument may contain an analog-to-digital converter, data transmission and data storage devices, a microprocessor, and a data display. The sensor is one of the essential elements because it interacts with the variable to be measured (the measurand), and generates an output signal proportional to that variable. At the other end of this chain, a data display is also essential, for the instrument must deliver data to the user. To understand a sensor, one must explore the physics of the sensor and of sensor interaction with the measurand. There is a wide variety of sensors available for measuring pressure, temperature, humidity, and so on, and this text discusses each individually. Therefore, each chapter must deal with many different physical principles. Sensor performance can be described by reference to a standardized set of performance definitions. These characteristics are used by manufacturers to describe instruments and as purchase specifications by buyers. Static characteristics are those obtained when the sensor input and output are static (i.e., not changing in time). Static sensitivity is an example of a static characteristic and is particularly useful in sensor analysis. When raw sensor output is plotted as a function of the input, the slope of this curve is called the static sensitivity. Relating static sensitivity to fundamental physical parameters is a systematic way of revealing sensor physics and leads to an understanding of the sensor and of how to improve the design. Dynamic characteristics are a way of defining a sensor response to a changing input. The most widely known dynamic performance parameter is the time constant, discussed in chap. 6.

scholarly journals The geodetic mass balance of Eyjafjallajökull ice cap for 1945–2014: processing guidelines and relation to climate

2019 ◽  
Vol 65 (251) ◽  
pp. 395-409 ◽  
Author(s):  
JOAQUÍN M. C. BELART ◽  
EYJÓLFUR MAGNÚSSON ◽  
ETIENNE BERTHIER ◽  
FINNUR PÁLSSON ◽  
GUðFINNA AÐALGEIRSDÓTTIR ◽  
...  
Keyword(s):  
Mass Balance ◽  
Robust Statistics ◽  
Winter Precipitation ◽  
Summer Temperature ◽  
Aerial Photographs ◽  
Mass Balances ◽  
Stereo Images ◽  
Surface Mass ◽  
Ice Cap ◽  
Static Sensitivity

ABSTRACTMass-balance measurements of Icelandic glaciers are sparse through the 20th century. However, the large archive of stereo images available allows estimates of glacier-wide mass balance ($\dot{B}$) in decadal time steps since 1945. Combined with climate records, they provide further insight into glacier–climate relationship. This study presents a workflow to process aerial photographs (1945–1995), spy satellite imagery (1977–1980) and modern satellite stereo images (since 2000) using photogrammetric techniques and robust statistics in a highly automated, open-source pipeline to retrieve seasonally corrected, decadal glacier-wide geodetic mass balances. In our test area, Eyjafjallajökull (S-Iceland, ~70 km2), we obtain a mass balance of $<![CDATA[ $ \dot{\curr B}_{\curr 1945}^{\curr 2014} \curr = -0.27 \pm 0.03\,{\rm \curr m\ w}{\rm. \curr e}{\rm.} {\rm \curr a}^{{\rm \ndash \curr 1}}$, with a maximum and minimum of $\dot{\curr B}_{\curr 1984}^{\curr 1989} \curr = 0.77 \curr \pm 0.19\,{\rm \curr m\ \curr w}{\rm\curr . e}{\rm\curr .} {\rm\curr a}^{{\rm\curr \ndash 1}}$ and $\dot{\curr B}_{\curr 1994}^{\curr 1998}\curr = -1.94 \curr \pm 0.34\,{\rm \curr m\ w}{\rm\curr . e}{\rm\curr .} {\rm \curr a}^{{\rm\curr \ndash 1}}$, respectively, attributed to climatic forcing, and $\dot{\curr B}_{\curr 2009}^{\curr 2010} \curr = -3.39{\rm \;} \curr \pm {\rm \;} \curr 0.43\,{\rm \curr m\ w}{\rm\curr . e}{\rm\curr .} {\rm\curr a}^{{\rm\curr \ndash 1}}$, mostly caused by the April 2010 eruption. The reference-surface mass balances correlate with summer temperature and winter precipitation, and linear regression accounts for 80% of the mass-balance variability, yielding a static sensitivity of mass balance to summer temperature and winter precipitation of − 2.1 ± 0.4 m w.e.a–1K–1 and 0.5 ± 0.3 m w.e.a–1 (10%)–1, respectively. This study serves as a template that can be used to estimate the mass-balance changes and glaciers' response to climate.

The Measurement of Carpet Static

1976 ◽  
Vol 46 (2) ◽  
pp. 129-134 ◽  
Author(s):  
K. Chakravarti ◽  
G. J. Pontrelli
Keyword(s):  
Low Voltage ◽  
Fiber Type ◽  
Threshold Level ◽  
Test Methods ◽  
Discharge Voltage ◽  
Test Method ◽  
Static Test ◽  
Factors Affecting ◽  
Static Sensitivity ◽  
Maximum Voltage

The various factors affecting static generation in carpets such as relative humidity, fiber type, shoe-sole material, carpet backing, and treadwear have been examined in this paper. Experiments to determine threshold level of static sensitivity in humans have been made. Critical evaluations of current carpet static test methods have been made, and the use of low voltage (discharge voltage) instead of maximum voltage value is suggested. A new carpet static test method using a motorized treadmill device has been described.

scholarly journals Static and Dynamic Evaluation of a Winding Deformation FBG Sensor for Power Transformer Applications

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4877 ◽  
Author(s):  
Aguinaldo Goes de Melo ◽  
Daniel Benetti ◽  
Luiz Alkimin de Lacerda ◽  
Rodrigo Peres ◽  
Claudio Floridia ◽  
...  
Keyword(s):  
Power Transformer ◽  
Dynamic Pressure ◽  
Short Circuit ◽  
Pressure Sensors ◽  
Electric Power Industry ◽  
Inrush Current ◽  
Polyether Ether Ketone ◽  
Dielectric Sensor ◽  
Transformer Winding ◽  
Static Sensitivity

Power transformer is the most important and expensive equipment used in the electric power industry. Fiber Bragg grating (FBG) sensors has stood out as a flexible and particularly suitable tool for power transformer monitoring being a passive and dielectric sensor element. In this work we evaluated the performance of FBG pressure sensors developed to monitor the static and dynamic pressure in high voltage winding transformers during events such as short-circuit and inrush current. Two types of sensors packaging materials were evaluated in laboratory: polyether ether ketone (PEEK) and transformerboard (TB). The sensors have been tested for high intensity and short duration impacts similar to those occurring in short circuits. In addition, we evaluated the time response of sensors using an interrogation system with a 5 kHz sweep in order to analyze the short circuit response time properly. The results pointed that FBG pressure sensors using PEEK and TB are suitable for transformer winding monitoring. The static sensitivity obtained to PEEK based sensors was 0.911 pm/N, in the range of 800 N to 1500 N. This sensitivity is 4.47 higher than TB based sensors sensitivity. Dynamical tests performance showed an excellent repeatability for both sensors, in agreement with static observation.

Static sensitivity of primary muscle spindle endings

1982 ◽  
Vol 13 (5) ◽  
pp. 387-394
Author(s):  
V. I. Zalkind
Keyword(s):  
Muscle Spindle ◽  
Primary Muscle Spindle Endings ◽  
Static Sensitivity ◽  
Muscle Spindle Endings

Regulation of the Dynamic and Static Sensitivity of Gastrocnemius-Soleus Muscle Spindle Afferents by Joint and Cutaneous Afferents in the Cat

1995 ◽  
pp. 151-156 ◽  
Author(s):  
P. H. Ellaway ◽  
N. J. Davey ◽  
M. J. Ljubisavljevic ◽  
N. P. Anissimova
Keyword(s):  
Muscle Spindle ◽  
Soleus Muscle ◽  
Cutaneous Afferents ◽  
Muscle Spindle Afferents ◽  
Static Sensitivity

Analysis of Power Monitoring on AC Induction Drive Systems

1990 ◽  
Vol 112 (2) ◽  
pp. 239-248 ◽  
Author(s):  
J. L. Stein ◽  
Churn-Hway Wang
Keyword(s):  
Signal To Noise Ratio ◽  
Graph Model ◽  
Input Power ◽  
Process Conditions ◽  
Machine Component ◽  
Cnc Milling ◽  
Spindle System ◽  
Power Monitoring ◽  
Static Sensitivity ◽  
Drive Systems

The performance of AC induction motor drive systems as torque sensors is evaluated. Their potential for monitoring tool wear or breakage as well as machine component failure is discussed. A bond graph model of the motor and drive train is developed to determine the relation between electric motor power consumption and the applied torques and to identify the machine components that dominate this dynamic relation. The model, as applied to a CNC milling machine spindle system, shows (1) that the rotor input power is linearly related to both static and dynamic cutting torques under normal process conditions and (2) that the static sensitivity of the spindle system as a sensor increases and the bandwidth of the sensor decreases as the spindle speed is increased. The sensitivity and bandwidth changes are due to changes in system parameters caused by altering the gear ratios between the motor and spindle. Experiments show that the signal-to-noise ratio is affected by the motor torque vibration induced by a geometric irregularity in the drive belt. The vibration, however, does not affect the bandwidth of the sensor system. Power monitoring appears to be a viable torque sensing technique.

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