On the Step-Response Method of Determining the Zero-Power Reactor Transfer Function

1969 ◽  
Vol 36 (1) ◽  
pp. 113-114
Author(s):  
D. M. Chapin
Keyword(s):  
Transfer Function ◽  
Power Reactor ◽  
Step Response ◽  
Reactor Transfer ◽  
Response Method

scholarly journals EXPERIMENTAL DETERMINATION OF THE ZERO POWER TRANSFER FUNCTION OF THE AKR-2

2021 ◽  
Vol 247 ◽  
pp. 21009
Author(s):  
Sebastian Hübner ◽  
Alexander Knospe ◽  
Marco Viebach ◽  
Carsten Lange ◽  
Antonio Hurtado
Keyword(s):  
Lower Bound ◽  
Transfer Function ◽  
Neutron Flux ◽  
Basic Characteristic ◽  
Nuclear Reactor ◽  
Power Reactor ◽  
Plateau Region ◽  
Reactor Transfer ◽  
Good Agreement

The transfer function is a basic characteristic of every nuclear reactor. It describes how a perturbation at a given place and time influences the neutron flux. In case of a known perturbation, the determination of characteristic reactor parameters is possible. The present paper shows an experimental method to determine the gain of the zero-power reactor transfer function (ZPTF) of the AKR-2 reactor at TU Dresden and the comparison to the theoretical shape of the ZPTF derived from kinetic parameters simulated with MCNP. For the experiments, a high-precision linear motor axis is used to insert an oscillating perturbation acting at frequencies smaller than the lower bound of the plateau region of the ZPTF. For higher frequencies, a rotating absorber is used. This device emulates an absorber of variable strength. The reactor response is detected with a He-3 counter. The data evaluation shows good agreement between measured and corresponding theoretical values of the gain of the ZPTF.

Dynamic System Identification Using a Step Input

2005 ◽  
Vol 24 (2) ◽  
pp. 125-134
Author(s):  
Manabu Kosaka ◽  
Hiroshi Uda ◽  
Eiichi Bamba ◽  
Hiroshi Shibata
Keyword(s):  
Steady State ◽  
System Identification ◽  
Transfer Function ◽  
Step Response ◽  
Output Data ◽  
Mass System ◽  
Input And Output ◽  
Rational Transfer ◽  
Line Identification ◽  
The Moment

In this paper, we propose a deterministic off-line identification method performed by using input and output data with a constant steady state output response such as a step response that causes noise or vibration from a mechanical system at the moment when it is applied but they are attenuated asymptotically. The method can directly acquire any order of reduced model without knowing the real order of a plant, in such a way that the intermediate parameters are uniquely determined so as to be orthogonal with respect to 0 ∼ N-tuple integral values of output error and irrelevant to the unmodelled dynamics. From the intermediate parameters, the coefficients of a rational transfer function are calculated. In consequence, the method can be executed for any plant without knowing or estimating its order at the beginning. The effectiveness of the method is illustrated by numerical simulations and also by applying it to a 2-mass system.

scholarly journals Modeling and Implementation of TEG-Based Energy Harvesting System for Steam Sterilization Surveillance Sensor Node

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6338
Author(s):  
Mateusz Daniol ◽  
Lukas Boehler ◽  
Ryszard Sroka ◽  
Anton Keller
Keyword(s):  
Power Generation ◽  
Thermal Gradient ◽  
Step Response ◽  
Steam Sterilization ◽  
Storage Unit ◽  
Ultra Low Power ◽  
Sensor Applications ◽  
Physical Prototype ◽  
Energy Harvesting System ◽  
Response Method

The aim of this work is a proof of concept, that medical Internet of Things (IoT) sterilization surveillance sensors can be powered by using the heat during a steam sterilization procedure. Hereby, the focus was on the use of thermo-electrical generators (TEG) to generate enough power for an ultra-low-power sensor application. Power generation requirement of the sensor was 1.6 mW over the single sterilization cycle. The thermal gradient across the TEG has been achieved using a highly efficient aerogel-foam-based thermal insulation, shielding a heat storage unit (HSU), connected to one side of the TEG. The evaluation of the developed system was carried out with thermal and electrical simulations based on the parameters extracted from the TEG manufacturer’s datasheet. The developed model has been validated with a real prototype using the thermal step response method. It was important for the authors to focus on rapid-prototyping and using off-the-shelf devices and materials. Based on comparison with the physical prototype, the SPICE model was adjusted. With a thermal gradient of 12 °C, the simulated model generated over 2 mW of power. The results show that a significant power generation with this system is possible and usable for sensor applications in medial IoT.

Dynamic cerebral autoregulation during brain activation paradigms

2005 ◽  
Vol 289 (3) ◽  
pp. H1202-H1208 ◽  
Author(s):  
Ronney B. Panerai ◽  
Michelle Moody ◽  
Penelope J. Eames ◽  
John F. Potter
Keyword(s):  
Transfer Function ◽  
Cerebral Autoregulation ◽  
Function Analysis ◽  
Brain Activation ◽  
Hemispheric Lateralization ◽  
Step Response ◽  
Arterial Blood ◽  
Transfer Function Analysis ◽  
The Right ◽  
Dynamic Cerebral Autoregulation

Dynamic cerebral autoregulation (CA) describes the transient response of cerebral blood flow (CBF) to rapid changes in arterial blood pressure (ABP). We tested the hypothesis that the efficiency of dynamic CA is increased by brain activation paradigms designed to induce hemispheric lateralization. CBF velocity [CBFV; bilateral, middle cerebral artery (MCA)], ABP, ECG, and end-tidal Pco2 were continuously recorded in 14 right-handed healthy subjects (21–43 yr of age), in the seated position, at rest and during 10 repeated presentations (30 s on-off) of a word generation test and a constructional puzzle. Nonstationarities were not found during rest or activation. Transfer function analysis of the ABP-CBFV (i.e., input-output) relation was performed for the 10 separate 51.2-s segments of data during activation and compared with baseline data. During activation, the coherence function below 0.05 Hz was significantly increased for the right MCA recordings for the puzzle tasks compared with baseline values (0.36 ± 0.16 vs. 0.26 ± 0.13, P < 0.05) and for the left MCA recordings for the word paradigm (0.48 ± 0.23 vs. 0.29 ± 0.16, P < 0.05). In the same frequency range, significant increases in gain were observed during the puzzle paradigm for the right (0.69 ± 0.37 vs. 0.46 ± 0.32 cm·s−1·mmHg−1, P < 0.05) and left (0.61 ± 0.29 vs. 0.45 ± 0.24 cm·s−1·mmHg−1, P < 0.05) hemispheres and during the word tasks for the left hemisphere (0.66 ± 0.31 vs. 0.39 ± 0.15 cm·s−1·mmHg−1, P < 0.01). Significant reductions in phase were observed during activation with the puzzle task for the right (−0.04 ± 1.01 vs. 0.80 ± 0.86 rad, P < 0.01) and left (0.11 ± 0.81 vs. 0.57 ± 0.51 rad, P < 0.05) hemispheres and with the word paradigm for the right hemisphere (0.05 ± 0.87 vs. 0.64 ± 0.59 rad, P < 0.05). Brain activation also led to changes in the temporal pattern of the CBFV step response. We conclude that transfer function analysis suggests important changes in dynamic CA during mental activation tasks.

A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

2010 ◽  
Vol 63 (4) ◽  
pp. 627-643 ◽  
Author(s):  
Mohammed El-Diasty ◽  
Spiros Pagiatakis
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Dynamic Response ◽  
Dynamic System ◽  
Least Squares ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Response Model ◽  
Response Method

We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

Comparison of Buck–Boost and Ćuk Converters Based on Time Domain Response

2018 ◽  
Vol 27 (14) ◽  
pp. 1850222
Author(s):  
J. Leema Rose ◽  
B. Sankaragomathi
Keyword(s):  
Pid Controller ◽  
Closed Loop ◽  
State Equation ◽  
Power Converter ◽  
Open Loop ◽  
Step Response ◽  
State Equations ◽  
Loop Control ◽  
Time Domain Response ◽  
Response Method

This paper presents the design and modeling of power electronic converters such as buck–boost and Ćuk operated under continuous conduction mode (CCM). The open-loop behavior of buck–boost and Ćuk converters needs modeling and simulation using modeled equations. The closed-loop control of these converters has a propositional–integral–derivative (PID) controller. PID controller parameters are obtained from Ziegler–Nichols step response method. These converters can be analyzed using the state equation. The MATLAB/SIMULINK tool is used for simulation of those state equations. Ćuk and buck–boost converters are designed and analyzed. The mathematical model of power Converter for simulation has been carried out using SIMULINK with/without any Sim Power System Elements. The open- and closed-loop results are compared.

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