scholarly journals Impulse Force Balance for Ultrashort Duration Hypersonic Test Facilities

2015 ◽  
Vol 2015 ◽  
pp. 1-8
Author(s):  
P. Singh ◽  
V. Menezes ◽  
K. J. Irimpan ◽  
H. Hosseini
Keyword(s):  
Impulse Response ◽  
Aerodynamic Force ◽  
Delta Wing ◽  
Shock Tunnel ◽  
Force Balance ◽  
Test Model ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Test Assembly ◽  
Hypersonic Shock

This paper presents the measurement of side force, pitching, and yawing moments on a model, using an accelerometer force balance, in a short duration hypersonic shock tunnel. The test model is a blunt-nosed, flapped delta wing, mounted on a support sting through a force balance. The flexible rubber bushes constituting the balance allow the model to float freely on the sting during the test. The accelerometers were located in the model to record accelerations in the directions of interest. The model was tested in shock tunnel at Mach 8 at different angles of incidence with the freestream. Dynamic calibration of the test assembly was carried out for the acquisition of impulse response functions for the above components of force and moments, using an impulse hammer. The convolution technique was applied to derive the impulse response functions. The accelerometer outputs from the model in the hypersonic freestream were processed using the respective impulse response functions to derive the unknown aerodynamic force and moments. The newly adopted convolution technique has been found very effective for data reduction from accelerometer force balances developed for shock tunnel applications.

Impulse-response functions and anthropogenic CO2

1995 ◽  
Vol 22 (4) ◽  
pp. 413-416 ◽  
Author(s):  
Francesco N. Tubiello ◽  
Michael Oppenheimer
Keyword(s):  
Impulse Response ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Anthropogenic Co2

IMPROVING EXTRACTION OF IMPULSE RESPONSE FUNCTIONS USING STATIONARY WAVELET SHRINKAGE IN TERAHERTZ REFLECTION IMAGING

2010 ◽  
Vol 09 (04) ◽  
pp. 387-394 ◽  
Author(s):  
YANG CHEN ◽  
YIWEN SUN ◽  
EMMA PICKWELL-MACPHERSON
Keyword(s):  
Experimental Data ◽  
Impulse Response ◽  
Response Function ◽  
Impulse Response Function ◽  
Terahertz Imaging ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Inverse Filtering ◽  
Wavelet Shrinkage ◽  
Gaussian Filtering

In terahertz imaging, deconvolution is often performed to extract the impulse response function of the sample of interest. The inverse filtering process amplifies the noise and in this paper we investigate how we can suppress the noise without over-smoothing and losing useful information. We propose a robust deconvolution process utilizing stationary wavelet shrinkage theory which shows significant improvement over other popular methods such as double Gaussian filtering. We demonstrate the success of our approach on experimental data of water and isopropanol.

A Comparison of Approaches to Select the Informativeness of Priors in BVARs

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Prüser ◽  
Christoph Hanck
Keyword(s):  
Impulse Response ◽  
Prior Information ◽  
Hierarchical Modeling ◽  
Small Samples ◽  
Model Parameters ◽  
Impulse Response Functions ◽  
Response Functions ◽  
Vector Autoregressions ◽  
The Rich ◽  
Bayesian Vars

Abstract Vector autoregressions (VARs) are richly parameterized time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, in small samples the rich parametrization of VAR models may come at the cost of overfitting the data, possibly leading to imprecise inference for key quantities of interest such as impulse response functions (IRFs). Bayesian VARs (BVARs) can use prior information to shrink the model parameters, potentially avoiding such overfitting. We provide a simulation study to compare, in terms of the frequentist properties of the estimates of the IRFs, useful strategies to select the informativeness of the prior. The study reveals that prior information may help to obtain more precise estimates of impulse response functions than classical OLS-estimated VARs and more accurate coverage rates of error bands in small samples. Strategies based on selecting the prior hyperparameters of the BVAR building on empirical or hierarchical modeling perform particularly well.

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