Engineering topics

Arman Mirhashemi

Publications and source records attributed to Arman Mirhashemi.

23 records · Page 2

NASA NTRSDate not supplied

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1%in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.

Machine Learning↗

NASA NTRSDate not supplied

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Machine Learning↗

NASA NTRSDate not supplied

Spacecraft Cabin Ventilation Fan Research at NASA

NASA has recently made the geometry and solid model for a spacecraft cabin ventilation fan prototype available to the public via electronic file downloads from the NASA Technical Report Server. This fan can be used for research and development by many organizations. The fan is 3.5 inches in diameter, 9 inches long, and weighed 3.6 lb. The NASA Quiet Space Fan was designed to make 3.64 inches of water pressure rise at 150.3 cfm of airflow at 12,000 rpm at standard air conditions of 70ºF and 14.7 psia. The performance of the metal version of the fan was measured to be 3.48 inches of water at 150.6 cfm at design speed. A low-noise blade-vane count was chosen to try to reduce tone noise generated by this fan by cutting off the first three blade passing frequency tones. In-duct microphone array measurements indicated that the most evident tones occur for frequencies of 1800 Hz (1 BPF) and 7200 Hz (4 BPF). Using reverberant room standard testing methods, the A-weighted sound power level for the fan operating at design point conditions was measured to be 71 dBA. This report describes the current set of publicly available information for this fan. The fan was designed, optimized, and tested with tools and techniques that NASA has traditionally used for turbofan engine research. This is one way that technology developed for aerospace applications can be used more broadly, since quiet and efficient fans are needed for many ventilation systems on spacecraft, aircraft, watercraft, land vehicles, and buildings.

fan, noise, ventilation↗

NASA NTRSDate not supplied

Spacecraft Cabin Ventilation Fan Research at NASA

NASA has recently made the geometry and solid model for a spacecraft cabin ventilation fan prototype available to the public via electronic file downloads from the NASA Technical Reports Server. This fan can be used for research and development by many organizations. The NASA Quiet Space Fan is 8.89 cm (3.50 in) in diameter, 22.9 cm (9.00 in) long, and the metal fan weighs 1.63 kg (3.6 lbm). The fan was designed to generate a system total pressure rise of 906 Pa (3.64 inches of water) at 0.709 m3/s (150.3 cfm) of airflow at 12,000 rpm (at 21.1ºC (70ºF) and 14.7 psia). Performance measured using standardized techniques showed good agreement with design and predicted values. A low-noise blade-vane count was chosen to reduce tonal noise generated by rotor-stator interaction for the first three blade passing frequency harmonics. In-duct microphone array measurements indicated that the most evident tones occur at frequencies of 1,800 Hz (1 BPF) and 7,200 Hz (4 BPF). Using reverberant room standard testing methods, the A-weighted sound power level for the fan operating at design point conditions was measured to be 71 dBA. This report describes the current set of publicly available information for this fan. The fan was designed, optimized, and tested with tools and techniques that NASA has traditionally used for turbofan engine research. This demonstrates that technology developed for aerospace applications can be used more broadly, since quiet and efficient fans are needed for many ventilation systems in spacecraft, aircraft, watercraft, land vehicles, and buildings.

fan↗

NASA NTRSDate not supplied

Turbo-Design: Open-Source Radial Equilibrium Turbomachinery Solver: Part I - Turbines

Advances in 3D Geometrical Designs and Cooling have played a significant role in improving the efficiency of turbomachinery. However, these advancements must be effectively translated back to the modeler. Machine learning can facilitate this transition. Specifically, machine learning–based loss models can bridge the gap between 3D and 1D designs, enabling modelers not only to predict velocity triangles but also to extract additional geometric features. Currently, the design tools used at NASA have not been updated to support such integration—until now. TurboDesign is an open-source, Python-based framework that replaces TD2 (LEW-11029-1) and AXOD2 (LEW-16323-1), both of which are radial equilibrium solvers for axial turbines. The goal of this update is to enable the integration of machine learning loss models into radial equilibrium equations. Additionally, TurboDesign is designed to support radial machines. This paper presents the governing equations, the assumptions underlying the code, the integration of legacy loss models, an example of machine learning model integration, and a validation comparison with CFD. All code, tutorials, and documentation are available at: https://www.github.com/nasa/turbo-design

Radial Equilibrium↗