Session: Research Posters

Paper Number: 172425

Factorial Analysis of Small Air Blower Acoustics and Vibration Spectra at Varying Temperatures and Air Flow Velocities 

Small electric blowers are critical in many cooling and ventilation systems in consumer and commercial settings. Noise and vibration can reduce performance, component life and user comfort. Current blower performance diagnostics lack a comprehensive, data‐driven framework to isolate how simultaneous changes in heat and flow settings drive noise and vibration. A controlled factorial experiment was conducted to quantify how blower temperature and air-flow velocity settings affect acoustic emissions and mechanical vibration outputs. A consumer grade air blower with programmable heat and speed settings served as the test object. The four heat levels – off, low, medium, and high, and three air velocity levels – low, medium and high were combined in a full 4x3 factorial design experiment. Each operating state was initially run for 15 seconds to stabilize that set condition and then recorded for an additional 30 seconds to ensure steady state was captured. The experiment was repeated three times at each condition for a total of 36 recordings. Three PCB ¼″ condenser microphones were arrayed around the blower output from 12 inches away at 0°, 45° and 270°. A PCB MEMS accelerometer was attached to the blower housing 3 inches from the outlet on the top face. All analog signals were acquired through a National Instruments DAQ through a single analog input module at 48 kHz. All acquired signals were filtered using a zero-phase band-pass filter from 20 Hz to 20 kHz to remove airflow rumble and hum. Acoustic metrics included A-weighting the sound pressure level (SPL) using ANSI S1.4-2014, power spectral densities (PSD) using Welch’s method (2^14 FFT), a Hamming window and a 50% overlap. Vibration metrics included root mean square acceleration (RMS). Cross-spectral coherence between each microphone and each accelerometer axis was also computed. A two-way Analysis of Variance (ANOVA) tested the main effects temperature and air velocity but also their interaction on mean SPL and mean RMS.  Strong main effects were observed for temperature (p < 0.001, η² = 0.72) and velocity (p < 0.001, η² = 0.85). A significant interaction (p=0.02) indicated that the influence of one factor depended on the level of the other. Here, p is the probability of observing such an effect by chance and η² (eta squared) is the proportion of total variance explained by each factor. Tukey’s Honest Significant Difference (HSD) controls the family-wise error rate when testing all pairs was done post-experiment to account for error and confirm the significance observed. It was confirmed each step increase in heat or velocity raised SPL by a statistically significant margin (p < 0.05). On average, we found that increasing velocity by one level increased A-weighted SPL by 8 dBA at constant heat. Also, increasing temperature by one level increased SPL by 5 dBA at constant velocity. It is observed that air velocity variations caused larger shifts in high-frequency spectral energy. Whereas temperature increase amplified low-frequency vibration modes below 200 Hz. Average vibration RMS rose by 0.15 g per temperature level at constant flow rate and 0.25 g per velocity level at constant heat. Coherence analysis showed up to 30% higher microphone-accelerometer coherence at 1 kHz under high-heat versus low-heat conditions (p < 0.01). These findings, supported by both objective statistical validation and psychoacoustic SPL measures, provide a robust framework for diagnosing noise-related performance issues in small blowers. They demonstrate that air-flow velocity dominates acoustic emissions, whereas temperature more strongly influences vibrations. These insights can guide low-noise blower design and predictive maintenance. The poster will present the experimental setup, data-processing workflow, ANOVA table, Turkey HSD findings, PSD and coherence visualizations, and design recommendations.

Presenting Author: Trisha Campanaro Penn State University - Harrisburg

Presenting Author Biography: Trisha Campanaro is a Ph.D. student in Engineering Systems at Penn State Harrisburg. She holds a B.S. in Mechanical Engineering Technology from Penn State University—where her undergraduate capstone focused on railroad-car connector design—and an M.S. in Engineering Management with a focus on Engineering Analytics and Project Management from Rowan University.
Her research spans acoustics, vibration analysis, and machine learning for electromechanical and/or mechanical systems. She leads experimental campaigns on blower noise and vibration, developing calibration protocols, signal-processing pipelines, and statistical analysis workflows. She has presented at multiple Penn State research poster competitions and won the Penn State Harrisburg competition in 2024.
In the classroom, she has taught Engineering Design I and served as a teaching assistant for Instrumentation and Advanced CAD courses. She mentors undergraduate STEM students in instrumentation and data analysis.
Outside academia, she works part-time as a special project coordinator at Rowan University, focusing on analytics, data science and project management. She is treasurer of the Graduate and Professional Student Council, member of ASME, and an active member of Women in STEM. She volunteers with the National FFA, judging Agriscience events, and supports AACSB accreditation efforts at Rowan University. Her broader research interests include biomedical engineering, medical devices, and materials science.

Authors:

Trisha Campanaro Penn State University - Harrisburg
Issam Abu-Mahfouz The Pennsylvania State University - Harrisburg