Jet Noise Prediction Comparisons with Scale Model Tests and Learjet Flyover Data

Recent interest in commercial supersonic flight has highlighted the need to accurately predict Effective Perceived Noise Levels (EPNL) for aircraft and, since the dominant noise source at takeoff will likely be jet noise, specifically jet noise contributions. The current study compares predictions from historical jet-noise models within NASAs Aircraft Noise Prediction Program and scale-model data to measurements made in a Learjet 25 flight test. The noise levels from the predictions and scale-model data were below those for the flight data by 2.5 3.5, 1 2, and 3 5 EPNdB for the SAE model, the Stone Jet model, and the scale-model data, respectively. Tones and broadband haystacks were identified in the flight spectra that are not associated with jet noise which increased the flight EPNL by at least 0.5 EPNdB over that computed from spectra with the tones and haystacks removed. The study highlights the need for accurate exhaust temperature measurements, aircraft flight position information, and averaging data across a line of microphones in flight tests. For example, a 100 F to 200 F difference in jet exhaust temperature is enough to explain the differences between flight, model scale, and prediction comparisons

NASA's Turbofan Engine Concept Study for a Next-Generation Single-Aisle Transport: Presentation to ICAO's Noise Technology Independent Expert Panel

No abstract availabl

Jet Noise Prediction Comparisons with Scale Model Tests and Learjet Flyover Data

Recent interest in commercial supersonic flight has highlighted the need to accurately predict Effective Perceived Noise Levels (EPNL) for aircraft and, since the dominant noise source at takeoff will likely be jet noise, specifically jet noise contributions. The current study compares predictions from historical jet-noise models within NASAs Aircraft Noise Prediction Program and scale-model data to measurements made in a Learjet 25 flight test. The noise levels from the predictions and scale-model data were below those for the flight data by 2.5 3.5, 1 2, and 3 5 EPNdB for the SAE model, the Stone Jet model, and the scale-model data, respectively. Tones and broadband haystacks were identified in the flight spectra that are not associated with jet noise which increased the flight EPNL by at least 0.5 EPNdB over that computed from spectra with the tones and haystacks removed. The study highlights the need for accurate exhaust temperature measurements, aircraft flight position information, and averaging data across a line of microphones in flight tests. For example, a 100 F to 200 F difference in jet exhaust temperature is enough to explain the differences between flight, model scale, and prediction comparisons

Comprehensive genomic characterization of squamous cell lung cancers

Lung squamous cell carcinoma is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in squamous cell lung cancers have not been comprehensively characterized, and no molecularly targeted agents have been specifically developed for its treatment. As part of The Cancer Genome Atlas, here we profile 178 lung squamous cell carcinomas to provide a comprehensive landscape of genomic and epigenomic alterations. We show that the tumour type is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumour. We find statistically recurrent mutations in 11 genes, including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations are seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2 and KEAP1 in 34%, squamous differentiation genes in 44%, phosphatidylinositol-3-OH kinase pathway genes in 47%, and CDKN2A and RB1 in 72% of tumours. We identified a potential therapeutic target in most tumours, offering new avenues of investigation for the treatment of squamous cell lung cancers.National Institutes of Health (U.S.) (Grant U24 CA126561)National Institutes of Health (U.S.) (Grant U24 CA126551)National Institutes of Health (U.S.) (Grant U24 CA126554)National Institutes of Health (U.S.) (Grant U24 CA126543)National Institutes of Health (U.S.) (Grant U24 CA126546)National Institutes of Health (U.S.) (Grant U24 CA126563)National Institutes of Health (U.S.) (Grant U24 CA126544)National Institutes of Health (U.S.) (Grant U24 CA143845)National Institutes of Health (U.S.) (Grant U24 CA143858)National Institutes of Health (U.S.) (Grant U24 CA144025)National Institutes of Health (U.S.) (Grant U24 CA143882)National Institutes of Health (U.S.) (Grant U24 CA143866)National Institutes of Health (U.S.) (Grant U24 CA143867)National Institutes of Health (U.S.) (Grant U24 CA143848)National Institutes of Health (U.S.) (Grant U24 CA143840)National Institutes of Health (U.S.) (Grant U24 CA143835)National Institutes of Health (U.S.) (Grant U24 CA143799)National Institutes of Health (U.S.) (Grant U24 CA143883)National Institutes of Health (U.S.) (Grant U24 CA143843)National Institutes of Health (U.S.) (Grant U54 HG003067)National Institutes of Health (U.S.) (Grant U54 HG003079)National Institutes of Health (U.S.) (Grant U54 HG003273