The HP-compressor of a twin-spool aero-engine experiences a rotating inlet distortion if a rotating stall in the upstream LP-compressor occurs. This may lead to HP-compressor instability like rotating stall or surge and has even more serious effects on the performance and behavior of the engine than rotating stall in the LP-compressor alone. Studies on compressor flow instabilities are carried out at the 5-stage HP-compressor Rig212, developed in the TurboUnion RB199 jet engine programme. In order to investigate rotating inlet distortions, the axial compressor test facility is equipped with a distortion generator rotating at high speeds in the compressor inlet duct. A disk with a sectorial total pressure loss screen simulates an upstream rotating stall. It produces a rotating inlet distortion with up to 65% of the compressor’s design speed in co- or counter-rotation direction. This paper extends the presentation of first results by Peters et al. [1] and covers both, co- and counter-rotating inlet distortions and their influence on the compressor surge margin. Hot-wire sensor data are analysed to investigate the stall inception process and the response of the compressor flow field to the excitation by the rotating inlet distortion. The experimental detection of aerodynamic eigenfrequencies of the compressor is compared with numerical results from a compressor model developed by Hu and Fottner [2].
Factors affecting the performance of the USEPA WINS PM2.5 separator have been systematically evaluated. In conjunction with the separator's laboratory calibrated penetration curve, analysis of the governing equation that describes conventional impactor performance was used to predict changes in cutpoint as a function of impactor dimensions, flow rate, uncertainties in ambient temperature and pressure measurement, and the temperature and pressure of the sampled air volume. By integrating the resulting performance curves with three idealized ambient aerosol size distributions, the effect of these parameters on measured PM2.5 concentration was predicted. Results showed that allowable variations in impactor jet width, flow rate, diffusion oil volumes, and ambient temperature and pressure measurement result in relatively minimal PM2.5 mass concentration measurement biases. Loading of the WINS well with previously collected particles slightly reduces the separator's cutpoint and thus slightly reduces expected PM2.5 mass concentrations. Variations in ambient pressure produce negligible changes in the performance of the WINS. While not causing a true measurement bias as defined by the regulations, low ambient temperatures naturally affect the airstream's properties and inherently shifts the WINS' cutpoint to slightly lower values. Laboratory-induced crystallization of the DOW 704 diffusion oil produced no appreciable changes in either the position or shape of the WINS separation curve.
We have developed a time-dependent simulation model to estimate in-room concentrations of multiple contaminants, i.e., ammonia (NH3), carbon dioxide (CO2), carbon monoxide (CO), and dust, as a function of increased ventilation with filtered recirculation for swine farrowing facilities. Energy and mass balance equations were used to simulate the indoor air quality (IAQ) and operating cost for a variety of ventilation conditions over a three-month winter period for a facility located in the Midwest U.S., using simplified and real-time production parameters, and results were compared to field data. The model was improved by minimizing the sum of squared errors (SSE) between modeled and measured NH3 and CO2. After optimizing NH3 and CO2, other IAQ results from the simulation were compared to field measurements using linear regression. For NH3, the coefficient of determination (R2) for simulation results and field measurements improved from 0.02 with the original model to 0.37 with the new model. For CO2, the R2 for simulation results and field measurements was 0.49 with the new model. When the makeup air was matched to hallway air CO2 concentrations (1,500 ppm), simulation results showed the smallest SSE. With the new model, the R2 for other contaminants were 0.34 for inhalable dust, 0.36 for respirable dust, and 0.26 for CO. Operation of the air cleaner decreased inhalable dust by 35% and respirable dust concentrations by 33%, while having no effect on NH3 and CO2, in agreement with field data, and increasing operating cost by $860 (58%) for the three-month period.
Nanocomposite materials may be considered as a low-risk application of nanotechnology, if the nanofillers remain embedded throughout the life-cycle of the products in which they are embedded. We hypothesize that release of free CNTs occurs by a combination of mechanical stress and chemical degradation of the polymer matrix. We experimentally address limiting cases: Mechanically released fragments may show tubular protrusions on their surface. Here we identify these protrusions unambiguously as naked CNTs by chemically resolved microscopy and a suitable preparation protocol. By size-selective quantification of fragments we establish as a lower limit that at least 95 % of the CNTs remain embedded. Contrary to classical fiber composite approaches, we link this phenomenon to matrix materials with only a few percent elongation at break, predicting which materials should still cover their CNT nanofillers after machining. Protruding networks of CNTs remain after photochemical degradation of the matrix, and we show that it takes the worst case combinations of weathering plus high-shear wear to release free CNTs in the order of mg/m2/year. Synergy of chemical degradation and mechanical energy input is identified as the priority scenario of CNT release, but its lab simulation by combined methods is still far from real-world validation.
To design a method that uses preliminary hazard mapping data to optimize the number and location of sensors within a network for a long-term assessment of occupational concentrations, while preserving temporal variability, accuracy, and precision of predicted hazards.Particle number concentrations (PNCs) and respirable mass concentrations (RMCs) were measured with direct-reading instruments in a large heavy-vehicle manufacturing facility at 80-82 locations during 7 mapping events, stratified by day and season. Using kriged hazard mapping, a statistical approach identified optimal orders for removing locations to capture temporal variability and high prediction precision of PNC and RMC concentrations. We compared optimal-removal, random-removal, and least-optimal-removal orders to bound prediction performance.The temporal variability of PNC was found to be higher than RMC with low correlation between the two particulate metrics (ρ = 0.30). Optimal-removal orders resulted in more accurate PNC kriged estimates (root mean square error [RMSE] = 49.2) at sample locations compared with random-removal order (RMSE = 55.7). For estimates at locations having concentrations in the upper 10th percentile, the optimal-removal order preserved average estimated concentrations better than random- or least-optimal-removal orders (P < 0.01). However, estimated average concentrations using an optimal-removal were not statistically different than random-removal when averaged over the entire facility. No statistical difference was observed for optimal- and random-removal methods for RMCs that were less variable in time and space than PNCs.Optimized removal performed better than random-removal in preserving high temporal variability and accuracy of hazard map for PNC, but not for the more spatially homogeneous RMC. These results can be used to reduce the number of locations used in a network of static sensors for long-term monitoring of hazards in the workplace, without sacrificing prediction performance.
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