The unwanted sound, noise, can lead to health problems, e.g. hearing loss and stress-related problems. A pre-knowledge of noise generation by machines is of great importance due to the ever-shorter product development cycles and stricter noise legislation. The noise from a machine radiates to the environment indirectly via the foundation structure and directly via the surrounding fluid. A fluid machine converts the energy from the fluid into mechanical energy or vice versa. Examples of the fluid machines are internal combustion engines (IC-engines), pumps, compressors, and fans. Predicting and controlling noise from a fluid machine requires a model of the noise sources themselves, i.e. acoustic source data. In the duct systems connected to the fluid machines, the acoustic source interacts strongly with the system boundaries, and the source characteristics must be described using in-duct methods. Above a certain frequency, i.e. first non-plane wave mode cut-on frequency, the sound pressure varies over the duct cross-section and non-plane waves are introduced. For a number of applications, the plane wave range dominates and the non-plane waves can be neglected. But for machines connected to large ducts, the non-plane wave range is also important. In the plane wave range, one-dimensional process simulation software can be used to predict, e.g. for IC-engines, the acoustic in-duct source characteristics. The high frequency phenomena with non-plane waves are so complicated, however, that it is practically impossible to simulate them accurately. Thus, in order to develop methods to estimate the sound produced, experimental studies are also essential. This thesis investigates the acoustic in-duct source characterization of fluid machines with applications to exhaust noise from medium speed IC-engines.  This corresponds to large engines used for power plants or on ships, for which the non-plane wave range also becomes important. The plane wave source characterization methods are extended into the higher frequency range with non-plane waves. In addition, methods to determine non-plane wave range damping for typical elements in exhaust systems, e.g. after-treatment devices, are discussed. / <p>QC 20151119</p>

A year-long study was conducted in Pinal County, Arizona to characterize fine and coarse particulate matter as a means of furthering our understanding of ambient concentrations and composition in rural, arid environments. Detailed measurement of ambient fine and coarse mass, ion, metal, and carbon concentrations at one-in-six day resolution was conducted at three sites from February 2009 to February 2010. Detailed organic carbon speciation was collected at 5-week resolution. A series of samples representing native soil, agricultural soil, road dust, and cattle feed lot material was collected, resuspended in the laboratory, and analyzed to provide a chemical source profile for each soil type yielding insights into unique source signatures. Observations within the chemical speciation data and subsequent modeling analysis show a strong impact from local sources at the Cowtown site where mass concentrations are highest. Source apportionment results confirm the significant impact from the cattle feedlot adjacent to the site. Chemical analysis of ambient particles and local feedlot material shows the presence of chemical marker species including phosphate which is unique to this source. Fugitive dust is a significant contributor to ambient particulate matter concentrations at all monitoring locations. Seasonal observations show higher concentrations during tilling and harvesting indicating the large role agricultural sources play on particle concentrations in this area. Chemical characterization and modeling show that re-entrained road dust is a significant factor. Fine particle modeling results indicate that concentrations are influenced significantly by motor vehicles including impacts from direct emissions including brake wear and indirect emissions including resuspended road dust. A significant fraction is also associated with crustal sources while about 5 g/m3 appears to be transported into the region from beyond the air shed. Detailed analysis of the local monsoon season indicates that monsoon rains serve to clean the atmosphere resulting in a marked decrease in ambient coarse mass and resulted in a period where local coarse PM concentrations measured at all sites became more uniform. The monsoon season also featured localized high wind events which severely increased coarse PM concentrations and often caused exceedences of the PM National Ambient Air Quality Standard.

A year-long study was conducted in Pinal County, Arizona to characterize fine and coarse particulate matter as a means of furthering our understanding of ambient concentrations and composition in rural, arid environments. Detailed measurement of ambient fine and coarse mass, ion, metal, and carbon concentrations at one-in-six day resolution was conducted at three sites from February 2009 to February 2010. Detailed organic carbon speciation was collected at 5-week resolution. A series of samples representing native soil, agricultural soil, road dust, and cattle feed lot material was collected, resuspended in the laboratory, and analyzed to provide a chemical source profile for each soil type yielding insights into unique source signatures. Observations within the chemical speciation data and subsequent modeling analysis show a strong impact from local sources at the Cowtown site where mass concentrations are highest. Source apportionment results confirm the significant impact from the cattle feedlot adjacent to the site. Chemical analysis of ambient particles and local feedlot material shows the presence of chemical marker species including phosphate which is unique to this source. Fugitive dust is a significant contributor to ambient particulate matter concentrations at all monitoring locations. Seasonal observations show higher concentrations during tilling and harvesting indicating the large role agricultural sources play on particle concentrations in this area. Chemical characterization and modeling show that re-entrained road dust is a significant factor. Fine particle modeling results indicate that concentrations are influenced significantly by motor vehicles including impacts from direct emissions including brake wear and indirect emissions including resuspended road dust. A significant fraction is also associated with crustal sources while about 5 g/m3 appears to be transported into the region from beyond the air shed. Detailed analysis of the local monsoon season indicates that monsoon rains serve to clean the atmosphere resulting in a marked decrease in ambient coarse mass and resulted in a period where local coarse PM concentrations measured at all sites became more uniform. The monsoon season also featured localized high wind events which severely increased coarse PM concentrations and often caused exceedences of the PM National Ambient Air Quality Standard.

Threat management of water distribution systems is essential for protecting consumers. In a contamination event, different strategies may be implemented to protect public health, including flushing the system through opening hydrants or isolating the contaminant by manipulating valves. To select the most effective options for responding to a contamination threat, the location and loading profile of the source of the contaminant should be considered. These characteristics can be identified by utilizing water quality data from sensors that have been strategically placed in a water distribution system. A simulation-optimization approach is described here to solve the inverse problem of source characterization, by coupling an evolutionary computation-based search with a water distribution system model. The solution of this problem may reveal, however, that a set of non-unique sources exists, where sources with significantly different locations and loading patterns produce similar concentration profiles at sensors. The problem of non-uniqueness should be addressed to prevent the misidentification of a contaminant source and improve response planning. This paper aims to address the problem of non-uniqueness through the use of Niched Co-Evolution Strategies (NCES). NCES is an evolutionary algorithm designed to identify a specified number of alternative solutions that are maximally different in their decision vectors, which are source characteristics for the water distribution problem. NCES is applied to determine the extent of non-uniqueness in source characterization for a virtual city, Mesopolis, with a population of approximately 150,000 residents. Results indicate that NCES successfully identifies non-uniqueness in source characterization and provides alternative sources of contamination. The solutions found by NCES assist in making decisions about response actions. Once alternative sources are identified, each source can be modeled to determine where the vulnerable areas of the system are, indicating the areas where response actions should be implemented.

Long and short-term exposure to particulate matter (PM) are linked to adverse heath endpoints. Evidence indicates that PM composition such as metals and organic carbon (OC) might drive the health effects. As airborne pollutants show significant intracity spatiotemporal variation, mobile sampling and distributed monitors are utilized to capture the variation pattern. The measurements are then fed to develop models to better characterize the relationship between exposure and health outcomes. Two sampling campaigns were conducted. One was sole mobile sampling in 2013 summer and winter in Pittsburgh, PA. Thirty-six sites were chosen based on three stratification variables: traffic density, proximity to point sources, and elevation. The other one was hybrid sampling network, incorporating a mobile sampling platform, 15 distributed monitors, and a supersite. We designed two case studies (transect and downtown), selected 14 neighborhoods (~1 km2), and conducted sampling in 2016 summer/fall and winter. Spatial variation of PM2.5 mass and composition was studied in the 2013 campaign. X-ray fluorescence (XRF) was used to analyze concentrations of 26 elements: Na, Mg, Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Zr, Cd, Sb, and Pb. Trace elements had a broad range of concentrations from 0 to 300 ng/m3. Comparison of data from mobile sampling with stationary monitors showed reasonable agreement. We developed Land use regression (LUR) models to describe spatial variation of PM2.5, Si, S, Cl, K, Ca, Ti, Cr, Fe, Cu, and Zn. Independent variables included traffic influence, land-use type, and facility emissions. Models had an average R2 of 0.57 (SD = 0.16). Traffic related variables explained the most variability with an average R2 contribution of 0.20 (SD = 0.20). Overall, these results demonstrated significant intra-urban spatial variability of fine particle composition. Spatial variation of OC was based on the 2013 campaign as well. We collected organic carbon (OC) on quartz filters, quantified different OC components with thermaloptical analysis, and grouped them based on volatility in decreasing order (OC1, OC2, OC3, OC4, and pyrolyzed carbon (PC)). We compared our ambient OC concentrations (both gas and particle phase) to similar measurements from vehicle dynamometer tests, cooking emissions, biomass burning emissions, and a highway traffic tunnel. OC2 and OC3 loading on ambient filters showed a strong correlation with primary emissions while OC4 and PC were more spatially homogenous. While we tested our hypothesis of OC2 and OC3 as markers of fresh source exposure for Pittsburgh, the relationship seemed to hold at a national level. Land use regression (LUR) models were developed for the OC fractions, and models had an average R2 of 0.64 (SD = 0.09). We demonstrate that OC2 and OC3 can be useful markers for fresh emissions, OC4 is a secondary OC indicator, and PC represents both biomass burning and secondary aerosol. People with higher OC exposure are likely inhaling more fresh OC2 and OC3, since secondary OC4 and PC varies much less drastically in space or with local primary sources. With the 2016 hybrid sampling campaign, we addressed the intracity exposure patterns, as they could be more complex than intercity ones because of local traffic, restaurants, land use, and point sources. This network studied a wide range of pollutants (CO2, CO, NO2, PM1 mass and composition, and particle number PN). Mobile measurements and distributed monitors show good agreement. PN hotspots are strongly associated with restaurants and highway traffic. PN at sites with large local source impacts tends to have larger diurnal variation than daily variation, while CO in downtown center shows the opposite trend. PN exhibits the largest spatial and temporal variations. Spatial variation is generally larger than temporal variation among all five pollutants (CO2, NO2, CO, PN, and PM1). These findings provide quantitative comparison between spatial and temporal variation in different scales, and support the theoretical validity of developing long-term exposure models from short-term mobile measurement. A combined sampling network with mobile and distributed monitor could prove more valuable in studying intracity air pollution. In the 2016 hybrid sampling campaign, we also studied spatial variability of air pollution in the vicinity of monitors. Monitoring network is essential for protecting public health, though evaluation is needed to assess spatial representativeness of monitors in different environments. Mobile sampling was conducted repeatedly around 15 distributed monitors. Substantial short-range spatial variability was observed. Spatial variation was consistently larger than temporal variation for NO2 and CO at different sites. Ultrafine particles were highly dynamic both in space and time. PM1 was less spatially and temporally variable. Urban locations had more frequent episodic source plume events compared with background sites. Using a single monitor measurement to represent surrounding ~1 km2 areas could introduce an average daily exposure misclassification of 46 ppb (SD = 26) for CO (30% of regional background), 3 ppb (SD = 2) for NO2 (43% of background), 4007 #/cm3 (SD = 1909) for ultrafine particle number (64% of background), and 1.2 μg/m3 (SD = 1.0) for PM1 (13% of background). Exposure differences showed fair correlation with traditional land use covariates such as traffic and restaurant density, and the magnitude of misclassification could be even bigger for urban neighborhoods.

In order to reduce uncertainty of atmospheric particle emissions and to examine the mechanism of new particle formation from precursor gases, measurements were conducted in a megacity (Paris, France), an urban area (Patras, Greece) and a remote location (Finokalia, Greece). At Finokalia, the composition of particles with diameter smaller than 1 μm (PM1) depended on air mass origin. The highest concentrations, and most frequent, were observed when air masses were coming from Europe. Organic aerosol was found to be 80% water soluble and the increased organic to elemental carbon ratio correlated with ozone concentration. These findings indicate that particulate matter (PM) at Finokalia was not emitted near the site but was transported from source regions hunderd of kilometers away and thus the area can be considered as a background of Europe. At Finokalia, atmospheric nucleation was observed more frequently during winter when sunlight intensity was below average and favored by air masses that crossed land before reaching the site. This behavior was explained by ammonia involvement in the nucleation process. PM1 was mainly acidic during summer and consumed all available ammonia, contrary to winter when, due to the lower sunlight intensity, particles were neutral and ammonia was available. During both seasons nucleation would only occur if particles were neutral which resulted in higher frequency of events during winter. Air masses that crossed land before reaching the site were enriched with ammonia, thus it was more likely for nucleation to occur. Number size distributions were monitored in Paris, France at fixed and mobile ground stations along with airborne measurements. The Paris plume was identified at a distance of at least 200 km from the city center and the number concentration was found to increase even by a 3-fold when air masses crossed Paris. During summer nucleation was observed approximately half of the campaign days; when the condensational sink was lower than average contrary to winter when no event was identified due to higher sink. Increased number concentration was observed at an altitude outside of the Paris plume simultaneously with new particle formation observed on the ground and was attributed to that phenomenon. At Patras, the legislated by E.U. daily PM10 standards were found to be violated. Exceedances were more frequent (58 of a total of 75) during the colder months (October to March) of the year. The warmer months (April to September) 80% of the PM2.5 was transported from other areas. Contrary during the colder months the contribution of transported PM reduced to 70% during autumn and 50% during winter, when the highest concentrations were observed on average. Local traffic contributed approximately 15% during winter and the remaining 35% was primarily due to domestic heating. PM2.5 and PM1 concentrations were found to exceed 100 μg m-3 on several occasions during nighttime due to domestic heating, either diesel or biomass combustion. Potassium, a tracer of biomass combustion, correlated well (R2=0.79) with PM2.5 during winter indicating a biomass source. Potassium concentrations were higher within the urban premises than a rural area located 36 km away from the city, indicating that at least a portion of the biomass combustion related PM2.5 were emitted locally. / Με σκοπό την μείωση της αβεβαιότητας των εκπομπών ατμοσφαιρικών σωματιδίων (ΑΣ) καθώς και διευκρίνισης του μηχανισμού σχηματισμού ΑΣ από την οξείδωση πρόδρομων αερίων, μετρήσεις πεδίου έλαβαν χώρα σε μία μεγαλούπολη (Παρίσι, Γαλλία), μία αστική περιοχή (Πάτρα, Ελλάδα) και σε μία απομακρυσμένη τοποθεσία (Φινοκαλιά, Ελλάδα). Στην Φινοκαλιά, η σύσταση των σωματιδίων με διάμετρο μικρότερη από 1 μm (ΑΣ1) εξαρτιόταν από την προέλευση των αερίων μαζών. Τις υψηλότερες συγκεντρώσεις εμφάνιζαν οι αέριες μάζες από τη Ευρώπη, που ήταν και οι πιο συχνές. Οργανικές ενώσεις των ΑΣ, εμφάνιζαν, υψηλή διαλυτότητα στο νερό (80%) και αυξημένο λόγο οργανικού προς στοιχειακό άνθρακα που συσχετιζόταν θετικά με τις συγκεντρώσεις όζοντος. Όλα τα παραπάνω υποδεικνύουν πως τα ΑΣ στην περιοχή της Φινοκαλιάς μεταφέρονταν από γειτονικές περιοχές εκατοντάδες χιλιόμετρα μακριά και συνεπώς η περιοχή μπορεί να θεωρηθεί ως σταθμός υποβάθρου για την Ευρώπη. Στην Φινοκαλιά, το φαινόμενο της ατμοσφαιρικής πυρηνογένεσης ήταν συχνότερο τους χειμερινούς μήνες, όταν η ένταση φωτός ήταν χαμηλότερη, και σε αέριες μάζες που παρέμεναν σημαντικό χρόνο πάνω από την στεριά πριν φτάσουν στον σταθμό. Αυτή η συμπεριφορά εξηγήθηκε με την συμμετοχή της αμμωνίας στην διαδικασία της πυρηνογένεσης. Τα ΑΣ1 το καλοκαίρι ήταν κατά κανόνα όξινα και κατανάλωναν όλη την διαθέσιμη αμμωνία σε αντίθεση με τον χειμώνα, όπου εξαιτίας της χαμηλότερης έντασης φωτός, τα ΑΣ1 ήταν ουδέτερα και υπήρχε διαθέσιμη. Και στις δύο περιόδους η πυρηνογένεση λάμβανε χώρα μόνο όταν τα σωματίδια ήταν ουδέτερα, το οποίο είχε ως αποτέλεσμα υψηλότερη συχνότητα του φαινομένου τους χειμερινούς μήνες. Οι αέριες μάζες όταν παρέμεναν πάνω από στεριά εμπλουτίζονταν με αμμωνία, αυξάνοντας την πιθανότητα πυρηνογένεσης. Κατανομές μεγέθους αριθμού μετρήθηκαν στο Παρίσι, Γαλλίας σε επίγειους σταθμούς, σταθερούς και κινητούς, καθώς και σε υψόμετρο. Ο θύσανος του Παρισιού ταυτοποιήθηκε σε απόσταση τουλάχιστον 200 km από την πόλη και οι συγκεντρώσεις αριθμού ΑΣ αύξαναν ακόμα και κατά 300% όταν οι αέριες μάζες προέρχονταν από το Παρίσι. Το καλοκαίρι πυρηνογένεση έλαβε χώρα τις μισές μέρες της δειγματοληψίας, όταν η διαθέσιμη επιφάνειας συμπύκνωσης ήταν χαμηλή, ενώ το χειμώνα, επειδή η διαθέσιμη επιφάνεια ήταν υψηλότερη, δεν ταυτοποιήθηκε το φαινόμενο. Αυξημένες συγκεντρώσεις αριθμού ΑΣ ταυτοποιήθηκαν εκτός του θυσάνου του Παρισιού ταυτόχρονα με πυρηνογένεση στο έδαφος και αποδόθηκαν σε αυτό το φαινόμενο. Στην Πάτρα τα θεσμοθετημένα από την Ε.Ε. ημερήσια όρια ΑΣ10 βρέθηκαν να παραβιάζονται. Οι υπερβάσεις ήταν πιο συχνές (58 από τις 75) τους ψυχρούς μήνες (Οκτώβριο - Μάρτιο). Τους θερμούς μήνες (Απρίλιο - Σεπτέμβριο) το 80% των ΑΣ2.5 μεταφέρονταν από άλλες περιοχές. Αντίθετα τους ψυχρούς μήνες η συνεισφορά από μεταφερόμενα ΑΣ μειωνόταν στο 70% το φθινόπωρο και 50% το χειμώνα, όταν και οι συγκεντρώσεις ΑΣ2.5 ήταν κατά μέσο όρο οι υψηλότερες στην περιοχή. Η τοπική κυκλοφορία συνείσφερε περίπου 15% τον χειμώνα ενώ ένα σημαντικό κομμάτι από το υπόλοιπο 35% οφειλόταν στην οικιακή θέρμανση. Συγκέντρωση ΑΣ2.5 και ΑΣ1 ίση ή μεγαλύτερη των 100 μg m-3 μετρήθηκε κατ'επανάληψη τις νυχτερινές ώρες των χειμερινών μηνών εξαιτίας της οικιακής θέρμανσης, είτε με πετρέλαιο είτε με καύση βιομάζας Η καύση βιομάζας υποδεικνύεται από την συσχέτιση (R2=0.79) των συγκεντρώσεων ΑΣ2.5 με τις συγκεντρώσεις καλίου, ένα δείκτη καύσης βιομάζας. Οι συγκεντρώσεις αυτού του δείκτη βρέθηκαν υψηλότερες μέσα στον αστικό ιστό από μία αγροτική περιοχή 36 km μακριά από την Πάτρα, αποκλείοντας την αποκλειστική μεταφορά ΑΣ2.5 καύσης βιομάζας από γειτονικές περιοχές.

Efficiency of probabilistic seismic hazard analysis mainly depends on the individual successes of its complementing components / such as source characterization and ground motion intensity prediction. This study contributes to major components of the seismic hazard workflow including magnitude &ndash / rupture dimension scaling relationships, and ground motion intensity prediction. The study includes revised independent models for predicting rupture dimensions in shallow crustal zones, accompanied by proposals for geometrically compatible rupture area-length-width models which satisfy the rectangular rupture geometry assumption. Second main part of the study focuses on developing a new ground motion prediction model using data from Turkish strong ground motion database. The series of efforts include, i) compilation and processing of a strong motion dataset, ii) quantifying parameter uncertainties of predictive parameters such as magnitude and source to site distance / and predicted accelerations due to uncertainty in site conditions and response, as well as uncertainty due to random orientation of the sensor, iii) developing a ground response model as a continuous function of peak ground acceleration and shear wave velocity, and finally, iv) removing bias in predictions due to uneven sampling of the dataset. Auxiliary components of the study include a systematic approach to source characterization problem, with products ranging from description of systematically idealized and documented seismogenic faults in Anatolia, to delineation, magnitude-recurrence parameterization, and selection of maximum magnitude earthquakes. Last stage of the study covers the development of a custom computer code for probabilistic seismic hazard assessment which meets the demands of modern state of practice.

Extracting speaker-specific information from speech is of great interest to both researchers and developers alike, since speaker recognition technology finds application in a wide range of areas, primary among them being forensics and biometric security systems. Several models and techniques have been employed to extract speaker information from the speech signal. Speech production is generally modeled as an excitation source followed by a filter. Physiologically, the source corresponds to the vocal fold vibrations and the filter corresponds to the spectrum-shaping vocal tract. Vocal tract-based features like the melfrequency cepstral coefficients (MFCCs) and linear prediction cepstral coefficients have been shown to contain speaker information. However, high speed videos of the larynx show that the vocal folds of different individuals vibrate differently. Voice source (VS)-based features have also been shown to perform well in speaker recognition tasks, thereby revealing that the VS does contain speaker information. Moreover, a combination of the vocal tract and VS-based features has been shown to give an improved performance, showing that the latter contains supplementary speaker information. In this study, the focus is on extracting speaker information from the VS. The existing techniques for the same are reviewed, and it is observed that the features which are obtained by fitting a time-domain model on the VS perform poorly than those obtained by simple transformations of the VS. Here, an attempt is made to propose an alternate way of characterizing the VS to extract speaker information, and to study the merits and shortcomings of the proposed speaker-specific features. The VS cannot be measured directly. Thus, to characterize the VS, we first need an estimate of the VS, and the integrated linear prediction residual (ILPR) extracted from the speech signal is used as the VS estimate in this study. The voice source linear prediction model, which was proposed in an earlier study to obtain the ILPR, is used in this work. It is hypothesized here that a speaker’s voice may be characterized by the relative proportions of the harmonics present in the VS. The pitch synchronous discrete cosine transform (DCT) is shown to capture these, and the gross shape of the ILPR in a few coefficients. The ILPR and hence its DCT coefficients are visually observed to distinguish between speakers. However, it is also observed that they do have intra-speaker variability, and thus it is hypothesized that the distribution of the DCT coefficients may capture speaker information, and this distribution is modeled by a Gaussian mixture model (GMM). The DCT coefficients of the ILPR (termed the DCTILPR) are directly used as a feature vector in speaker identification (SID) tasks. Issues related to the GMM, like the type of covariance matrix, are studied, and it is found that diagonal covariance matrices perform better than full covariance matrices. Thus, mixtures of Gaussians having diagonal covariances are used as speaker models, and by conducting SID experiments on three standard databases, it is found that the proposed DCTILPR features fare comparably with the existing VS-based features. It is also found that the gross shape of the VS contains most of the speaker information, and the very fine structure of the VS does not help in distinguishing speakers, and instead leads to more confusion between speakers. The major drawbacks of the DCTILPR are the session and handset variability, but they are also present in existing state-of-the-art speaker-specific VS-based features and the MFCCs, and hence seem to be common problems. There are techniques to compensate these variabilities, which need to be used when the systems using these features are deployed in an actual application. The DCTILPR is found to improve the SID accuracy of a system trained with MFCC features by 12%, indicating that the DCTILPR features capture speaker information which is missed by the MFCCs. It is also found that a combination of MFCC and DCTILPR features on a speaker verification task gives significant performance improvement in the case of short test utterances. Thus, on the whole, this study proposes an alternate way of extracting speaker information from the VS, and adds to the evidence for speaker information present in the VS.

Since the 1950s the jet aeroacoustics community has been involved in predicting and measuring the noise distribution in jets. In this work, cylindrical and planar Fourier near-field acoustical holography are used to investigate radiation from a full-scale, installed jet engine. Practical problems involving measurement aperture and the highly directional nature of the source are addressed. Insights from numerical simulations reveal usable reconstruction regions. A comparison of cylindrical and planar NAH for the respective measurement apertures shows cylindrical NAH outperforms planar NAH on reconstructions both towards and away from the source.