Air Quality and Airflow Characteristic Studies for Passenger Aircraft Cabins

This chapter summarizes the work done at the Airliner Cabin Environment Research Lab (ACERL) related to air quality, airflow characteristics, and human thermal comfort inside aircraft cabins. The laboratory is part of the Institute for Environmental Research (IER) at Kansas State University. It has a Boing 767 mockup cabin, bleed air simulator, and a Boeing 737 actual aircraft section that were all utilized to conduct experimental studies to understand air quality inside aircraft cabins. The studies summarized in this chapter include particle image velocimetry (PIV) investigations, particle dispersion, computational fluid dynamics (CFD) simulations, tracer gas and smoke visualization studies, and bleed air investigations. The chapter also summarizes other related studies including virus dispersion, air quality monitoring devices, and related developed air quality standards. The scope of this chapter is to summarize the setup and results of each of the above categories. This summary along with the cited references provides results for full size aircraft cabin environments, helps validate data for CFD simulations, and provides comparison data for other similar studies. This helps improve the design of future aircraft cabins and their ventilation systems and recommends changes to maintenance practices done that can improve the health and safety of humans inside these enclosed compartments

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Net-Zero Energy Buildings: Principles and Applications

Global warming and climate change are rising issues during the last couple of decades. With residential and commercial buildings being the largest energy consumers, sources are being depleted at a much faster pace in the recent decades. Recent statistics shows that 14% of humans are active participant to protect the environment with an additional 48% sympathetic but not active. In this chapter, net-zero energy buildings design tools and applications are presented that can help designers in the commercial and residential sectors design their buildings to be net-zero energy buildings. Case studies with benefits and challenges will be presented to illustrate the different designs to achieve a net-zero energy building (NZEB)

Optimizing Solar Cooling Systems

This papers discusses solar powered absorption cycle performance by simulating different component temperatures. The main components that were investigated included a generator, condenser, absorber and evaporator. The COP was optimized against the generator temperature while varying the other temperatures one at a time. The considered range for the generator temperature was 55–85�C (131–185 F). The optimum value for the evaporator temperature was 10�C (50 F), while that for the condenser and absorber was 30�C (86 F). The optimized COP was around 0.776 with the above selected components’ temperatures and for generator temperatures higher than 70�C (158 F). A simulation for the proposed optimized system was run for a 250 m2 (2691 ft2 ) house located in Indiana, USA and it was found that 13 solar collectors, having a 2 m2 (21.5 ft2 ) surface area each, were needed to run the generator along with a storage tank ranging in size from 1300 to 1700 L (343–450 gallons). The initial cost for such systems is much higher than that for conventional cooling systems, but the savings from the sustainable running cost offsets such higher initial costs over the long time. With the significant drop in collector prices and available incentives from the government and state agencies to use such sustainable systems, the payback period could be significantly improved

Vulnerability of mangroves to sea level rise in Qatar: Assessment and identification of vulnerable mangroves areas

Qatar is one of few countries in Arabian Gulf where mangrove ecosystem exist. They are essential number of ecosystem function; however, this valuable ecosystem is threatened by both anthropogenic and global climatic factors. This study is aimed at investigating the vulnerability of mangroves resulting from the rise in sea level. Remote sensing, GIS and soil analysis were used to achieve this assessment. Four main research questions including the change in mangrove area over time, the endangered area by sea level rise, the potentially expected migration area and the management strategies were answered. Thus the first objective of identifying potentially endangered mangrove areas by sea level rise in Qatar and second objective of enhancing the mangrove protection and resilience to sea level rise were achieved. The results of comparative analysis of satellite images show a 50 % increase of the growth of mangrove ecosystems. Comparison of soil within mangrove and outside mangrove area showed the same pH values with slightly different salinity, and similar soil Type. This will positively affect the migration process for existing mangroves. High exposure to sea level rise is estimated from overlaying recent mangrove layer over elevation layers of expected sea level rise scenarios. The result showed that endangered mangrove areas were 35% and 45% with 0.52 m and 0.74 m sea level rise respectively. Outward migration using spatial techniques was observed, while new conservation strategies are recommended to minimize the vulnerability of mangroves

How to utilize hedging and a fuel surcharge program to stabilize the cost of fuel

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 101-103).This paper looks at some of these travails as well as the common tools used to approach a volatile priced commodity, diesel fuel. It focuses on the impacts of hedging for companies that are directly impacted through the consumption of diesel fuel in addition to companies that are indirectly impacted because they outsource their transportation. It examines the impact of a fuel surcharge and how it distributes risk throughout the supply chain. To complement the research, analysis was conducted in the form of a survey to benchmark the industry with respect to current practices of hedging and fuel surcharges, a sensitivity test of a fuel surcharge matrix to find its appropriate usage, and a simulation to provide guidance as to the appropriate strategy for hedging. Lessons learned from the survey flowed into the sensitivity testing and simulation. These three segments of analysis highlighted the problem of volatility, increasing cost, and inability to pass on the cost, proving the true pain of fuel in the market. Ultimately, the paper answers: How to utilize hedging and a fuel surcharge program to stabilize the cost of fuel? The survey showed the wide adoption of fuel surcharges, confirming the academic research. The sensitivity test proved the need to keep the escalator variable in line with a carrier's actual fuel efficiency and standardize for all carriers. The simulation recommended longer term derivatives. Putting this together, the fuel surcharge establishes stability for the carrier, at the risk of the shipper. The shipper must maintain that stability through its maintenance of the escalator in the fuel surcharge matrix. Additionally, the shipper should hedge fuel via long term derivatives to establish personal fuel cost stability, creating a competitive advantage and enabling the shipper to compete more effectively.by Charles A. Shehadi, III and Michael R. Witalec.M.Eng.in Logistic

Machine Learning-assisted Bayesian Inference for Jamming Detection in 5G NR

The increased flexibility and density of spectrum access in 5G NR have made jamming detection a critical research area. To detect coexisting jamming and subtle interference that can affect legitimate communications performance, we introduce machine learning (ML)-assisted Bayesian Inference for jamming detection methodologies. Our methodology leverages cross-layer critical signaling data collected on a 5G NR Non-Standalone (NSA) testbed via supervised learning models, and are further assessed, calibrated, and revealed using Bayesian Network Model (BNM)-based inference. The models can operate on both instantaneous and sequential time-series data samples, achieving an Area under Curve (AUC) in the range of 0.947 to 1 for instantaneous models and between 0.933 to 1 for sequential models including the echo state network (ESN) from the reservoir computing (RC) family, for jamming scenarios spanning multiple frequency bands and power levels. Our approach not only serves as a validation method and a resilience enhancement tool for ML-based jamming detection, but also enables root cause identification for any observed performance degradation. Our proof-of-concept is successful in addressing 72.2\% of the erroneous predictions in sequential models caused by insufficient data samples collected in the observation period, demonstrating its applicability in 5G NR and Beyond-5G (B5G) network infrastructure and user devices

Airflow distribution and turbulence analysis in the longitudinal direction of a Boeing 767 mockup cabin

Doctor of PhilosophyDepartment of Mechanical and Nuclear EngineeringM. H. HosniB. W. JonesThis dissertation focuses on airflow distribution in the longitudinal direction of a wide-body mockup aircraft cabin, turbulence energy and dissipation rates, and the effect of thermal plumes, generated by passengers, on airflow distribution within the cabin. The mockup cabin utilized for this study mimics a Boeing 767 passenger cabin and includes 11 rows in the longitudinal direction with each row consisting of seven seats. Each seat is occupied by an inflatable manikin which is instrumented with a 10 meters long wire heater generating approximately 100 Watts of distributed sensible heat, representing heat load from a sedentary human being. In order to investigate the fluid dynamics characteristics of the airflow within the cabin, different experimental techniques were implemented. Smoke visualization was used to qualitatively visualize the general airflow pattern inside the cabin. A tracer gas composed mainly of carbon dioxide was used to track the airflow distribution inside the cabin. The tracer gas was released in several locations and then sampled at various locations throughout the mockup cabin. The release and sampling of the tracer gas allowed tracing the airflow inside the cabin using non dispersive infrared sensors. Combining results from different release-sampling scenarios gave better understanding of the chaotic and three-dimensional nature of the airflow behavior inside the cabin. Air speed and turbulence parameters were evaluated using omni-directional probes. Finally, the effect of the heat generated by the thermal manikins on the airflow behavior was investigated. The results from the airflow visualization and the tracer gas were complementary and showed that there were multiple air circulations along the length of the cabin. The dimension of the circulations were controlled by the minimum physical distance inside the cabin. The identified-isotropic turbulence were spread over the full width of the cabin in the front and middle sections of the cabin, whereas, multiple-smaller circulations were identified in the rear section. Cabin sections identified with high speed fluctuations were associated with higher turbulence kinetic energy levels and lower local dissipation rates. These sections served as driving forces to create the circulations identified in the tracer gas experiments. Furthermore, the heat generated by the thermal manikins was shown to significantly impact the behavior of the gaseous flow inside the cabin, the turbulence parameters, and speed fluctuations. Detailed uncertainty analysis was conducted to estimate the uncertainty limits for the measurements taken. The uncertainty estimates obtained for the tracer gas results ranged from ±14% for the test cases with the heated manikins to ±17% with the corresponding unheated manikins cases. The data uncertainty limits for the turbulence parameters were of higher levels due to limitations associated with the omni-directional probes used to measure the speed. With flow repeatability phenomena in same locations inside the mockup cabin during different days reaching up to ±10%, the uncertainty estimates were considered acceptable for these chaotic and highly random airflow conditions within the cabin