Abstract: Recent concerns of the growing impact of aviation on
climate change has prompted the emergence of a field referred to as
Sustainable or “Green” Aviation dedicated to mitigating the harmful
impact of aviation related CO2 emissions and noise pollution on
the environment. In the current paper, a unique “green” business
jet aircraft called the TransAtlantic was designed (using analytical
formulation common in conceptual design) in order to show the
feasibility for transatlantic passenger air travel with an aircraft
weighing less than 10,000 pounds takeoff weight. Such an advance in
fuel efficiency will require development and integration of advanced
and emerging aerospace technologies. The TransAtlantic design is
intended to serve as a research platform for the development of
technologies such as active flow control. Recent advances in the field
of active flow control and how this technology can be integrated
on a sub-scale flight demonstrator are discussed in this paper. Flow
control is a technique to modify the behavior of coherent structures
in wall-bounded flows (over aerodynamic surfaces such as wings and
turbine nozzles) resulting in improved aerodynamic cruise and flight
control efficiency. One of the key challenges to application in manned
aircraft is development of a robust high-momentum actuator that
can penetrate the boundary layer flowing over aerodynamic surfaces.
These deficiencies may be overcome in the current development
and testing of a novel electromagnetic synthetic jet actuator which
replaces piezoelectric materials as the driving diaphragm. One of
the overarching goals of the TranAtlantic research platform include
fostering national and international collaboration to demonstrate (in
numerical and experimental models) reduced CO2/ noise pollution
via development and integration of technologies and methodologies
in design optimization, fluid dynamics, structures/ composites,
propulsion, and controls.
Abstract: Many inherited diseases and non-hereditary disorders are common in the development of renal cystic diseases. Polycystic kidney disease (PKD) is a disorder developed within the kidneys in which grouping of cysts filled with water like fluid. PKD is responsible for 5-10% of end-stage renal failure treated by dialysis or transplantation. New experimental models, application of molecular biology techniques have provided new insights into the pathogenesis of PKD. Researchers are showing keen interest for developing an automated system by applying computer aided techniques for the diagnosis of diseases. In this paper a multilayered feed forward neural network with one hidden layer is constructed, trained and tested by applying back propagation learning rule for the diagnosis of PKD based on physical symptoms and test results of urinalysis collected from the individual patients. The data collected from 50 patients are used to train and test the network. Among these samples, 75% of the data used for training and remaining 25% of the data are used for testing purpose. Further, this trained network is used to implement for new samples. The output results in normality and abnormality of the patient.
Abstract: This paper describes part of a project about Learningby-
Modeling (LbM). Studying complex systems is increasingly
important in teaching and learning many science domains. Many
features of complex systems make it difficult for students to develop
deep understanding. Previous research indicates that involvement
with modeling scientific phenomena and complex systems can play a
powerful role in science learning. Some researchers argue with this
view indicating that models and modeling do not contribute to
understanding complexity concepts, since these increases the
cognitive load on students. This study will investigate the effect of
different modes of involvement in exploring scientific phenomena
using computer simulation tools, on students- mental model from the
perspective of structure, behavior and function. Quantitative and
qualitative methods are used to report about 121 freshmen students
that engaged in participatory simulations about complex phenomena,
showing emergent, self-organized and decentralized patterns. Results
show that LbM plays a major role in students' concept formation
about complexity concepts.
Abstract: In this paper, the requirement for Coke quality
prediction, its role in Blast furnaces, and the model output is
explained. By applying method of Artificial Neural Networking
(ANN) using back propagation (BP) algorithm, prediction model has
been developed to predict CSR. Important blast furnace functions
such as permeability, heat exchanging, melting, and reducing
capacity are mostly connected to coke quality. Coke quality is further
dependent upon coal characterization and coke making process
parameters. The ANN model developed is a useful tool for process
experts to adjust the control parameters in case of coke quality
deviations. The model also makes it possible to predict CSR for new
coal blends which are yet to be used in Coke Plant. Input data to the
model was structured into 3 modules, for tenure of past 2 years and
the incremental models thus developed assists in identifying the
group causing the deviation of CSR.