Class of 2015 Trident Scholar

Presentations & Banquet

United States Naval Academy

April 23-25

1FOREWORD

The Naval Academy instituted the Trident Scholar Program in 1963 to provide an exciting opportunity for some of our most capable students to engage in extended independent study and research throughout their senior year. Under the Trident Scholar Program, midshipmen in the top 10 percent of their class at the end of

the first semester of their junior year are invited to submit research project proposals for evaluation by a committee composed of senior-level professors and officers who represent all academic departments. Based on their academic qualifications and the findings of this Trident Scholar Committee, the Academic Dean and Provost appoints new Trident Scholars for the next academic year.

Each midshipman selected to participate in the Trident Scholar Program is afforded an unusually exciting and unique educational experience. A Trident Scholars research is carried out independently, but is also under the close watch of faculty advisors who are all well qualified in the subject field of study. Each scholars academic class load is adjusted to allow for the significant time that they will spend on their research project, while at the same time allowing them to complete the requirements of their regular academic major. Special funding provided by the Office of Naval Research (ONR) for the Trident Scholar Program helps to make certain that materials, instrumentation, and travel opportunities are available to each scholar. ONRs generous help ensures that each students experience is as educationally complete and as rewarding as possible. Generous support for the Trident Scholar Program is also provided by members of the Naval Academy Class of 1979.

Traditionally, at the end of each academic year each Trident Scholar presents the results of his or her research in a public lecture at the Academy, in a written archived report, and in a poster session prior to a formal dinner. The public lectures and dinner bring together the entire spectrum of Naval Academy research, including both graduating and newly designated scholars, their advisors and sponsors, members of the Trident Committee and other invited guests. From the work presented by the scholars each year, one is selected as the most outstanding research project. This midshipman is awarded the Class of 1979 Trident Scholar Prize (formerly known as the Office of Naval Intelligence Harry E. Ward Trident Scholar Prize).

Many Trident Scholars are given the opportunity to undertake immediate graduate studies at other universities prior to reporting to their first duty assignment. Many also complete advanced degrees during their time on active duty and return later in their careers for teaching assignments at the Naval Academy.

Let me use this opportunity to congratulate the Trident Scholars of the Class of 2015 for their great individual achievement, and pass along my wishes for their continued success throughout their naval careers and beyond.

A.T. PHILLIPS Academic Dean and Provost

2TRIDENT SCHOLAR COMMITTEE

Committee ChairProfessor Maria J. Schroeder

Associate Director of Midshipman Research

Division of Engineering and Weapons

Associate Professor Randy BroussardAssociate Professor David Fredriksson

Associate Professor Luksa LuznikAssociate Professor David Miklosovic

Division of Mathematics and Science

Associate Professor Christopher BrownAssociate Professor Clare Gutteridge

Professor Deborah KonkowskiAssociate Professor Christopher Morgan

Division of Humanities and Social Sciences

Associate Professor Lori BogleAssociate Professor Thomas Burgess

Lieutenant Commander Liam Corley, USNProfessor Nancy Mace

We honor those former Trident Scholars who attained flag rank and served their Navy and their nation at the highest levels.

ADM Donald Lee Pilling, USN (Ret.) Class of 1965

Vice Chief of Naval Operations

RADM Robert Michael Nutwell, USN (Ret.) Class of 1966

Deputy Director, Space, Information Warfare, Command and Control

VADM John Scott Redd, USN (Ret.) Class of 1966

Director, Strategic Plans and PolicyOffice of the Joint Chiefs of Staff

ADM Richard Willard Mies, USN (Ret.) Class of 1967

Commander in Chief,United States Strategic Command

VADM George Peter Nanos, Jr., USN (Ret.) Class of 1967

Commander, Naval Sea Systems Command

RADM Jay Martin Cohen, USN (Ret.) Class of 1968

Chief of Naval Research

RADM Jeffrey Alan Cook, USN (Ret.) Class of 1968

Vice Commander, Naval Air Systems Command

VADM Evan Martin Chanik, USN (Ret.) Class of 1973

Commander, Second FleetDirector, Combined Joint Operations from

the Sea Center of Excellence

RADM Paul John Ryan, USN (Ret.) Class of 1973

Commander, Mine Warfare Command

VADM Joseph Ambrose Sestak, Jr., USN (Ret.)Class of 1974

Deputy Chief of Naval Operations,Warfare Requirements & Programs (N6/N7)

RADM Samuel J. Cox, USN Class of 1980

Director, National Maritime Intelligence-Integration Office Commander, Office of Naval Intelligence

VADM William Hunter Hilarides, USN Class of 1981

Commander, Naval Sea Systems Command

ADM John Michael Richardson, USN Class of 1982

Director, Naval Nuclear Propulsion Program

3TRIDENT SCHOLAR PROGRAM52 Years of Tradition

The U.S. Naval Academy instituted the Trident Scholar Program in 1963 to provide an opportunity for exceptionally capable midshipmen to engage in independent study and research during their senior year. Over its 52-year history, more than 500 midshipmen have participated in the program, contributing their talents,

creativity, and enthusiasm to their field of study. Last year, the Class of 1979 committed to supporting the Trident Scholar Program. We are grateful to members of the Class of 1979 for their generous support. A few

representatives from the Class of 1979 and their spouses are in attendance at the banquet.

Lieutenant Commander Sean T. Cate, USN (Ret.)

17th Company

Commander Fredrick K. Gerheiser, USN, CEC (Ret.)

16th Company

Mr. Jeffrey E. McFadden36th CompanyTrident Scholar

"Chivalry and the Military Officer: An Historical and Literary Inquiry"

Advisor: Professor Wilson L. Heflin

Captain Charles B. Dixon, USN (Ret.)

19th Company

Captain David M. Jackson, USN (Ret.)

17th Company

Midshipman 1st Class Jeffrey E. McFadden with his advisor Professor Wilson L. Heflin in 1979.

41255Midshipman 1st Class Michael K. Johnson

Probe-Independent EEG Assessment of Mental Workload in Pilots

The goal of this project was to develop an algorithm to measure mental workload based on brain signals recorded noninvasively,

and without the need for an externally introduced probe stimulus. Several signal processing and machine learning techniques were compared based on their performance in classifying behavioral

challenges of varying complexity.

1335Midshipman 1st Class David A. Stevens

Computational Sensitivity Analysis for the Aerodynamic Design of Supersonic and Hypersonic Air Vehicles

The sensitivities of a hypersonic aircrafts aerodynamics to changes in its geometry were calculated using a highly-automated geometry and computational fluid dynamics framework. This research will assist designers in avoiding vehicle redesigns later in the design process,

thereby reducing project delays and costs.

1415Midshipman 1st Class Eric A. Swanson

Quantum Mechanics in Relativistic SpacetimeThe thermodynamics of a black hole is at the interface of General Relativity (GR) and Quantum Mechanics (QM). This project

constructs the quantum states of a free particle interacting with a black hole. This differs from the conventional method of calculating black

hole entropy which uses quantum field theory.

1455Midshipman 1st Class A. Eileen Dilks

Towards a Personalized Prescription Tool for Diabetic Treatment

This project combines a mathematical model of glucose digestion with models of the absorption of slow and fast acting insulin

injections. A personalized prescription tool is created by applying an adaptive predictive control algorithm to determine the best insulin

therapy routine for a patient based on past glucose readings.

TRIDENT SCHOLAR PRESENTATIONS

ModeratorProfessor Maria J. Schroeder

Associate Director of Midshipman Research

Thursday, April 23, 20151255-1530

Rickover Hall, Room 103

5Morning Session

0810Midshipman 1st Class Brian R. He

A Theoretical and Experimental Analysis of Post-Compression Water Injection in a Rolls-Royce M250 Gas

Turbine EngineThis project investigates the effects on performance and emissions

caused by injecting water at different flow rates and temperatures into the compressor discharge of a Rolls-Royce M250 turboshaft gas turbine.

Analysis of the results aims to improve the current understanding of water injection in low pressure-ratio gas turbines.

0850Midshipman 1st Class Gabriel Tang Ying Kit

Cooperative Control of Unmanned Surface Vessels and Unmanned Underwater Vessels for Asset Protection

This research projects aims to develop an effective method for multi-modal cooperation techniques for autonomous vehicles. Utilizing cooperative control through a novel capability function as well as a localization algorithm, this project develops a control system that

allows USVs and UUVs to cooperate together effectively in carrying out asset protection.

0930Midshipman 1st Class Benjamin C. Etringer

A Modeling and Data Analysis of Laser Beam Propagation in the Maritime Domain

This project investigates the impact of the maritime environment on the propagation of laser beams. Two distinct methods will be

employed: 1) Data analysis is carried out by computing probability density functions using three approximation techniques; and 2) preliminary steps are taken to develop a stochastic partial

differential equation model for laser beam propagation in a complex environment.

1010Midshipman 1st Class Samuel S. Lacinski

Multiple Sensor Discrimination of Closely-Spaced Objects on a Ballistic Trajectory

Multiple sensor discrimination requires the integration of dissimilar sensor measurements to improve the likelihood of identifying an object of interest among closely-spaced objects on a ballistic trajectory. This project developed and tested via simulation the

algorithms necessary for fusing the sensors data using a Target Object Map for correlation and Dempster-Shafer discrimination logic.

1050Midshipman 1st Class Daniel R. Kuerbitz

An Examination of a Pumping Rotor Blade Design for Brownout Mitigation

Brownout poses a serious hazard to rotorcraft operations. A pumping rotor blade design was tested as a means to diffuse tip vortices,

thereby mitigating the rotors brownout effects. The pumping designs effectively diffused tip vortices at lower thrust conditions. At higher

thrust conditions, tip vortices persisted in the flow.

Afternoon Session

1255Midshipman 1st Class Andrea R. Howard

Measuring Oman's Food Security Outlook for Crisis Aversion

This project develops a model to measure the effects of crises that threaten Omans minimum threshold of food and water supplies.

Using a Bayesian belief network, the model quantifies Omans sensitivity to changes in eighteen variables that affect food security.

1335Midshipman 1st Class Zane A. Markel

Machine Learning Based Malware DetectionMachine learning based malware detection could considerably enhance antivirus software, yet existing methods suffer from flawed test procedures and poor performance. We identify promising file characteristics for malware detection, explore

the difficulty of detecting malware amongst abundant benign software, and propose a more rigorous procedure for

demonstrating expected realistic performance.

1415Midshipman 1st Class Steven T. Hallgren

An Exploration of Structures in the Transitional Odd-Odd Nucleus 160Lu

We investigated the nuclear structure of 160Lu, a nucleus that is neither completely spherical nor completely deformed. We collected gamma rays emitted by rapidly-spinning 160Lu nuclei as they slow

down to determine the nuclear shape. Our results suggest new structural features originating from the transitional nature of the

nucleus.

1455Midshipman 1st Class Fletcher D. Rydalch

A Characterization of the Ship-Effect in a Maritime Environment and Special Nuclear Material Detection

A spatial characterization of the radiation ship effect was completed in the vicinity of a Navy warship. Results show

increasing background radiation levels approaching the ship, as expected. Modeling has shown this increased background to

have a negative impact on the stand-off detection range of special nuclear material on board.

TRIDENT SCHOLAR PRESENTATIONSModerator

Professor Maria J. SchroederAssociate Director of Midshipman Research

Friday, April 24, 20150810-1125 and 1255-1530 Rickover Hall, Room 103

6TRIDENT SCHOLAR BANQUET

Trident Banquet and Induction CeremonySaturday, April 25, 2015

Alumni Hall

Poster Session and Social Hour1800-1900

Master of CeremoniesDr. Andrew T. Phillips

Academic Dean and Provost

InvocationLieutenant Yonatan M. Warren, CHC, USN

4th Battalion Chaplain

Guest SpeakerRear Admiral Mathias W. Winter, USN

Chief of Naval Research

Award of Trident Scholar Certificates to Class of 2015 Trident Scholars

Induction of Class of 2016 Trident Scholars

7Rear Admiral Mathias Winter, a 1984 graduate of the University of Notre Dame with a Bachelor of Science in Mechanical Engineering, received his commission through the Naval Reserve Officers Training Corps and was designated a naval flight officer in 1985. Winter served operational tours as an A-6E Intruder Bombardier/Navigator with Attack Squadrons 42, 85 and 34, making multiple deployments aboard aircraft carriers USS Saratoga (CV 60), USS America (CV 66), USS Dwight D. Eisenhower (CVN 69) and USS George Washington (CVN 73). Winters acquisition tours include assistant deputy program manager (DPM) for the Joint Standoff Weapon System; executive assistant to the Joint Strike Fighter (JSF) program director; chief engineer for JSF Integrated Flight and Propulsion Control; DPM for the Tactical Tomahawk All-Up-Round development program; chief of staff to the Program Executive Officer (PEO) for Tactical Aircraft Programs; and his major acquisition command tour as the Precision Strike Weapons (PMA-201) program manager. Winter has served flag tours as the commander, Naval Air Warfare Center Weapons Division, China Lake/Point Mugu, California, assistant commander for Test and Evaluation, Naval Air Systems Command and PEO for Unmanned Aviation and Strike Weapons. In December 2014, he became the 25th Chief of Naval Research with concurrent flag responsibilities as Director, Innovation Technology Requirements, and Test & Evaluation. Winter holds a master's degree in computer science from the Naval Postgraduate School and another in national resource strategy from National Defense Universitys Industrial College of the Armed Forces; and a Level III certification in Program Management and Test & Evaluation from the Defense System Management College. His personal awards include the Legion of Merit (3), Defense Meritorious Service Medal (2), Navy Meritorious Service Medal (2), Navy and Marine Corps Commendation Medal (4), Joint Service Achievement Medal (2), Navy and Marine Corps Achievement Medal, Air Force Acquisition Excellence Award, Southwest Asia Service Medal, Kuwait Liberation Medal, and various unit and sea service awards.

REAR ADMIRAL MATHIAS W. WINTER, USN

Chief of Naval ResearchDirector, Innovation Technology Requirements,

and Test & Evaluation (N84)

8For diabetic patients, insulin is unable to effectively assist in transporting glucose into cells to be used for energy. Type I diabetes arises when the pancreas does not produce enough insulin, and type II diabetes develops when cell receptors become insensitive to insulin. Both conditions present the danger of causing unhealthy glucose levels in the blood stream and are treated with insulin injection therapy to trigger glucose uptake in the cells.

A mathematical model of natural glucose and insulin control allows for a quantitative understanding of the internal glucose-insulin dynamics of healthy and diabetic patients. Cobelli et. al. presented a simulation model composed of glucose and insulin subsystems and four unit process models which use differential equations to describe the kinetics of glucose digestion and absorption that occur after a meal. This model has achieved the greatest physiological accuracy to date and was used as a basis for a computer simulator for type 1 diabetes that received FDA approval as a substitute to animal trials for preclinical testing. This model provides the means for developing a systematic approach to prescribing insulin injection therapy for diabetic patients in order to maintain healthy glucose levels.

This research extends the Cobelli model of glucose and insulin dynamics to include both long- and short-acting insulin inputs currently used to treat diabetic patients. The project will introduce a personalized approach to treatment by adapting the set of average diabetic Cobelli model parameters over time in response to observed patient feedback data. The personalized model will be combined with a nonlinear model predictive control strategy to determine the best insulin injection routine to achieve healthy glucose levels in diabetic patients. This work will contribute to the development of an individualized prescription tool which physicians can use to more effectively treat diabetic patients.

FACULTY ADVISOR

Professor Richard T. O'Brien Weapons and Systems Engineering Department

B.S., Brown UniversityM.Sc., Ph.D., Johns Hopkins University

External Collaborator: Dr. Ledys J. DiMarsico, M.D., Sinai Hospital of Baltimore

A. Eileen DilksMidshipman 1st Class

Towards a Personalized Prescription Tool for Diabetic Treatment

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9In this project we will investigate the impact of the maritime environment on the propagation of laser beams. This study will primarily use data collected at the Naval Academy with the goal of quantifying the correlation between the statistics of the environmental parameters and the statistics of laser beam intensity at the target.

The project will have two parts to it: 1) we present a computational analysis of different probability density function approximation techniques; and 2) we introduce preliminary steps towards developing a stochastic model for the maritime laser beam propagation. In the first part of this work, we will apply three mathematical methods to construct the probability density function of the data: i) the Kernel Density Estimator (KDE) method, ii) the Barakat Method using lower-order moments, and iii) the Bayesian Mixture Model. We will compare and contrast the features of the three approximation techniques, first in the context of a synthetic datum whose true pdf is known, and next in the context of the laser data.

In the second task, we will analyze how a complex medium causes the photons of the laser light to behave differently than if they were acting in freespace, by focusing on the stochastic behavior that our data exhibits. We will develop a stochastic paraxial wave equation in order to have a mathematical model capable of accepting statistical parameters from the atmosphere as input to allow us to investigate the statistical properties of light intensity at a specified target.

FACULTY ADVISOR

Professor Reza Malek-MadaniMathematics Department

B.S., M.S., Southern Illinois UniversityPh.D., Brown University

Benjamin C. EtringerMidshipman 1st Class

A Modeling and Data Analysis of Laser Beam Propagation in the Maritime Domain

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Steven T. HallgrenMidshipman 1st Class

An Exploration of Structures in the Transitional Odd-Odd Nucleus 160Lu

FACULTY ADVISOR

Professor Daryl J. HartleyPhysics Department

B.S., Furman UniversityPh.D., Florida State University

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Contrary to popular belief, not all nuclei are spherical. Nuclei tend to be spherical when they have close to a full shell of nucleons. This concept is similar to the stability of noble gases caused by full shells of electrons. Past studies have determined that shells fill at certain magic numbers of nucleons when either the proton number (Z) or the neutron number (N) of the nucleus is, among other values, 82. The further away a nucleus Z or N is from these magic numbers, the more deformed it will be.

Lutetium has 71 protons (Z=71). A wide variety of isotopes of lutetium exist, which have the same Z but different N. It is possible to watch how the nucleus deforms by observing isotopes with more and more neutrons. 157Lu is still nearly spherical even with 86 neutrons, 4 beyond the magic number of 82. However, at N = 90 (161Lu), the nucleus becomes well-deformed and exhibits very few properties of a spherical nucleus. As such, the transition between generally spherical lutetium nuclei and generally non-spherical nuclei occurs between N = 86 and N = 90, where 160Lu (N = 89) lies.

This project set out to investigate the structure of such a transitional nucleus and identify the predominant factors influencing its shape. The study required creating rapidly-spinning 160Lu nuclei by means of a particle accelerator at Argonne National Laboratory. Once excited, an instrument called Gammasphere was employed to capture information about the high-energy photons (gamma rays) that these excited states emit as they cool. Our results indicate the possibility that, at lower spins, 160Lu is asymmetric in shape, being deformed along all three dimensions. At higher spins, our data suggests that the nucleus returns to the one-dimensional deformation typically associated with such rare-earth nuclei. This phenomenon is not observed in neighboring nuclei (those with similar Z or N). We propose that the behavior observed in 160Lu is a result of increased malleability of transitional nuclei when compared to near-magic number nuclei and an extra unpairing of the nucleons that is not typically observed in nearby nuclei.

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FACULTY ADVISOR

Professor Martin R. Cerza Mechanical Engineering Department

B.S., M.S., Ph.D., Rutgers University

The gas turbine engine is one of the most common methods of energy generation and propulsion used by the military today. Its applications include surface ships, aircraft, and tanks, and it is highly regarded due to its high power-to-weight ratio and ability to operate using a wide variety of fuels. Spurred by ongoing concerns regarding air pollution from energy generation sources, researchers have

explored numerous systems for reducing gas turbine emissions and improving efficiency. One of these systems involves a time-honored technique of spraying water into the gas turbine in order to improve power output and reduce nitrous oxide concentration.

Water injection is typically implemented in one of two ways: direct water injection, which involves spraying at either the combustion chamber or compressor discharge; or compressor inlet fogging, which entails spraying water at the inlet of the engine. Previous research has examined the effects of the two water injection methods on high pressure-ratio gas turbines, such as the LM2500, as well as the effects of compressor inlet fogging on low pressure-ratio gas turbines, such as the Rolls-Royce M250. However, there are few conclusive results regarding the use of direct water injection on low pressure-ratio gas turbines. This project investigates the effects of injecting water at the compressor discharge of a Rolls-Royce M250 with regard to its power output, efficiency, operating conditions, and emissions.

Experiments will be conducted using an original spray assembly with one of USNAs Rolls-Royce M250 gas turbine engines. The effects of varying the temperature and flow rate of the injected water will be examined based on measured brake horsepower, torque, operating temperatures and pressures, and emissions concentrations. The analysis involves comparing the experimental data with simulated direct water injection results as well as with data from a previous compressor inlet fogging project using the same gas turbine. The results will help yield a better understanding of the effects of using water injection systems with low pressure-ratio gas turbines for possible implementation in the future.

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Brian R. HeMidshipman 1st Class

A Theoretical and Experimental Analysis of Post-Compression Water Injection in a Rolls-Royce M250 Gas Turbine Engine

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Insecurity of food and water supplies in the Arabian Gulf is an important concern for stability in the region, where national security policy and food security policy interrelate. Even with three wars in Libya, Yemen, and Syria and several government overthrows in 2011 a year marked by doubled world grain prices Arabian Gulf nations, other than Qatar, appear hesitant to publically declare the severity of impending food and water insecurity. In Oman, population growth at 4.98% between 2003 and 2013, an

expatriate community comprising 44% of the total population, salinization issues and sinking groundwater tables, rising obesity, a culture of overindulgence, an overreliance on imported food, and instability in the international marketplace threaten the adequacy of the food and water supply.

This project endeavors to quantify the sensitivity of Omans food security strategy to various shocks with a Bayesian belief network (BBN). A BBN is a model that estimates changes in conditional probability, given assumptions about the causal relationships between variables. In this present study, the probability that the daily energy supply (DES) exceeds a healthy lower bound, estimated at 2100 kilocalories/person/day, serves as the primary output of the BBN. The inputs to the BBN are eighteen variables organized into four categories: energy, trade, domestic agriculture, and human factors. Statistical analyses connect each of these input variables to historical effects on the output variable, DES.

The BBN is then used to test the sensitivity of DES in possible future scenarios. Example scenarios include (1) an international refusal to sell cereals to Oman, (2) a plummet in the price of oil, and (3) the mass emigration of the expatriate workforce from Oman. By focusing on DES, the model meets the standard international definition of a food secure nation and provides an indication of how possible future events could affect the food security of Oman. Beyond the specific model results, this effort also serves as a template and model for building future studies that could help identifyand avertcrises before they happen.

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FACULTY ADVISORS

Assistant Professor Michael R. KellermannPolitical Science Department

B.A., University of MichiganM.Sc., London School of Economics

Ph.D., Harvard University

Associate Professor Deborah WheelerPolitical Science Department

B.A., University of CaliforniaM.A., Ph.D., University of Chicago

Assistant Professor Gina R. HendersonOceanography Department

B.A., University College of DublinM.Sc., Ph.D., University of Delaware

Associate Professor Patrick A. CatonMechanical Engineering Department

B.A., B.S., M.S., Ph.D., Stanford University

Professor Frederick L. CrabbeComputer Science Department

B.A., Dartmouth CollegeM.S., Ph.D., University of California, Los Angeles

Andrea R. HowardMidshipman 1st Class

Measuring Oman's Food Security Outlook for Crisis Aversion

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Mental workload can be described as a ratio between task-complexity and a persons cognitive capacity to meet task demands. This description captures the intuitive idea that mental workload depends both on external factors such as the objective difficulty of required tasks, and internal factors such as a persons past experiences and skill set. There is a growing body of research focused on developing quantitative methods to assess mental workload in order to improve the mental resiliency of

people in high-stress environments. Various metrics derived from physiological signals such as heart rate, blood pressure, galvanic skin response, and eye-gaze have been investigated as biomarkers of mental workload. These signals have been used to distinguish mental workload levels with accuracies significantly better than chance, but there are still no widely accepted standards or commercial products for mental workload monitoring.

With recent improvements in the ease-of-use, reliability, and costs of portable electroencephalography (EEG) systems, there has been increasing interest in using brain signals to measure mental workload. It is hypothesized that EEG offers a more direct assay of mental workload than other physiological biomarkers because of the proximity of EEG sensors to the neural substrates of cognitive stress.

Existing approaches for quantifying mental workload using electroencephalography often rely on probe stimuli to elicit stereotyped neural responses such as the P300 wave. The goal of this research was to develop an EEG-based algorithm to classify different levels of task complexity that does not rely upon an auditory probe. By choosing subjects with a similar level of task-experience, we partially control for differences in the capacity to perform the experimental task and therefore use task-complexity as a surrogate for mental workload. As we were particularly interested in understanding the response of aircraft pilots to the cognitive demands imposed by their flight-missions, we used flight simulator tasks of varying challenge-level as our experimental paradigm. Furthermore, since pilots are typically in persistent radio or intercom communications via headset during flight, this also represents a scenario that would be particularly well-suited to a probe-independent index of cognitive workload.

Michael K. JohnsonMidshipman 1st Class

Probe-Independent EEG Assessment of Mental Workload in Pilots

FACULTY ADVISOR

Assistant Professor Justin A. Blanco Electrical and Computer Engineering Department

Sc.B., Brown UniversityM.S., Stanford University

Ph.D., University of Pennsylvania

External Collaborators: Professor Bradley Hatfield, University of Maryland

Assistant Professor Rodolphe Gentili, University of MarylandHyuk Oh, University of Maryland

Kyle Jaquess, University of Maryland

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Daniel R. KuerbitzMidshipman 1st Class

An Examination of a Pumping Rotor Blade Design for Brownout Mitigation

Brownout is a phenomenon encountered when a rotorcraft hovers over an unprepared surface and becomes engulfed in a cloud of sediment. The generated brownout cloud obscures a pilots vision, greatly increasing flight risks. Brownout also reduces the service life of mechanical components (i.e. rotor blades, engines, etc.), significantly increasing maintenance costs and reducing operational readiness. The problem of brownout therefore poses a significant hazard to naval rotorcraft operations.

Brownout is caused by the interaction between the rotor wake and loose sediment on the surface. The trailed tip vortices are the primary means by which sediment is entrained into the airflow. Therefore, faster diffusing tip vortices would be expected to reduce brownout intensity. However, when a rotorcraft operates near the ground, the tip vortex filaments are stretched, thereby reintensifying their vorticity and arresting their diffusion rate. Rotor blade design can significantly affect tip vortices. A slotted tip design, with intakes on the leading edge near the tip, was shown to diffuse tip vortices. However, it also incurred a 2% power penalty due to increased profile drag on the slots.

It was hypothesized that moving the intake slots to the hub, where dynamic pressure is lower, would reduce the profile power penalty while still effectively diffusing tip vortices. With this in mind, the present study investigated a pumping blade design with an intake slot at the hub and various upward orientations of the exit slot at the blade tip.

Rotor performance measurements showed that at lower thrust conditions, where profile losses dominate, the baseline (i.e. non-pumping) blade required less power. However, at higher thrust conditions, where induced losses dominate, the power required for the baseline and pumping blade designs began to converge.

Flowfield measurements were taken using flow visualization and particle image velocimetry. It was found that the pumping blade designs initially produced more diffused tip vortices as compared to the baseline blade. However, at the higher thrust condition, this initial diffusion was not sufficient to overcome the reintensification process resulting from the ground. At the lower thrust condition, the pumping blade effectively diffused the tip vortices.

FACULTY ADVISORS

Assistant Professor Joseph I. MilluzzoAerospace Engineering Department

B.S., Virginia Polytechnic Institute and State UniversityM.S., Ph.D., University of Maryland

Associate Professor David MiklosovicAerospace Engineering Department

B.S., M.S., Ph.D., Ohio State University

External Collaborator:Professor J. Gordon Leishman, Embry-Riddle Aeronautical University

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Samuel S. LacinskiMidshipman 1st Class

Multiple Sensor Discrimination of Closely-Spaced Objects on a Ballistic Trajectory

FACULTY ADVISORS

External Collaborator:Dr. Thierry B. Copie, MIT, Lincoln Laboratories

One of the challenges associated with defending against ballistic missiles is to identify and track the object of interest among multiple closely-spaced objects (CSOs) that travel on a ballistic trajectory. One approach that will improve discrimination performance is to combine data from multiple sensors. Multiple sensor correlation and discrimination involves the integration of several sensor returns that

are often collected by dissimilar sensors placed on the ground and on-orbit to improve the likelihood of identifying and tracking an object of interest within the CSOs.

This project investigates the development of the algorithms necessary for fusing data obtained from multiple, dissimilar sensors. The algorithms employ a target object map (TOM) created using multiple sensor measurements for correlation. The object of interest is then selected using a probability-based Dempster-Shafer discrimination algorithm combined with the TOM correlation probability.

A simulation environment was developed to examine the performance of these algorithms. The environment includes relevant characteristics of the sensors in the discrimination algorithm, a modeling process for the ballistic trajectories of the CSOs, and a decision-making process for handling the multi-sensor data to correlate and discriminate the object of interest. This simulation environment was utilized to assess system performance characteristics using a Monte Carlo simulation by changing system parameters such as sensor measurement accuracy, sensor locations, Kalman filtering approaches, state propagation algorithms, TOM correlation approaches, probability distribution of characteristics and the number of CSOs.

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Assistant Professor Tracie A. SeversonWeapons and Systems Engineering Department

B.S., U.S. Naval AcademyM.S., University of Michigan

Ph.D., University of Maryland

Associate Professor Tae W. LimAerospace Engineering Department

B.S., Yonsei University, South KoreaM.S., Ph.D., University of Virginia

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Zane A. MarkelMidshipman 1st Class

Machine Learning Based Malware Detection

FACULTY ADVISOR

Commander Michael B. Bilzor, USNPermanent Military ProfessorComputer Science Department

B.S., U.S. Naval AcademyM.S., Johns Hopkins University

Ph.D., U.S. Naval Postgraduate School

Current antivirus software is effective at detecting well known threats, but cannot keep up with the rate at which new malware is authored or modern antivirus avoidance techniques, such as using polymorphic code. Some studies have investigated augmenting current antivirus techniques with machine learning, which could potentially detect some previously unknown malware. However, previously proposed methods either do not detect malware with satisfactory performance, or they have only been tested on laboratory software databases that cannot suitably be projected into realistic performance.

This work explores several aspects of machine learning based malware detection. First, we propose an approach to learn primarily from program metadata, particularly header data in the 32-bit Windows Portable Executable (PE32) file format. We identify learning methods that learn effectively from this metadata, explore which metadata features can be trivially modified and are not appropriate for malware detection, test it on approximately realistic datasets, and find that it performs favorably compared to Windows API imports, another category of file characteristic that shows promise for machine learning based malware detection. Additionally, we find and explore the drastic performance drop which occurs when using a realistically low proportion of malware in test datasets instead of datasets split evenly between malware and benign software.

Ensemble learning, which commonly alleviates this problem in other similar machine learning applications, does not appreciably help in this context. Training with datasets that have the same proportion of malware as the test datasets optimizes performance, yet the file characteristics that are informative for malware detection change with the proportion of malware in the training dataset. We conclude that file characteristics must be trained on and tested in approximately realistic settings in order to demonstrate their robustness in operational malware detection, and we propose a test procedure which meets these standards.

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Fletcher D. RydalchMidshipman 1st Class

A Characterization of the Ship Effect in a Maritime Environment and Special Nuclear Material Detection

The interdiction of nuclear and radiological materials out of regulatory control is of utmost importance in national security. In the maritime environment where such material may be moved on large vessels, detection is complicated due to the environment, the ships motion, time constraints, and the amount of potential shielding; either incidental or purposefully placed. Additionally, the level of the radiation background on and in the immediate vicinity of the ship (where an illicit source might be detected) is affected

by the ship effect. The neutron and gamma radiation ship effect is a phenomenon involving high energy physics where cosmic radiation interacts preferentially with high atomic number materials to produce additional background radiation. A classic example (for which the effect is named) is that of a ship afloat.

The objectives of this research project were to spatially characterize the ship effect in the vicinity of a naval warship and to gage the impact of the characterized environment on detection feasibility for onboard radioactive sources. The results of this characterization will inform the development of survey protocols and equipment, allowing improved identification of nuclear material aboard a maritime vessel by stand-off radiation detection. The project included completion of the following tasks: (1) integrating a suitable radiation detection system, (2) conducting measurements of the background neutron and gamma radiation both on land and surrounding a ship on the water, (3) simulation of a radiation signature emitted from nuclear material aboard a ship using radiation transport software, and (4) comparing the measured radiation signatures and modeled source signatures to show the impact on detection feasibility of nuclear material in a ship effect environment.

The objectives of this research project have been met. Results show an expected increase in radiation background while approaching a ship, with the greatest increase measured to be greater than 40% near the ships center of mass. The effect of this increased background radiation on detection feasibility has been estimated. For one configuration, a modeled detectors response to a notional on-board source has been combined with the characterized background to show the decrease in detectable range due to the ship effect.

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FACULTY ADVISORS

External Collaborators:Commander Raoul J. Bustamante, U.S. Naval Academy Waterfront Readiness DivisionCaptain Monte L. Ulmer, Commanding Officer, Naval Support Activity Washington

Major Andrew W. Decker, Deputy Director, J9-NTW (NSERC), Defense Threat Reduction Agency

Assistant Professor Marshall MillettMechanical Engineering Department

B.S., U.S. Naval AcademyM.S., Ph.D., University of Maryland

Professor Martin E. NelsonMechanical Engineering Department

B.S., University of WisconsinM.S., Ph.D., University of Virginia

Vice Admiral Joe Leidig, USN (Ret.) Corbin A. McNeill Endowed Chair in Engineering

Mechanical Engineering DepartmentB.S., U.S. Naval Academy

M.A., U.S. Naval War College

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David A. StevensMidshipman 1st Class

Computational Sensitivity Analysis for the Aerodynamic Design of Supersonic and Hypersonic Air Vehicles

FACULTY ADVISOR

Associate Professor Chris L. PettitAerospace Engineering Department

B.S., M.S., University of California, Los AngelesPh.D., Johns Hopkins University

External Collaborator: Philip S. Beran, Principal Research Aerospace Engineer, Air Force Research Laboratory

The conceptual design of hypersonic vehicles relies on computational methods to produce estimates of aerodynamic, structural, thermal protection, and propulsion design requirements. Additionally, conceptual vehicle designs must begin to solve the multidisciplinary optimization problem presented by these competing factors. Solving the design optimization problem is not possible using traditional

low-fidelity vehicle models and flow simulations because these models represent the underlying physics of the combined disciplines too poorly to be dependable. Moreover, it may not even be possible to solve the design optimization problem because these methods may preclude unconventional vehicle designs that do not fall within historical design paradigms. Additionally, designs based on these undependable methods may suffer from unanticipated phenomena during testing that lead to costly vehicle redesigns and program delays.

The sensitivities of a hypersonic air vehicles aerodynamics to geometric variations were calculated using a computational framework developed for this project. This framework automates the process of design space sampling, vehicle model generation, flow solution, response surface generation, and global sensitivity analysis. It is unique in its integration of modern design tools such as parametric vehicle models, automated volumetric mesh generation and refinement, Eulerian flow simulations, Kriging response surface generation, and global sensitivity analysis. As a stepping stone to solving the multidisciplinary design optimization problem, this work focuses on quantifying the sensitivity of the vehicles lift-to-drag ratio and aerodynamic moments to changes in the planform of the vehicles lifting surfaces. The analysis of these aerodynamic forces and moments provides insight into the vehicles range and provides a baseline for future structural and control design analyses.

The framework was verified by computing the sensitivity of the vehicles lift-to-drag ratio to changes in the wings dihedral angle and span. This study demonstrated that the hypersonic vehicles lift-to-drag ratio was approximately three times more sensitive to variations in the wings span compared to variations in the wings dihedral. Future work will expand the framework to include a higher dimensional design space and additional aerodynamic forces and moments.

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Eric A. SwansonMidshipman 1st Class

Black Hole Entropy: Quantum Mechanics in Relativistic Spacetime

The thermodynamic properties of any system can be calculated from knowing the quantum states of that system. The connection between thermal physics and quantum mechanics is well understood. Near a black hole, the effects of gravity, understood through general relativity, affect the particle states. In general, quantum mechanics and general relativity are not compatible in describing a system, but the extreme environment of a black hole demands the use of both theories.

The starting point for this project is calculating the thermodynamics of a system in a Minkowski spacetime. This is the general flat spacetime geometry that we experience in our day to day lives. This should reproduce the same thermodynamic properties that we are familiar with in classical physics. The benefit is that we have a framework to introduce the effects of the black hole. The Minkowski spacetime is a relativistically correct spacetime in the absence of gravity. The form of the equation allows for the effect of the black hole to be input directly and calculated as a perturbation of the flat spacetime solution.

The black hole spacetime introduces mathematical difficulties that are absent in the more simple case. In taking a limiting case of a particle constrained to move in a radial motion (not orbiting), the Hawking Temperature of a black hole can be reproduced. Allowing for general motion of the particle introduces angular momentum to the problem. The challenges of this extra term can be dealt with using the group theory of representation.

After calculating the wave-function of a particle on the black hole spacetime, thermodynamic quantities such as temperature and entropy can be calculated trivially.

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FACULTY ADVISORS

Assistant Professor Eyo E. ItaPhysics Department

B.S., U.S. Naval AcademyM.S., Johns Hopkins UniversityPh.D., University of Cambridge

Commander Richard H. Downey, USNPermanent Military Professor

Physics DepartmentB.S., Massachusetts Institute of Technology

M.S., University of IllinoisPh.D., U.S. Naval Postgraduate School

Associate Professor Carl E. MunganPhysics Department

B.Sc., Queen's University, CanadaM.S., Ph.D., Cornell University

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Gabriel Tang Ying KitMidshipman 1st Class

Cooperative Control of Unmanned Surface Vessels and Unmanned Underwater Vessels for Asset Protection

FACULTY ADVISOR

Professor Bradley E. BishopWeapons and Systems Engineering Department

B.S., Michigan State UniversityM.S., Ph.D., University of Illinois, Urbana-Champaign

The field of cooperative autonomous control has traditionally focused on a swarm of homogeneous vehicles working together to fulfill a task. However, heterogeneous swarms working cooperatively in a multi-modal manner have the potential to synergize the disparate functional capabilities in order to better fulfill mission requirements.

This project focuses on the development of a cooperative control system for a heterogeneous swarm of unmanned surface vessels (USVs) and unmanned underwater vessels (UUVs) specifically utilized for the task of asset protection. Relying on a hybrid control scheme that combines both behavior-based and systems-theoretic concepts, the swarm provides better adaptability, robustness and overall performance than traditional control methods. Instead of simply defining the unit positions or the shape of the unit distribution desired for the swarm state, a novel capability function is used as the driver for the swarm. This capability function uses real time data in order to define the actual mission parameters for example, the probability of detection of a patrol vessel. Based on the capability desired, the swarm then maneuvers itself to generate the required capability.

A well-recognized difficulty with UUVs is the persistent localization problem. Typical methods for localization underwater suffer from buildup of uncertainty over time, reducing the efficacy of the UUV units due to positioning errors. Surfacing in order to get a GPS fix causes a temporary reduction in the quality of sensing by moving the underwater units to the same plane as the surface vessels, and may also reduce stealth for the UUVs and jeopardize mission fulfillment. As such, long baseline techniques were implemented in a secondary controller in order to incorporate cooperation between the USV and UUV units as a tool for improving localization. Using the USVs as navigation beacons, the UUVs were able to ascertain their position, mitigating the localization uncertainty while still ensuring that the full heterogeneous swarm provides the desired asset protection capability.

Overall, this model of a hybrid cooperative control has proven itself to be effective, robust and easily manipulated to suit different secondary objectives setting the foundation for future models of control systems for multi-modal swarms.

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We are proud to announce the United States Naval Academy Class of 2016 Trident Scholars.

MIDSHIPMAN 2/C IAN E. SHAWMathematics Honors Major

Construction of Rational Maps on the Projective Line with Given Dynamical Structure

Advisors: Associate Professor Amy E. Ksir, Mathematics Department; LT Brian J. Stout, USN, Mathematics

Department

MIDSHIPMAN 2/C AARON M. SIMSSystem Engineering Honors Major

Control of Multi-Vehicle Formations with Coordinated Inter-Vehicle Communication

Advisors: Associate Professor Levi D. DeVries, Systems and Weapons Engineering Department; VADM Joe Leidig, USN

(Ret.), Mechanical Engineering Department

MIDSHIPMAN 2/C TIMOTHY E. TRACEYMechanical Engineering Major

Measurement and Modeling of High Energy Laser (HEL)-Droplet Interactions

Advisors: Associate Professor Cody J. Brownell, Mechanical Engineering Department; CDR Stuart R. Blair, USN,

Mechanical Engineering Department

MIDSHIPMAN 2/C THOMAS J. WESTERApplied Mathematics Honors Major

Mathematical Modeling: Immune System Dynamics in the Presence of Cancer and Immunodeficiency In Vivo

Advisors: Associate Professor Sonia M. Garcia, Mathematics Department; Assistant Professor Daniel D. Isaac, Chemistry

Department

MIDSHIPMAN 2/C MICHAEL A. WOULFEPhysics Major

Towards a Theory of a Nearly Two-Dimensional Dipolar Bose Gas

Advisor: Assistant Professor Ryan M. Wilson, Physics Department

MIDSHIPMAN 2/C ALVIN A. ABES Systems Engineering Major

Modeling and Control of the Cobelli Model as a Personalized Prescriptive Tool for Diabetes Treatment

Advisor: Professor Richard OBrien, Weapons and Systems Engineering Department

External Collaborator: Dr. Ledys J. DiMarsico, M.D., Sinai Hospital of Baltimore

MIDSHIPMAN 2/C BENJAMIN I. BRANSON Aerospace Engineering Major

An Examination of a Centrifugal Pumping Blade Design as a Means of Vortex Mitigation

Advisor: Assistant Professor Joseph I. Milluzzo, Aerospace Engineering Department

MIDSHIPMAN 2/C RYAN J. BURMEISTERComputer Science Major

Fast, Distributed Algorithms for Training of Deep NetworksAdvisor: Assistant Professor Gavin W. Taylor, Computer

Science DepartmentExternal Collaborator: Assistant Professor Thomas Goldstein,

University of Maryland

MIDSHIPMAN 2/C JAMES F. COOKESystems Engineering Honors Major

Uncalibrated Three-Dimensional Microrobot ControlAdvisors: Associate Professor Jenelle A. Piepmeier, Weapons and Systems Engineering Department; Professor Samara L.

Firebaugh, Electrical and Computer Engineering Department; Assistant Professor Hatem Elbidweihy, Electrical and Computer

Engineering Department

MIDSHIPMAN 2/C MIGUEL A. NIEVES IIOperations Research Major

Markov Decision Process Model of the Accept/Decline Decision in Liver Transplantation

Advisor: Associate Professor Sommer E. Gentry, Mathematics Department

External Collaborator: Associate Professor Dorry Segev, Johns Hopkins University School of Medicine

MIDSHIPMAN 2/C SPENCER C. SHABSHABElectrical Engineering Major

Nonlinear Control Method for Synchronization of Converter-Interfaced Generators

Advisors: Assistant Professor Daniel F. Opila, Electrical and Computer Engineering Department; CDR John D. Stevens,

USN, Electrical and Computer Engineering DepartmentExternal Collaborators: Assistant Professor Sairaj Dhople,

University of Minnesota; Dr. Brian Johnson, Ph.D., Power Systems Engineering Center, National Renewable Energy

Laboratory

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Midshipman 1/C A. Eileen DilksSystems Engineering Major Lawrenceville, Georgia

Midshipman 1/C Daniel R. Kuerbitz

Aerospace Engineering MajorHighland Heights, Ohio

Midshipman 1/C Brian R. HeMechanical Engineering Major

Danville, California

Midshipman 1/C Benjamin C. Etringer

Applied Mathematics (Honors) MajorMayodan, North Carolina

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Midshipman 1/C Eric A. SwansonPhysics Major Shoreview, Minnesota

Midshipman 1/C David A. Stevens Aerospace Engineering MajorTucson, Arizona

Midshipman 1/C Fletcher D. RydalchMechanical Engineering Major Rexburg, Idaho

Midshipman 1/C Zane A. MarkelComputer Science Major Bismarck, North Dakota

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Midshipman 1/C Gabriel Tang Ying KitSystems Engineering (Honors) Major Singapore, Republic of Singapore

Midshipman 1/C Andrea R. HowardPolitical Science Major Norcross, Georgia

Midshipman 1/C Michael K. JohnsonElectrical Engineering Major Euless, Texas

Midshipman 1/C Samuel S. LacinskiAerospace Engineering Major

North Royalton, Ohio

Midshipman 1/C Steven T. HallgrenPhysics MajorCoeur d'Alene, Idaho

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