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Daniel Guggenheim School of Aerospace Engineering
Daniel Guggenheim School of Aerospace Engineering
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ItemIAD wind-tunnel test data analysis & IAD structural modeling(Georgia Institute of Technology, 2010-12-31) Jagoda, Jechiel I. ; Tanner, Chris ; Braun, Robert D.
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ItemSubgrid combustion modeling for the next generation national combustion ...(Georgia Institute of Technology, 2010-12-31) Menon, Suresh ; Sen, Baris A. ; Srinivasan, Srikant ; Smith, Andrew
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ItemAdaptive control of a slender launch vehicle(Georgia Institute of Technology, 2010-12-30) Calise, Anthony J. ; Craig, James I.
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ItemDevelopment and application of a rapid military model development framework(Georgia Institute of Technology, 2010-12-20) Andriano, Nelson GregoryMilitary operations are complex systems composed of the interactions of many smaller discrete systems, or assets: aircraft, watercraft, troops, etc. Historically, the requirements for new assets have been created based on standalone optimization. It is not just necessary to optimize requirements for a single scenario, such as a wartime operation, but instead to optimize the requirements that will benefit the entire military operation as a whole in a number of different scenarios, such as wartime and peace time. To better define future military assets it is necessary sample a large number of scenarios. To capture all of the interactions and develop a complete understanding of the overall system, it is necessary to model both combat and logistics, which have traditionally been modeled and analyzed separately. To characterize military operations and the assets that contribute to them, it is necessary to move beyond the traditional models that use aggregated approximations for combat and stand alone nodal analysis for logistics. A unique need for a framework which captures the complex interaction between combat and logistics while allowing a large number of automated cases and scenarios to run with no human in the loop. The framework this paper discusses was created to facilitate the making of models to analyze and characterize military operations and the effects that future assets will have on entire operations. The framework is agent-based, allowing bottom up definition and the gathering of emergent behavior, and uses a modified Hughes salvo method for combat, the Foundation for Intelligent Physical Agents messaging structure, and the beliefs, desires, and intentions (BDI) agent model. The modeling of communication and BDI creates myopic agents that are constrained by the information they can obtain, process, and react to. In this paper, the framework is first depicted and then validated by the creation of a model with the purposes of defining the requirements for a future asset, the Transformable Craft. The creation and testing of the model prove that the requirements for the framework have been met with success. The potential applications of the framework ranges from data-farming military operations models for future asset requirement, characterizing military operations systems, and providing a stepping stone for future agent-based military operations modeling and simulation work.
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ItemWave propagation in nonlinear periodic structures(Georgia Institute of Technology, 2010-12-20) Narisetti, Raj K.A periodic structure consists of spatially repeating unit cells. From man-made multi-span bridges to naturally occurring atomic lattices, periodic structures are ubiquitous. The periodicity can be exploited to generate frequency bands within which elastic wave propagation is impeded. A limitation to the linear periodic structure is that the filtering properties depend only on the structural design and periodicity which implies that the dispersion characteristics are fixed unless the overall structure or the periodicity is altered. The current research focuses on wave propagation in nonlinear periodic structures to explore tunability in filtering properties such as bandgaps, cut-off frequencies and response directionality. The first part of the research documents amplitude-dependent dispersion properties of weakly nonlinear periodic media through a general perturbation approach. The perturbation approach allows closed-form estimation of the effects of weak nonlinearities on wave propagation. Variation in bandstructure and bandgaps lead to tunable filtering and directional behavior. The latter is due to anisotropy in nonlinear interaction that generates low response regions, or "dead zones," within the structure.The general perturbation approach developed has also been applied to evaluate dispersion in a complex nonlinear periodic structure which is discretized using Finite Elements. The second part of the research focuses on wave dispersion in strongly nonlinear periodic structures which includes pre-compressed granular media as an example. Plane wave dispersion is studied through the harmonic balance method and it is shown that the cut-off frequencies and bandgaps vary significantly with wave amplitude. Acoustic wave beaming phenomenon is also observed in pre-compressed two-dimensional hexagonally packed granular media. Numerical simulations of wave propagation in finite lattices also demonstrated amplitude-dependent bandstructures and directional behavior so far observed.
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ItemQuantitative Assessment of Human Control on Landing Trajectory Design(Georgia Institute of Technology, 2010-12-02) Chua, Zarrin K.An increased thirst for scientific knowledge and a desire to advance humanity's presence in space prompts the need for improved technology to send crewed vehicles to places such as the Moon, Mars, and nearby passing asteroids. Landing at any of these locations will require vehicle capabilities greater than that previously used during the Apollo program or those applied in Low Earth Orbit. In particular, the vehicle and the on-board crew must be capable of executing precision landing in sub-optimal landing conditions during time-critical, high-stakes mission scenarios, such as Landing Point Designation (LPD) , or the critical phase of determining the vehicle's final touchdown point. Most proposed solutions involve automated control of landing vehicles, accepting no input from the on-board crew - effectively relegating them to payload. While this method is satisfactory for some missions, an automation-only approach during this critical mission phase may be placing the system at a disadvantage by neglecting the human capability of [what?]. Therefore, the landing system may result in a lack of dynamic flexibility to unexpected landing terrain or in-flight events. It is likely that executing LPD will require an ideal distribution of authority between the on-board crew and an automated landing system. However, this distribution is application-specific and not easily calculated. Current science does not provide enough detailed or explicit theories regarding allocation of automation, and the advantages provided by biological and digital pilots (either acting as the sole authoritarian or as a coordinated team) are difficult to describe in quantitative measures. Despite previous experience in piloting vehicles on the Moon, few cognitive models describing the decision-making process exist. The specialization of the pilot and the application pose significant practical challenges in regular observations in the target environment. The lack of quantitative knowledge results in predominantly qualitative design trade-offs during pre-mission planning. While qualitative analyses have proven to be useful to the mission designer, an understanding founded on quantitative metrics regarding the relationship between human control and mission design will provide the sufficient supplementary information necessary for overall success. In particular, increased knowledge of the impact of human control on landing trajectory design would allow for more efficient and thorough conceptual mission planning. This knowledge would allow visualization of the flight envelope possible for various degrees of human control and help establish conceptual estimations of critical mission parameters such as fuel consumption or task completion time. This report details an experiment undertaken to further understanding of the impact of moderate degrees of human control on landing trajectory design or vice versa during LPD. This report briefly summarizes current understanding and modeling of moderate control during LPD and similar applications, reviews previous and current efforts in implementing LPD, examines the pilot study to observe subjects in a simulated LPD task, and discusses the significance of findings from the pilot study.
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ItemCross-flow dilution air jet studies(Georgia Institute of Technology, 2010-12-01) Seitzman, Jerry M. ; Lieuwen, Timothy C. ; Wilde, Ben ; Noble, Bobby
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ItemDetection and control of instabilities and blowoff for low emissions combustors(Georgia Institute of Technology, 2010-12) Seitzman, Jerry M.
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ItemTurbulent flame speed measurements and modeling of syngas fuels(Georgia Institute of Technology, 2010-11-30) Seitzman, Jerry M. ; Lieuwen, Timothy C.
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ItemA data analytics approach to gas turbine prognostics and health management(Georgia Institute of Technology, 2010-11-19) Diallo, Ousmane NasrAs a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equipments covered under LTSA contracts. As a matter of fact, a recent study by the Electric Power Research Institute estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA technology compressor at $10 to $20 million. Therefore, in this dissertation, a two-phase data analytics approach is proposed to use the existing monitoring gas path and vibration sensors data to first develop a proactive strategy that systematically detects and validates catastrophic failure precursors so as to avoid the failure; and secondly to estimate the residual time to failure of the unhealthy items. For the first part of this work, the time-frequency technique of the wavelet packet transforms is used to de-noise the noisy sensor data. Next, the time-series signal of each sensor is decomposed to perform a multi-resolution analysis to extract its features. After that, the probabilistic principal component analysis is applied as a data fusion technique to reduce the number of the potentially correlated multi-sensors measurement into a few uncorrelated principal components. The last step of the failure precursor detection methodology, the anomaly detection decision, is in itself a multi-stage process. The obtained principal components from the data fusion step are first combined into a one-dimensional reconstructed signal representing the overall health assessment of the monitored systems. Then, two damage indicators of the reconstructed signal are defined and monitored for defect using a statistical process control approach. Finally, the Bayesian evaluation method for hypothesis testing is applied to a computed threshold to test for deviations from the healthy band. To model the residual time to failure, the anomaly severity index and the anomaly duration index are defined as defects characteristics. Two modeling techniques are investigated for the prognostication of the survival time after an anomaly is detected: the deterministic regression approach, and parametric approximation of the non-parametric Kaplan-Meier plot estimator. It is established that the deterministic regression provides poor prediction estimation. The non parametric survival data analysis technique of the Kaplan-Meier estimator provides the empirical survivor function of the data set comprised of both non-censored and right censored data. Though powerful because no a-priori predefined lifetime distribution is made, the Kaplan-Meier result lacks the flexibility to be transplanted to other units of a given fleet. The parametric analysis of survival data is performed with two popular failure analysis distributions: the exponential distribution and the Weibull distribution. The conclusion from the parametric analysis of the Kaplan-Meier plot is that the larger the data set, the more accurate is the prognostication ability of the residual time to failure model.