Exploration Medical Capability Medical System Requirements Overview and Update
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Bardina, Jorge↗
Engineering topics
Bardina, Jorge
Publications and source records attributed to Bardina, Jorge.
Exploration Medical Capability Medical System Recommendations for Gateway
- Medical System Content Development - 2019: Develop clinical content to inform medical system design; Iterate on content with wider ExMC (Exploration Medical Capability) team; Capture processes used to perform these tasks. - Using model content to inform system design - SME (Subject Matter Expert) collaboration to refine systems using clinical content.
Hailey, Melinda↗
Ground and Range Operations for a Heavy-Lift Vehicle: Preliminary Thoughts
This paper discusses the ground and range operations for a Shuttle derived Heavy-Lift Vehicle being launched from the Kennedy Space Center on the Eastern range. Comparisons will be made between the Shuttle and a heavy lift configuration (SLS-ETF MPCV April 2011) by contrasting their subsystems. The analysis will also describe a simulation configuration with the potential to be utilized for heavy lift vehicle processing/range simulation modeling and the development of decision-making systems utilized by the range. In addition, a simple simulation model is used to provide the required critical thinking foundations for this preliminary analysis.
Rabelo, Luis↗
NASA System-Level Design, Analysis and Simulation Tools Research on NextGen
A review of the research accomplished in 2009 in the System-Level Design, Analysis and Simulation Tools (SLDAST) of the NASA's Airspace Systems Program is presented. This research thrust focuses on the integrated system-level assessment of component level innovations, concepts and technologies of the Next Generation Air Traffic System (NextGen) under research in the ASP program to enable the development of revolutionary improvements and modernization of the National Airspace System. The review includes the accomplishments on baseline research and the advancements on design studies and system-level assessment, including the cluster analysis as an annualization standard of the air traffic in the U.S. National Airspace, and the ACES-Air MIDAS integration for human-in-the-loop analyzes within the NAS air traffic simulation.
Bardina, Jorge↗
Micro-Flying Robotics in Space Missions
The Columbia Accident Investigation Board issued a major recommendation to NASA. Prior to return to flight, NASA should develop and implement a comprehensive inspection plan to determine the structural integrity of all Reinforced Carbon-Carbon (RCC) system components. This inspection plan should take advantage of advanced non-destructive inspection technology. This paper describes a non-intrusive technology with a micro-flying robot to continuously monitor inside a space vehicle for any stress related fissures, cracks and foreign material embedded in walls, tubes etc.
Bardina, Jorge↗
Decision Facilitator for Launch Operations using Intelligent Agents
Launch operations require millions of micro-decisions which contribute to the macro decision of 'Go/No-Go' for a launch. Knowledge workers"(such as managers and technical professionals) need information in a timely precise manner as it can greatly affect mission success. The intelligent agent (web search agent) uses the words of a hypertext markup language document which is connected through the internet. The intelligent agent's actions are to determine if its goal of seeking a website containing a specified target (e.g., keyword or phrase), has been met. There are few parameters that should be defined for the keyword search like "Go" and "No-Go". Instead of visiting launch and range decision making servers individually, the decision facilitator constantly connects to all servers, accumulating decisions so the final decision can be decided in a timely manner. The facilitator agent uses the singleton design pattern, which ensures that only a single instance of the facilitator agent exists at one time. Negotiations could proceed between many agents resulting in a final decision. This paper describes details of intelligent agents and their interaction to derive an unified decision support system.
Thirumalainambi, Rajkumar↗
Modeling and Simulation of Shuttle Launch and Range Operations
The simulation and modeling test bed is based on a mockup of a space flight operations control suitable to experiment physical, procedural, software, hardware and psychological aspects of space flight operations. The test bed consists of a weather expert system to advise on the effect of weather to the launch operations. It also simulates toxic gas dispersion model, impact of human health risk, debris dispersion model in 3D visualization. Since all modeling and simulation is based on the internet, it could reduce the cost of operations of launch and range safety by conducting extensive research before a particular launch. Each model has an independent decision making module to derive the best decision for launch.
Bardina, Jorge↗
Web-Based Toxic Gas Dispersion Model for Shuttle Launch Operations
During the launch of the Space Shuttle vehicle, the burning of liquid hydrogen fuel with liquid oxygen at extreme high temperatures inside the three space shuttle main engines, and the burning of the solid propellant mixture of ammonium perchlorate oxidizer, aluminum fuel, iron oxide catalyst, polymer binder, and epoxy curing agent in the two solid rocket boosters result in the formation of a large cloud of hot, buoyant toxic exhaust gases near the ground level which subsequently rises and entrains into ambient air until the temperature and density of the cloud reaches an approximate equilibrium with ambient conditions. In this paper, toxic gas dispersion for various gases are simulated over the web for varying environmental conditions which is provided by rawinsonde data. The model simulates chemical concentration at ground level up to 10 miles (1 KM grids) in downrange up to an hour after launch. The ambient concentration of the gas dispersion and the deposition of toxic particles are used as inputs for a human health risk assessment model. The advantage of the present model is the accessibility and dissemination of model results to other NASA centers over the web. The model can be remotely operated and various scenarios can be analyzed.
Bardina, Jorge↗
Debris Dispersion Model Using Java 3D
This paper describes web based simulation of Shuttle launch operations and debris dispersion. Java 3D graphics provides geometric and visual content with suitable mathematical model and behaviors of Shuttle launch. Because the model is so heterogeneous and interrelated with various factors, 3D graphics combined with physical models provides mechanisms to understand the complexity of launch and range operations. The main focus in the modeling and simulation covers orbital dynamics and range safety. Range safety areas include destruct limit lines, telemetry and tracking and population risk near range. If there is an explosion of Shuttle during launch, debris dispersion is explained. The shuttle launch and range operations in this paper are discussed based on the operations from Kennedy Space Center, Florida, USA.
Thirumalainambi, Rajkumar↗
Human Health Risk Assessment Simulations in a Distributed Environment for Shuttle Launch
During the launch of a rocket under prevailing weather conditions, commanders at Cape Canaveral Air Force station evaluate the possibility of whether wind blown toxic emissions might reach civilian and military personnel in the near by area. In our model, we focused mainly on Hydrogen chloride (HCL), Nitrogen oxides (NOx) and Nitric acid (HNO3), which are non-carcinogenic chemicals as per United States Environmental Protection Agency (USEPA) classification. We have used the hazard quotient model to estimate the number of people at risk. It is based on the number of people with exposure above a reference exposure level that is unlikely to cause adverse health effects. The risk to the exposed population is calculated by multiplying the individual risk and the number in exposed population. The risk values are compared against the acceptable risk values and GO or NO-go situation is decided based on risk values for the Shuttle launch. The entire model is simulated over the web and different scenaria can be generated which allows management to choose an optimum decision.
Thirumalainambi, Rajkumar↗
Internet Based Simulations of Debris Dispersion of Shuttle Launch
The debris dispersion model (which dispersion model?) is so heterogeneous and interrelated with various factors, 3D graphics combined with physical models are useful in understanding the complexity of launch and range operations. Modeling and simulation in this area mainly focuses on orbital dynamics and range safety concepts, including destruct limits, telemetry and tracking, and population risk. Particle explosion modeling is the process of simulating an explosion by breaking the rocket into many pieces. The particles are scattered throughout their motion using the laws of physics eventually coming to rest. The size of the foot print explains the type of explosion and distribution of the particles. The shuttle launch and range operations in this paper are discussed based on the operations of the Kennedy Space Center, Florida, USA. Java 3D graphics provides geometric and visual content with suitable modeling behaviors of Shuttle launches.
Bardina, Jorge↗
Integration of Dynamic Models in Range Operations
This work addresses the various model interactions in real-time to make an efficient internet based decision making tool for Shuttle launch. The decision making tool depends on the launch commit criteria coupled with physical models. Dynamic interaction between a wide variety of simulation applications and techniques, embedded algorithms, and data visualizations are needed to exploit the full potential of modeling and simulation. This paper also discusses in depth details of web based 3-D graphics and applications to range safety. The advantages of this dynamic model integration are secure accessibility and distribution of real time information to other NASA centers.
Bardina, Jorge↗
Intelligent Launch and Range Operations Virtual Test Bed (ILRO-VTB)
Intelligent Launch and Range Operations Virtual Test Bed (ILRO-VTB) is a real-time web-based command and control, communication, and intelligent simulation environment of ground-vehicle, launch and range operation activities. ILRO-VTB consists of a variety of simulation models combined with commercial and indigenous software developments (NASA Ames). It creates a hybrid software/hardware environment suitable for testing various integrated control system components of launch and range. The dynamic interactions of the integrated simulated control systems are not well understood. Insight into such systems can only be achieved through simulation/emulation. For that reason, NASA has established a VTB where we can learn the actual control and dynamics of designs for future space programs, including testing and performance evaluation. The current implementation of the VTB simulates the operations of a sub-orbital vehicle of mission, control, ground-vehicle engineering, launch and range operations. The present development of the test bed simulates the operations of Space Shuttle Vehicle (SSV) at NASA Kennedy Space Center. The test bed supports a wide variety of shuttle missions with ancillary modeling capabilities like weather forecasting, lightning tracker, toxic gas dispersion model, debris dispersion model, telemetry, trajectory modeling, ground operations, payload models and etc. To achieve the simulations, all models are linked using Common Object Request Broker Architecture (CORBA). The test bed provides opportunities for government, universities, researchers and industries to do a real time of shuttle launch in cyber space.
Bardina, Jorge↗
Training Data Requirement for a Neural Network to Predict Aerodynamic Coefficients
Basic aerodynamic coefficients are modeled as functions of angle of attack, speed brake deflection angle, Mach number, and side slip angle. Most of the aerodynamic parameters can be well-fitted using polynomial functions. We previously demonstrated that a neural network is a fast, reliable way of predicting aerodynamic coefficients. We encountered few under fitted and/or over fitted results during prediction. The training data for the neural network are derived from wind tunnel test measurements and numerical simulations. The basic questions that arise are: how many training data points are required to produce an efficient neural network prediction, and which type of transfer functions should be used between the input-hidden layer and hidden-output layer. In this paper, a comparative study of the efficiency of neural network prediction based on different transfer functions and training dataset sizes is presented. The results of the neural network prediction reflect the sensitivity of the architecture, transfer functions, and training dataset size.
Korsmeyer, David↗
Prediction of Aerodynamic Coefficients using Neural Networks for Sparse Data
Basic aerodynamic coefficients are modeled as functions of angles of attack and sideslip with vehicle lateral symmetry and compressibility effects. Most of the aerodynamic parameters can be well-fitted using polynomial functions. In this paper a fast, reliable way of predicting aerodynamic coefficients is produced using a neural network. The training data for the neural network is derived from wind tunnel test and numerical simulations. The coefficients of lift, drag, pitching moment are expressed as a function of alpha (angle of attack) and Mach number. The results produced from preliminary neural network analysis are very good.
Rajkumar, T.↗
Prediction of Aerodynamic Coefficient using Genetic Algorithm Optimized Neural Network for Sparse Data
Wind tunnels use scale models to characterize aerodynamic coefficients, Wind tunnel testing can be slow and costly due to high personnel overhead and intensive power utilization. Although manual curve fitting can be done, it is highly efficient to use a neural network to define the complex relationship between variables. Numerical simulation of complex vehicles on the wide range of conditions required for flight simulation requires static and dynamic data. Static data at low Mach numbers and angles of attack may be obtained with simpler Euler codes. Static data of stalled vehicles where zones of flow separation are usually present at higher angles of attack require Navier-Stokes simulations which are costly due to the large processing time required to attain convergence. Preliminary dynamic data may be obtained with simpler methods based on correlations and vortex methods; however, accurate prediction of the dynamic coefficients requires complex and costly numerical simulations. A reliable and fast method of predicting complex aerodynamic coefficients for flight simulation I'S presented using a neural network. The training data for the neural network are derived from numerical simulations and wind-tunnel experiments. The aerodynamic coefficients are modeled as functions of the flow characteristics and the control surfaces of the vehicle. The basic coefficients of lift, drag and pitching moment are expressed as functions of angles of attack and Mach number. The modeled and training aerodynamic coefficients show good agreement. This method shows excellent potential for rapid development of aerodynamic models for flight simulation. Genetic Algorithms (GA) are used to optimize a previously built Artificial Neural Network (ANN) that reliably predicts aerodynamic coefficients. Results indicate that the GA provided an efficient method of optimizing the ANN model to predict aerodynamic coefficients. The reliability of the ANN using the GA includes prediction of aerodynamic coefficients to an accuracy of 110% . In our problem, we would like to get an optimized neural network architecture and minimum data set. This has been accomplished within 500 training cycles of a neural network. After removing training pairs (outliers), the GA has produced much better results. The neural network constructed is a feed forward neural network with a back propagation learning mechanism. The main goal has been to free the network design process from constraints of human biases, and to discover better forms of neural network architectures. The automation of the network architecture search by genetic algorithms seems to have been the best way to achieve this goal.
Rajkumar, T.↗
Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network
A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.
Rajkumar, T.↗
Microphone Array Phased Processing System (MAPPS): Version 4.0 Manual
A processing system has been developed to meet increasing demands for detailed noise measurement of individual model components. The Microphone Array Phased Processing System (MAPPS) uses graphical user interfaces to control all aspects of data processing and visualization. The system uses networked parallel computers to provide noise maps at selected frequencies in a near real-time testing environment. The system has been successfully used in the NASA Ames 7- by 10-Foot Wind Tunnel.
Watts, Michael E.↗