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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

LA17-V-InverseRadTransportProblem-PD2Kb. Final Project Report

In the inverse radiation transport problem, a radiation signal emanating from an unknown object is used to reconstruct geometric and material parameters of the object , e.g., the mass of special nuclear material (SNM), the isotopic composition of the SNM (e.g., the enrichment of 235U), the thickness of shielding, etc. The inverse radiation transport problem is one of the most fundamental and important in the emergency response mission, and has been addressed in previous NA-22 and NA-42/84 investments by using derivative-free optimization methods. Our project was aimed at (i) consolidating previous work by applying polynomial chaos expansion (PCE) for reducing significantly the number of forward model simulations required by derivative-free optimization methods; (ii) developing a general-purpose framework and stand-alone software for solving the inverse radiation transport problem by applying a new “predictive modeling” methodology (predictive modeling of coupled multi-physics systems, PM_CMPS) which enables the fusing of experimental and computational information; and (iii) developing several benchmarks (for unscattered gamma rays) for demonstrating the quantification of non-Gaussian features (e.g., skewness and kurtosis, which characterize asymmetries and long tails) of response distributions by deterministically and efficiently propagating parameter uncertainties (covariances and higher-order moments, where available) using high-order sensitivities computed by the recently developed comprehensive adjoint sensitivity analysis methodology (C-ASAM). With regard to (i), application of PCE, the most significant accomplishment of this project was an implementation of PCE into INVERSE that significantly reduced computation time for the DiffeRential Evolution Adaptive Metropolis (DREAM) and Differential Evolution (DE) methods. For inverse transport problems with measurements of neutron leakage, neutron multiplication, and neutron-induced gamma-rays, the solution times for the DREAM and DE methods were reduced by factors of 30-300, reducing run times from hours to minutes while maintaining the same accuracy as the standard (non-PCE) DREAM and DE. Speedups were further improved by exploring sparse grid quadrature sets using the Toolkit for Adaptive Stochastic Modeling and Non-Intrusive ApproximatioN (Tasmanian) toolset. An initial exploration of sparse grids indicated that further speedups factors of 5-10 are obtainable. The implementation of PCE in INVERSE is not in a form that users can easily access. With regard to (ii), solving the inverse problem using a predictive modeling paradigm, the most significant accomplishment of this project were the derivation and implementation of adjoint-based second derivatives in neutron and photon transport theory. There are two codes that compute these derivatives. SENSPG computes them for uncollided gamma rays and SENSMG computes them for neutron leakage. SENSMG is open-source code available on github. Other significant accomplishments included a stand-alone code, MULTI-PRED, that implements the predictive modeling paradigm, along with a detailed manual. The code was implemented in the existing INVERSE code for inverse problems, but a head-to-head comparison of inverse methods was not achieved. We also derived and implemented the capability to compute sensitivities for multiplicity measurements into the SENSMG code, and this capability was used in a data assimilation application, but it was not used for solving inverse problems. With regard to (iii), developing benchmarks to demonstrate the importance of high-order sensitivity and uncertainty analysis, the most significant accomplishment of this project were two test problems that showed how important second-order sensitivities are.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Exploration of multifidelity UQ sampling strategies for computer network applications.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Here, two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat – A linearized version of 3-dimensional experimental data of size 61×81×21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat – 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work. This file contains five columns for the parameters J_1, J_2, J_3, J_(3^' ) and D respectively. 3D_Simulation_Data.dat – The simulated structure factor, S(Q) data are included in this file. Each raw contains a linearized array of 3D volumes of S(Q) calculated for the parameter set given in the corresponding row of the file Parameters.dat. The size of the volume data was matched to the experimental data. Qx(h,-h,0).dat, Qy(k,k,-2k).dat, Qz(l,l,l).dat – These files contain the h, k, and l values along with the reciprocal vectors [h,-h,0], [k,k,-2k] and [l,l,l] respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimized Renewable Energy Integration for EV High-Power Dynamic Wireless Charging Systems

Dynamic wireless charging for electric vehicles is an emerging technology to reduce on-board battery size and extend driving range. Due to its unique characteristic of vehicle-speed-related pulse-like load profile, the high-power dynamic wireless charging system (DWCS) introduces high stress to the utility grid. In this paper, an optimization model for renewable energy integration in the DWCS is proposed to mitigate the grid impact and minimize the operation costs of the whole system. As the load profile of DWCS is related to the traffic volume and various approaching vehicle speeds, the annual average daily traffic data and a stochastic model are used to develop 24-hour load profile of DWCS. To find a tradeoff between grid impact mitigation and operation costs minimization, relationships among power demand from power grid, photovoltaic (PV) capacity, wind energy (WE) capacity and energy storage (ES) capacity are analyzed, and the optimization objective and constraints are developed. Numerical simulation results demonstrate that energy storage integration can greatly mitigate the grid impact of DWCS, and optimal ratio of PV and WE can significantly reduce the operation cost of DWCS.

Zeng, Rong↗
NASA NTRS2024

Experimental Results for Mars Rotorcraft Airfoils (roamx-0201 and clf5605) at Low Reynolds Number and Compressible Flow in a Mars Wind Tunnel

Experimental results are obtained for a roamx-0201 type airfoil and the clf5605 airfoil at highsubsonic, low Reynolds number conditions using the Tohoku University Mars Wind Tunnel, Japan. The tests are conducted at a Mach number of M = 0.60, and a Reynolds number of Re = 20,000 to reflect representative aerodynamics of a rotor blade for Mars exploration. The angle of attack is varied between α = −2.0 deg and α = 6.0 deg. The roamx-0201 type airfoil is an unconventional airfoil optimized for the chosen tunnel operating conditions using the Evolutionary aLgorithm for Iterative Studies of Aeromechanics (ELISA), developed under the Rotor Optimization for the Advancement of Mars eXploration (ROAMX) project. ELISA is utilized here to optimize aerodynamic airfoil performance using a Genetic Algorithm and two-dimensional high-fidelity CFD simulations, ultimately resulting in a Pareto-optimal airfoil set. The clf5605 airfoil is the outboard airfoil used on the Ingenuity Mars Helicopter and provides a baseline against which the roamx-0201, as well as possible future airfoil profiles for the compressible low Reynolds number regime, can be compared against. Lift and drag data are recorded using a balance, pressure distributions are obtained using Pressure Sensitive Paint (PSP) application, and Schlieren images are obtained to visualize the flowfield. The data is tabulated to aid future research.

Roamx↗
DOE OSTI2022

Bayesian optimization for inverse calibration of expensive computer models: A case study for Johnson-Cook model in machining

Inverse model calibration for identifying the constitutive model parameters can be computationally demanding for expensive-to-evaluate simulation models. Here, this paper presents a modified Bayesian optimization (BO) method, denoted as BO-bound, that incorporates theoretical bounds on the quantity of interest. A case study for the inverse calibration of the Johnson Cook (J-C) flow stress model parameters is presented using machining (cutting) force data. The results show fast calibration of the five J-C parameters within 25 simulations. In general, the BO-bound method is applicable for inverse calibration of any expensive simulation models as well as optimization problems with known bounds.

Bayesian optimization↗
DOE OSTI2019

Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence

Particle-laden turbulent flows subject to radiative heating are relevant in many applications, for example concentrated solar power receivers. Efficient and accurate simulations provide valuable insights and enable optimization of such systems. However, as there are many uncertainties inherent in such flows, uncertainty quantification is fundamental to improve the predictive capabilities of the numerical simulations. For large-scale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as most strategies require a large number of high-fidelity solves. This requirement might result in an infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method exploits the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. Here, the application of this bi-fidelity approximation results in accurate estimates of the quantities of interest statistics, while requiring a small number of high-fidelity model evaluations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Optimising response to an introduction of African swine fever in wild pigs

African swine fever virus (ASFv) is a virulent pathogen that threatens domestic swine industries globally and persists in wild boar populations in some countries. Persistence in wild boar can challenge elimination and prevent disease-free status, making it necessary to address wild swine in proactive response plans. In the United States, invasive wild pigs are abundant and found across a wide range of ecological conditions that could drive different epidemiological dynamics among populations. Information on the size of the control areas required to rapidly eliminate the ASFv in wild pigs and how this area should change with management constraints and local ecology is needed to optimize response planning. We developed a spatially explicit disease transmission model contrasting wild pig movement and contact ecology in two ecosystems in Southeastern United States. We simulated ASFv spread and determined the optimal response area (reported as the radius of a circle) for eliminating ASFv rapidly over a range of detection times (when ASFv was detected relative to the true date of introduction), culling capacities (proportion of wild pigs in the culling zone removed weekly) and wild pig densities. Large radii for response areas (14 km) were needed under most conditions but could be shortened with early detection (≤ 8 weeks) and high culling capacities (≥ 15% weekly). Under most conditions, the ASFv was eliminated in less than 22 weeks using optimal control radii, although ecological conditions with high rates of wild pig movement required higher culling capacities (≥ 10% weekly) for elimination within 1 year. The results highlight the importance of adjusting response plans based on local ecology and show that wild pig movement is a better predictor of the optimal response area than the number of ASFv cases early in the outbreak trajectory. Furthermore, our framework provides a tool for determining optimal control plans in different areas, guiding expectations of response impacts, and planning resources needed for rapid elimination.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Multi-scale simulations of wind farm performance with complex terrain and weather events

Predicting the response of wind farms to local weather is necessary for optimal design and operation. In this work, simulation and analysis of a frontal passage through a utility scale wind farm is achieved for the first time using a seamless multi-scale modeling approach. A generalized actuator disk (GAD) wind turbine model is used to represent turbine wakes, and results are compared to novel radar observations during the frontal passage. The Weather Research and Forecasting (WRF) model is employed with a nested grid setup that allows for coupling between multi-scale atmospheric conditions and turbine response. Starting with mesoscale forcing, the atmosphere is dynamically downscaled to the region of interest, where the interaction between turbulent flows and individual wind turbines is simulated with the GAD at10 m resolution. Several improvements are made to the GAD model to mimic realistic turbine operation, including a yawing capability and a power output calculation. Ultimately, the model is able to capture both the dynamics of the frontal passage and the turbine response; predictions show good agreement with observed turbine wake structure, velocity deficit, and power output after accounting for a phase shift in the mesoscale forcing. This study demonstrates the utility of the WRF-GAD framework for simulating wind farm performance under complex atmospheric conditions.

17 WIND ENERGY↗
DOE OSTI2016

Xyce Parallel Electronic Simulator Users' Guide Version 6.6.

This manual describes the use of the Xyce Parallel Electronic Simulator. Xyce has been de- signed as a SPICE-compatible, high-performance analog circuit simulator, and has been written to support the simulation needs of the Sandia National Laboratories electrical designers. This development has focused on improving capability over the current state-of-the-art in the following areas: Capability to solve extremely large circuit problems by supporting large-scale parallel com- puting platforms (up to thousands of processors). This includes support for most popular parallel and serial computers. A differential-algebraic-equation (DAE) formulation, which better isolates the device model package from solver algorithms. This allows one to develop new types of analysis without requiring the implementation of analysis-specific device models. Device models that are specifically tailored to meet Sandia's needs, including some radiation- aware devices (for Sandia users only). Object-oriented code design and implementation using modern coding practices. Xyce is a parallel code in the most general sense of the phrase -- a message passing parallel implementation -- which allows it to run efficiently a wide range of computing platforms. These include serial, shared-memory and distributed-memory parallel platforms. Attention has been paid to the specific nature of circuit-simulation problems to ensure that optimal parallel efficiency is achieved as the number of processors grows. The information herein is subject to change without notice. Copyright c 2002-2016 Sandia Corporation. All rights reserved. Acknowledgements The BSIM Group at the University of California, Berkeley developed the BSIM3, BSIM4, BSIM6, BSIM-CMG and BSIM-SOI models. The BSIM3 is Copyright c 1999, Regents of the University of California. The BSIM4 is Copyright c 2006, Regents of the University of California. The BSIM6 is Copyright c 2015, Regents of the University of California. The BSIM-CMG is Copyright c 2012 and 2016, Regents of the University of California. The BSIM-SOI is Copyright c 1990, Regents of the University of California. All rights reserved. The Mextram model has been developed by NXP Semiconductors until 2007, Delft University of Technology from 2007 to 2014, and Auburn University since April 2015. Copyrights c of Mextram are with Delft University of Technology, NXP Semiconductors and Auburn University. The MIT VS Model Research Group developed the MIT Virtual Source (MVS) model. Copyright c 2013 Massachusetts Institute of Technology (MIT). The EKV3 MOSFET model was developed by the EKV Team of the Electronics Laboratory-TUC of the Technical University of Crete. Trademarks Xyce TM Electronic Simulator and Xyce TM are trademarks of Sandia Corporation. Orcad, Orcad Capture, PSpice and Probe are registered trademarks of Cadence Design Systems, Inc. Microsoft, Windows and Windows 7 are registered trademarks of Microsoft Corporation. Medici, DaVinci and Taurus are registered trademarks of Synopsys Corporation. Amtec and TecPlot are trademarks of Amtec Engineering, Inc. All other trademarks are property of their respective owners. Contacts World Wide Web http://xyce.sandia.gov https://info.sandia.gov/xyce (Sandia only) Email xyce@sandia.gov (outside Sandia) xyce-sandia@sandia.gov (Sandia only) Bug Reports (Sandia only) http://joseki-vm.sandia.gov/bugzilla http://morannon.sandia.gov/bugzilla

47 OTHER INSTRUMENTATION↗
NASA NTRS2019

Numerical Investigation and Optimization of a Flushwall Injector for Scramjet Applications at Hypervelocity Flow Conditions

An investigation utilizing Reynolds-averaged simulations (RAS) was performed in order to find optimal designs for an interdigitated flushwall injector suitable for scramjet applications at hypervelocity conditions. The flight Mach number, duct height, spanwise width, and injection angle were the design variables selected to maximize two objective functions: the thrust potential and combustion efficiency. A Latin hypercube sampling design-of-experiments method was used to select design points for RAS. A methodology was developed that automated building geometries and generating grids for each design. The ensuing RAS analysis generated the performance database from which the two objective functions of interest were computed using a one-dimensional performance utility. The data were fitted using four surrogate models: an artificial neural network (ANN) model, a cubic polynomial, a quadratic polynomial, and a Kriging model. Variance-based decomposition showed that both objective functions were primarily driven by changes in the duct height. Multiobjective design optimization was performed for all four surrogate models via a genetic algorithm method. Optimal solutions were obtained at the upper and lower bounds of the flight Mach number range. The Kriging model obtained an optimal solution set that predicted high values for both objective functions. Additionally, three challenge points were selected to assess the designs on the Pareto fronts. Further sampling among the designs of the Pareto fronts are required in order to lower the errors and perform more accurate surrogate-based optimization. sed optimization.

Shenoy, Rajiv R.↗
DOE OSTI2019

Linear Regression Based Multi-fidelity Surrogate for Disturbance Amplification in Multi-phase Explosion

When simulations are very expensive and many are required, as for optimization or uncertainty quantification, a way to reduce cost is using surrogates. With multiple simulations to predict the quantity of interest, some being very expensive and accurate (high-fidelity simulations) and others cheaper but less accurate (low-fidelity simulations), it may be worthwhile to use multifidelity surrogates (MFSs). Moreover, if we can afford just a few high-fidelity simulations or experiments, MFS becomes necessary. Co-Kriging, which is probably the most popular MFS, replaces both low-fidelity and high-fidelity simulations by a single MFS. A recently proposed linear regression–based MFS (LR-MFS) offers the option to correct the LF simulations instead of correcting the LF surrogate in the MFS. When the low-fidelity simulation is cheap enough for use in an application, such as optimization, this may be an attractive option. Here, we explore the performance of LR-MFS using exact and surrogate-replaced low-fidelity simulations. The concern studied is a cylindrical dispersal of 100-μ m-diameter solid particles after detonation and the quantity of interest is a measure of the amplification of the departure from axisymmetry. We find very substantial accuracy improvements for this problem using the LR-MFS with exact low-fidelity simulations. Inspired by these results, we also compare the performance of co-Kriging to the use of Kriging to correct exact low-fidelity simulations and find a similar accuracy improvement when simulations are directly used. For this problem, further improvements in accuracy are achievable by taking advantage of inherent parametric symmetries. These findings may alert users of MFSs to the possible advantages of using exact low-fidelity simulations when this is affordable.

42 ENGINEERING↗
DOE OSTI2021

Development of high B s Fe–Ni-based metal amorphous nanocomposite by optimization of glass-forming ability

Thermocalc simulations identified compositions with good glass-forming ability (GFA) by locating minima in liquidus temperature, and solidification range in (Fe 70 Ni 30 ) x (B-Si-Nb) 100-x alloys with x = 82% and x = 85%, increased compared to previously developed x = 80% alloys. 3 compositions in the x = 82% system and 1 in the x = 85% system were successfully cast as amorphous magnetic ribbon. Magnetic properties of as-cast alloys showed improvements in Curie temperature and saturation induction, especially for the 85% alloy. Crystallization behavior in the 85% alloy was determined to occur as a 2-step process. Annealing studies showed (Fe 70 Ni 30 ) 82 Nb 2 Si 0 B 16 and (Fe 70 Ni 30 ) 82 Nb 3 Si 0 B 15 alloys to exhibit low coercivity and high B s with annealing. TEM results show significant crystallization in as-cast ribbon. The (Fe 70 Ni 30 ) 85 Nb 0.5 Si 0 B 14.5 alloy as-cast has small, uniformly distributed grains, allowing a useful nanocrystalline alloy to be produced as cast. Lastly, the parameter ΔT xg , the temperature range in which the material can be thermomechanically processed, was in the range of 12–23 °C.

36 MATERIALS SCIENCE↗
NASA NTRS1981

An approach to thermal-vacuum test optimization

An analytical model is presented that simulates failure rate characteristics of spacecraft equipment progressing through component and system level thermal-vacuum test programs and on into orbit. Costs for the test programs are part of the model as well as costs attributable to the mission. A lost value function combines the performance of the equipment with these costs and allows comparisons to be made among various test combinations and the resulting predicted mission performance. A minimum overall cost program is obtained by selecting that test program that yields the lowest lost value figure.

Kruger, R.↗
DOE OSTI2024

Implicit neural representations for experimental steering of advanced experiments

Scattering measurements using electrons, neutrons, or photons are essential for obtaining microscopic insights into materials. However, limited facility availability and high-dimensional scattering data necessitate more efficient experimental steering techniques. Here, we report a machine learning method that guides scattering data collection and facilitates real-time estimation of model parameters, given a reliable forward model to simulate experimental signals. We employ implicit neural representations as efficient surrogates that link model parameters with simulated spectroscopies. This enables a Bayesian optimal experimental design framework to estimate the probability distributions of parameters from high-dimensional scattering data. We demonstrate the proposed method using inelastic neutron scattering with simulated and real experimental data, highlighting the method’s ability to provide real-time parameter estimation with quantified uncertainties and to deliver informed experimental guidance that reduces experimental time while maximizing scientific output. This approach paves the way for accelerated discoveries in condensed matter through scattering measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Ultrafast Dynamics of Photoexcited Charge Carriers in In{sub 0.53}Ga{sub 0.47}As/In{sub 0.52}Al{sub 0.48}As Superlattices under Femtosecond Laser Excitation

The results of experimental studies of the time dynamics of photoexcited charge carriers in In{sub 0.53}Ga{sub 0.47}As/In{sub 0.52}Al{sub 0.48}As superlattices grown by molecular-beam epitaxy on a GaAs substrate with a metamorphic buffer are reported. On the basis of the results of the numerical simulation of band diagrams, the optimal thickness of the In{sub 0.52}Al{sub 0.48}As barrier layer (4 nm) is chosen. At this thickness, the electron wave functions in In{sub 0.53}Ga{sub 0.47}As substantially overlap the In{sub 0.52}Al{sub 0.48}As barriers. This makes it possible to attain a short lifetime of photoexcited charge carriers (τ ~ 3.4 ps) at the wavelength λ = 800 nm and the pumping power 50 mW without doping of the In{sub 0.53}Ga{sub 0.47}As layer with beryllium. It is shown that an increase in the wavelength to λ = 930 nm (at the same pumping power) yields a decrease in the lifetime of photoexcited charge carriers to τ ~ 2 ps. This effect is attributed to an increase in the capture cross section of trapping states for electrons with lower energies and to a decrease in the occupancy of traps at lower excitation densities.

36 MATERIALS SCIENCE↗
NASA NTRS2011

Examining Passenger Flow Choke Points at Airports Using Discrete Event Simulation

The movement of passengers through an airport quickly, safely, and efficiently is the main function of the various checkpoints (check-in, security. etc) found in airports. Human error combined with other breakdowns in the complex system of the airport can disrupt passenger flow through the airport leading to lengthy waiting times, missing luggage and missed flights. In this paper we present a model of passenger flow through an airport using discrete event simulation that will provide a closer look into the possible reasons for breakdowns and their implications for passenger flow. The simulation is based on data collected at Norfolk International Airport (ORF). The primary goal of this simulation is to present ways to optimize the work force to keep passenger flow smooth even during peak travel times and for emergency preparedness at ORF in case of adverse events. In this simulation we ran three different scenarios: real world, increased check-in stations, and multiple waiting lines. Increased check-in stations increased waiting time and instantaneous utilization. while the multiple waiting lines decreased both the waiting time and instantaneous utilization. This simulation was able to show how different changes affected the passenger flow through the airport.

Brown, Jeremy R.↗
NASA NTRS2009

Open-Loop Pitch Table Optimization for the Maximum Dynamic Pressure Orion Abort Flight Test

NASA has scheduled the retirement of the space shuttle orbiter fleet at the end of 2010. The Constellation program was created to develop the next generation of human spaceflight vehicles and launch vehicles, known as Orion and Ares respectively. The Orion vehicle is a return to the capsule configuration that was used in the Mercury, Gemini, and Apollo programs. This configuration allows for the inclusion of an abort system that safely removes the capsule from the booster in the event of a failure on launch. The Flight Test Office at NASA's Dryden Flight Research Center has been tasked with the flight testing of the abort system to ensure proper functionality and safety. The abort system will be tested in various scenarios to approximate the conditions encountered during an actual Orion launch. Every abort will have a closed-loop controller with an open-loop backup that will direct the vehicle during the abort. In order to provide the best fit for the desired total angle of attack profile with the open-loop pitch table, the table is tuned using simulated abort trajectories. A pitch table optimization program was created to tune the trajectories in an automated fashion. The program development was divided into three phases. Phase 1 used only the simulated nominal run to tune the open-loop pitch table. Phase 2 used the simulated nominal and three simulated off nominal runs to tune the open-loop pitch table. Phase 3 used the simulated nominal and sixteen simulated off nominal runs to tune the open-loop pitch table. The optimization program allowed for a quicker and more accurate fit to the desired profile as well as allowing for expanded resolution of the pitch table.

Stillwater, Ryan A.↗