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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Sensitivity of the MAR Regional Climate Model Snowpack to the Parameterization of the Assimilation of Satellite-Derived Wet-Snow Masks on the Antarctic Peninsula

Both regional climate models (RCMs) and remote sensing (RS) data are essential tools in understanding the response of polar regions to climate change. RCMs can simulate how certain climate variables, such as surface melt, runoff and snowfall, are likely to change in response to different climate scenarios but are subject to biases and errors. RS data can assist in reducing and quantifying model uncertainties by providing indirect observations of the modeled variables on the present climate. In this work, we improve on an existing scheme to assimilate RS wet snow occurrence data with the “Modèle Atmosphérique Régional” (MAR) RCM and investigate the sensitivity of the RCM to the parameters of the scheme. The assimilation is performed by nudging the MAR snowpack temperature to match the presence of liquid water observed by satellites. The sensitivity of the assimilation method is tested by modifying parameters such as the depth to which the MAR snowpack is warmed or cooled, the quantity of water required to qualify a MAR pixel as “wet” (0.1 % or 0.2 % of the snowpack mass being water), and assimilating different RS datasets. Data assimilation is carried out on the Antarctic Peninsula for the 2019–2021 period. The results show an increase in meltwater production (+66.7 % on average, or +95 Gt), along with a small decrease in surface mass balance (SMB) (−4.5 % on average, or −20 Gt) for the 2019–2020 melt season after assimilation. The model is sensitive to the tested parameters, albeit with varying orders of magnitude. The prescribed warming depth has a larger impact on the resulting surface melt production than the liquid water content (LWC) threshold due to strong refreezing occurring within the top layers of the snowpack. The values tested for the LWC threshold are lower than the LWC for typical melt days (approximately 1.2 %) and impact results mainly at the beginning and end of the melting period. The assimilation method will allow for the estimation of uncertainty in MAR meltwater production and will enable the identification of potential issues in modeling near-surface snowpack processes, paving the way for more accurate simulations of snow processes in model projections.

regional climate models↗
NASA NTRSDate not supplied

Fluid-Structure Interaction Simulations Of Supersonic Parachute Inflation: Model Sensitivities

Fluid-structure interaction simulations have the potential to accelerate the certification process of parachute systems for interplanetary robotic exploration missions while helping to mitigate risks by covering a wider range of scenarios than can be flight tested. The Launch, Ascent, and Vehicle Aerodynamics team is working toward actualizing this potential by further developing the capability to perform such fluid-structure interaction simulations and validating it with the best supersonic parachute flight test data available. We perform several simulations of the Advanced Supersonic Parachute Inflation Research Experiments’ third flight test (SR03) where we independently vary several parameters to determine their sensitivity on the predicted drag. We investigate sensitivity of the drag to the added mass of the confluence fitting, to the effective porosity, to how the gore seams are modeled, to viscous effects, and to deceleration. Modeling the added mass of the confluence fitting has virtually no impact on the drag, but is worthwhile to include to better match the overall system’s mass, which is important for the simulations with deceleration. Parachute drag is only marginally sensitive to porosity and viscous effects. Maximum parachute drag is most sensitive to how the gore seams are modeled, causing a nearly 20% drop when they are modeled as stiffer radials, and to deceleration, causing an additional 10% reduction. Results show improved agreement with flight test measurements when the FSI model is closest to the as-built configuration and physics experienced in flight: with the confluence fitting, viscous effects, the gore seams modeled as stiff radials, and including deceleration.

ESM↗
DOE OSTI2025

Improved Bayesian regularization of inverse problems in vibrations and acoustics using noise-only measurements

Here, this paper studies Tikhonov regularization (ridge regression) parameter selection for problems in vibrations and acoustics. The selection method is based on a popular Bayesian method, but it incorporates measurements of sensor noise. The regularization parameter is closely related to the ratio of system input energy to noise energy, so noise measurements inform the inference procedure and improve parameter identification. In cases where standard Bayesian regularization identifies zero as the optimal regularization parameter, noise measurements guarantee a unique nonzero optimum. Sufficient theoretical criteria are developed for this guarantee. The method is verified in even-determined and under-determined configurations in an acoustic source localization simulation and a vibration load identification experiment. It is shown to yield significant improvements over existing empirical Bayesian regularization. Improvements are larger in the even-determined case and smaller in the under-determined case, wherein the inverse solution is less sensitive to the regularization parameter.

42 ENGINEERING↗
DOE OSTI2024

Refining water and carbon fluxes modeling in terrestrial ecosystems via plant hydraulics integration

Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. Here, in this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model’s relative sensitivity to newly introduced leaf-, stem and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters

High-resolution space-based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise ratio. Different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.

Intrinsic dimensionality↗
NASA NTRS1993

Characterization and durability testing of plasma-sprayed zirconia-yttria and hafnia-yttria thermal barrier coatings. Part 1: Effect of spray parameters on the performance of several lots of partially stabilized zirconia-yttria powder

Initial experiments conducted on thermal barrier coatings prepared in the newly upgraded research plasma spray facility and the burner rig test facilities are discussed. Part 1 discusses experiments which establish the spray parameters for three baseline zirconia-yttria coatings. The quality of five similar coating lots was judged primarily by their response to burner rig exposure supplemented by data from other sources such as specimen characterizations and thermal diffusivity measurements. After allowing for burner rig variability, although there appears to be an optimum density (i.e., optimum microstructure) for maximum burner rig life, the distribution tends to be rather broad about the maximum. In Part 2, new hafnia-yttria-based coatings were evaluated against both baseline and alternate zirconia-yttria coatings. The hafnia-yttria coatings and the zirconia-yttria coatings that were prepared by an alternate powder vendor were very sensitive to plasma spray parameters, in that high-quality coatings were only obtained when certain parameters were employed. The reasons for this important observation are not understood. Also not understood is that the first of two replicate specimens sprayed for Part 1 consistently performed better than the second specimen. Subsequent experiments did not display this spray order affect, possibly because a chiller was installed in the torch cooling water circuit. Also, large changes in coating density were observed after switching to a new lot of electrodes. Analyses of these findings were made possible, in part, because of the development of a sensitive density measurement technique described herein in detail. The measured thermal diffusivities did not display the expected strong relationship with porosity. This surprising result was believed to have been caused by increased microcracking of the denser coatings on the stainless steel substrates.

Miller, Robert A.↗
NASA NTRS1981

Multiobjective insensitive design of airplane control systems with uncertain parameters

A multiobjective computer-aided design algorithm has been developed which minimizes the sensitivity of the design objectives to uncertainties in system parameters. The more important uncertain parameters are described by a gaussian random vector with known covariance matrix, and a vector sensitivity objective function is defined as the probabilities that the design objectives will violate specified requirements constraints. Control system parameters are found which minimize the sensitivity vector in a Pareto-optimal sense, using constrained minimization algorithms. Example results are shown for lateral stability augmentation system (SAS) design for three Shuttle flight conditions.

Schy, A. A.↗
NASA NTRS1978

Instrumental variables algorithm for modal parameter identification in flutter testing

The paper is concerned with the task of estimating modal parameters from system response measurement in aircraft flutter testing. A frequency-domain derivation of an instrumental-variables algorithm is presented for a linear time-invariant dynamic system of order n. Basically, this algorithm fits a set of poles and zeros to the measured transfer function. An illustrative example is provided regarding the application of the algorithm to aeroelasticity testing. It is shown that the algorithm can be implemented for on-line data reduction with a microcomputer-based analysis system. By using instrumental variables the sensitivity of the modal parameter estimates to noise in the system-response measurements is reduced greatly. The algorithm is expected to be a powerful and valuable tool for on-line estimation of modal parameters in flutter testing and should be useful in control system and structural dynamics tests.

Johnson, W.↗
NASA NTRSDate not supplied

PACE OCI Polarization Sensitivity Based on Pre-launch Testing

The Ocean Color Instrument (OCI) is a sensor on the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in early 2024. OCI is a grating spectrometer with hyperspectral coverage from the ultraviolet (about 310 nm) to near-infrared (about 900 nm), with additional filtered channels in the short-wave infrared (940 nm – 2260 nm). This instrument will provide ocean color science data to continue the data sets collected by heritage sensors MODIS, SeaWiFs, and VIIRS, but with increased spectral coverage and improved accuracy. In order to achieve the high levels of accuracy demanded by the science community, a rigorous ground test program was conducted to calibrate the instrument and ensure that the calibration can be transferred to on-orbit operations. Some calibration parameters can only be measured during pre-launch testing; one such parameter is the polarization sensitivity. Polarization testing measured the Mueller matrix components needed to determine the polarization sensitivity for all spectral bands for a series of telescope scan angles covering the expected on-orbit scan range. Results indicate that the sensitivity is below 0.6 % except at the shortest wavelengths (less than 340 nm) and was characterized to better than 0.1 % above 340 nm. This indicates that any polarized scenes measured on orbit can be corrected for with a high degree of confidence.

PACE↗
NASA NTRSDate not supplied

PACE OCI Polarization Sensitivity Based on Pre-launch Testing

The Ocean Color Instrument (OCI) is a sensor on the upcoming Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, scheduled for launch in early 2024. OCI is a grating spectrometer with hyperspectral coverage from the ultraviolet (about 310 nm) to near-infrared (about 900 nm), with additional filtered channels in the short-wave infrared (940 nm – 2260 nm). This instrument will provide ocean color science data to continue the data sets collected by heritage sensors MODIS, SeaWiFs, and VIIRS, but with increased spectral coverage and improved accuracy. In order to achieve the high levels of accuracy demanded by the science community, a rigorous ground test program was conducted to calibrate the instrument and ensure that the calibration can be transferred to on-orbit operations. Some calibration parameters can only be measured during pre-launch testing; one such parameter is the polarization sensitivity. Polarization testing measured the Mueller matrix components needed to determine the polarization sensitivity for all spectral bands for a series of telescope scan angles covering the expected on-orbit scan range. Results indicate that the sensitivity is below 0.6 % except at the shortest wavelengths (less than 340 nm) and was characterized to better than 0.1 % above 340 nm. This indicates that any polarized scenes measured on orbit can be corrected for with a high degree of confidence.

PACE↗
NASA NTRS1995

Grid Sensitivity and Aerodynamic Optimization of Generic Airfoils

An algorithm is developed to obtain the grid sensitivity with respect to design parameters for aerodynamic optimization. The procedure is advocating a novel (geometrical) parameterization using spline functions such as NURBS (Non-Uniform Rational B- Splines) for defining the airfoil geometry. An interactive algebraic grid generation technique is employed to generate C-type grids around airfoils. The grid sensitivity of the domain with respect to geometric design parameters has been obtained by direct differentiation of the grid equations. A hybrid approach is proposed for more geometrically complex configurations such as a wing or fuselage. The aerodynamic sensitivity coefficients are obtained by direct differentiation of the compressible two-dimensional thin-layer Navier-Stokes equations. An optimization package has been introduced into the algorithm in order to optimize the airfoil surface. Results demonstrate a substantially improved design due to maximized lift/drag ratio of the airfoil.

Sadrehaghighi, Ideen↗
NASA NTRS1994

Utilitarian models of the solar nebula

Models of the primitive solar nebula based on a combination of theory, observations of T Tauri stars, and global conservation laws are presented. The models describe the motions of nebular gas, mixing of interstellar material during the formation of the nebula, and evolution of thermal structure in terms of several characteristic parameters. The parameters describe key aspects of the protosolar cloud (its rotation rate and collapse rate) and the nebula (its mass relative to the Sun, decay time, and density distribution). For most applications, the models are heuristic rather than predicted. Their purpose is to provide a realistic context for the interpretation of solar system data, and to distinquish those nebular characteristics that can be specified with confidence, independently of the assumtions of particular models, form those that are poorly constrained. It is demonstrated that nebular gas typically experienced large radial excursions during the evolution of the nebula and that both inward and outward mean radial velocities on the order of meters per second occured in the terrestrial planet region, with inward velocities predominant for most ofthe evolution. However, the time history of disk size, surface density, and radial velocities are sensitive to the total angular momentun of the protosolar cloud, which cannot be constrained by purely theoretical considerations.It is shown that a certain amount of 'formational' mixing of interstellar material was an inevitable consequenc of nebular mass and angular momentum transport during protostellar collapse, regardless of the specific transport mechanisms invloved. Even if the protosolar cloud was initially homogeneous, this mixing was important because it had the effect of mingling presolar material that had experienced different degrees of thermal processing during collapse and passage through the accertion shock. Nebular thermal structure is less sensitive to poorly constrained parameters than is dynamical history. A simple criterion is derived for the condition that silicate grains are evaporated at midplane, and it is argued that this condition was probably fulfilled early in nebular history. Cooling of a hot nebula due tocoagulation of dust and consequent local reduction of optical depth is examined, and it is shown how such a process leads naturally to an enrichment of rock-forming elements in the gas phase.

Cassen, Patrick↗
NASA NTRS1991

Temperature sensitivity of differential absorption lidar measurements of water vapor in the 720-nm region

Recently measured properties of water vapor (H2O) absorption lines have been used in calculations to evalute the temperature sensitivity of differential absorption lidar (Dial) H2O measurements. This paper estimates the temperature sensitivity of H2O lines in the 717-733-nm region for both H2O mixing ratio and number density measurements, and discusses the influence of the H2O line ground state energies E-double-prime, the H2O absorption linewidths, the linewidth temperature dependence parameter, and the atmospheric temperature and pressure variations with altitude and location on the temperature sensitivity calculations. Line parameters and temperature sensitivity calculations for 67 H2O lines in the 720-nm band are given which can be directly used in field experiments. Water vapor lines with E-double-prime values in the 100-300/cm range were found to be optimum for Dial measurements of H2O number densities, while E-double-prime values in the 250-500/cm range were found to be optimum for H2O mixing ratio measurements.

Browell, Edward V.↗
DOE OSTI2026

Peridynamic modeling of cementitious materials for nuclear waste management

Radioactive and hazardous waste generated from fuel processing plants, nuclear reactors, and hospitals, requires effective management strategies. Cementitious materials are widely applied for these needs, serving as structural materials, reactive barriers, or waste forms. In these applications cracking poses a significant risk to performance, driven by inconsistent shrinkage of the components and the varying strength and permeability of their interfaces. Traditional modeling approaches face challenges in representing the complex fracture behavior of cementitious materials due to the reliance on spatial derivatives and difficulties with mesh generation. Here, to overcome these limitations, we employ peridynamics, a novel continuum mechanics formulation that uses integrals to describe mechanical equilibrium, avoiding discontinuities associated with traditional methods. Through incorporation of a bi-linear softening model and quasistatics, an experimentally validated model for Portland cement concrete samples was created. Mechanical parameters, including compressive strength and elastic modulus were validated and variation due to aggregate packing was evaluated. Additionally, sensitivity analysis of the peridynamic parameters to the bulk material properties was established. The results lay the groundwork for evaluating the impact of unique conditions of cementitious waste forms that can be assessed to improve the reliability of waste management strategies.

Aggregates↗
NASA NTRS1994

GA-optimization for rapid prototype system demonstration

An application of the Genetic Algorithm (GA) is discussed. A novel scheme of Hierarchical GA was developed to solve complicated engineering problems which require optimization of a large number of parameters with high precision. High level GAs search for few parameters which are much more sensitive to the system performance. Low level GAs search in more detail and employ a greater number of parameters for further optimization. Therefore, the complexity of the search is decreased and the computing resources are used more efficiently.

Kim, Jinwoo↗
NASA NTRS1972

Combined loading criterial influence on structural performance

An investigation was conducted to determine the influence of combined loading criteria on the space shuttle structural performance. The study consisted of four primary phases: Phase (1) The determination of the sensitivity of structural weight to various loading parameters associated with the space shuttle. Phase (2) The determination of the sensitivity of structural weight to various levels of loading parameter variability and probability. Phase (3) The determination of shuttle mission loading parameters variability and probability as a function of design evolution and the identification of those loading parameters where inadequate data exists. Phase (4) The determination of rational methods of combining both deterministic time varying and probabilistic loading parameters to provide realistic design criteria. The study results are presented.

Kuchta, B. J.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases.1 Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission.2 Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an Advanced Design Methods (ADM) based approach. This approach applies the concepts of Design of Experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development e ort. In order to t a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of Ground Rules and Assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated \what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are t to the outputs of each environment and are integrated via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any \what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will have the potential to lower LCC of launch vehicle designs originating from ACO.

Zwack, Mathew R.↗
NASA NTRS2016

Application of Design of Experiments and Surrogate Modeling within the NASA Advanced Concepts Office, Earth-to-Orbit Design Process

Decisions made during early conceptual design have a large impact upon the expected life-cycle cost (LCC) of a new program. It is widely accepted that up to 80% of such cost is committed during these early design phases. Therefore, to help minimize LCC, decisions made during conceptual design must be based upon as much information as possible. To aid in the decision making for new launch vehicle programs, the Advanced Concepts Office (ACO) at NASA Marshall Space Flight Center (MSFC) provides rapid turnaround pre-phase A and phase A concept definition studies. The ACO team utilizes a proven set of tools to provide customers with a full vehicle mass breakdown to tertiary subsystems, preliminary structural sizing based upon worst-case flight loads, and trajectory optimization to quantify integrated vehicle performance for a given mission. Although the team provides rapid turnaround for single vehicle concepts, the scope of the trade space can be limited due to analyst availability and the manpower requirements for manual execution of the analysis tools. In order to enable exploration of a broader design space, the ACO team has implemented an advanced design methods (ADM) based approach. This approach applies the concepts of design of experiments (DOE) and surrogate modeling to more exhaustively explore the trade space and provide the customer with additional design information to inform decision making. This paper will first discuss the automation of the ACO tool set, which represents a majority of the development effort. In order to fit a surrogate model within tolerable error bounds a number of DOE cases are needed. This number will scale with the number of variable parameters desired and the complexity of the system's response to those variables. For all but the smallest design spaces, the number of cases required cannot be produced within an acceptable timeframe using a manual process. Therefore, automation of the tools was a key enabler for the successful application of an ADM approach to an ACO design study. Following the overview of the tool set automation, an example problem will be given to illustrate the implementation of the ADM approach. The example problem will first cover the inclusion of ground rules and assumptions (GR&A) for a study. The GR&A are very important to the study as they determine the constraints within which a trade study can be conducted. These trades must ultimately reconcile with the customer's desired output and any anticipated "what if" questions. The example problem will then illustrate the setup and execution of a DOE through the automated ACO tools. This process is accomplished more efficiently in this work by splitting the tools into two separate environments. The first environment encompasses the structural optimization and mass estimation tools, while the second is focused on trajectory optimization. Surrogate models are fit to the outputs of each environment and are "integrated" via connection of the surrogate equations. Throughout this process, checks are implemented to compare the output of the surrogates to the output of manually run cases to ensure that the error of the final surrogates is at an acceptable level. The conclusion of the example problem demonstrates the utility of the ADM based approach. Using surrogate models gives the ACO team the ability to visualize vehicle sensitivities to various design parameters and identify regions of interest within the design space. The ADM approach can thus be used to inform concept down selection and isolate promising vehicle configurations to be explored in more detail through the manual design process. In addition it provides the customer with an almost instantaneous turnaround on any ''what if" questions that may arise within the bounds of the surrogate model. This approach ultimately expands the ability of the ACO team to provide its customer with broad and rapid turnaround trade studies for launch vehicle conceptual design. The ability to identify a selection of designs which can meet the customer requirements will help ensure lower LCC of launch vehicle designs originating from ACO.

Zwack, Mathew R.↗