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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Coaxial Rotor Flow Phenomena in Forward Flight

Coaxial rotors are nding use in advanced rotorcraft concepts. Combined with lift offset rotor technology, they offer a solution to the problems of dynamic stall and re-verse ow that often limit single rotor edgewise forward ight speeds. Lower tip speed means reduced high-speed impulsive noise. The need for an anti-torque tail rotor is eliminated, a major boon during operation in conned areas. However, the operation of two counter-rotating ro-tors in close proximity generates many possibilities for aerodynamic interactions between rotor blades, blades and vortices, and between vortices. The parameter de-sign space is very large, and requires efcient computations as well as basic experiments to explore important physics to determine performance, loads, and acoustics. Computations are done on the classic HarringtonDingeldein rotor test case from the 1950s using the ROTUNS Navier Stokes code. Two regimes are explored: very low advance ratio as a perturbation from hover, and high advance ratio. Flow eld properties from RotUNS are used with 2-D OVERFLOW computations to capture blade crossing effects including those of higher subsonic Mach numbers. Bladeblade and bladevortex intersection events are captured using a MatLab-based predictor.

Flow Phenomena↗
NASA NTRSDate not supplied

A Model-Based Framework for NASA Science Mission Formulation

This paper details an effort to implement NASA systems engineering standards and practices using a Digital System Model (DSM), which we also refer to as the system architecture model (SAM). Creating a SAM at the beginning of a design effort helps systems engineer identify errors, inconsistencies, and miscommunications as early as possible. Such issues can then be resolved before it becomes costly and requires significant rework to do so. A pre-formulation SAM also enables advanced trade studies at the earliest stage of concept development. This results in “win-wins” where architecture changes can reduce cost without decreasing effectiveness, or increase effectiveness without increasing cost. SAMs also streamline the creation of project documentation and facilitate superior dialogue between science, engineering, and management stakeholders. Common artifacts such as Master Equipment Lists (MELs), Science Traceability Matrices (STMs), and Mission Traceability Matrices (MTMs), can be automatically maintained through requirements and structural models in the SAM. Key performance parameters (and changes to them) can be tied to simulations in the SAM, allowing far more rapid and extensive trade space exploration. As will be shown in this work, these advantages have been realized for the first time in an actual NASA Goddard Space Flight Center (GSFC) pre-phase A and phase A concept study. Modelbased design tools have been developed in a general, modular manner to maximize reuse on future programs. Focus is placed on structural and requirements architecture modeling. Data is ingested into the SAM from existing discipline model outputs (e.g. MS Excel spreadsheets) and used to update a SysML structural model. A mission requirements model is created within the SAM, containing mission specific as well as standard GSFC mission requirements. With the linked requirements model and structural model, automated compliance checking will be performed as mission parameters evolve without the need for discipline engineers to work directly with the SAM in SysML.

MBSE↗
DOE OSTI2023

Probing the Electroweak Phase Transition with Exotic Higgs Decays

An essential goal of the Higgs physics program at the LHC and beyond is to explore the nature of theHiggs potential and shed light on the mechanism of electroweak symmetry breaking. An important classof models alter the thermal history of electroweak symmetry breaking from the predictions of the StandardModel (SM). This paper reviews the existence of a region of parameter space where a strong first-orderelectroweak phase transition is compatible with exotic decays of the SM-like Higgs boson. A dedicatedsearch for exotic Higgs decays can actively explore this framework at the Large Hadron Collider (LHC),while future exotic Higgs decay searches at the high-luminosity LHC and future Higgs factories will bevital to conclusively probe the scenario.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Duality and supersymmetry constraints on the weak gravity conjecture

Positivity bounds coming from consistency of UV scattering amplitudes are not always sufficient to prove the weak gravity conjecture for theories beyond Einstein-Maxwell. Additional ingredients about the UV may be necessary to exclude those regions of parameter space which are naïvely in conflict with the predictions of the weak gravity conjecture. In this paper we explore the consequences of imposing additional symmetries inherited from the UV theory on higher-derivative operators for Einstein-Maxwell-dilaton-axion theory. Using black hole thermodynamics, for a preserved SL(2, $\mathbb{R}$) symmetry we find that the weak gravity conjecture then does follow from positivity bounds. For a preserved O(d, d; $\mathbb{R}$) symmetry we find a simple condition on the two Wilson coefficients which ensures the positivity of corrections to the charge-to-mass ratio and that follows from the null energy condition alone. We find that imposing supersymmetry on top of either of these symmetries gives corrections which vanish identically, as expected for BPS states.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Graphene-Based Interconnect Exploration for Large SRAM Caches for Ultrascaled Technology Nodes

Graphene-based interconnects are considered promising replacements for traditional copper (Cu) interconnect due to their great electric properties. In this article, an interconnect-memory co- design framework is developed to efficiently optimize various graphene-based interconnect technologies. Four interconnect materials and heterogeneous design schemes are benchmarked against their traditional Cu counterparts to optimize large cache-level SRAM performance in terms of delay and energy per access, energy-delay product (EDP), and energy-delay-area product (EDAP). Here, a large design space exploration is performed based on realistic subarray design and device technology. Various interconnect- and array-level design parameters are studied to quantify the true potential of graphene-based wires for optimal memory performance.

42 ENGINEERING↗
DOE OSTI2022

FutureTense

Protective vaccines and reliable diagnostics are essential tools for controlling viral diseases. However, the efficacy of these tools can be diminished by mutations in viral genomes. The delay between the emergence of new viral strains and the redesign of vaccines and diagnostics allows for continued viral transmission. Is it possible to address this challenge by computationally predicting viral genome sequence evolution? Can we “future-proof” vaccines and diagnostics by targeting both current and anticipated future sequence variants? While predicting viral evolution is still an unsolved, “grand challenge” problem in biology, the large, and rapidly growing, number of SARS-CoV-2 genome sequences provide an opportunity to quantify the ability of machine learning to predict viral genome sequence evolution. Towards this end, we have developed a simple computational model for predicting viral evolution at the level of individual nucleotides. The key metric for quantifying the per-base, prediction accuracy for viral evolution is the Mann-Whitney U statistic (or, equivalently, the area under the receiver operator curve). Since the Mann-Whitney U statistic is not a differentiable function, existing deep leaning packages (like Pytorch and Keras/TensorFlow) are not useful, as they require that the accuracy metric/objective function be analytically differentiable with respect to the model parameters. To overcome this challenge, we have implemented custom software, “FutureTense”, that can train a machine learning model by maximizing the non-differentiable Mann-Whitney U statistic. This software trains a machine learning model by exploring along the direction of the discrete gradient of the Mann-Whitney U statistic in the model parameter space. Parallel computing and genome sequence-specific optimizations are used to accelerate model training. The resulting machine learning model learns the observed high C->U mutation rates in the SARS-CoV-2 genome (which are potentially induced by host defenses) and provides prediction accuracies that are significantly better than one would expect from random chance. While predicting viral evolution is still quite far from a solved problem, the surprising performance of this simple model gives hope that the accuracy of predicting viral genome evolution can be further increased by more sophisticated approaches.

Gans, Jason↗
DOE OSTI2025

Aerodynamic Sensitivities over Separable Shape Tensors

Here, we present a comprehensive aerodynamic sensitivity analysis of airfoil parameterization informed by separable shape tensors. This parameterization approach uniquely benefits the design process by isolating various well-studied shape characteristics, such as airfoil thickness, and providing a well-regulated low-dimensional parameter domain for aerodynamic designs. Exploring the aerodynamic sensitivities of this novel parameterization can provide valuable insights for more robust designs and future manufacturing efforts. We construct a data-driven parameter space of airfoils using principal geodesic analysis of separable shape tensors informed by a curated database containing almost 20,000 suitable engineering airfoils. Analyzing the shape reconstruction error and the maximum mean discrepancy between joint distributions of aerodynamic quantities, we study the dimensionality of the learned parameter space. This simple numerical experiment demonstrates a dramatic dimension reduction that retains design effectiveness and promotes regularity of the shape representations. Finally, we generate new airfoils and use the HAM2D Reynolds-averaged Navier–Stokes solver to predict lift, drag, and moment coefficients. We compute multiple sensitivity metrics to quantify and assert the consistency of parameter influence on the aerodynamic quantities. We also explore low-dimensional polynomial ridge approximations to motivate physical intuitions and offer explanations of the approximated sensitivities.

17 WIND ENERGY↗
NASA NTRSDate not supplied

Dynamic Adjustment of Model Parameters Using Assimilated Boundary Layer Height

The NASA Global Modeling and Assimilation Office is exploring the assimilation of boundary layer height (PBLH) derived from a variety of observations, including radiosondes, GNSS-RO, space- and ground-based lidar, and radar wind profilers. Previous work using the Goddard Earth Observing System (GEOS) has shown that within the lower troposphere, the representation of thermodynamic structure is strongly affected by model parameterizations. In assimilation experiments, parameterizations can compensate for analysis thermodynamic increments and reduce the observation information carried forward in time. To mitigate this effect, the present study explores a dynamic parameter adjustment based on the PBLH analysis field. The PBLH analysis increment is used to adjust model parameters that influence the depth of parameterized boundary layer mixing in both stable and convective regimes. Replay (nudging) experiments and cycled data assimilation experiments are used to evaluate the approach. The parameter adjustment is shown to reduce model compensation of the analysis thermodynamic tendencies and bring model parameterized mixing depths into closer agreement with observed PBLH.

Nathan Arnold↗
NASA NTRS2019

Bayesian Inference and the Effects of Varying Uncertainty Models in Charring Ablator Calibration and Uncertainty Quantification Problems

The Mars Science Laboratory (MSL) vehicle utilized a heat shield constructed from NASA’s Phenolic-Impregnated Carbon Ablator (PICA) material to protect the main structure from the high enthalpy environment encountered during hypersonic atmospheric entry. During the vehicle’s descent through Martian atmosphere, multiple thermocouples embedded within the heat shield captured in-depth material temperature data that allow for studies to be conducted on current material response reconstruction tools. In the present work, material temperature data obtained from thermocouples within the MISP-4 plug (MEDLI (Mars Science Laboratory Entry, Descent, and Landing Instrument) Integrated Sensor Plug) are utilized in the calibration of Theoretical Ablative Composite for Open Testing (TACOT) model parameters in conjunction with NASA’s Porous material Analysis Toolbox (PATO) through Bayesian inference where uncertainty due to parametric, modeling, and experimental sources is simultaneously quantified. Prior to the study, a sensitivity analysis is performed through computation of the robust Sobol indices in an effort to study the relationship between input space and model response and to reduce the dimensionality of the statistical inverse problem. The Bayesian inference methodology necessitates an a-priori choice to be made for the uncertainty model for which numerous possibilities are available. Across most works, however, only basic additive or multiplicative models are utilized with pre-defined magnitudes of uncertainty based on a-priori knowledge or to-be-calibrated multipliers of static covariance matrix structures. The present effort explores the effects of informed uncertainty models, ones with temporal dependence that are simultaneously calibrated through Bayesian inference, on calibrated results for parameters that make up the uncertain input space.

Sensitivity Analysis↗
DOE OSTI2022

Exploring the phenomenology of weak adjoint scalars in minimal R -symmetric models

We examine the phenomenology of the scalar fields in weak and Higgs sectors of minimal R-symmetric models, in particular the ‘swino’ and ‘sbino’, the scalar partners to the chiral fields that marry the electroweak gauge bosons in Dirac gaugino models. These fields are in adjoint representations of SU(2) and U(1) and have both CP-even and CP-odd components. The interactions of these new states are summarized, and decay widths are computed analytically to one loop order. We discuss the tree level contributions of these new states to the mass spectrum of MSSM sfermions. We also explore production cross sections and decay signatures at colliders for several chosen benchmarks. We find that large regions of parameter space are unconstrained by present collider data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Design of novel refractory equiatomic multi-principal elemental alloys based on Mo-Nb-Ti system for Gen IV reactor applications

Excellent irradiation damage resistance demonstrated by multi-principal elemental alloys (MPEAs) has sparked significant interest among researchers, prompting exploration into their vast compositional space, to validate their suitability for nuclear applications. A combined approach of thermodynamic and empirical parameters calculations alongside CALPHAD (CALculation of PHAse Diagrams) for phase formation predictions enable high-throughput material selection for sophisticated applications like nuclear, overcoming laborious and time-consuming experiments. Key thermodynamic and empirical parameters for eight novel equiatomic MPEAs, based on seven low thermal neutron cross section refractory elements, for predicting phase formation were calculated, and equilibrium and non-equilibrium simulations in CALPHAD were employed to comprehensively model the systems. Pseudo binary phase diagram simulations showed that Zr, V or equiatomic CrV additions to the base MoNbTi alloy (MoNbTi-Zr, MoNbTi-V and MoNbTi-CrV alloys) favor the formation of isomorphous body-centered cubic (BCC) phase at high temperatures, while Cr, Al, equiatomic ZrV, or equiatomic CrAl additions (MoNbTi-Cr, MoNbTi-Al, MoNbTi-ZrV or MoNbTi-CrAl alloys) limit the solubility of them. Equilibrium CALPHAD simulations at 750 oC were consistent with XRD results on MoNbTi, MoNbTiZr and MoNbTiCr alloys, and partially for others. Notably, elemental segregation observed in the backscattered electron (BSE) scanning electron microscopy (SEM) images of the alloys was accurately simulated through non-equilibrium Scheil solidification calculations in CALPHAD, further verified by experiments. The precipitation of TiCr2 Laves phase in Cr containing MoNbTiCr and MoNbTiCrAl was accurately predicted while discrepancies were noted in MoNbTiCrV. The equilibrium simulations also provided insights into phase compositions at specific temperatures offering a pathway for tailoring the desired microstructure and properties of these systems. Empirical parameters calculations successfully predicted random solid solution in the base MoNbTi alloy, and with an exception in MoNbTiV and MoNbTiAl, predicted intermetallic precipitation in the rest, especially, Laves phase precipitation in Cr containing alloys.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

Investigation of low-energy particle remnants in high-energy collisions at the LHC with a skipper-CCD detector

We deployed the Mobile Skipper Testing Apparatus ∼33 m away from the Compact Muon Solenoid collision point, the first skipper-CCD detector probing low-energy particles produced in high-energy collisions at the Large Hadron Collider. In this work, we search for beam-related events using data collected in 2024 during beam-on and beam-off periods. The dataset corresponds to integrated luminosities of 113.3 fb −1 and 1.54 nb −1 for the proton-proton and Pb-Pb collision periods, respectively. We report observed event rates in a model-independent framework across two ionization regions: ≤ 20⁢𝑒 − and > 20⁢𝑒 − . For the low-energy region, we perform a likelihood analysis to test the null hypothesis of no beam-correlated signal. We found no significant correlation during proton-proton and Pb-Pb collisions. For the high-energy region, we present the energy spectra for both collision periods and compare event rates for images with and without luminosity. We observe a slight increase in the event rate following the Pb-Pb collisions, coinciding with a rise in the single-electron rate, which will be investigated in future work. Using the low-energy proton-proton results, we place 95% confidence level constraints on the mass-millicharge parameter space of millicharged particles. Overall, the results in this work demonstrate the viability of skipper-CCD technology to explore new physics at high-energy colliders and motivate future searches with more massive detectors.

Cervantes-Vergara, Brenda A. [Fermi National Accel↗
DOE OSTI2024

Dissipative Dark Substructure: The Consequences of Atomic Dark Matter on Milky Way Analog Subhalos

Using cosmological hydrodynamical zoom-in simulations, we explore the properties of subhalos in Milky Way analogs that contain a subcomponent of atomic dark matter (ADM). ADM differs from cold dark matter (CDM) due to the presence of self-interactions that lead to energy dissipation, analogous to standard model baryons. This model can arise in dark sectors that are natural and theoretically motivated extensions to the standard model. The simulations used in this work were carried out using GIZMO and utilize the FIRE-2 galaxy formation physics in the standard model baryonic sector. For the parameter points we consider, the ADM gas cools efficiently, allowing it to collapse to the center of subhalos. This increases a subhalo's central density and affects its orbit, with more subhalos surviving small pericentric passages. The subset of subhalos that host satellite galaxies have cuspier density profiles and smaller stellar half-mass radii relative to CDM. The entire population of dwarf galaxies produced in the ADM simulations is more compact than those seen in CDM simulations, unable to reproduce the entire diversity of observed dwarf galaxy structures. Additionally, we also identify a population of highly compact subhalos that consist nearly entirely of ADM and form in the central region of the host, where they can leave distinctive imprints in the baryonic disk. This work presents the first detailed exploration of subhalo properties in a strongly dissipative dark matter scenario, providing intuition for how other regions of ADM parameter space, as well as other dark sector models, would impact galactic-scale observables.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Complementarity between neutrinoless double beta decay and collider searches for heavy neutrinos in composite-fermion models

Composite-fermion models predict excited quarks and leptons with mass scales which can potentially be observed at high-energy colliders like the LHC; the most recent exclusion limits from the CMS and ATLAS Collaborations corner excited-fermion masses and the compositeness scale to the multi-TeV range. At the same time, hypothetical composite Majorana neutrinos would lead to observable effects in neutrinoless double beta decay ($0\nu \beta \beta$) experiments. In this work, we show that the current composite-neutrino exclusion limit $M_N>4.6$ TeV, as extracted from direct searches at the LHC, can indeed be further improved to $M_N>8.8$ TeV by including the bound on the nuclear transition $^{136}$Xe $\to ^{136}$Ba + $2e^-$. Looking ahead, the forthcoming HL-LHC will allow probing a larger portion of the parameter-space, nevertheless, it will still benefit from the complementary limit provided by $0 \nu \beta \beta$ future detectors to explore composite-neutrino masses up to $12.6$ TeV.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Enhancing the activity of Fe-N-C oxygen reduction reaction electrocatalysts by high-throughput exploration of synthesis parameters

Here, the most active class of platinum group metal-free (PGM-free) oxygen reduction reaction (ORR) electrocatalysts in acidic electrolytes are those synthesized by heat treatment of iron, carbon, nitrogen precursors (Fe-N-C). Due to the large number of possible precursor compounds, a small fraction of the synthesis variable space has been explored. Correlation of synthesis variables with Fe speciation and ORR activity has been limited. In this work, an automation platform and a multi-port ball-milling were utilized to evaluate the effects of synthesis variables, such as identity of iron precursor, iron loading, and carbon and nitrogen sources on the ORR activity of iron -nitrogen-carbon catalysts in acidic electrolyte. The ORR activity is correlated with catalyst Fe speciation determined using Fe K-edge X-ray absorption spectroscopy (XAFS).

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

Searching for Dark Matter and Dark Sectors

The overall objective of this project was to explore new particle and forces beyond those known in the Standard Model of particle physics. In particular, the research focused on low-mass dark sectors, especially below the mass of the proton (about 1 GeV). Dark sectors, which are particles that are not charged directly under the Standard Model forces, are an exciting theoretical possibility that could address several shortcomings of the Standard Model, including the existence of dark matter. The research accomplished the following: (1) Developed novel techniques and strategies for direct detection of sub-GeV dark matter, and provided the theoretical support for ongoing direct-detection efforts. (2) Investigated new models of dark sectors, and derived new constraints from cosmology, astrophysics, and terrestrial experiments. This includes an investigation of models with dark-matter self-interactions that could help form supermassive black holes in the early Universe. (3) Explored new opportunities for dark-sector searches with existing and planned experiments. This includes providing the theoretical support to two experiments I helped propose, the A' Experiment (APEX) and the Heavy Photon Search (HPS), both at Jefferson Lab. The proposed research program significantly advanced the search for new particles and new forces. The outcomes and impact were: (1) It further enabled direct-detection experiments to probe vast regions of uncharted dark matter parameter space down to MeV masses, about three orders of magnitude lower in mass than traditional direct-detection strategies. (2) It exposed gaps in current experimental exploration, prompting new ideas for how these might be probed, and revealed how dark sectors might impact structure in our Universe. (3) It increased the discovery potential of dark sectors at three experimental frontiers–the energy, intensity, and cosmic frontiers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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.↗
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.↗