SEARCH · Engineering Papers

Results for “adaptable tools”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,639 records · Page 91

All sources NASA NTRS DOE OSTI

DOE OSTI2016

SU-G-BRA-12: Development of An Intra-Fractional Motion Tracking and Dose Reconstruction System for Adaptive Stereotactic Body Radiation Therapy in High-Risk Prostate Cancer

Purpose: A clinical trial on stereotactic body radiation therapy (SBRT) for high-risk prostate cancer is undergoing at our institution. In addition to escalating dose to the prostate, we have increased dose to intra-prostatic lesions. Intra-fractional prostate motion deteriorates well planned radiation dose, especially for the small intra-prostatic lesions. To solve this problem, we have developed a motion tracking and 4D dose-reconstruction system to facilitate adaptive re-planning. Methods: Patients in the clinical trial were treated with VMAT using four arcs and 10 FFF beam. KV triggered x-ray projections were taken every 3 sec during delivery to acquire 2D projections of 3D anatomy at the direction orthogonal to the therapeutic beam. Each patient had three implanted prostate markers. Our developed system first determined 2D projection locations of these markers and then 3D prostate translation and rotation via 2D/3D registration of the markers. Using delivery log files, our GPU-based Monte Carlo tool (goMC) reconstructed dose corresponding to each triggered image. The calculated 4D dose distributions were further aggregated to yield the delivered dose. Results: We first tested each module in our system. MC dose engine were commissioned to our treatment planning system with dose difference of <0.5%. For motion tracking, 1789 kV projections from 7 patients were acquired. The 2D marker location error was <1 mm. For 3D motion tracking, root mean square (RMS) errors along LR, AP, and CC directions were 0.26mm, 0.36mm, and 0.01mm respectively in simulation studies and 1.99mm, 1.37mm, and 0.22mm in phantom studies. We also tested the entire system workflow. Our system was able to reconstruct delivered dose. Conclusion: We have developed a functional intra-fractional motion tracking and 4D dose re-construction system to support our clinical trial on adaptive high-risk prostate cancer SBRT. Comprehensive evaluations have shown the capability and accuracy of our system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Integrated Simulation Development and Decision Support Tool-Set for Utility Market and Distributed Solar Power Generation

This project will enable utilities to develop long-term strategic plans that integrate high levels of renewable energy generation, and to better plan power system operations under high renewable penetration. The program developed forecast data streams for decision support and effective integration of centralized and distributed solar power generation in utility operations. This toolset focused on real time simulation of distributed power generation within utility grids with the emphasis on potential applications in day ahead (market) and real time (reliability) utility operations. The project team developed and demonstrated methodologies for quantifying the impact of distributed solar generation on core utility operations, identified protocols for internal data communication requirements, and worked with utility personnel to adapt the new distributed generation (DG) forecasts seamlessly within existing Load and Generation procedures through a sophisticated DMS. This project supported the objectives of the SunShot Initiative and SUNRISE by enabling core utility operations to enhance their simulation capability to analyze and prepare for the impacts of high penetrations of solar on the power grid. The impact of high penetration solar PV on utility operations is not only limited to control centers, but across many core operations. Benefits of an enhanced DMS using state-of-the-art solar forecast data were demonstrated within this project and have had an immediate direct operational cost savings for Energy Marketing for Day Ahead generation commitments, Real Time Operations, Load Forecasting (at an aggregate system level for Day Ahead), Demand Response, Long term Planning (asset management), Distribution Operations, and core ancillary services as required for balancing and reliability. This provided power system operators with the necessary tools and processes to operate the grid in a reliable manner under high renewable penetration.

14 SOLAR ENERGY↗

DOE OSTI2014

Final Technical Report for Contract No. DE-EE0006332, "Integrated Simulation Development and Decision Support Tool-Set for Utility Market and Distributed Solar Power Generation"

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Extension of the ADAPT Framework for Multiple Simulators

Probabilistic Risk Assessment (PRA) uses fault tree/event tree analysis to evaluate the environmental impacts of nuclear power plants due to internal or external initiating events. In a fault tree, basic failure events are assembled using primarily And/Or logic to determine the combinations of failures that lead to failure of the system as a whole. An event tree is forward-facing, and begins with a single initiating event. From there the analysis considers what event may occur next, and branches out among the possible configurations of occurrence and non-occurrence of that event. This branching continues until end states are reached. End states may include a safe and stable configuration of the plant, or any number of differing failure states. Both fault and event trees require basic event probabilities as inputs to provide insight on the likelihood of different outcomes. PRA has been applied to nuclear power plants as an analysis tool since the 1975 Reactor Safety Study. In a traditional PRA, the order of events is prescribed by the analyst and each event is typically a binary aleatory uncertainty: occurrence or non-occurrence. Discrete dynamic event tree (DDET) analysis eliminates this subjectivity in ordering of events by using the output of a dynamic system model (simulator) to inform the branching. Branching conditions are triggered by the existence of a relevant plant state in the code, and therefore only occur as physically appropriate. The DDET approach also allows consideration of both epistemic and aleatory uncertainties on a phenomenologically and stochastically consistent platform. One limitation of the DDET driver codes developed to date (namely ADAPT, ADS, and MCDET ) is that the tree is driven by a single simulator. The goal of this work is a general multi-simulator driver. This limits the parameter space that may be explored in a single DDET to that which may be covered by a single simulator. In this work, the Analysis of Dynamic Accident Progression Trees (ADAPT) code is extended to support multiple arbitrary simulators. This capability will increase the depth and breadth of phenomena that may be analyzed in a single DDET. For example, a large-scale system code may pass the analysis to a more detailed code if certain conditions are met, in order to capture relevant phenomena that would be lost if only the system code were to be used. In addition, the time required to cover a given uncertainty space may be reduced by dynamically identifying and running only the situations that are appropriate given the plant conditions in each simulator. The use of multiple simulators is expected to further reduce subjectivity in PRA by enabling high fidelity modeling of reality. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Automated Energy-Dispersive X-ray Spectroscopy Analysis for Multi-Modal Few-Shot Learning

Scanning transmission electron microscopy (STEM) is a powerful tool that allows for the atomic-scale analysis of a materials’ structure, chemistry, and defect domains (Akers et al. 2021). The current generation of microscopes generate vast amounts of data, surpassing the limits of effective manual analysis traditionally performed by domain experts (Spurgeon et al. 2021). While recent strides in machine learning have significantly enhanced the processing of large and intricate datasets acquired through electron microscopy, the prevalent use of proprietary software packages for initial data collection poses a challenge. In many cases, these software packages act as a ‘black box’, constraining user functionality and hindering the output of data in a format that is conducive to seamless integration into machine learning models. This work addresses these challenges by adapting HyperSpy, an open-source Python library, for the analysis and quantification of raw energy dispersive spectroscopy (EDS) data acquired through STEM. The modified HyperSpy code successfully facilitates user-defined segmentation of the data, enabling the integration of atomic %, weight %, and raw EDS spectra for each segmented region into an existing few-shot machine learning model. While initial results reveal discrepancies in quantified atomic and weight percentages when compared to proprietary software, ongoing efforts aim to rectify this issue by refining the fit of the HyperSpy model to the EDS spectra. Overall, this research underscores the potential of open-source tools like HyperSpy to enhance the accessibility of analytical tools, fostering a transparent and user-friendly environment for seamlessly incorporating electron microscopy data into machine learning models.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Geothermal play fairway analysis, part 1: Example from the Snake River Plain, Idaho

The Snake River Plain (SRP) volcanic province overlies the track of the Yellowstone hotspot, a thermal anomaly that extends deep into the mantle. Most of the area is underlain by a basaltic volcanic province that overlies a mid-crustal intrusive complex, which in turn provides the long-term heat flux needed to sustain geothermal systems. Previous studies have identified several known geothermal resource areas within the SRP. For the geothermal study presented herein, our goals were to: (1) adapt the methodology of Play Fairway Analysis (PFA) for geothermal exploration to create a formal basis for its application to geothermal systems, (2) assemble relevant data for the SRP from publicly available and private sources, and (3) build a geothermal PFA model for the SRP and identify the most promising plays, using GIS-based software tools that are standard in the petroleum industry. The study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the SRP: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for heat source were heat flow, distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Reservoir and recharge permeability was inferred from the analysis of stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetics and gravity data. Data for cap or seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment maps for heat, permeability, and seal, which were combined to create a Composite Common Risk Segment map for all southern Idaho that reflects the risk associated with geothermal resource exploration and identifies favorable resource tracks. Our regional assessment indicated that undiscovered geothermal resources may be located in several areas of the SRP. Two of these areas, the western SRP and Camas Prairie, were selected for more detailed assessment, during which heat, permeability, and seal were evaluated using newly collected field data and smaller grid parameters to refine the location of potential resources. These higher resolution assessments illustrate the flexibility of our approach over a range of scales.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1991

Bar-Code-Scribing Tool

Proposed hand-held tool applies indelible bar code to small parts. Possible to identify parts for management of inventory without tags or labels. Microprocessor supplies bar-code data to impact-printer-like device. Device drives replaceable scribe, which cuts bar code on surface of part. Used to mark serially controlled parts for military and aerospace equipment. Also adapts for discrete marking of bulk items used in food and pharmaceutical processing.

Badinger, Michael A.↗

NASA NTRS1992

A decision support tool for synchronizing technology advances with strategic mission objectives

Successful accomplishment of the objectives of many long-range future missions in areas such as space systems, land-use planning, and natural resource management requires significant technology developments. This paper describes the development of a decision-support data-derived tool called MisTec for helping strategic planners to determine technology development alternatives and to synchronize the technology development schedules with the performance schedules of future long-term missions. Special attention is given to the operations, concept, design, and functional capabilities of the MisTec. The MisTec was initially designed for manned Mars mission, but can be adapted to support other high-technology long-range strategic planning situations, making it possible for a mission analyst, planner, or manager to describe a mission scenario, determine the technology alternatives for making the mission achievable, and to plan the R&D activity necessary to achieve the required technology advances.

Hornstein, Rhoda S.↗

NASA NTRS2003

Adaptation of SUBSTOR for controlled-environment potato production with elevated carbon dioxide

The SUBSTOR crop growth model was adapted for controlled-environment hydroponic production of potato (Solanum tuberosum L. cv. Norland) under elevated atmospheric carbon dioxide concentration. Adaptations included adjustment of input files to account for cultural differences between the field and controlled environments, calibration of genetic coefficients, and adjustment of crop parameters including radiation use efficiency. Source code modifications were also performed to account for the absorption of light reflected from the surface below the crop canopy, an increased leaf senescence rate, a carbon (mass) balance to the model, and to modify the response of crop growth rate to elevated atmospheric carbon dioxide concentration. Adaptations were primarily based on growth and phenological data obtained from growth chamber experiments at Rutgers University (New Brunswick, N.J.) and from the modeling literature. Modified-SUBSTOR predictions were compared with data from Kennedy Space Center's Biomass Production Chamber for verification. Results show that, with further development, modified-SUBSTOR will be a useful tool for analysis and optimization of potato growth in controlled environments.

NASA Discipline Life Support Systems↗

NASA NTRS2011

Estimation of Time-Varying Pilot Model Parameters

Human control behavior is rarely completely stationary over time due to fatigue or loss of attention. In addition, there are many control tasks for which human operators need to adapt their control strategy to vehicle dynamics that vary in time. In previous studies on the identification of time-varying pilot control behavior wavelets were used to estimate the time-varying frequency response functions. However, the estimation of time-varying pilot model parameters was not considered. Estimating these parameters can be a valuable tool for the quantification of different aspects of human time-varying manual control. This paper presents two methods for the estimation of time-varying pilot model parameters, a two-step method using wavelets and a windowed maximum likelihood estimation method. The methods are evaluated using simulations of a closed-loop control task with time-varying pilot equalization and vehicle dynamics. Simulations are performed with and without remnant. Both methods give accurate results when no pilot remnant is present. The wavelet transform is very sensitive to measurement noise, resulting in inaccurate parameter estimates when considerable pilot remnant is present. Maximum likelihood estimation is less sensitive to pilot remnant, but cannot detect fast changes in pilot control behavior.

Zaal, Peter M. T.↗

DOE OSTI2014

Employing the radiological and nuclear risk assessment methods (RNRAM) for assessing radiological and nuclear detection architectures

The United States Department of Homeland Security’s Domestic Nuclear Detection Office (DNDO) is charged with implementing domestic nuclear detection efforts to protect the U.S. from radiological and nuclear threats. DNDO is also responsible for coordinating the development of the Global Nuclear Detection Architecture (GNDA). DNDO utilizes a unique risk analysis tool to conduct a holistic risk assessment of the GNDA known as the Radiological and Nuclear Risk Assessment Methods (RNRAM). The capabilities of this tool will be used to support internal DNDO analyses and has also been used for other entities such as the International Atomic Energy Agency. The model uses a probabilistic risk assessment methodology and includes the ability to conduct a risk assessment of the effectiveness of layered architectures in the GNDA against an attack by an intelligent, adaptive adversary. This paper overviews the basic structure, capabilities, and use of RNRAM as used to assess different architectures and how various risk components are calculated through a series of interconnected modules. Also highlighted is flexible structure of RNRAM which can accommodate new modules in order to examine a variety of threat detection architectures and concepts.

Brigantic, Robert T.↗

DOE OSTI2020

Metrics and Analytical Frameworks for Valuing Energy Efficiency and Distributed Energy Resources in the Built Environment: Preprint

This paper summarizes efforts to develop new—and enhance existing—analytical frameworks and metrics to quantify the value that grid-interactive efficient homes with solar (GEB-solar homes) can provide. Industry is working to characterize and understand these capabilities and benefits, but existing analytical frameworks for evaluating energy efficiency (EE) are often siloed from those that evaluate distributed energy resources (DERs). Five metrics were adapted from an extensive literature review and applied to case studies of a modeled home in Riverside, California: ramp up/down, cover factor demand/supply, and curtailable load. Eight different technology scenarios were analyzed using a more tightly connected suite of building-to-grid models (BEopt, REopt, ReEDS, and PLEXOS). Additionally, an initial version of the Cambium tool was developed, characterizing the marginal prices and emissions from NREL’s 2018 Standard Scenarios. These grid costs were extended to a time-varying proxy retail rate and applied as part of a new grid alignment metric. In the results, a more integrated combination of GEB-solar technologies led to a higher cover factor demand—the percentage of gross home load covered by on-site solar—however, a benchmark was required to determine what range of cover factor was “best” for given grid conditions. To that end, a grid alignment cost metric was applied to the case study scenarios. The average cost to serve the net load of the home decreased from a median of ~$0.24/kWh to ~$0.10/kWh when the most integrated technology scenario was optimized towards the grid pricing proxy versus the time-of-use (TOU) rate.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2023

Directed Energy Deposition Process Modeling, Validation, and Process-Informed Optimization

The directed energy deposition (DED) process, one of the most popular additive manufacturing techniques in use today, involves various complex physical mechanisms that are not yet well understood. In this regard, computational tools show promise for elucidating the manufacturing process and enabling nondestructive performance evaluations of manufactured parts. To better control and optimize the DED process?thereby improving the manufactured product? the present work develops and demonstrates a novel artificial intelligence (AI)-based process control and optimization technique. Specifically, a geometry-free thermo-mechanical model with adaptive subdomain construction is developed to accurately capture the material?s thermo-mechanical response under cyclical reheating and high cooling rates [1]. The model is demonstrated and validated with experimental measurements, in light of various geometries and processing parameters. Moreover, based on this thermo-mechanical model, a physics-informed reduced-order model is developed to enable quick predictions of the temperature field at every time step. Furthermore, an AI-based controller is developed that can adapt to the ever-changing system states by automatically adjusting the manufacturing process parameters. This entire work is based on the open-source Multiphysics Object-Oriented Simulation Environment (MOOSE) [2] and its recent integration with Libtorch (the C++ frontend of PyTorch [3]). The combined development of the adaptive material deposition scheme, thermo-mechanical model, associated reduced-order model, and AI-based process controller carries great potential for improving advanced manufacturing processes.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The Wind Turbine Rotors of the Future: A Research Agenda from the Big Adaptive Rotor Project

This white paper outlines a roadmap to inform future research efforts around the wind turbine rotors of the future. The document is authored by researchers that have been working on the Big Adaptive Rotor (BAR) and related initiatives at the National Renewable Energy Laboratory and Sandia National Laboratories. The Wind Energy Technologies Office of the U.S. Department of Energy has been funding BAR since 2018. The learnings from BAR have been documented in dozens of publications. This new paper identifies unresolved critical challenges in wind turbine rotor technology and structures them in four large topic areas: 1. Predictive and validated numerical tools. 2. Design methods and standards. 3. Improvements in manufacturing. 4. Technological innovations. Although wind turbines have not changed in their core architecture, with the market almost entirely dominated by three-bladed upwind rotors, a vast amount of research has allowed to introduce technological innovations resulting in a reduction in levelized cost of energy that made wind competitive with all traditional sources of electricity. Focusing on the rotor, blades will keep following the trend of mass and stiffness reduction, will adopt segmentation, and will keep growing in length, both land-based and offshore. To support this continuous innovation, this white paper argues that research efforts in rotor technology at the national labs are best directed to the first three topic areas and to low technology-readiness-levels (1-3) innovations, leaving the task of implementing higher-TRL (4-9) technological innovations to industry stakeholders. This research roadmap is also well aligned with the upcoming roadmap defined during the International Energy Agency 109th Topical Expert Meeting on the Grand Challenges of Wind Energy.

17 WIND ENERGY↗

DOE OSTI2023

Systems Engineering of Rhodococcus opacus to Enable Production of Drop-in Fuels from Lignocellulose

Production of drop-in fuels from lignocellulose using Rhodococcus opacus PD630 (hereafter R. opacus) is a challenging goal. During the grant period we have pushed the field forward significantly in several areas of research. Towards the end goal of accelerating the adoption of R. opacus in biofuel production, during the grant period we have expanded the phenotypic characterization of R. opacus grown in single aromatic (model lignocellulosic) compounds or their mixtures, modeling the growth conditions in lignocellulosic biomass. Harnessing the power of adaptive evolution, we produced evolved R. opacus isolates with superior lignin valorization capabilities and identified differentially expressed genes and pathways after adaptation. We used next generation multi-omic techniques such as genomic, transcriptomic, and metabolomic analyses, to identify the catabolic pathways used by R. opacus to degrade aromatic compounds and funnel these degradation products into central metabolism, as well as the aromatic transport genes required for increased tolerance and utilization. Taking this information one step further, we identified endogenous transcription factors and regulatory mechanisms important for degradation of five model aromatic compounds. To accurately estimate R. opacus growth and consumption on model lignin compounds we pioneered the use of novel extraction procedures prior to GC-MS analysis. Alongside 13 C-metabolic flux analysis, we have elucidated the metabolic routes preferred by Rhodococcus opacus during aromatic compound degradation. Finally, we used in tandem lipidomics and high-resolution mass spectrometry to identify the modulation of mycolic acids and phospholipid membrane composition modification as a strategy for aromatic tolerance in R. opacus. Being a non-model organism, R. opacus lacks the breadth of tools and technical foundation which drive biofuel research in more well-understood microbes such as Escherichia coli. To reduce this burden for use, we designed and produced new tools for genomic manipulation and engineering in R. opacus. These engineering breakthroughs support efficient genomic editing, enabling gene overexpression, repression, and genetic alteration. Using these tools, we have generated synthetically engineered strains with increased lipogenesis and growth, both positive traits required for increased lignin valorization. Optimizing engineered strains for biofuel production from lignocellulose requires extremely sophisticated synthetic rewiring of metabolism. To facilitate systems-level reorganization of metabolism in R. opacus, we created a genome-scale model that accurately predicts metabolic flux and growth rates on the aromatic compound phenol. Lignin requires extensive pre-treatment before biological degradation by R. opacus. Towards an eventual goal of degrading real-world lignin, we developed new depolymerization processes to generate lignin breakdown products (LBP). We optimized LBP storage and composition analysis techniques, enabling accurate prediction of specific LBP compound integration into cell wall components. Overall, through the work funded by this grant we generated 20 manuscripts (17 published, 3 in review/preparation), methods for increased accuracy in metabolomics of aromatic compounds, multiple genetic tools for altering the R. opacus genome, genome scale models for predicting flux through metabolic pathways, as well as multi-omic data for community use. The work funded by this grant has increased the knowledge of aromatic degradation in bacteria and advanced our efforts to optimize R. opacus for lignin valorization.

09 BIOMASS FUELS↗

NASA NTRS2011

TAMU: Blueprint for A New Space Mission Operations System Paradigm

The Transferable, Adaptable, Modular and Upgradeable (TAMU) Flight Production Process (FPP) is a System of System (SOS) framework which cuts across multiple organizations and their associated facilities, that are, in the most general case, in geographically disperse locations, to develop the architecture and associated workflow processes of products for a broad range of flight projects. Further, TAMU FPP provides for the automatic execution and re-planning of the workflow processes as they become operational. This paper provides the blueprint for the TAMU FPP paradigm. This blueprint presents a complete, coherent technique, process and tool set that results in an infrastructure that can be used for full lifecycle design and decision making during the flight production process. Based on the many years of experience with the Space Shuttle Program (SSP) and the International Space Station (ISS), the currently cancelled Constellation Program which aimed on returning humans to the moon as a starting point, has been building a modern model-based Systems Engineering infrastructure to Re-engineer the FPP. This infrastructure uses a structured modeling and architecture development approach to optimize the system design thereby reducing the sustaining costs and increasing system efficiency, reliability, robustness and maintainability metrics. With the advent of the new vision for human space exploration, it is now necessary to further generalize this framework to take into consideration a broad range of missions and the participation of multiple organizations outside of the MOD; hence the Transferable, Adaptable, Modular and Upgradeable (TAMU) concept.

Ruszkowski, James T.↗

DOE OSTI2015

Simulation of Internal Combustion Engines with High-Performance Computing Tools

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly griddependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was developed and implemented by Senecal et al. (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against nonevaporating, evaporating, and reacting data from the literature. The current numerical study focusses on further demonstration of the grid-convergent modeling approach for simulating a single-cylinder Cat® compression ignition engine. The simulated injector is characterized with a nominal nozzle exit diameter of 259 μm. Simulations using various minimum grid sizes (ranging from 125 μm to 1000 μm) are compared for engine performance and emissions parameters of interest such as pressure, heat release rate, ignition delay, NOx, HC, and soot emissions. The peak cell-count for the highest resolution simulation was on the order of 34 million. These computationally expensive simulations were facilitated at a high-performance computing facility at Argonne National Laboratory. METIS load-balancing algorithm was developed and implemented in Converge code for the simulations. Scaling studies were also performed. The validity of previously recommended grid settings (ASME-ICEF2012-92043) for accuracy/runtime trade-off is further assessed. Efficacy of a simplified combustion model is also compared against a detailed chemical kinetics based combustion model.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2016

Aerodynamic Flight-Test Results for the Adaptive Compliant Trailing Edge

The aerodynamic effects of compliant flaps installed onto a modified Gulfstream III airplane were investigated. Analyses were performed prior to flight to predict the aerodynamic effects of the flap installation. Flight tests were conducted to gather both structural and aerodynamic data. The airplane was instrumented to collect vehicle aerodynamic data and wing pressure data. A leading-edge stagnation detection system was also installed. The data from these flights were analyzed and compared with predictions. The predictive tools compared well with flight data for small flap deflections, but differences between predictions and flight estimates were greater at larger deflections. This paper describes the methods used to examine the aerodynamics data from the flight tests and provides a discussion of the flight-test results in the areas of vehicle aerodynamics, wing sectional pressure coefficient profiles, and air data.

aerodynamics↗