Learning turbulence with machines: data-driven closures and generative models
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general physics↗
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Diffusion model approach to simulating electron-proton scattering events
Generative artificial intelligence is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to experiments like the Continuous Electron Beam Accelerator Facility and the future Electron-Ion Collider (EIC). These experiments play a critical role in advancing our understanding of nucleons and nuclei in terms of quark and gluon degrees of freedom. The use of generative models for simulating collider events faces several challenges such as the sparsity of the data, the presence of global or eventwide constraints, and steeply falling particle distributions. In this work, we focus on the implementation of diffusion models for the simulation of electron-proton scattering events at EIC energies. Our results demonstrate that diffusion models can reproduce relevant observables such as momentum distributions and correlations of particles, momentum sum rules, and the leading electron kinematics, all of which are of particular interest in electron-proton collisions. Although the sampling process is relatively slow compared to other machine-learning architectures, we find diffusion models can generate high-quality samples. We foresee various applications of our work including inference for nuclear structure, interpretable generative machine learning, and searches of physics beyond the Standard Model. Published by the American Physical Society 2024
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Usage of ChatGPT for Engineering Design and Analysis Tool Development
ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.
Design↗
Usage of ChatGPT for Engineering Design and Analysis Tool Development
ChatGPT, a generative AI large language model, has recently captured significant attention in both the computer science community and the broader public domain. It has demonstrated a wide range of capabilities, from answering simple questions to writing fully functional computer code. This study spotlights both the capabilities and limitations of ChatGPT when addressing engineering problems. The model's capacity to generate practical engineering tools is highlighted through an example of a prompt that leads to an interactive plotting tool, enabling the examination of the fluid boundary layer around a fan blade. Subsequently, the paper also uncovers potential pitfalls in ChatGPT’s application, shown through an unsuccessful attempt to use ChatGPT to automate a process in Ansys Workbench through scripting. The research further investigates ChatGPT's proficiency in addressing inquiries and providing explanations about the functionalities of OpenMDAO, an open-source, multidisciplinary design, analysis, and optimization tool developed at NASA Glenn Research Center. Finally, an optimization methodology, developed with ChatGPT’s help, is applied to the structural optimization of a fan blade. The developed optimization method utilizes T-Blade3 for geometry generation, Ansys Mechanical for meshing and finite element analysis, and sci-kit learn’s MLPRegressor method to generate a trained neural network model of the design space. OpenMDAO is then used to find the optimal point within the design space. The outcome is a significant reduction in stress in the optimized model—less than one-fifth of the stress value in the baseline model.
Design↗
Physically constrained 3D diffusion for inverse design of fiber-reinforced polymer composite materials
Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for applications such as energy absorption in crash structures, flexible robotics, and impact-resistant protective gear. However, the inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with targeted nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. We propose a loss-guided, learning-free approach to generate physically feasible microstructure designs by explicitly enforcing physical constraints during the generation process. For training purposes, 1.35 million FRPC samples were created, and their corresponding stress-strain curves were computed using established physics-based computational models. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.
Xu, Pei [Clemson Univ., SC (United States)]↗
Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data
Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.
quantum↗
Resolving the Coverage Dependence of Surface Reaction Kinetics with Machine Learning and Automated Quantum Chemistry Workflows
Microkinetic models for catalytic systems require estimation of many thermodynamic and kinetic parameters that can be calculated for isolated species and transition states using ab initio methods. However, the presence of nearby coadsorbates on the surface can dramatically alter these thermodynamic and kinetic parameters causing them to be dependent on species coverage fractions. As there are combinatorially many coadsorbed configurations on the surface, computing the coverage dependence of these parameters is far less straightforward. We present a framework for generating and applying machine learning models to predict coverage-dependent parameters for microkinetic models. Our toolkit enables automatic calculation and evaluation of coadsorbed configurations allowing us to sample 2,000 coadsorbed adsorbates and transition states (TSs) for a diverse set of 9 reactions on Cu(111), a challenging surface, with four possible coadsorbates. This dataset was then used to train subgraph isomorphic decision trees (SIDTs) to predict the stability and association energy of configurations. We were able to achieve mean absolute errors (MAEs) of 0.106 eV on adsorbates, 0.172 eV on TSs, and due to natural error cancellation in SIDTs for relative properties, 0.130 eV on reaction energies and 0.180 eV on activation barriers. In conclusion, we describe how to use these models to predict coverage-dependent corrections for adsorbates and TSs and demonstrate on H*, HO*, and O* comparing the generated SIDT model with an iteratively refined version.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
AC/DC Power Systems with Applications for future Lunar/Mars base and Crew Exploration Vehicle
ABSTRACT The Power Systems branch at JSC faces a number of complex issues as it readies itself for the President's initiative on future space exploration beyond low earth orbit. Some of these preliminary issues - those dealing with electric power generation and distribution on board Mars-bound vehicle and that on Lunar and Martian surface may be summarized as follows: Type of prime mover - Because solar power may not be readily available on parts of the Lunar/Mars surface and also during the long duration flight to Mars, the primary source of power will most likely be nuclear power (Uranium fuel rods) with a secondary source of fuel cell (Hydrogen supply). The electric power generation source - With nuclear power being the main prime mover, the electric power generation source will most likely be an ac generator at a yet to be determined frequency. Thus, a critical issue is whether the generator should generate at constant or variable frequency. This will decide what type of generator to use - whether it is a synchronous machine, an asynchronous induction machine or a switched reluctance machine. The type of power distribution system - the distribution frequency, number of wires (3- wire, 4-wire or higher), and ac/dc hybridization. Building redundancy and fault tolerance in the generation and distribution sub-systems so that the system is safe; provides 100% availability to critical loads; continues to operate even with faulted sub-systems; and requires minimal maintenance. This report descril_es results of a summer faculty fellowship spent in the Power Systems Branch with the specific aim of investigating some of the lessons learned in electric power generation and usage from the terrestrial power systems industry, the aerospace industry as well as NASA's on-going missions so as to recommend novel surface and vehicle-based power systems architectures in support of future space exploration initiatives. A hybrid ac/dc architecture with source side and load side redundancies and including emergency generators on both ac and dc sides is proposed. The generation frequency is 400 Hz mostly because of the technology maturity at this frequency in the aerospace industry. Power will be distributed to several ac load distribution buses through solid state variable speed, constant frequency converters on the ac side. A segmented dc ring bus supplied from ac/dc converters and with the capability of connecting/disconnecting the segments will supply power to multiple de load distribution buses. The system will have the capability of reverse flow from dc to ac side in the case of an extreme emergency on the main ac generation side.
Chowdhury, Badrul H.↗
Stirling Convertor Controller Development at NASA Glenn Research Center
For nearly two decades, NASA Glenn Research Center (GRC) has been supporting the development of Radioisotope Power Systems (RPS). NASA desires higher conversion efficiency RPS options that are reliable and robust with long life design. Dynamic conversion, such as Stirling and Brayton, offer the potential for higher conversion efficiencies than current RPS but have yet to be demonstrated in a flight application. The RPS program sent out a solicitation to investigate options for dynamic conversion technologies. As a result of this solicitation, four dynamic power convertor (DPC) technologies were selected for design and three are proceeding to the fabrication phase of prototype dynamic convertors. One lesson learned from the Advanced Stirling Radioisotope Generator (ASRG) project is that controller development should be coordinated with the development of a dynamic convertor. As a result of this, NASA GRC has been utilizing hardware from past Stirling convertor projects including that of the ASRG to support controller development for the DPC's. NASA GRC has developed a strong knowledge base on both analog and digital Stirling dynamic power convertor controllers and will continue to expand and apply that knowledge to the DPC's. Over the past 15 years, controllers were developed at GRC, at Lockheed Martin (LM) and by the Johns Hopkins University/Applied Physics Laboratory (JHU/APL). Various generations of the controllers have been developed as lessons were learned through various component and system level tests. Some of the tests performed were fault tolerance, flight acceptance vibration, electromagnetic interference (EMI), spacecraft integration, and extended operation. The fault tolerance test characterized the controller's ability to handle various fault conditions, including high or low bus power consumptions, total open load or short circuit, and replacing a failed controller card while the backup maintains control of the Stirling convertor. The vibration test confirms the controller's ability to control an ASC during launch. The EMI test characterized the AC and DC magnetic and electric fields emitted by the single ASC and if the controller has an impact on the radiated EMI. Spacecraft integration testing in the Radioisotope Power Systems System Integration Laboratory (RSIL) provided insight into the electrical interactions between the representative RPS, its associated control schemes, and realistic electric system loads. The extended operation test allows data to be collected over a period of thousands of hours to obtain long term performance data of the system. This paper describes the history of controller development at NASA GRC, tests performed on these controllers, and lessons learned.
Dugala, Gina M.↗
Stirling Convertor Controller Development at NASA Glenn Research Center
Over the past decade, NASA Glenn Research Center (GRC) has been supporting the development of Radioisotope Power Systems (RPS). NASA desires higher conversion efficiency RPS options that are reliable and robust with long life design. Dynamic conversion, such as Stirling and Brayton, offer the potential for higher conversion efficiencies but have yet to be demonstrated in a flight application. The RPS program sent out a solicitation to investigate options for dynamic conversion technologies. As a result of this solicitation, four dynamic power convertor (DPC) technologies were selected for design and fabrication of a prototype dynamic convertor. One lesson learned from the Advanced Stirling Radioisotope Generator (ASRG) project is that controller development should start early in the development of a dynamic convertor. As a result of this, NASA GRC has been utilizing hardware from past Stirling convertor projects including that of the ASRG to support controller development for the DPC's. NASA GRC has developed a strong knowledge base on both analog and digital Stirling dynamic power convertor controllers and will continue to expand and apply that knowledge to the four DPC's. Over the past 15 years, controllers were developed in-house at GRC, at Lockheed Martin Coherent Technologies (LMCT) and by the Johns Hopkins University/Applied Physics Laboratory (JHU/APL). Various generations of the controllers, have been developed as lessons were learned through various component and system level tests. Some of the tests performed were fault tolerance, qualification vibration level, electromagnetic interference, Radioisotope Power System Systems Integration (RSIL) tests, and extended operation. The fault tolerance test characterized the controller's ability to handle various fault conditions, including high or low bus power consumptions, total open load or short circuit, and replacing a failed controller card while the backup maintains control of the ASC. The vibration test confirms the controller's ability to control an ASC during launch. The EMI test characterized the AC and DC magnetic and electric fields emitted by the single ASC and if the controller has an impact on the radiated EMI. RSIL testing provided insight into the electrical interactions between the representative RPS, its associated control schemes, and realistic electric system loads. The extended operation test allows data to be collected over a period of thousands of hours to obtain long term performance data of the system. This paper describes the history of controller development at NASA GRC, tests performed on these controllers, and lessons learned.
Dugala, Gina M.↗
Stirling Convertor Controller Development at NASA Glenn Research Center
For nearly two decades, NASA Glenn Research Center has been supporting the development of radioisotope power systems (RPS). NASA desires higher conversion efficiency RPS options that are reliable and robust with long-life design. Dynamic conversion, such as Stirling and Brayton, offer the potential for higher conversion efficiencies than current RPS but have yet to be demonstrated in a flight application. The RPS program sent out a solicitation to investigate options for dynamic conversion technologies. As a result of this solicitation, four dynamic power convertor (DPC) technologies were selected for design and three are proceeding to the fabrication phase of prototype dynamic convertors. One lesson learned from the Advanced Stirling Radioisotope Generator (ASRG) project is that controller development should be coordinated with the development of a dynamic convertor. As a result of this, Glenn has been utilizing hardware from past Stirling convertor projects, including that of the ASRG, to support controller development for the DPCs. Glenn has developed a strong knowledge base on both analog and digital Stirling DPC controllers and will continue to expand and apply that knowledge to the DPCs. Over the past 15 years, controllers were developed at Glenn, at Lockheed Martin (LM), and by the Johns Hopkins University Applied Physics Laboratory (APL). Various generations of the controllers have been developed as lessons were learned through various component- and system-level tests. Some of the tests performed were fault tolerance, flight acceptance vibration, electromagnetic interference (EMI), spacecraft integration, and extended operation. The fault tolerance test characterized the controller’s ability to handle various fault conditions, including high or low bus power consumption, total open load or short circuit, and replacing a failed controller card while the backup maintains control of the Stirling convertor. The vibration test confirms the controller’s ability to control an Advanced Stirling Convertor (ASC) during launch. The EMI test characterized the alternating-current (AC) and direct-current (DC) magnetic and electric fields emitted by the single ASC and if the controller has an impact on the radiated EMI. Spacecraft integration testing in the Radioisotope Power Systems (RPS), System Integration Laboratory (RSIL) provided insight into the electrical interactions between the representative RPS, its associated control schemes, and realistic electric system loads. The extended operation test allows data to be collected over a period of thousands of hours to obtain long-term performance data of the system. This paper describes the history of controller development at Glenn, tests performed on these controllers, and lessons learned.
Dugala, Gina M.↗
Learning and Tuning of Fuzzy Rules
In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.
Berenji, Hamid R.↗
LevSeq: Rapid Generation of Sequence-Function Data for Directed Evolution and Machine Learning
Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization. The pipeline facilitates data-driven protein engineering by consolidating sequence-function data to inform directed evolution and provide the requisite data for machine learning-guided protein engineering (MLPE). LevSeq enables quality control of mutagenesis libraries prior to screening, which reduces time and resource costs. Simulation studies demonstrate LevSeq’s ability to accurately detect variants under various experimental conditions. Lastly, we show LevSeq’s utility in engineering protoglobins for new-to-nature chemistry. Widespread adoption of LevSeq and sharing of the data will enhance our understanding of protein sequence-function landscapes and empower data-driven directed evolution.
59 BASIC BIOLOGICAL SCIENCES↗
Reusing Data and Metadata to Create New Metadata Through Machine-Learning & Other Programmatic Methods
Recent improvements in natural language processing (NLP) enable metadata to be created programmatically from reused original metadata or even the dataset itself. Transfer-learning applied to NLP has greatly improved performance and reduced training data requirements. In this talk, we’ll compare machine-generated metadata to human-generated metadata and discuss characteristics of metadata and data archives that affect suitability for machine-learning reuse of metadata. Where as human-generated metadata is often populated once, populated from the perspective of data supplier, populated by many individuals with different words for the same thing, and limited in length, machine-generated metadata can be updated any number of times, generated from the perspective of any user, constrained to a standardized set of terms that can be evolved over time, and be any length required. Machine-learning generated metadata offers benefits but also additional needs in terms of version control, process transparency, human-computer interaction, and IT requirements. As a successful example, we’ll discuss how a dataset of abstracts and associated human-tagged keywords from a standardized list of several thousand keywords were used to create a machine-learning model that predicted keyword metadata for open-source code projects on code.nasa.gov. We’ll also discuss a less successful example from data.nasa.gov to show how data archive architecture and characteristics of initial metadata can be strong controls on how easy it is to leverage programmatic methods to reuse metadata to create additional metadata.
Gosses, Justin↗
A deep learning and finite element approach for exploration of inverse structure–property designs of lightweight hybrid composites
Hybrid composites have important applications, such as high-performance and lightweight materials in aerospace and automotive industries. Hybrid composites utilize the synergy of diverse fillers to achieve desired material properties, but usually have more complicated microstructures. While topology optimization can optimize a particular property, designing hybrid composites for customized mechanical performances, e.g. full-range stress–strain curve, remains challenging. Here, a computational framework that integrated finite element analysis (FEA) and artificial intelligence (AI) methods of Conditional Generative Adversarial Networks (cGAN) deep learning and transfer learning was developed to establish inverse structure–property relationships and design tailor-made hybrid composites. Based on FEA-generated datasets of hybrid fiber-particle–matrix microstructures and their corresponding full-range stress–strain curves, a cGAN architecture was trained to generate tailored microstructures and establish structure–property relationships. Similarity in microstructural features and well-matched stress–strain curves based on the AI-generated composites were achieved. In conclusion, transfer learning was used to expand the pre-trained model for designing different materials systems.
Hybrid composites↗
Decoding the effects of synonymous variants
Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs suffer from the lack of gold-standard training/evaluation data and exhibit over-reliance on sequence conservation signals. We developed synVep (synonymous Variant effect predictor), a machine learning-based method that overcomes both of these limitations. Our training data was a combination of variants reported by gnomAD (observed) and those unreported, but possible in the human genome (generated). We used positive-unlabeled learning to purify the generated variant set of any likely unobservable variants. We then trained two sequential extreme gradient boosting models to identify subsets of the remaining variants putatively enriched and depleted in effect. Our method attained 90% precision/recall on a previously unseen set of variants. Furthermore, although synVep does not explicitly use conservation, its scores correlated with evolutionary distances between orthologs in cross-species variation analysis. synVep was also able to differentiate pathogenic vs. benign variants, as well as splice-site disrupting variants (SDV) vs. non-SDVs. Thus, synVep provides an important improvement in annotation of sSNVs, allowing users to focus on variants that most likely harbor effects.
Zishuo Zeng↗
Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study
To improve the performance of electrochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strained regions, on atomic-scale transport and stability. Molecular dynamics simulations are used to explore the combined effect of far-from-equilibrium atomic structures and the choice of interatomic potential on ionic diffusivity predictions for Li 7 La 3 Zr 2 O 12 (LLZO), a promising solid electrolyte for all-solid-state batteries. Amorphization and high strain are considered using both classical Buckingham interatomic potentials and machine learning force fields. Here we find that both crystalline expansion and amorphization tend to slow diffusion, although the different physical encodings in the two potentials impact the properties in different ways. We trace these variations to a combination of structural and transport factors, the contributions of which are deconvoluted computationally. Graph-based analysis reveals that the variations for amorphous LLZO arise from the connectivity of diffusion pathways within the predicted structures, which generally correlates with diffusivity and is notably higher for structures generated by the machine learning force fields. Our study provides additional insight into the relationship between atomic structure and diffusivity in LLZO, while also highlighting the need for care in choosing and validating potentials to simulate far from equilibrium structures.
25 ENERGY STORAGE↗
System Identification for Integrated Aircraft Development and Flight Testing [l'Identification Des Systemes Pour le Developpement Integre des Aeronefs et les Essais en Vol]
Over the last decades flight vehicles such as aircraft and helicopters entering service and requiring increased operational effectiveness have with few exceptions experienced prolonged flight test development to achieve full certification. In many cases the original requirements had later to be reduced to enable release to service. The impact on the customer, and manufacturer has been considerable leading to increased costs and or reduced operational capabilities. These costly experiences are largely a result of the flight vehicle not behaving as modelled and designed. The evaluation of flight test data can be used as a tool for validating windtunnel results and mathematical models describing the flight dynamical behaviour. In this sense the uncertainty of important aerodynamic stability and control parameters can be reduced and the confidence of aircraft mathematical models improved. An additional important factor comes from the implementation of active control systems offering the promise of significantly increased flight vehicle performance and operational capability. This approach extends the traditional trade-offs between aerodynamics, structures and propulsion systems to include full- time, full-authority fly-by-wire/light systems. It is imperative that the aerodynamic stability and control parameters of such integrated flight and propulsion control systems have to turn out inflight as predicted, since inherent stability margins will be lower and the flight control system must correct these deficiencies to provide flight critical redundancy and safety. With the methodology of system identification from flight tests it is possible to sense the control inputs and the flight vehicle reactions Such as accelerations, rates and attitudes. The mathematical model, e.g. the model structure and parameters, has to be determined from the relationship of the measured control inputs and the system's responses. The aim of this symposium was to review the present state of the art of flight vehicle system and parameter identification techniques, and to provide a critical appraisal of current methods developed and applied to flight test data in a number of NATO nations. Particular emphasis was placed on practical aspects and lessons learned in order to generate information useful to the flight test community in industry and government agencies. The technical papers share invaluable experience and emphasize the advances of flight vehicle system identification over the last years to the point where confidence and robustness level is now reasonably high. The symposium covered overviews of identification methodologies, flight test techniques, recent aircraft and helicopter application programs, and a session of short papers covering up-to-the-minute flight test results. A final discussion included prepared comments from experts and concluded with key issues learned in the application of system identification and future research needs. The essential benefits to NATO nations can be condensed as follows: More accurate mathematical models for high bandwidth flight control systems, Improved assessment and evaluation of flying qualities, High fidelity mathematical models for flight vehicle development and mission training simulators, and generally, Reduced flight test time and costs.
Advisory Group for Aerospace Research and Developm↗