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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Metallic Rotor Sizing and Performance Model for Flywheel Systems

The NASA Glenn Research Center (GRC) is developing flywheel system requirements and designs for terrestrial and spacecraft applications. Several generations of flywheels have been designed and tested at GRC using in-house expertise in motors, magnetic bearings, controls, materials and power electronics. The maturation of a flywheel system from the concept phase to the preliminary design phase is accompanied by maturation of the Integrated Systems Performance model, where estimating relationships are replaced by physics based analytical techniques. The modeling can incorporate results from engineering model testing and emerging detail from the design process.

Moore, Camille J.↗
DOE OSTI2017

MOFA: Multi-objective Flux Analysis for the COBRA Toolbox

Environments of biological organisms are frequently nutrient and energy limited. This necessitates that systems exert tight regulation of trade-offs among key biological processes. Quantitative knowledge of these tradeoffs is essential for successful manipulation of biosystems (e.g., metabolic engineering projects). Using genome-scale constraint-based models of metabolism (GSMs), Multi-Objective Flux Analysis (MOFA) allows analyses of trade-offs among a system’s key biological tasks. The leading software for conducting a variety of constraint-based analyses using GSMs is the COBRA Toolbox for MATLAB. Results: We have developed a new add-on tool for COBRA that performs MOFA for a number of objectives only limited by computation power.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Optimizing thermodynamic trajectories using evolutionary reinforcement learning

Using a model heat engine we show that neural network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We use an evolutionary learning algorithm to evolve a population of neural networks, subject to a directive to maximize the efficiency of a trajectory composed of a set of elementary thermodynamic processes; the resulting networks learn to carry out the maximally-efficient Carnot, Stirling, or Otto cycles. Given additional irreversible processes this evolutionary scheme learns a hitherto unknown thermodynamic cycle. Our results show how the reinforcement learning strategies developed for game playing can be applied to solve physical problems conditioned upon path-extensive order parameters.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1988

Life prediction modeling based on cyclic damage accumulation

A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, where initiation was defined as a 0.030 inch surface length crack. A principal engineering feature of the CDA method is the minimum data base required for implementation. Model constants can be evaluated through a few simple specimen tests such as monotonic loading and rapic cycle fatigue. The method was expanded to account for the effects on creep-fatigue life of complex loadings such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. A significant data base was generated on the behavior of the cast nickel-base superalloy B1900+Hf, including hundreds of specimen tests under such loading conditions. This information is being used to refine and extend the CDA life prediction model, which is now nearing completion. The model is also being verified using additional specimen tests on wrought INCO 718, and the final version of the model is expected to be adaptable to most any high-temperature alloy. The model is currently available in the form of equations and related constants. A proposed contract addition will make the model available in the near future in the form of a computer code to potential users.

Nelson, Richard S.↗
DOE OSTI2017

Radiative Heat Transfer modelling in a Heavy-Duty Diesel Engine

Detailed radiation modelling in piston engines has received relatively little attention to date. Recently, it is being revisited in light of current trends towards higher operating pressures and higher levels of exhaust-gas recirculation, both of which enhance molecular gas radiation. Advanced high-efficiency engines also are expected to function closer to the limits of stable operation, where even small perturbations to the energy balance can have a large influence on system behavior. Here several different spectral radiation property models and radiative transfer equation (RTE) solvers have been implemented in an OpenFOAM-based engine CFD code, and simulations have been performed for a heavy-duty diesel engine. Differences in computed temperature fields, NO and soot levels, and wall heat transfer rates are shown for different combinations of spectral models and RTE solvers. The relative importance of molecular gas radiation versus soot radiation is examined. And the influence of turbulence-radiation interactions is determined by comparing results obtained using local mean values of composition and temperature to compute radiative emission and absorption with those obtained using a particle-based transported probability density function method.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2014

Interdisciplinary Interactions During R&D and Early Design of Large Engineered Systems

Designing Large-Scale Complex Engineered Systems (LaCES) such as aircraft and submarines requires the input of thousands of engineers and scientists whose work is proximate in neither time nor space. Comprehensive knowledge of the system is dispersed among specialists whose expertise is in typically one system component or discipline. This study examined the interactive work practices among such specialists seeking to improve engineering practice through a rigorous and theoretical understanding of current practice. This research explored current interdisciplinary practices and perspectives during R&D and early LaCES design and identified why these practices and perspectives prevail and persist. The research design consisted of a three-fold, integrative approach that combined an open-ended survey, semi-structured interviews, and ethnography. Significant empirical data from experienced engineers and scientists in a large engineering organization were obtained and integrated with theories from organization science and engineering. Qualitative analysis was used to obtain a holistic, contextualized understanding. The over-arching finding is that issues related to cognition, organization, and social interrelations mostly dominate interactions across disciplines. Engineering issues, such as the integration of hardware or physics-based models, are not as significant. For example, organization culture is an important underlying factor that guided researchers more toward individual sovereignty over cross-disciplinarity. The organization structure and the engineered system architecture also serve as constraints to the engineering work. Many differences in work practices were observed, including frequency and depth of interactions, definition or co-construction of requirements, clarity or creation of the system architecture, work group proximity, and cognitive challenges. Practitioners are often unaware of these differences resulting in confusion and incorrect assumptions regarding work expectations. Cognitively, the enactment and coconstruction of knowledge are the fundamental tasks of the interdisciplinary interactions. Distributed and collective cognition represent most of the efforts. Argument, ignorance, learning, and creativity are interrelated aspects of the interactions that cause discomfort but yield benefits such as problem mitigation, broader understanding, and improved system design and performance. The quality and quantity of social interrelations are central to all work across disciplines with reciprocity, respectful engagement, and heedful interrelations being significant to the effectiveness of the engineering and scientific work.

McGowan, Anna-Maria Rivas↗
DOE OSTI2015

Band gap engineering in finite elongated graphene nanoribbon heterojunctions: Tight-binding model

A simple model based on the divide and conquer rule and tight-binding (TB) approximation is employed for studying the role of finite size effect on the electronic properties of elongated graphene nanoribbon (GNR) heterojunctions. In our model, the GNR heterojunction is divided into three parts: a left (L) part, middle (M) part, and right (R) part. The left part is a GNR of width W{sub L}, the middle part is a GNR of width W{sub M}, and the right part is a GNR of width W{sub R}. We assume that the left and right parts of the GNR heterojunction interact with the middle part only. Under this approximation, the Hamiltonian of the system can be expressed as a block tridiagonal matrix. The matrix elements of the tridiagonal matrix are computed using real space nearest neighbor orthogonal TB approximation. The electronic structure of the GNR heterojunction is analyzed by computing the density of states. We demonstrate that for heterojunctions for which W{sub L} = W{sub R}, the band gap of the system can be tuned continuously by varying the length of the middle part, thus providing a new approach to band gap engineering in GNRs. Our TB results were compared with calculations employing divide and conquer rule in combination with density functional theory (DFT) and were found to agree nicely.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Modeling Multi-View Impedance-Based Cross-Geometry SOH Estimator for Li-ion Batteries

Abstract: Accurately estimating battery’s State of Health (SOH) remains challenging when models must generalize across cell designs and operating conditions. Most Electrochemical Impedance Spectroscopy (EIS)-based approaches either (i) hand-engineer a few Nyquist-plot features for shallow models—fast but does not generalize across geometries—or (ii) learn directly from Nyquist plots with deep networks, which removes manual feature extraction, yet still limited to a single plot type. As a result, cross-geometry robustness and deployability on constrained Internet of Things (IoT) devices remain open problems. We propose a compact Convolutional Neural Network (CNN) (∼ 10k parameters) that takes multi-representation EIS inputs—Nyquist (real/imaginary) and phase–magnitude (|Z|/ϕ) stacked as four channels, so the model can learn complementary degradation signatures while remaining small enough for fast inference. We build a dataset from cyclic aging of two geometries (LG INR18650MJ1 cylindrical cells and LIR2032 coin cells), acquire EIS every ten cycles from 10 kHz to 10 mHz (10 points/decade), and evaluate with leave-one-cell-out testing strategy. We further study fusion vs. single-representation inputs and assess feasibility for on-device deployment (e.g., NVIDIA Jetson device). The results show that training on multiple EIS representations improves SOH estimation accuracy and cross-geometry generalization compared to single-representation models, which uses only Nyquist or phase–magnitude plots. This design targets accurate, generalizable SOH prediction without manual feature engineering while enabling practical real-time use.

Bakr, Ahmed [The University of Alabama (UA)]↗
NASA NTRS1986

Configuration evaluation and criteria plan. Volume 2: Evaluation critera plan (preliminary). Space Transportation Main Engine (STME) configuration study

The unbiased selection of the Space Transportation Main Engine (STME) configuration requires that the candidate engines be evaluated against a predetermined set of criteria which must be properly weighted to emphasize critical requirements defined prior to the actual evaluation. The evaluation and selection process involves the following functions: (1) determining if a configuration can satisfy basic STME requirements (yes/no); (2) defining the evaluation criteria; (3) selecting the criteria relative importance or weighting; (4) determining the weighting sensitivities; and (5) establishing a baseline for engine evaluation. The criteria weighting and sensitivities are cost related and are based on mission models and vehicle requirements. The evaluation process is used as a coarse screen to determine the candidate engines for the parametric studies and as a fine screen to determine concept(s) for conceptual design. The criteria used for the coarse and fine screen evaluation process is shown. The coarse screen process involves verifying that the candidate engines can meet the yes/no screening requirements and a semi-subjective quantitative evaluation. The fine screen engines have to meet all of the yes/no screening gates and are then subjected to a detailed evaluation or assessment using the quantitative cost evaluation processes. The option exists for re-cycling a concept through the quantitative portion of the screening and allows for some degree of optimization. The basic vehicle is a two stage LOX/HC, LOX/LH2 parallel burn vehicle capable of placing 150,000 lbs in low Earth orbit (LEO).

Bair, E. K.↗
NASA NTRS2017

Mission Assurance: A Model-Based Approach

The purpose of Safety and Mission Assurance (SMA) at NASA’s Jet Propulsion Laboratory is to ensure mission safety and success. As the complexity of technical systems increases, the tools, processes, and infrastructure used to facilitate SMA activities must evolve. A transition from a document-centric approach to a data-driven, model-based architecture is critical in allowing SMA efforts to grapple with complex system designs. This work describes how a sample subset of existing SMA processes can be executed in a model-based environment, while also describing ways in which a model-based architecture can further augment SMA efforts. One key aspect is supporting the complex interplay between the different SMA processes and the systems engineering effort to provide additional insight into the system from an SMA perspective. The proposed architecture for model-based mission assurance provides an avenue for assuring mission safety and success in increasingly complex systems.

Schreiner, Samuel S.↗
DOE OSTI2023

Multidisciplinary concurrent optimization framework for multi-phase building design process

Modern day building design projects require multidisciplinary expertise from architects and engineers across various phases of the design (conceptual, preliminary, and detailed) and construction processes. The Architecture Engineering and Construction (AEC) community has recently shifted gears toward leveraging design optimization techniques to make well-informed decisions in the design of buildings. However, most of the building design optimization efforts are either multidisciplinary optimization confined to just a specific design phase (conceptual/preliminary/detailed) or single disciplinary optimization (structural/thermal/daylighting/energy) spanning across multiple phases. Complexity in changing the optimization setup as the design progresses through subsequent phases, interoperability issues between modeling and physics-based analysis tools used at later stages, and the lack of an appropriate level of design detail to get meaningful results from these sophisticated analysis tools are few challenges that limit multi-phase multidisciplinary design optimization (MDO) in the AEC field. Here this paper proposes a computational building design platform leveraging concurrent engineering techniques such as interactive problem structuring, simulation-based optimization using meta models for energy and daylighting (machine learning based) and tradespace visualization. The proposed multi-phase concurrent MDO framework is demonstrated by using it to design and optimize a sample office building for energy and daylighting objectives across multiple phases. Furthermore, limitations of the proposed framework and future avenues of research are listed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1986

Stratified processes to analyze SSME parts and subsystems using Weibull methodology

Due to the various parts found in a Space Shuttle Main Engine (SSME), analyzation of every part is not feasible or needed. Based on previous mathematical modeling experience of high velocity cryogenic equipment, the environmental traits of location of the part, temperature range, fluid velocity, pressure range, and elevation variation of fluid flow as being significant factors were determined. Six parts categories were developed. The part categories are: (1) fuel turbomachinery, (2) oxidizer, (3) combustion devices, (4) valves, (5) ducts, and (6) lines. Due to a suspicion that superficial failure modes exist, six status codes were developed. The codes are: (1) part is in service presently, (2) part is out-of-service due to its own failure, (3) part is out-of-service due to engine (or some other part failure), (4) part is out-of-service because it's retired/obsolete (time related), (5) never fired-scrapped due to own problem or part is store, and (6) part is out-of-service for repair. It was suggested that to properly model failure behavior of SSME parts that: (1) significant factors contributing to failure must be examined, (2) relatively precision based on real life data must be examined, and (3) alternative definitions of failure must be initialized by the engineers.

Gray, Lou Allen Bell↗
DOE OSTI2024

Regional-scale fault-to-structure earthquake simulations with the EQSIM framework: Workflow maturation and computational performance on GPU-accelerated exascale platforms

Continuous advancements in scientific and engineering understanding of earthquake phenomena, combined with the associated development of representative physics-based models, is providing a foundation for high-performance, fault-to-structure earthquake simulations. However, regional-scale applications of high-performance models have been challenged by the computational requirements at the resolutions required for engineering risk assessments. The EarthQuake SIMulation (EQSIM) framework, a software application development under the US Department of Energy (DOE) Exascale Computing Project, is focused on overcoming the existing computational barriers and enabling routine regional-scale simulations at resolutions relevant to a breadth of engineered systems. This multidisciplinary software development—drawing upon expertise in geophysics, engineering, applied math and computer science—is preparing the advanced computational workflow necessary to fully exploit the DOE’s exaflop computer platforms coming online in the 2023 to 2024 timeframe. Achievement of the computational performance required for high-resolution regional models containing upward of hundreds of billions to trillions of model grid points requires numerical efficiency in every phase of a regional simulation. This includes run time start-up and regional model generation, effective distribution of the computational workload across thousands of computer nodes, efficient coupling of regional geophysics and local engineering models, and application-tailored highly efficient transfer, storage, and interrogation of very large volumes of simulation data. This article summarizes the most recent advancements and refinements incorporated in the workflow design for the EQSIM integrated fault-to-structure framework, which are based on extensive numerical testing across multiple graphics processing unit (GPU)-accelerated platforms, and demonstrates the computational performance achieved on the world’s first exaflop computer platform through representative regional-scale earthquake simulations for the San Francisco Bay Area in California, USA.

58 GEOSCIENCES↗
NASA NTRS2026

Development of the METMAN Thermal Desktop Human Model for System Integration

Human thermal analysis and model development inform NASA’s space suit development, vehicle/habitat design, and survivability studies. Historically, human thermal models such as the 41-node metabolic man (METMAN) and the Wissler model have been standalone software tools developed with FORTRAN, a programming language known for its high performance in computationally intensive applications. Though efficient, these standalone programs pose challenges to coupled human-system analysis with detailed life support and thermal control subsystem models in other commercial software. This paper describes the conversion of the METMAN human thermal model from a standalone FORTRAN program to a model in Thermal Desktop (Ansys), a commonly used CAD-based simulation software for engineering that specializes in heat transfer, thermal radiation, and fluid flow analysis. This format was chosen to best facilitate model sharing and compatibility, enabling the direct integration of METMAN human thermal analysis with subsystem models across NASA programs and commercial partners.

Elizabeth Thurston↗
DOE OSTI2019

Turbulent nonpremixed cool flames: Experimental measurements, Direct Numerical Simulation, and manifold-based combustion modeling

Turbulence, low-temperature chemistry, and their interactions in the form of turbulent cool flames are critical to understanding and improving advanced engines. Design of such engines requires tractable simulations which in turn necessitate turbulent combustion models that can account for cool flames. While manifold-based turbulent combustion models are an attractive option for hot flames, their applicability to cool flames is not yet fully understood. This is partially due to the lack of turbulent cool flame experiments, which has made model validation difficult. This work addresses these points with a combined experimental and computational investigation of turbulent nonpremixed dimethyl ether cool flames. First, a Co-flow Axisymmetric Reactor-Assisted Turbulent (CARAT) burner is developed and tested. Turbulent cool flames are studied using the formaldehyde planar laser-induced fluorescence (PLIF), acetone PLIF, and planar Rayleigh scattering techniques. The acetone PLIF signals are converted into mixture fraction values, and measurements of time-averaged temperature are derived from the Rayleigh scattering signals by taking advantage of the similarities between cool flames and unburned mixtures. Second, a Direct Numerical Simulation (DNS) of the CARAT burner is conducted. The flame is shown to be sensitive to thermal boundary conditions such that the stabilization method is dependent on a 10 K change in inlet temperature. Comparisons of first- and second-order statistics of temperature, mixture fraction, and formaldehyde between the DNS and experiment show good agreement. The validity of manifold-based turbulent combustion models in turbulent cool flames is then explored by first studying the effective Lewis numbers in the flame using a local differential diffusion parameter analysis. Turbulent cool flames are shown to have effective unity Lewis number transport even at a much lower Reynolds number than typically required for hot flames. Good agreement is shown for temperature and formaldehyde between DNS conditional means and one-dimensional nonpremixed flame solutions. Additionally, the motion of the cool flame in mixture fraction space is shown to be well described by one-dimensional nonpremixed flame solutions. These points indicate that the fundamental physics of turbulent cool flames are analogous to turbulent hot flames, implying that the same reduced-order manifold-based modeling approaches utilized for turbulent hot flames can be utilized for turbulent cool flames.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2001

Robust Strategy for Rocket Engine Health Monitoring

Monitoring the health of rocket engine systems is essentially a two-phase process. The acquisition phase involves sensing physical conditions at selected locations, converting physical inputs to electrical signals, conditioning the signals as appropriate to establish scale or filter interference, and recording results in a form that is easy to interpret. The inference phase involves analysis of results from the acquisition phase, comparison of analysis results to established health measures, and assessment of health indications. A variety of analytical tools may be employed in the inference phase of health monitoring. These tools can be separated into three broad categories: statistical, rule based, and model based. Statistical methods can provide excellent comparative measures of engine operating health. They require well-characterized data from an ensemble of "typical" engines, or "golden" data from a specific test assumed to define the operating norm in order to establish reliable comparative measures. Statistical methods are generally suitable for real-time health monitoring because they do not deal with the physical complexities of engine operation. The utility of statistical methods in rocket engine health monitoring is hindered by practical limits on the quantity and quality of available data. This is due to the difficulty and high cost of data acquisition, the limited number of available test engines, and the problem of simulating flight conditions in ground test facilities. In addition, statistical methods incur a penalty for disregarding flow complexity and are therefore limited in their ability to define performance shift causality. Rule based methods infer the health state of the engine system based on comparison of individual measurements or combinations of measurements with defined health norms or rules. This does not mean that rule based methods are necessarily simple. Although binary yes-no health assessment can sometimes be established by relatively simple rules, the causality assignment needed for refined health monitoring often requires an exceptionally complex rule base involving complicated logical maps. Structuring the rule system to be clear and unambiguous can be difficult, and the expert input required to maintain a large logic network and associated rule base can be prohibitive.

Santi, L. Michael↗
DOE OSTI2013

Volume-translated cubic EoS and PC-SAFT density models and a free volume-based viscosity model for hydrocarbons at extreme temperature and pressure conditions

This research focuses on providing the petroleum reservoir engineering community with robust models of hydrocarbon density and viscosity at the extreme temperature and pressure conditions (up to 533 K and 276 MPa, respectively) characteristic of ultra-deep reservoirs, such as those associated with the deepwater wells in the Gulf of Mexico. Our strategy is to base the volume-translated (VT) Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) cubic equations of state (EoSs) and perturbed-chain, statistical associating fluid theory (PC-SAFT) on an extensive data base of high temperature (278–533 K), high pressure (6.9–276 MPa) density rather than fitting the models to low pressure saturated liquid density data. This high-temperature, high-pressure (HTHP) data base consists of literature data for hydrocarbons ranging from methane to C{sub 40}. The three new models developed in this work, HTHP VT-PR EoS, HTHP VT-SRK EoS, and hybrid PC-SAFT, yield mean absolute percent deviation values (MAPD) for HTHP hydrocarbon density of ~2.0%, ~1.5%, and <1.0%, respectively. An effort was also made to provide accurate hydrocarbon viscosity models based on literature data. Viscosity values are estimated with the frictional theory (f-theory) and free volume (FV) theory of viscosity. The best results were obtained when the PC-SAFT equation was used to obtain both the attractive and repulsive pressure inputs to f-theory, and the density input to FV theory. Both viscosity models provide accurate results at pressures to 100 MPa but experimental and model results can deviate by more than 25% at pressures above 200 MPa.

02 PETROLEUM↗
NASA NTRS2012

Constructing an Efficient Self-Tuning Aircraft Engine Model for Control and Health Management Applications

Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulations.Self-tuning aircraft engine models can be applied for control and health management applications. The self-tuning feature of these models minimizes the mismatch between any given engine and the underlying engineering model describing an engine family. This paper provides details of the construction of a self-tuning engine model centered on a piecewise linear Kalman filter design. Starting from a nonlinear transient aerothermal model, a piecewise linear representation is first extracted. The linearization procedure creates a database of trim vectors and state-space matrices that are subsequently scheduled for interpolation based on engine operating point. A series of steady-state Kalman gains can next be constructed from a reduced-order form of the piecewise linear model. Reduction of the piecewise linear model to an observable dimension with respect to available sensed engine measurements can be achieved using either a subset or an optimal linear combination of "health" parameters, which describe engine performance. The resulting piecewise linear Kalman filter is then implemented for faster-than-real-time processing of sensed engine measurements, generating outputs appropriate for trending engine performance, estimating both measured and unmeasured parameters for control purposes, and performing on-board gas-path fault diagnostics. Computational efficiency is achieved by designing multidimensional interpolation algorithms that exploit the shared scheduling of multiple trim vectors and system matrices. An example application illustrates the accuracy of a self-tuning piecewise linear Kalman filter model when applied to a nonlinear turbofan engine simulation. Additional discussions focus on the issue of transient response accuracy and the advantages of a piecewise linear Kalman filter in the context of validation and verification. The techniques described provide a framework for constructing efficient self-tuning aircraft engine models from complex nonlinear simulatns.

Armstrong, Jeffrey B.↗