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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2001

On Multifunctional Collaborative Methods in Engineering Science

Multifunctional methodologies and analysis procedures are formulated for interfacing diverse subdomain idealizations including multi-fidelity modeling methods and multi-discipline analysis methods. These methods, based on the method of weighted residuals, ensure accurate compatibility of primary and secondary variables across the subdomain interfaces. Methods are developed using diverse mathematical modeling (i.e., finite difference and finite element methods) and multi-fidelity modeling among the subdomains. Several benchmark scalar-field and vector-field problems in engineering science are presented with extensions to multidisciplinary problems. Results for all problems presented are in overall good agreement with the exact analytical solution or the reference numerical solution. Based on the results, the integrated modeling approach using the finite element method for multi-fidelity discretization among the subdomains is identified as most robust. The multiple method approach is advantageous when interfacing diverse disciplines in which each of the method's strengths are utilized.

Ransom, Jonathan B.↗
NASA NTRS2003

Transient Mathematical Modeling for Liquid Rocket Engine Systems: Methods, Capabilities, and Experience

The subject of mathematical modeling of the transient operation of liquid rocket engines is presented in overview form from the perspective of engineers working at the NASA Marshall Space Flight Center. The necessity of creating and utilizing accurate mathematical models as part of liquid rocket engine development process has become well established and is likely to increase in importance in the future. The issues of design considerations for transient operation, development testing, and failure scenario simulation are discussed. An overview of the derivation of the basic governing equations is presented along with a discussion of computational and numerical issues associated with the implementation of these equations in computer codes. Also, work in the field of generating usable fluid property tables is presented along with an overview of efforts to be undertaken in the future to improve the tools use for the mathematical modeling process.

Martin, Michael A.↗
NASA NTRS2005

Transient Mathematical Modeling for Liquid Rocket Engine Systems: Methods, Capabilities, and Experience

The subject of mathematical modeling of the transient operation of liquid rocket engines is presented in overview form from the perspective of engineers working at the NASA Marshall Space Flight Center. The necessity of creating and utilizing accurate mathematical models as part of liquid rocket engine development process has become well established and is likely to increase in importance in the future. The issues of design considerations for transient operation, development testing, and failure scenario simulation are discussed. An overview of the derivation of the basic governing equations is presented along with a discussion of computational and numerical issues associated with the implementation of these equations in computer codes. Also, work in the field of generating usable fluid property tables is presented along with an overview of efforts to be undertaken in the future to improve the tools use for the mathematical modeling process.

Seymour, David C.↗
DOE OSTI2025

Performance evaluation of automated data-driven feature extraction and selection methods for practical and scalable building energy consumption prediction models

Here, this study quantifies the impact of automated feature engineering methods (feature extraction and selection) on the quality and accuracy of machine learning models that predict building energy consumption. The case study compares model performance for three main scenarios: baseline (no feature extraction and selection), feature extraction only, and feature extraction combined with feature selection (filter and/or wrapper methods) for fully trained machine learning models for 200 metered/sub-metered energy measurements across 118 real buildings. For consistency, the same machine learning model architecture (a black box deep learning neural network with probabilistic forecast output) was used for all scenarios. Based on results, all feature engineering methods provided noticeable prediction accuracy improvements (e.g., 29%-68% median prediction improvement) compared to baseline scenarios. However, in this application, feature selection methods provide little practical value due to their limited performance gains and high computational cost. Smarter algorithm development supported by better computational environments will be needed before feature selection methods can reliably and efficiently improve predictive model performance.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Engineering Launch Methods for Non-Spherical Hypervelocity Projectiles

The use of carbon-fiber-reinforced polymer (CFRP) in modern satellites has led to a significant amount of space debris, including fragments of CFRP and high-density metals such as steel and copper, produced during catastrophic breakup events. To address this issue, NASA's White Sands Test Facility (WSTF) has been developing the capability to launch "flake-like" and long "needle-like" projectiles. WSTF tested projectiles with different length-to-diameter ratios (L/D), impacting aluminum Whipple shields with thermal blankets on the outer surface, at velocities exceeding 6 km/s. The results of the study suggest that launching and imaging shaped projectiles at high velocities is feasible. Ongoing research is now focused on improving the techniques for launching and imaging shaped projectiles under hypervelocity conditions.

Engineering↗
NASA NTRS1990

Knowledge-based system verification and validation

The objective of this task is to develop and evaluate a methodology for verification and validation (V&V) of knowledge-based systems (KBS) for space station applications with high reliability requirements. The approach consists of three interrelated tasks. The first task is to evaluate the effectiveness of various validation methods for space station applications. The second task is to recommend requirements for KBS V&V for Space Station Freedom (SSF). The third task is to recommend modifications to the SSF to support the development of KBS using effectiveness software engineering and validation techniques. To accomplish the first task, three complementary techniques will be evaluated: (1) Sensitivity Analysis (Worchester Polytechnic Institute); (2) Formal Verification of Safety Properties (SRI International); and (3) Consistency and Completeness Checking (Lockheed AI Center). During FY89 and FY90, each contractor will independently demonstrate the user of his technique on the fault detection, isolation, and reconfiguration (FDIR) KBS or the manned maneuvering unit (MMU), a rule-based system implemented in LISP. During FY91, the application of each of the techniques to other knowledge representations and KBS architectures will be addressed. After evaluation of the results of the first task and examination of Space Station Freedom V&V requirements for conventional software, a comprehensive KBS V&V methodology will be developed and documented. Development of highly reliable KBS's cannot be accomplished without effective software engineering methods. Using the results of current in-house research to develop and assess software engineering methods for KBS's as well as assessment of techniques being developed elsewhere, an effective software engineering methodology for space station KBS's will be developed, and modification of the SSF to support these tools and methods will be addressed.

Johnson, Sally C.↗
NASA NTRS2015

Skylon Aerodynamics and SABRE Plumes

An independent partial assessment is provided of the technical viability of the Skylon aerospace plane concept, developed by Reaction Engines Limited (REL). The objectives are to verify REL's engineering estimates of airframe aerodynamics during powered flight and to assess the impact of Synergetic Air-Breathing Rocket Engine (SABRE) plumes on the aft fuselage. Pressure lift and drag coefficients derived from simulations conducted with Euler equations for unpowered flight compare very well with those REL computed with engineering methods. The REL coefficients for powered flight are increasingly less acceptable as the freestream Mach number is increased beyond 8.5, because the engineering estimates did not account for the increasing favorable (in terms of drag and lift coefficients) effect of underexpanded rocket engine plumes on the aft fuselage. At Mach numbers greater than 8.5, the thermal environment around the aft fuselage is a known unknown−a potential design and/or performance risk issue. The adverse effects of shock waves on the aft fuselage and plumeinduced flow separation are other potential risks. The development of an operational reusable launcher from the Skylon concept necessitates the judicious use of a combination of engineering methods, advanced methods based on required physics or analytical fidelity, test data, and independent assessments.

Mehta, Unmeel↗
NASA NTRS1981

Approximate heating analysis for the windward-symmetry plane of Shuttle-like bodies at large angle of attack

An engineering method has been developed for computing the windward-symmetry plane convective heat-transfer rates on Shuttle-like vehicles at large angles of attack. The engineering code includes an approximate inviscid flowfield technique, laminar and turbulent heating-rate expressions, an approximation to account for the variable-entropy effects on the surface heating and the concept of an equivalent axisymmetric body to model the windward-ray flowfields of Shuttle-like vehicles at angles of attack from 25 to 45 degrees. The engineering method is validated by comparing computed heating results with corresponding experimental data measured on Shuttle and advanced transportation models over a wide range of flow conditions and angles of attack from 25 to 40 degrees and also with results of existing prediction techniques. The comparisons are in good agreement.

Zoby, E. V.↗
DOE OSTI2023

Chapter 14 - Reliability of Wind Turbines

The global wind energy industry has grown at a fast pace during the past half-decade. Advancements from design and manufacturing to operation and maintenance have led to reduced capital and maintenance costs, which make wind power an indispensable source for a comprehensive solution to global electricity needs. Once wind turbines are installed, the opportunity to lower wind power costs is mainly through improved operation and maintenance practices. Modern wind turbines are equipped with tens or hundreds of measurement channels and are generating an abundance of data, with lot of efforts being put into data analysis by both the research community and the industry. One type of analysis is through the exploration of reliability engineering methods based on readily available data or maintenance records collected at typical wind power plants. If adopted and conducted appropriately, these analyses can quickly save operation and maintenance costs in a potentially impactful manner. The wind industry has adopted this discipline more broadly in recent years. This chapter discusses wind turbine reliability by highlighting the methodology of reliability engineering life data analysis. It first briefly discusses the fundamentals of wind turbine reliability and the current industry status. Then, the reliability engineering method for life analysis, including data collection, model development, and forecasting, is presented in detail and illustrated through two case studies. The chapter concludes with some remarks on potential opportunities to improve wind turbine reliability. An owner and operator's perspective is taken and mechanical components are used to exemplify the potential benefits of reliability engineering analysis to improve wind turbine reliability and availability.

database↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
NASA NTRS1990

Global optimization methods for engineering design

The problem is to find a global minimum for the Problem P. Necessary and sufficient conditions are available for local optimality. However, global solution can be assured only under the assumption of convexity of the problem. If the constraint set S is compact and the cost function is continuous on it, existence of a global minimum is guaranteed. However, in view of the fact that no global optimality conditions are available, a global solution can be found only by an exhaustive search to satisfy Inequality. The exhaustive search can be organized in such a way that the entire design space need not be searched for the solution. This way the computational burden is reduced somewhat. It is concluded that zooming algorithm for global optimizations appears to be a good alternative to stochastic methods. More testing is needed; a general, robust, and efficient local minimizer is required. IDESIGN was used in all numerical calculations which is based on a sequential quadratic programming algorithm, and since feasible set keeps on shrinking, a good algorithm to find an initial feasible point is required. Such algorithms need to be developed and evaluated.

Arora, Jasbir S.↗
NASA NTRS1972

An engineering optimization method with application to STOL-aircraft approach and landing trajectories

An optimization method has been developed that computes the optimal open loop inputs for a dynamical system by observing only its output. The method reduces to static optimization by expressing the inputs as series of functions with parameters to be optimized. Since the method is not concerned with the details of the dynamical system to be optimized, it works for both linear and nonlinear systems. The method and the application to optimizing longitudinal landing paths for a STOL aircraft with an augmented wing are discussed. Noise, fuel, time, and path deviation minimizations are considered with and without angle of attack, acceleration excursion, flight path, endpoint, and other constraints.

Jacob, H. G.↗
NASA NTRS1990

Genetic-evolution-based optimization methods for engineering design

This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.

Rao, S. S.↗