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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Active vibration mitigation of distributed parameter, smart-type structures using Pseudo-Feedback Optimal Control (PFOC)

A new, near-optimal feedback control technique is introduced that is shown to provide excellent vibration attenuation for those distributed parameter systems that are often encountered in the areas of aeroservoelasticity and large space systems. The technique relies on a novel solution methodology for the classical optimal control problem. Specifically, the quadratic regulator control problem for a flexible vibrating structure is first cast in a weak functional form that admits an approximate solution. The necessary conditions (first-order) are then solved via a time finite-element method. The procedure produces a low dimensional, algebraic parameterization of the optimal control problem that provides a rigorous basis for a discrete controller with a first-order like hold output. Simulation has shown that the algorithm can successfully control a wide variety of plant forms including multi-input/multi-output systems and systems exhibiting significant nonlinearities. In order to firmly establish the efficacy of the algorithm, a laboratory control experiment was implemented to provide planar (bending) vibration attenuation of a highly flexible beam (with a first clamped-free mode of approximately 0.5 Hz).

Patten, W. N.↗
NASA NTRS1990

A method for optimal actuator and sensor placement for large flexible structures

A method of finding the optimal sensor and actuator locations for the control of flexible structures has been developed. The method is based on the orthogonal projection of structural modes into the intersection subspace of the controllable and observable subspaces. The controllability and observability grammians are then used to weight the projections to reflect the degrees of controllability and observability. This method produces a three-dimensional design space wherein sets of optimal actuator and sensors may be selected.

Lim, K. B.↗
NASA NTRS1991

A mathematical basis for the design optimization of adaptive trusses in precision control

Optimal actuator placement schemes are presently studied for cases of adaptive truss precision control and prestressing control, with a view to the maximization of actuator efficiencies. In statically indeterminate truss structures, the optimal placement criteria and techniques differ, depending on whether the primary determinate structure is known. A suboptimal actuator-placement solution to the global optimization problem which combines displacement control and prestressing control is suggested, by combining the separate displacement control and prestressing control optimization results. Attention is given to the results obtained for the illustrative case of a two-bay, three-dimensional precision truss structure.

Das, S. K.↗
NASA NTRS1981

Optimal damper location in the vibration control of large space structures

The problem of finding the optimal location of active dampers for the vibration control of space structures is investigated. The optimal location is where maximum damping can be introduced to a particular vibration mode. To this end, classical root locus techniques are used to study the effect on eigenvalues of placing a damper on an undamped structure. An efficient formulation is derived which avoids the solution of the damped eigenvalue problem. Based on the observation of the results for a free-free beam model, a Minimum Constrained Frequency Criterion (MCFC) for locating the optimal damper location is proposed.

Wang, B. P.↗
DOE OSTI2025

Optimizing Management of Persistent Data Structures in High-Performance Analytics

Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.

Computer science↗
NASA NTRS1992

Dynamic Optimization

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Laird, Philip↗
NASA NTRSDate not supplied

Use of Design of Experiments and Rule-Based Inference in Determining Neural Network Architectures for Loss of Control Detection

In this work, we describe methods for selecting the neural network architectures and input spaces to implement belief state inference on generic commercial transport aircraft. First, we highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). We present a structured method that can be used in a number of aerospace applications, to optimize the structure and training parameters of the CVAE for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from a NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications. While theoretically, we could create a single CVAE with an input space the size of all measurable flight variables and environmental dynamics, it becomes intractable to use such a neural network in an in-situ intelligent multi-agent system. Using the recommendations from our case study, we introduce a technical approach for feasibly describing the belief space by (1) identifying significant statistical relationships among flight variables using rule induction, (2) using a set of rules that cover all features to define the input space of multiple CVAEs, and (3) forming a belief space based on the joint probability density of their collective latent spaces. This results in a series of relatively small matrix multiplications that can be performed in real time, as opposed to large matrix computations in a single CVAE. We demonstrate the application of this approach on the T-2 flight loss-of control experiments, using the architecture and hyperparameter recommendations from the case study. We compare the utilities of an individual CVAE trained on all flight variables and multiple CVAEs defined on subsets of flight variables for detecting qualitative changes in flight. We demonstrate that the use of multiple CVAEs with smaller input spaces permits the CVAE to capture more granular relationships in the latent space, permitting better state space characterization and loss-of-control detection.

Design of experiments↗
NASA NTRS1989

Airframe structural dynamic considerations in rotor design optimization

An an overview and discussion of those aspects of airframe structural dynamics that have a strong influence on rotor design optimization is provided. Primary emphasis is on vibration requirements. The vibration problem is described, the key vibratory forces are identified, the role of airframe response in rotor design is summarized, and the types of constraints which need to be imposed on rotor design due to airframe dynamics are discussed. Some considerations of ground and air resonance as they might affect rotor design are included.

Kvaternik, Raymond G.↗
NASA NTRS1992

Joint Japan/U.S. Conference on Adaptive Structures, 2nd, Nagoya, Japan, Nov. 12-14, 1991, Collection of Papers

The present conference discusses the development status of adaptive structures in Europe and in Japan, the 'Cosmo-Lab' structures/robotics cooperation concept, active-adhesion concepts for in-orbit structural assembly, adaptively controlled truss structures, object-oriented modeling in structural analysis, the control effectiveness and energy efficiency of an active mass damper, a space truss with experimental tendon control, and piezoelectric actuator-based space trusses. Also discussed is the control of resonant frequencies in adaptive structures through prestressing, active control of vortex-excited vibrations of flexible cylindrical structures, shape adjustment of a flexible space antenna reflector, the SDIO Adaptive Structures Program, optimal trajectories of iterative manipulation for space robots, a docking device as an adaptive structure, shape-memory polymers and their hybrid composites, and fuzzy control methods for structural dynamics.

Matsuzaki, Yuji↗
NASA NTRS1993

The use of antioptimization to compare alternative structural models

Structural models are usually tested by comparing their response with that of a reference structure (an actual structure or a more refined model) to a limited number of arbitrary loads. This test is not always reliable because the loads are arbitrary. An antioptimization-based method is proposed to test structural models. This method compares a structural model with a reference model or an actual structure under the worst loading case that maximizes the error in the model. Specifically, the method identifies the loading case that maximizes the difference between the responses of two models of the same structure using optimization. This method can be used to design experiments in order to validate a structural model. It can also be applied to identify damage in a structure by determining the load that maximizes the difference in the behavior of the damaged and the intact structure. The proposed method is illustrated by applying it to a plate and an automotive structure.

Gangadharan, S. N.↗
DOE OSTI2026

Microstructure‐Informed Analysis Framework for Lattice Structures: Guiding Topology‐Material Synergy in Titanium Alloys

In this work we discuss a microstructure‐informed analysis framework for lattice structures that maps the material's microstructural response to guide topology selection and mechanical performance optimization. By coupling geometrical topology with intrinsic material behavior, we demonstrate how anisotropic microstructural response can inform the design of optimized lattice structures. To illustrate this concept, we focus on two distinct classes of titanium alloys: Ti5553 (Ti‐5Al‐5Mo‐5V‐3Cr wt%), which exhibits a predominantly ‐phase microstructure, and Ti64 (Ti‐6Al‐4V wt%), which features a dual‐phase structure. These alloys exhibit markedly different mechanical responses under multiaxial loading in “fully dense” solid form. The strut‐level stress analysis of these alloys reveals how specific microstructural characteristics can guide the selection of appropriate lattice topologies. Two representative lattice configurations, one stretching‐dominated and one bending‐dominated, are evaluated under identical loading conditions to explore how microstructure‐driven design can lead to topology choices that are better suited to accommodate shear or other critical local stress states, thereby enhancing mechanical performance. A strut‐level mechanics‐based analysis is performed to evaluate shear stress distribution and highlight the role of topology‐microstructure synergy and compatibility in determining overall lattice behavior. The findings emphasize the importance of designing structures that are both load‐aware and microstructure‐responsive, enabling more effective material utilization in advanced engineering applications.

36 MATERIALS SCIENCE↗
NASA NTRS1989

Improved performance in NASTRAN (R)

Three areas of improvement in COSMIC/NASTRAN, 1989 release, were incorporated recently that make the analysis program run faster on large problems. Actual log files and actual timings on a few test samples that were run on IBM, CDC, VAX, and CRAY computers were compiled. The speed improvement is proportional to the problem size and number of continuation cards. Vectorizing certain operations in BANDIT, makes BANDIT run twice as fast in some large problems using structural elements with many node points. BANDIT is a built-in NASTRAN processor that optimizes the structural matrix bandwidth. The VAX matrix packing routine BLDPK was modified so that it is now packing a column of a matrix 3 to 9 times faster. The denser and bigger the matrix, the greater is the speed improvement. This improvement makes a host of routines and modules that involve matrix operation run significantly faster, and saves disc space for dense matrices. A UNIX version, converted from 1988 COSMIC/NASTRAN, was tested successfully on a Silicon Graphics computer using the UNIX V Operating System, with Berkeley 4.3 Extensions. The Utility Modules INPUTT5 and OUTPUT5 were expanded to handle table data, as well as matrices. Both INPUTT5 and OUTPUT5 are general input/output modules that read and write FORTRAN files with or without format. More user informative messages are echoed from PARAMR, PARAMD, and SCALAR modules to ensure proper data values and data types being handled. Two new Utility Modules, GINOFILE and DATABASE, were written for the 1989 release. Seven rigid elements are added to COSMIC/NASTRAN. They are: CRROD, CRBAR, CRTRPLT, CRBE1, CRBE2, CRBE3, and CRSPLINE.

Chan, Gordon C.↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T 2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

Loss of Control↗
NASA NTRSDate not supplied

Use of Design of Experiments in Determining Neural Network Architectures for Loss of Control Detection

Abstract—We describe empirical methods for selecting a neural network architecture to implement belief state inference on generic commercial transport aircraft. We highlight a case study on the planning, execution, and analysis of a set of experiments to determine the configurations of a conditional variational autoencoder (CVAE). Our main contribution is the application of a structured method that can be used for machine learning in many aerospace applications. This method optimizes the structure and training parameters of a neural network for belief state inference, using Design of Experiments (DOE) statistical methodologies. The motivation for this specific DOE analysis was to identify the appropriate hyperparameters for measuring the CVAE reconstruction probability and latent space, such that the measurements can be used to infer qualitative state changes for the aircraft. We demonstrate that this process yields information about a trained neural network’s utility for this specific application, along with a quantifiable range of certainty. We execute 84 experiments using loss-of-control flight maneuver data from the NASA T-2 aircraft, demonstrating that this empirical process allows us to construct cheap and simple models with specific attributes amenable to belief state inference in aerospace applications.

neural networks↗
DOE OSTI2025

First-Principles Studies on Sc 2 RuZ (Z = Si, Ge, Sn) Inverse Heusler Alloys: Structural, Electronic, and Transport Properties

The continuous demand for efficient, nontoxic, and thermally stable materials for room-temperature energy conversion motivates the exploration of novel thermoelectric systems beyond the traditional magnetic Heusler alloys. While full and half-Heusler compounds, especially Co-, Ni-, and Mn-based systems, have demonstrated promising thermoelectric properties, their typically high operating temperatures and magnetic complexities limit their applicability in ambient thermal management. In this context, we investigate whether Sc-based inverse Heusler alloys can offer a viable nonmagnetic alternative with competitive thermoelectric performance. In this work, we perform a systematic first-principles study of the inverse Heusler compounds Sc 2 RuZ (Z = Si, Ge, Sn), focusing on their structural, electronic, mechanical, and thermodynamic-thermoelectric properties. Density Functional Theory (DFT) was employed to compute optimized lattice structures and band dispersion, while dynamical stability was assessed via phonon calculations. Thermoelectric transport coefficients, including Seebeck coefficient, electrical conductivity, and thermal conductivity, were estimated using the semiclassical Boltzmann transport theory within the constant relaxation time approximation. Our results show that all Sc 2 RuZ compounds are thermodynamically stable semiconductors with indirect band gaps of 0.12–0.16 eV and exhibit high elastic moduli, especially Sc 2 RuSn, which demonstrates superior stiffness and incompressibility. Importantly, all compounds display promising room-temperature thermoelectric characteristics, including high Seebeck coefficients and power factors. These findings reveal that Sc 2 RuZ alloys represent a rare class of stable, nonmagnetic inverse Heusler semiconductors with intrinsic thermoelectric potential at room temperature, unlike many existing Heusler systems optimized for spintronics or high-temperature operation. This work expands the known design space for Heusler-based thermoelectrics and offers a theoretical basis for experimental realization of efficient, low-temperature, nonmagnetic thermoelectric materials.

alloys↗
DOE OSTI2024

Performance Analysis of an Optimization Algorithm for Metamaterial Design on the Integrated High-Performance Computing and Quantum Systems

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system.

Kim, Seongmin↗
NASA NTRS1978

Implementation of natural frequency analysis and optimality criterion design

A description is presented of an effective optimality criterion computer design approach for member size selection to improve frequency characteristics for moderately large structure models. It is shown that the implementation of the simultaneous iteration method within a natural frequency structural design optimization provides a method which is more efficient in isolating the lowest natural frequency modes than the frequently applied Stodola method. Additional computational advantages are derived by using previously converged eigenvectors at the start of the iterations during the second and the following design cycles. Vectors with random components can be used at the first design cycle, which, in relation to the entire computer time for the design program, results in only a moderate computational penalty.

Levy, R.↗