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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

MME-based attitude dynamics identification and estimation for SAMPEX

A method is described for obtaining optimal attitude estimation algorithms for spacecraft lacking attitude rate measurement devices (rate gyros), and then demonstrated using actual flight data from the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX) spacecraft. SAMPEX does not have on-board rate sensing, and relies on sun sensors and a three-axis magnetometer for attitude determination. Problems arise since typical attitude estimation is accomplished by filtering measurements of both attitude and attitude rates. Rates are nearly always sampled much more densely than are attitudes. Thus, the absence/loss of rate data normally reduces both the total amount of data available and the sampling density (in time) by a substantial fraction. As a result, the sensitivity of the estimates to model uncertainty and to measurement noise increases. In order to maintain accuracy in the attitude estimates, there is increased need for accurate models of the rotational dynamics. The proposed approach is based on the minimum model error (MME) optimal estimation strategy, which has been successfully applied to estimation of poorly modeled dynamic systems which are relatively sparsely and/or noisily measured. The MME estimates may be used to construct accurate models of the system dynamics (i.e. perform system model identification). Thus, an MME-based approach directly addresses the problems created by absence of attitude rate measurements.

Depena, Juan↗
DOE OSTI2015

Hexagonal Uniformly Redundant Arrays (HURAs) for scintillator based coded aperture neutron imaging

A series of Monte Carlo simulations have been conducted, making use of the EJ-426 neutron scintillator detector, to investigate the potential of using hexagonal uniformly redundant arrays (HURAs) for scintillator based coded aperture neutron imaging. This type of scintillator material has a low sensitivity to gamma rays, therefore, is of particular use in a system with a source that emits both neutrons and gamma rays. The simulations used an AmBe source, neutron images have been produced using different coded-aperture materials (boron- 10, cadmium-113 and gadolinium-157) and location error has also been estimated. In each case the neutron image clearly shows the location of the source with a relatively small location error. Neutron images with high resolution can be easily used to identify and locate nuclear materials precisely in nuclear security and nuclear decommissioning applications. (authors)

42 ENGINEERING↗
DOE OSTI2015

Processing system of jaws tomograms for pathology identification and surgical guide modeling

The aim of the study is to create an image processing system, which allows dentists to find pathological resorption and to build surgical guide surface automatically. X-rays images of jaws from cone beam tomography or spiral computed tomography are the initial data for processing. One patient’s examination always includes up to 600 images (or tomograms), that’s why the development of processing system for fast automation search of pathologies is necessary. X-rays images can be useful not for only illness diagnostic but for treatment planning too. We have studied the case of dental implantation – for successful surgical manipulations surgical guides are used. We have created a processing system that automatically builds jaw and teeth boundaries on the x-ray image. After this step, obtained teeth boundaries used for surgical guide surface modeling and jaw boundaries limit the area for further pathologies search. Criterion for the presence of pathological resorption zones inside the limited area is based on statistical investigation. After described actions, it is possible to manufacture surgical guide using 3D printer and apply it in surgical operation.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRSDate not supplied

Aero-Propulsive Modeling for eVTOL Aircraft Using Wind Tunnel Testing with Multisine Inputs

A novel approach for modeling the aero-propulsive characteristics of an electric vertical takeoff and landing (eVTOL) aircraft was developed and demonstrated in wind tunnel testing. The approach was applied to the NASA LA-8 tandem tilt-wing eVTOL aircraft, using an efficient, hybrid experiment design composed of a static I-optimal response surface design for slowly-varying test variables, and dynamic orthogonal phase-optimized multisine excitations for the control surfaces and electric propulsors. Both the static and dynamic experiment designs were executed simultaneously to collect informative data for model identification. Statistically-weighted multivariate orthogonal function modeling was used to combine local modeling results computed in the frequency domain using data collected with dynamic excitations operating on the control effectors to form an aggregate aero-propulsive model. The final identified model exhibited good predictive capability when compared to validation data acquired separately from the data used to develop the model. The required test time using these new techniques was reduced by at least a factor of five compared to previous static wind tunnel testing for the LA-8 aircraft, while providing more informative data, greater parameterization flexibility, and high-quality models.

system identification↗
NASA NTRS1991

Correlation techniques to determine model form in robust nonlinear system realization/identification

The fundamental challenge in identification of nonlinear dynamic systems is determining the appropriate form of the model. A robust technique is presented which essentially eliminates this problem for many applications. The technique is based on the Minimum Model Error (MME) optimal estimation approach. A detailed literature review is included in which fundamental differences between the current approach and previous work is described. The most significant feature is the ability to identify nonlinear dynamic systems without prior assumption regarding the form of the nonlinearities, in contrast to existing nonlinear identification approaches which usually require detailed assumptions of the nonlinearities. Model form is determined via statistical correlation of the MME optimal state estimates with the MME optimal model error estimates. The example illustrations indicate that the method is robust with respect to prior ignorance of the model, and with respect to measurement noise, measurement frequency, and measurement record length.

Stry, Greselda I.↗
DOE OSTI2014

k-OptForce: Integrating Kinetics with Flux Balance Analysis for Strain Design

Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest. It enables identification of a minimal set of interventions comprised of both enzymatic parameter changes (for reactions with available kinetics) and reaction flux changes (for reactions with only stoichiometric information). Application of k-OptForce to the overproduction of L-serine in E. coli and triacetic acid lactone (TAL) in S. cerevisiae revealed that the identified interventions tend to cause less dramatic rearrangements of the flux distribution so as not to violate concentration bounds. In some cases the incorporation of kinetic information leads to the need for additional interventions as kinetic expressions render stoichiometry-only derived interventions infeasible by violating concentration bounds, whereas in other cases the kinetic expressions impart flux changes that favor the overproduction of the target product thereby requiring fewer direct interventions. A sensitivity analysis on metabolite concentrations shows that the required number of interventions can be significantly affected by changing the imposed bounds on metabolite concentrations. Furthermore, k-OptForce was capable of finding non-intuitive interventions aiming at alleviating the substrate-level inhibition of key enzymes in order to enhance the flux towards the product of interest, which cannot be captured by stoichiometry-alone analysis. This study paves the way for the integrated analysis of kinetic and stoichiometric models and enables elucidating system-wide metabolic interventions while capturing regulatory and kinetic effects.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Safeguards Lessons Learned from the Rokkasho Reprocessing Plant - 19502

Once operational, the Rokkasho Reprocessing Plant (RRP) will have an operating throughput of 800 MT of uranium in irradiated LWR fuel per year. In order to establish effective and efficient safeguards at RRP, the safeguards discussion with the IAEA was initiated in 1988. However, at that time there were no available models or guidelines that could be used as a reference and multiple difficulties were recognized for the implementation of safeguards into a large scale reprocessing plant. Therefore, from 1988 to 1992, a multinational forum, referred to as LASCAR (Large Scale Reprocessing Plant Safeguards) [1], provided the recommendations on how an effective safeguards approach could be implemented into a large scale reprocessing plant while efficiently using existing resources. During the course of safeguards discussions between the IAEA, the State, and JNFL, the use of operator's equipment and measurement systems (by signal splitting with authentication) for inspections was studied. JNFL also examined incorporating inspection requirements into the process design: to reduce the costs associated with system installation and future system maintenance and to ensure effective, efficient, and credible safeguards implementation into the RRP (this approach has essentially become the 'Safeguards by Design (SBD)' process). From these studies and JNFL's experience of implementing safeguards into a new plant [2], JNFL established and applied signal splitting from operator's equipment as a means for providing inspectors required safeguards information. In addition, the LASCAR findings were utilized to establish the safeguards implemented into the RRP. Safeguards implementation into RRP commenced March 31, 2006. On June 4, 2008, the IAEA approved the 'integrated safeguards approach' for RRP. This paper provides a high level overview of the major safeguards implemented at RRP and the lessons learned from this implementation that can be applied to existing or future similar facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Full-Envelope Aero-Propulsive Model Identification for Lift+Cruise Aircraft Using Computational Experiments

This paper describes a process utilizing computational fluid dynamics experiments to develop a reduced-order, integrated propulsion-airframe aerodynamic model for a lift+cruise electric vertical takeoff and landing (eVTOL) reference aircraft concept. eVTOL configurations exhibit aerodynamic characteristics of both fixed-wing and rotary-wing aircraft as well as complex phenomena, such as propulsion-airframe interactions. Consequently, conventional aircraft aerodynamic modeling strategies require modification when applied to these unique aircraft. A novel aero-propulsive modeling approach using design of experiments techniques and computational fluid dynamics simulations is postulated for modeling a lift+cruise aircraft configuration. Aero-propulsive models are developed throughout the operational flight envelope to enable continuous flight dynamics simulations from hover through forward flight. The model adequacy is assessed using validation data acquired separately from data used to identify the model and indicates that the models have sufficient predictive capability. Research findings are presented with a discussion of unique lift+cruise aircraft aerodynamic characteristics and practical strategies to inform future aero-propulsive modeling and simulation efforts for eVTOL aircraft.

lift+cruise↗
DOE OSTI2025

Identification and control of the exhaust using gas perturbations in the DIII-D tokamak

This paper presents perturbative experiments that enable the validation and development of control-oriented models for exhaust control. We identify the response of the divertor plasma and scrape-off layer in the DIII-D tokamak to deuterium and nitrogen multi-sine perturbations, in favorable and unfavorable field directions for H-mode access. We obtained good signal-to-noise ratios in the 1–10 Hz frequency range by measuring Balmer-alpha, Lyman-alpha, and N 4+ line emission, radiated power, and neutral pressure. We find a similar phase response across gas species and magnetic field directions, while the gain response is nonlinear. With these experiments, we identify a control-oriented model to design a divertor radiated power controller to track specified reference waveforms in conjunction with resonant magnetic perturbations. Although the physics basis for compatibility between detachment and resonant magnetic perturbation edge-localized mode suppression remains to be demonstrated, the present results provide a robust controller that represents a promising step toward future joint control strategies.

detachment control↗
DOE OSTI2023

A compact dication source for Ba 2+ tagging and heavy metal ion sensor development

Here, we present a tunable metal ion beam that delivers controllable ion currents in the picoamp range for testing of dry-phase ion sensors. Ion beams are formed by sequential atomic evaporation and single or multiple electron impact ionization, followed by acceleration into a sensing region. Controllability of the ionic charge state is achieved through tuning of electrode potentials that influence the retention time in the ionization region. Barium, lead, and cadmium samples have been used to test the system, with ion currents identified and quantified using a quadrupole mass analyzer. Realization of a clean Ba 2+ ion beam within a bench-top system represents an important technical advance toward the development and characterization of barium tagging systems for neutrinoless double beta decay searches in xenon gas. This system also provides a testbed for investigation of novel ion sensing methodologies for environmental assay applications, with dication beams of Pb 2+ and Cd 2+ also demonstrated for this purpose.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Witnessing Entanglement and Quantum Correlations in Condensed Matter: A Review

The detection and certification of entanglement and quantum correlations in materials is of fundamental and far-reaching importance, and has seen significant recent progress. It impacts both the understanding of the basic science of quantum many-body phenomena as well as the identification of systems suitable for novel technologies. Frameworks suitable to condensed matter that connect measurements to entanglement and coherence have been developed in the context of quantum information theory. These take the form of entanglement witnesses and quantum correlation measures. Here, the underlying theory of these quantities, their relation to condensed matter experimental techniques, and their application to real materials are comprehensively reviewed. In addition, their usage in, e.g., protocols, the relative advantages and disadvantages of witnesses and measures, and future prospects in, e.g., correlated electrons, entanglement dynamics, and entangled spectroscopic probes, are presented. Consideration is given to the interdisciplinary nature of this emerging research and substantial ongoing progress by providing an accessible and practical treatment from fundamentals to application. Particular emphasis is placed on quantities accessible to collective measurements, including by susceptibility and spectroscopic techniques. This includes the magnetic susceptibility witness, one-tangle, concurrence and two-tangle, two-site quantum discord, and quantum coherence measures such as the quantum Fisher information.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Inverse load calculation procedure for offshore wind turbines and application to a 5‐MW wind turbine support structure

Abstract A significant number of wind turbines installed today have reached their designed service life of 20 years, and the number will rise continuously. Most of these turbines promise a more economical performance if they operate for more than 20 years. To assess a continued operation, we have to analyze the load‐bearing capacity of the support structure with respect to site‐specific conditions. Such an analysis requires the comparison of the loads used for the design of the support structure with the actual loads experienced. This publication presents the application of a so‐called inverse load calculation to a 5‐MW wind turbine support structure. The inverse load calculation determines external loads derived from a mechanical description of the support structure and from measured structural responses. Using numerical simulations with the software fast , we investigated the influence of wind‐turbine‐specific effects such as the wind turbine control or the dynamic interaction between the loads and the support structure to the presented inverse load calculation procedure. fast is used to study the inverse calculation of simultaneously acting wind and wave loads, which has not been carried out until now. Furthermore, the application of the inverse load calculation procedure to a real 5‐MW wind turbine support structure is demonstrated. In terms of this practical application, setting up the mechanical system for the support structure using measurement data is discussed. The paper presents results for defined load cases and assesses the accuracy of the inversely derived dynamic loads for both the simulations and the practical application. Copyright © 2017 John Wiley & Sons, Ltd.

Pahn, T.↗
DOE OSTI2022

Knowledge-matching based computational framework for genome-scale metabolic model refinement

Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Harnessing on-machine metrology data for prints with a surrogate model for laser powder directed energy deposition

In this study, we leverage the massive amount of multi-modal on-machine metrology data generated from Laser Powder Directed Energy Deposition (LP-DED) to construct a comprehensive surrogate model of the 3D printing process. By employing Dynamic Mode Decomposition with Control (DMDc), a data-driven technique, we capture the complex physics inherent in this extensive dataset. This physics-based surrogate model emphasizes thermodynamically significant quantities, enabling us to accurately predict key process outcomes. The model ingests 21 process parameters, including laser power, scan rate, and position, while providing outputs such as melt pool temperature, melt pool size, and other essential observables. Furthermore, it incorporates uncertainty quantification to provide bounds on these predictions, enhancing reliability and confidence in the results. We then deploy the surrogate model on a new, unseen part and monitor the printing process as validation of the method. Our experimental results demonstrate that the predictions align with actual measurements with high accuracy, confirming the effectiveness of our approach. Furthermore, this methodology not only facilitates real-time predictions but also operates at process-relevant speeds, establishing a basis for implementing feedback control in LP-DED.

Digital twins↗
DOE OSTI2019

Cell-machine interfaces for characterizing gene regulatory network dynamics

Gene regulatory networks and the dynamic responses theyproduce offer a wealth of information about how biologicalsystems process information about their environment.Recently, researchers interested in dissecting these networkshave been outsourcing various parts of their experimentalworkflow to computers. Here we review how, using microfluidicor optogenetic tools coupled with fluorescence imaging, it isnow possible to interface cells and computers. These platformsenable scientists to perform informative dynamic stimulationsof genetic pathways and monitor their reaction. It is alsopossible to close the loop and regulate genes in real time,providing an unprecedented view of how signals propagatethrough the network. Finally, we outline new tools that can beused within the framework of cell–machine interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Optimization-based control using input convex neural networks

Input convex neural networks (ICNNs) are a family of deep learning models where the outputs are constructed to be convex functions of the inputs. By parameterizing system models using ICNNs, optimization-based control problems can be solved as convex optimization problems, leading to improved performance and robustness. This work proposes a novel framework where the control objective function and constraints are modelled using ICNNs. A case study of optimization-based control with output constraints is conducted on a process with input multiplicity and nonminimum phase behavior. The simulation results demonstrate improved economic yield compared with normal neural networks. Additionally, the input convexity formulation is compared with simple regularization techniques, and unique benefits such as improved data efficiency and robustness of the proposed formulation are shown. Finally, by explicitly incorporating prior knowledge about convexity, this framework provides a good balance between the universal approximation power of deep learning and computational feasibility required by control.

42 ENGINEERING↗