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At least 721 records · Page 40

Drive Rig Mufflers for Model Scale Engine Acoustic Testing

Testing of air breathing propulsion systems in the 9x15 foot wind tunnel at NASA Glenn Research Center depends on compressed air turbines for power. The drive rig turbines exhaust directly to the wind tunnel test section, and have been found to produce significant unwanted noise that reduces the quality of the acoustic measurements of the model being tested. In order to mitigate this acoustic contamination, a muffler can be attached downstream of the drive rig turbine. The modern engine designs currently being tested produce much less noise than traditional engines, and consequently a lower noise floor is required of the facility. An acoustic test of a muffler designed to mitigate this extraneous noise is presented, and a noise reduction of 8 dB between 700 Hz and 20 kHz was documented, significantly improving the quality of acoustic measurements in the facility.

Stephens, David↗

Inter-Calibration of Nine UV Sensing Instruments Over Antarctica and Greenland Since 1980

Nadir-viewed intensities (radiances) from nine UV sensing satellite instruments are calibrated over the East Antarctic Plateau and Greenland during summer. The calibrated radiances from these UV instruments ultimately will provide a global long-term record of cloud trends and cloud response from ENSO events since 1980. We first remove the strong solar zenith angle dependence from the intensities using an empirical approach rather than a radiative trans-fer model. Then small multiplicative adjustments are made to these solar zenith angle normalized intensities in order to minimize differences when two or more instruments tempo-rally overlap. While the calibrated intensities show a negligible long-term trend over Antarctica and a statistically in-significant UV albedo trend of−0.05 % per decade over the interior of Greenland, there are small episodic reductions in intensities which are often seen by multiple instruments. Three of these darkening events are explained by boreal forest. Other events are caused by surface melting or volcanoes. We estimate a 2-sigma uncertainty of 0.35% for the calibrated radiances

Nadir-viewed intensities (radiances) from nine UV ↗

Physics Guided Simulation of Electrostatic Discharge: Technical Report

Triboelectrically-charged objects may create threshold sparks, electrostatic discharge (ESD) events, to equilibrate charge between themselves and other relatively charged objects. ESD events exhibit many complex physical phenomena. They are a nexus of several fields of physics with disparate characteristic scales: plasma physics, chemical kinetics, hydrodynamics, circuit models, etc. These scales can span many orders of magnitude from the varied collisions thermalizing information within a plasma on the $\mathcal{O}(fs/ps)$ to the physical size of the plasma channel on the $\mathcal{O}(100µm)$, to the speed of a nonlinear hydrodynamic wave propagating at $\mathcal{O}(µm, ns)$. These threshold ESD events may occur in situations of programmatic importance, delivering energy and power profiles to a “victim load” generating deleterious consequences. To predict and mitigate these consequences we must answer questions about the spark’s energy budget: how much energy goes into producing the spark channel; how much gets radiated away; how much energy is advected away into the hydrodynamics; and how much energy is delivered to a victim load. An ESD simulation toolset has been created and evolved in order to answer these questions. An appropriate, physics guided implementation for simulation can be done by gaining insight into its constituent physics and leveraging that intuition to choose a suitable numerical operator. We examine in detail the chemical kinetics, circuit discharge, and hydrodynamics to deter mine dominant regimes, values, timescales, and interactions to uncover the underlying physical dynamics. We also examine and propose model reduction schemes for high-dimensional chemical kinetics. We use past and current work with experimentally validated and theoretically-verified hydrodynamics to calculate applicability limits of the non-ionizing strong shock limit. We quantify the energy budget from a hydrodynamic perspective and demonstrate that a significant fraction of the stored energy is “earmarked” for hydrodynamic advection as an energy terminus. Lastly, we combine the constituent physics of an ESD event (chemical kinetics, circuit model, and hydrodynamics) into a cohesive, actionable toolset and obtain promising results from an isothermal test case. We then propose a viable, modular evolution of the ESD toolset based upon the performed examination of the physics uncovering dominant physical scales and the stiffness of the compositional differential system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Effect of rod gap spacing on a suction panel for laminar flow and noise control in supersonic wind tunnels

Results are presented of a coordinated experimental and theoretical study of a sound shield concept which aims to provide a means of noise reduction in the test section of supersonic wind tunnels at high Reynolds numbers. The model used consists of a planar array of circular rods aligned with the flow, with adjustable gaps between them for boundary layer removal by suction, i.e., laminar flow control. One of the basic requirements of the present sound shield concept is to achieve sonic cross flow through the gaps in order to prevent lee-side flow disturbances from penetrating back into the shielded region. Tests were conducted at Mach 6 over a local unit Reynolds number range from about 1.2 x 10 to the 6th power to 13.5 x 10 to the 6th power per foot. Measurements of heat transfer, static pressure, and sound levels were made to establish the transition characteristics of the boundary layer on the rod array and the sound shielding effectiveness.

Harvey, W. D.↗

Lessons Learned on Operating and Preparing Operations for a Technology Mission from the Perspective of the Earth Observing-1 Mission

The New Millennium Program's first Earth-observing mission (EO-1) is a technology validation mission. It is managed by the NASA Goddard Space Flight Center in Greenbelt, Maryland and is scheduled for launch in the summer of 2000. The purpose of this mission is to flight-validate revolutionary technologies that will contribute to the reduction of cost and increase of capabilities for future land imaging missions. In the EO-1 mission, there are five instrument, five spacecraft, and three supporting technologies to flight-validate during a year of operations. EO-1 operations and the accompanying ground system were intended to be simple in order to maintain low operational costs. For purposes of formulating operations, it was initially modeled as a small science mission. However, it quickly evolved into a more complex mission due to the difficulties in effectively integrating all of the validation plans of the individual technologies. As a consequence, more operational support was required to confidently complete the on-orbit validation of the new technologies. This paper will outline the issues and lessons learned applicable to future technology validation missions. Examples of some of these include the following: (1) operational complexity encountered in integrating all of the validation plans into a coherent operational plan, (2) initial desire to run single shift operations subsequently growing to 6 "around-the-clock" operations, (3) managing changes in the technologies that ultimately affected operations, (4) necessity for better team communications within the project to offset the effects of change on the Ground System Developers, Operations Engineers, Integration and Test Engineers, S/C Subsystem Engineers, and Scientists, and (5) the need for a more experienced Flight Operations Team to achieve the necessary operational flexibility. The discussion will conclude by providing several cost comparisons for developing operations from previous missions to EO-1 and discuss some details that might be done differently for future technology validation missions.

Mandl, Dan↗

Experimental validation of Order (n) DISCOS

An order (n) algorithm is developed for use in the industry-standard DISCOS (Dynamics Interaction Simulation of Controls and Structures) program for flexible multibody system dynamics simulation. This version should be more computationally efficient than conventional DISCOS for problems where a large number of bodies are involved. Program-dependent changes to DISCOS are described for reducing the number of bodies required to model gear reduction and nonlinear stiffness as internal effects. The gear reduction modification can be extended to general applications, such as gear box, rack and pinion, lever joint, and screw rotation models. Upgrading DISCOS for future use include the addition of robotics capabilities such as event-driven topology changes, surface sliding, pick-and-place, and multiarm hand-off.

Chun, Hon M.↗

Computational analysis of methods for reduction of induced drag

The purpose of this effort was to perform a computational flow analysis of a design concept centered around induced drag reduction and tip-vortex energy recovery. The flow model solves the unsteady three-dimensional Euler equations, discretized as a finite-volume method, utilizing a high-resolution approximate Riemann solver for cell interface flux definitions. The numerical scheme is an approximately-factored block LU implicit Newton iterative-refinement method. Multiblock domain decomposition is used to partition the field into an ordered arrangement of blocks. Three configurations are analyzed: a baseline fuselage-wing, a fuselage-wing-nacelle, and a fuselage-wing-nacelle-propfan. Aerodynamic force coefficients, propfan performance coefficients, and flowfield maps are used to qualitatively access design efficacy. Where appropriate, comparisons are made with available experimental data.

Janus, J. M.↗

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗

Drag characteristics of V-groove and transverse curvature riblets

The paper examines the drag characteristics of longitudinally ribbed surfaces where the rib dimensions are the order of the initial turbulent wall burst region near the wall. Direct drag measurements are presented for symmetric and unsymmetric V-groove, and (larger wavelength) transverse curvature riblets. The direct drag data indicate net drag reductions as large as 7% for certain V-groove riblets with heights in terms of law of the wall coordinates h+ less than 30. For one of the riblet models, the drag reduction is confirmed by reductions in the momentum thickness, turbulent velocity fluctuations, and Reynolds stress profiles. Although the direct drag data for the transverse curvature riblets showed drag increases, certain models did show that the drag increase diminished with increasing velocity. This trend is opposite to the normal one found for rough surfaces and should be investigated further.

Walsh, M. J.↗

The Accuracy of Parameter Estimation in System Identification of Noisy Aircraft Load Measurement

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

Kong, Jeffrey↗

Global Response Patterns of Major Rainfed Crops to Adaptation by Maintaining Current Growing Periods and Irrigation

Increasing temperature trends are expected to impact yields of major field crops by affecting various plant processes, such as phenology, growth, and evapotranspiration. However, future projections typically do not consider the effects of agronomic adaptation in farming practices. We use an ensemble of seven Global Gridded Crop Models to quantify the impacts and adaptation potential of field crops under increasing temperature up to 6 K, accounting for model uncertainty. We find that without adaptation, the dominant effect of temperature increase is to shorten the growing period and to reduce grain yields and production. We then test the potential of two agronomic measures to combat warming-induced yield reduction: (i) use of cultivars with adjusted phenology to regain the reference growing period duration and (ii) conversion of rainfed systems to irrigated ones in order to alleviate the negative temperature effects that are mediated by crop evapotranspiration. We find that cultivar adaptation can fully compensate global production losses up to 2 K of temperature increase, with larger potentials in continental and temperate regions. Irrigation could also compensate production losses, but its potential is highest in arid regions, where irrigation expansion would be constrained by water scarcity. Moreover, we discuss that irrigation is not a true adaptation measure but rather an intensification strategy, as it equally increases production under any temperature level. In the tropics, even when introducing both adapted cultivars and irrigation, crop production declines already at moderate warming, making adaptation particularly challenging in these areas.

Minoli, Sara↗

Uncertainty reduction in residual stress measurements by an optimised inverse solution using nonconsecutive polynomials

Many destructive methods for measuring residual stresses such as the slitting method require an inverse analysis to solve the problem. The accuracy of the result as well as an uncertainty component (the model uncertainty) depends on the basis functions used in the inverse solution. The use of a series expansion as the basis functions for the inverse solution was analysed in a previous work for the particular case where functions orders grew consecutively. The present work presents a new estimation of the model uncertainty and a new improved methodology to select the final basis functions for the case where the basis is composed of polynomials. Including nonconsecutive polynomial orders in the basis generates a larger space of possible solutions to be evaluated and allows the possibility to include higher-order polynomials. The paper includes a comparison with two other inverse analyses methodologies applied to synthetically generated data. With the new methodology, the final error is reduced and the uncertainty estimation improved.

36 MATERIALS SCIENCE↗

Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing a enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlight potential aspects of the model for real-life applications.

Mathieu, Reynold↗

Sensitivity analyses of a homogeneous model and a RZ model of the MYRRHA reactor in its critical configuration

Two homogenized models of the MYRRHA (Multi-purpose hybrid Research Reactor for High-tech Applications) reactor in its critical configuration are presented in this study. Both models will be verified by comparing the main reactor parameters (K{sub eff}, β{sub eff} and Λ) with those of the heterogeneous model and a sensitivity analysis will be performed. A sensitivity analysis of some important parameters related to reactivity, namely the vacuum coefficient, the Doppler and the power peaking factors, is also presented. The study of the vacuum coefficient has been carried out by analyzing two possible scenarios: 50% vacuum in the coolant (lead bismuth eutectic) and 100%, obtaining a list of the 10 reactions and nuclides that most affect the k{sub eff} value. With respect to the Doppler coefficient, it will be seen that a 300 degree increase in fuel temperature (starting at 800 K) results in a reduction of the total reactivity of the system by 134 pcm.. Finally, the map of the power peaking factors will be shown, as well as a sensitivity analysis. For making the sensitivity calculations Serpent-2 reactor physics Monte Carlo code has been used and JEFF-3.3 library has been chosen in order to continue with the OECD/NEA WPEC SG46 work evaluating the nuclear data of this library. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Water Weakening of Calcium Oxide

Chemomechanical processes such as water weakening can control the permeability and deformation of rocks and manmade materials. Here, atomistic modeling and nanomechanical experiments were used to identify molecular origins of chemomechanical effects in calcium oxide (CaO) and its effect on observed elastic, plastic, and brittle deformation. Classical molecular dynamics simulations with the bond order-based reactive force-field ReaxFF were used to assess brittle fracture. In the presence of water, CaO fractured earlier and more often during quasi-static loading, with a calculated reduction in fracture toughness of ~80% associated with changes in the stress distribution around the crack tip. Experimentally, elastic and plastic deformation of CaO surfaces exposed to water was assessed experimentally using in situ liquid nanoindentation. Nanoindentation showed that following exposure to water, the contact hardness decreased by 1–2 orders of magnitude and decreased the modulus by 2–3 orders of magnitude due to surface hydroxylation. Furthermore, the strong chemomechanical effects on the mechanical processes in CaO suggests that minerals with similar structures may exhibit comparable effects, influencing the stability of cements and geomaterials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Tuning the Quantum Dot (QD)/Mediator Interface for Optimal Efficiency of QD-Sensitized Near-Infrared-to-Visible Photon Upconversion Systems

Lead sulfide (PbS) quantum dots (QDs) have shown promising performance as a sensitizer in infrared-to-visible photon upconversion systems. To investigate the key design rules, we compare three PbS-sensitized upconversion systems using three mediator molecules with the same tetracene triplet acceptor at different distances from the QD. Using transient absorption spectroscopy, we directly measure the triplet energy-transfer rates and efficiencies from the QD to the mediator and from the mediator to the emitter. With increasing distance between the mediator and PbS QD, the efficiency of the first triplet energy transfer from the QD to the mediator decreases because of a decrease in the rate of this triplet energy-transfer step, while the efficiency of the second triplet energy transfer from the mediator to the emitter increases because of a reduction in the QD-induced mediator triplet state decay. The latter effect is a result of the slow rate constant of the second triplet energy-transfer process, which is 3 orders of magnitude slower than the diffusion-limited value. The combined results lead to a net decrease of the steady-state upconversion quantum yield with distance, which could be predicted by our kinetic model. Our result shows that the QD/mediator interface affects both the first and second triplet energy transfer processes in the photon upconversion system, and the QD/mediator distance has an opposite effect on the efficiencies of the first and second triplet energy transfer. Lastly, these findings provide important insight for the further rational improvement of the overall efficiency of QD-based upconversion systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction

4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an important inverse problem in non-destructive evaluation. Conventional back-projection based reconstruction methods assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. Here, we use a forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued 4D object coordinates. Unlike previous neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.

36 MATERIALS SCIENCE↗