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At least 703 records · Page 39

The 15th Aerospace Mechanisms Symposium

The purpose of the symposium was to provide a forum for the interchange of information among those active in the field of mechanism technology. To that end, 27 papers were presented on aeronautics and space flight, with special emphasis on actuators and aerospace applications for pointing systems, latches, connectors, and other mechanisms for large space structures. The papers were authored by engineers from a broad aerospace spectrum including the U. S. aerospace industry, NASA, and several European participants. Technological areas covered include: aerospace propulsion; aerodynamic devices; crew safety; space vehicle control; spacecraft deployment, positioning, and pointing; deployable antennas/reflectors; and large space structures. Devices for payload deployment, payload retention, and crew extravehicular activities on the space shuttle orbiter are also described.

Actuators↗

Measurements of reactive gaseous rocket injector admittances

The paper describes the results of an experimental study of the quantitative determination of the capabilities of the combustion processes associated with coaxial gaseous propellant rocket injectors to drive combustor pressure oscillations. The data, obtained by employing the modified impedance tube technique with compressed air as the oxidizer and acetylene gas as the fuel, describe the frequency dependence of the admittance of the combined injector-combustion process. The measured data are compared with the predictions of the Feiler and Heidmann analytical model utilizing different values for the characteristic combustion time tau sub b. The values of tau sub b which result in a best fit between the measured and predicted data are indicated for different equivalence ratios. It is shown that for the coaxial injector investigated in this study the tau sub b varies between 0.7 and 1.2 msec for equivalence ratios in the range of 0.57 to 1.31. In addition, the experimental data indicate that the tested injector system could drive combustion instabilities over a frequency range that is in qualitative agreement with the predictions of the Feiler and Heidmann model.

Janardan, B. A.↗

Cultivating an Emergent Earth Observation Analytics Ecosystem in the Cloud

A diverse set of data analytics systems for Earth Observations are sprouting up in the Earth Science community, with a wealth of processing algorithms and analysis methods. There is a similar wealth of data resources available via myriad data providers and clearinghouses, including large institutional systems like the Earth Observing System Data and Information System, Comprehensive Large Scale Array-data Stewardship System, and Federated Earth Observation Missions gateway. With Earth system science driving a need to work with more datasets together, and the community developing more analysis tools (some of them dataset-specific), how can we develop analysis workflows that incorporate far-flung datasets and leverage analysis resources from multiple organizations? Cloud computing points the way toward a solution in two different respects. Firstly, the access to and abstraction of virtually unlimited storage and computing power provides an environment that enables more straightforward means of pulling datasets and analysis resources together. Just as importantly, however, cloud computing serves as an example of an "ecosystem" of interoperating services, since the essence of cloud computing is the presentation of all resources as a service, from hardware to infrastructure to platform to software. This enables the combination of off-the-shelf, diverse services to construct entire systems that emerge out of an equally diverse community of architects and developers. This approach can be similarly applied to the data and analysis resources in the Earth Observation community. By exposing these resources via well understood services, and consuming resources in the same way, different organizations can construct bespoke analysis workflows and systems for their own purposes. The key leap the community needs to make is to develop analysis systems in components that interact with other components via services. The result would be a rich ecosystem of analytics components that can be combined to analyze datasets at scale and in conjunction with other datasets from other sources.

chaos↗

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

Hybrid Approaches to Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Systems Health Management↗

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗

Unitary Thermal Energy Management for Propulsion Range Augmentation (UTEMPRA) (CRADA Final Report)

NREL will work with Delphi to develop technology that integrates the thermal management of electric drive vehicle sub-systems into a unified thermal system that reduces auxiliary loads and increases vehicle range. The ultimate goal is to create a cost-effective system which improves climate control efficiency through heat pump operation and waste heat recovery, while maintaining occupant thermal comfort and meeting powertrain thermal requirements. The technology will also aim to reduce the number of independent cooling systems required in electric drive vehicles.

33 ADVANCED PROPULSION SYSTEMS↗

Interlayer correlated fractional quantum Hall state in the ν = 4 / 5 bilayer system

We perform exact diagonalization studies for fractional quantum Hall states at filling factor 4/5 in a bilayer system, on a torus with various aspect ratios and angles. In this work we find that in the absence of tunneling, two weakly coupled 2/5 layers undergo a phase transition into an interlayer-correlated regime, which is also Abelian with the fivefold degeneracy on the torus. In the limit of zero layer separation, this phase becomes a singlet in the pseudospin variable describing the layer degree of freedom. By studying the Chern-number matrix, we show that the K matrix describing the interlayer-correlated regime requires a matrix dimension larger than two and this regime is in particular not described by a Halperin state. A detailed analysis of possible 4 × 4 K matrices having the requisite symmetries and quantum numbers shows that there is only one equivalence class of such matrices. A model wave function representing this universality class is constructed. The role of separate particle number conservation in both layers is discussed, and it is argued that this additional symmetry allows for the further distinction of two different symmetry-protected Abelian phases in the interlayer correlated regime. Interlayer tunneling breaks this symmetry and can drive the system into a single-layer regime when strong enough. A qualitative phase diagram in the tunneling-layer separation parameter space is proposed based on our numerical results.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Electron/hole selectivity in organic semiconductor contacts for solar energy conversion (Final Report)

Four major and accomplishments for the project "Electron/hole selectivity in organic semiconductor contacts for solar energy conversion.'' are reported. The first is a development of a model describing the contact-determined behavior of a solar cell. The most basic solar cell consists of an intrinsic (pure) semiconductor that acts as an absorber to which contacts are made that provide the asymmetry needed to create a driving force (voltage) for the directional flow of electrons (current), in other words, to convert sunlight into electrical energy. The asymmetry at the contacts comes from the different rate at which electrons and holes, the charge carriers generated by illumination, are collected. We developed a model that describes the ideal current-voltage, and hence energy converting properties, of an ideal photovoltaic that is entirely determined by the kinetics for these charge collection processes. Second, we measured how organic semiconductor interfacial layers impact contact electron and hole transfer rates at semiconductor interfaces and described how these concepts do or do not determine the open-circuit voltage of a solar cell. The work clearly controverts common general misconceptions about the action of contact interfacial layers, such as the idea that improved selectivity for one carrier over the other results from decreased recombination, and quantitatively demonstrates how specific interfacial layer materials act to improve the efficiency of a solar cell. Third, we measured the sub-band gap external quantum efficiency (EQE) of a series of organometal halide perovskite (“perovskite”) solar cells. These measurements quantified band-tailing and revealed defect states, which can cause recombination, that correlate with composition, performance, and hysteresis. Finally, we developed a semiconductor bipolar membrane that uses light to pump ions. This structure is unique among systems that drive ion gradients using light in that it is designed to pump salt rather than one sign of ion; it is a photochemical salt pump.

14 SOLAR ENERGY↗

Spin-Up Instability of a Levitated Molten Drop in MHD-Flow Transition to Turbulence

When an alternating magnetic field interacts with induced eddy currents in a conducting body, there will be a repulsive force between the body and the driving coil system generating the field. This repulsive force is the basis of electromagnetic levitation, which allows containerless processing of different materials. The eddy currents in the conducting body also generate Joule heating. Axial rotation of electromagnetically levitated objects is a common observation in levitation systems and often an undesirable side effect of such experiments on 1-g and -g. There have been recent efforts to use magnetic damping and suppress this tendency of body rotation. The first report of rotation in EML drops was attributed to a slight asymmetry of the shape and location of the levitation coils could change the axis and speed of rotation. Other theories of sample rotation include a frequency difference in the traveling electromagnetic waves and a phase difference in two different applied fields of the same frequency. All of these different mechanisms share the following characteristics: the torque is small, constant for constant field strength, and very weakly dependent on the sample's temperature and phase (solid or liquid). During experiments on the MSL-1 (First Microgravity Science Laboratory) mission of the Space Shuttle (STS-83 and STS-94, April and July 1997), a droplet of palladium-silicon alloy was electromagnetically levitated for viscosity measurements. For the non-deforming droplet, the resultant MHD flow inside the drop is inferred from motion of impurities on the surface. These observations indicate formation of a pair of co-rotating toroidal flow structures inside the spheroidal levitated drop that undergo secondary flow instabilities. As rise in the fluid temperature rises, the viscosity falls and the internal flow accelerates and becomes oscillatory; and beyond a point in the experiments, the surface impurities exhibit non-coherent chaotic motion signifying emergence of turbulence inside the drop. In this work, a background of these set of observations will be given followed by a presentation of our results on the digital particle tracking analysis that has been performed on a number of available videos. The analysis indicates that the levitated drop attains a constant rotational speed during the melting phase and formation of the co-rotating axi-symmetric laminar toroidal structures. However, the rate of axial rotation increases dramatically during the deformation of the toroidal structures anti their breakup into chaotic entities. This new data suggests an interaction between the flow inside the levitated molten drop and the driving coils in the experiments. Possible mechanisms for this interaction will be reviewed. The data will also be used to make an assessment of existing theories on droplet rotation.

Abedian, B.↗

Operational space for lower hybrid heating scenarios in the full tungsten environment of WEST

In tungsten—W—Environment in Steady-state Tokamak (WEST), the lower hybrid current drive (LHCD) system is key for achieving long pulse operation by providing most of the non-inductive plasma current, as well as a crucial source of electron heating. Therefore, determining the operational space for its application is fundamental. In the present study, the LHCD operational space is deeply analyzed for 0.5 MA pulses. This space is bounded by three limits: (i) the ratio of the LHCD power over density must be above a threshold to compensate tungsten radiation with enough core heating, (ii) the line-averaged density must be high enough to allow good coupling of the hybrid wave with the plasma, and (iii) fast electron ripple losses must be below a limit to avoid reaching a thermal threshold on plasma-facing components. If the tungsten radiation peak or burn-through phase is not safely overcome, a maximum electron temperature of 1.5 keV is obtained, confinement is degraded, and magnetohydrodynamic activity is frequently triggered, potentially causing a disruption. From experimental measurements and interpretative simulations, we highlight the main mechanisms that prevent the plasma from heating up during LHCD power ramp-up. Three parameters play a major role: plasma density, tungsten concentration and LHCD power deposition. A strategy to overcome this limitation is found: a precise density ramp-up performed simultaneously with the increase in LHCD power. Additionally, we show that boronization greatly facilitates the burn-through of tungsten by lowering its content during the heating phase. Finally, taking into account the three constraints given above, the LHCD operational space is determined at power ramp-up and during constant heating phases.

lower hybrid heating and current drive↗

A PWM transistor inverter for an ac electric vehicle drive

A prototype system consisting of closely integrated motor, inverter, and transaxle has been built in order to demonstrate the feasibility of a three-phase ac transistorized inverter for electric vehicle applications. The microprocessor-controlled inverter employs monolithic power transistors to drive an oil-cooled, three-phase induction traction motor at a peak output power of 30 kW from a 144 V battery pack. Transistor safe switching requirements are discussed, and a circuit is presented for recovering trapped snubber inductor energy at transistor turn-off.

Slicker, J. M.↗

Electron Bifurcating Hydrogenases

The importance of electron-bifurcating enzymes is manifest by their ability to maximize energy efficiency. Specifically, they couple a downhill oxidation-reduction (redox) reaction with an uphill redox reaction. Since the rapid increase in the discovery of bifurcating enzymes starting in 2008, there has been interest in incorporating their mechanistic principles into artificial/semiartificial systems to drive chemically challenging reactions. This has yet to be achieved, partly because the details of electron bifurcation, i.e. mechanisms, are largely elusive. Nevertheless, much progress has been made in understanding reactivities, structures, and some mechanistic aspects of these enzymes. Notable examples are electron-bifurcating hydrogenases, which are the focus of this chapter. The chapter is organized as follows. Section 11.1 provides an overview of hydrogenases and electron bifurcation. In Section 11.2, some physiological roles of electron-bifurcating hydrogenases are highlighted. Additionally, electron-bifurcating subunit compositions and biochemical reactivities are comprehensively tabulated, and some key points/considerations about these are noted. In Section 11.3, we discuss the known structures of these enzymes, which provide insight into their complex arrangements of redox cofactors, such as iron-sulfur clusters. Also provided are tabulations and discussions of some biophysical properties of the cofactors. In Section 11.4, we discuss the mechanistic proposals of these enzymes, which are primarily based on structural information. Areas of research that are much needed are outlined in Section 11.5. We conclude on the note that what is learned from electron-bifurcating hydrogenases has applicability to other bifurcating enzymes, nonbifurcating analogs, and mechanistic enzymology at large.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Enhancing Early Systems R&D Capabilities with Systems —Theoretic Process Analysis

Systems engineering today faces a wide array of challenges, ranging from new operational environments to disruptive technological — necessitating approaches to improve research and development (R&D) efforts. Yet, emphasizing the Aristotelian argument that the “whole is greater than the sum of its parts” seems to offer a conceptual foundation creating new R&D solutions. Invoking systems theoretic concepts of emergence and hierarchy and analytic characteristics of traceability, rigor, and comprehensiveness is potentially beneficial for guiding R&D strategy and development to bridge the gap between theoretical problem spaces and engineering-based solutions. In response, this article describes systems–theoretic process analysis (STPA) as an example of one such approach to aid in early-systems R&D discussions. STPA—a ‘top-down’ process that abstracts real complex system operations into hierarchical control structures, functional control loops, and control actions—uses control loop logic to analyze how control actions (designed for desired system behaviors) may become violated and drive the complex system toward states of higher risk. By analyzing how needed controls are not provided (or out of sequence or stopped too soon) and unneeded controls are provided (or engaged too long), STPA can help early-system R&D discussions by exploring how requirements and desired actions interact to either mitigate or potentially increase states of risk that can lead to unacceptable losses. This article will demonstrate STPA's benefit for early-system R&D strategy and development discussion by describing such diverse use cases as cyber security, nuclear fuel transportation, and US electric grid performance. Together, the traceability, rigor, and comprehensiveness of STPA serve as useful tools for improving R&D strategy and development discussions. In conclusion, leveraging STPA as well as related systems engineering techniques can be helpful in early R&D planning and strategy development to better triangulate deeper theoretical meaning or evaluate empirical results to better inform systems engineering solutions.

42 ENGINEERING↗

Carbodiimide‐Induced Formation of Transient Polyether Cages**

Abstract The use of “fuel” compounds to drive chemical systems out of equilibrium is currently of interest because of the potential for temporally controlled, responsive behavior. We have recently shown that transiently formed crown ethers exhibit counterintuitive templation effects when generated in the presence of alkali metal cations: “matched” cations, such as K + with an 18‐crown‐6 analogue, suppress the formation of the macrocycles (negative templation). In this work, we describe two macrocyclic diacids that, on treatment with carbodiimides, give transient macrobicyclic cages analogous to polyether cages. Negative templation effects are observed for the smaller cage when generated in the presence of K + and Na + , but there is a weak, but reproducible, positive templation effect in the presence of Li + . The larger cage behaves similarly in the presence of Li + , K + , Rb + , and Cs + , but differently with Na + , which appears to bind to both the cage and the initial macrocycle.

Hossain, Mohammad Mosharraf↗

Coupled Boltzmann transport equations of heavy quarks and quarkonia in quark-gluon plasma

We develop a framework of coupled transport equations for open heavy flavor and quarkonium states, in order to describe their transport inside the quark-gluon plasma. Our framework is capable of studying simultaneously both open and hidden heavy flavor observables in heavy-ion collision experiments and can account for both, uncorrelated and correlated recombination. Our recombination implementation depends on real-time open heavy quark and antiquark distributions. We carry out consistency tests to show how the interplay among open heavy flavor transport, quarkonium dissociation and recombination drives the system to equilibrium. We then apply our framework to study bottomonium production in heavy-ion collisions. We include Υ(1S), Υ(2S), Υ(3S), χb (1P) and χb (2P) in the framework and take feed-down contributions during the hadronic gas stage into account. Cold nuclear matter effects are included by using nuclear parton distribution functions for the initial primordial heavy flavor production. A calibrated 2 + 1 dimensional viscous hydrodynamics is used to describe the bulk QCD medium. We calculate both the nuclear modification factor R AA of all bottomonia states and the azimuthal angular anisotropy coefficient v 2 of the Υ(1S) state and find that our results agree reasonably with experimental measurements. Our calculations indicate that correlated cross-talk recombination is an important production mechanism of bottomonium in current heavy-ion experiments. The importance of correlated recombination can be tested experimentally by measuring the ratio of R AA ( χb (1P)) and R AA (Υ(2S)).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Real-time chiral dynamics at finite temperature from quantum simulation

In this study, we explore the real-time dynamics of the chiral magnetic effect (CME) at a finite temperature in the (1+1)-dimensional QED, the massive Schwinger model. By introducing a chiral chemical potential μ 5 through a quench process, we drive the system out of equilibrium and analyze the induced vector currents and their evolution over time. The Hamiltonian is modified to include the time-dependent chiral chemical potential, thus allowing the investigation of the CME within a quantum computing framework. We employ the quantum imaginary time evolution (QITE) algorithm to study the thermal states, and utilize the Suzuki-Trotter decomposition for the real-time evolution. This study provides insights into the quantum simulation capabilities for modeling the CME and offers a pathway for studying chiral dynamics in low-dimensional quantum field theories.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Dynamic modelling of slip in a wind turbine spherical roller main bearing

This paper considers the problem of the dynamic modelling of macro slip in spherical roller bearings. By revisiting the fundamental physics which drive these systems, potential issues in existing models have been identified. Furthermore, in pure rolling conditions it was found that governing differential equations become "stiff", requiring the use of implicit methods of time integration. The problem of individual roller macro slip in a wind turbine main bearing is then investigated using a simplified representation of system dynamics. Model results indicate clear links between slip/friction and the operational strategy of the wind turbine, as well as significantly higher frictional effects in the downwind main bearing row. Due to modelling simplifications, these results should not yet be considered conclusive, with further work required.

17 WIND ENERGY↗