Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Random Effects”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Hamiltonian simulation in Zeno subspaces

Here, we investigate the quantum Zeno effect as a framework for designing and analyzing quantum algorithms for Hamiltonian simulation. We show that frequent projective measurements of an ancilla qubit register can be used to simulate quantum dynamics on a target qubit register with a circuit complexity similar to randomized approaches. The classical sampling overhead in the latter approaches is traded for ancilla qubit overhead in Zeno-based approaches. A second-order Zeno sequence is developed to improve scaling and implementations through unitary kicks are discussed. We derive rigorous error bounds that allow for identifying the associated circuit complexities for the first- and second-order Zeno sequences. We show that the circuits over the combined register can be identified as a subroutine commonly used in post-Trotter Hamiltonian simulation methods. We build on this observation to reveal connections between different Hamiltonian simulation algorithms.

Hamiltonian simulation↗
NASA NTRS2006

Update On the Status of the FLUKA Monte Carlo Transport Code*

The FLUKA Monte Carlo transport code is a well-known simulation tool in High Energy Physics. FLUKA is a dynamic tool in the sense that it is being continually updated and improved by the authors. We review the progress achieved since the last CHEP Conference on the physics models, some technical improvements to the code and some recent applications. From the point of view of the physics, improvements have been made with the extension of PEANUT to higher energies for p, n, pi, pbar/nbar and for nbars down to the lowest energies, the addition of the online capability to evolve radioactive products and get subsequent dose rates, upgrading of the treatment of EM interactions with the elimination of the need to separately prepare preprocessed files. A new coherent photon scattering model, an updated treatment of the photo-electric effect, an improved pair production model, new photon cross sections from the LLNL Cullen database have been implemented. In the field of nucleus-- nucleus interactions the electromagnetic dissociation of heavy ions has been added along with the extension of the interaction models for some nuclide pairs to energies below 100 MeV/A using the BME approach, as well as the development of an improved QMD model for intermediate energies. Both DPMJET 2.53 and 3 remain available along with rQMD 2.4 for heavy ion interactions above 100 MeV/A. Technical improvements include the ability to use parentheses in setting up the combinatorial geometry, the introduction of pre-processor directives in the input stream. a new random number generator with full 64 bit randomness, new routines for mathematical special functions (adapted from SLATEC). Finally, work is progressing on the deployment of a user-friendly GUI input interface as well as a CAD-like geometry creation and visualization tool. On the application front, FLUKA has been used to extensively evaluate the potential space radiation effects on astronauts for future deep space missions, the activation dose for beam target areas, dose calculations for radiation therapy as well as being adapted for use in the simulation of events in the ALICE detector at the LHC.

Ferrari, A.↗
DOE OSTI2022

Leveraging Machine Learning and Geo-Tagged Citizen Science Data to Disentangle the Factors of Avian Mortality Events at the Species Level

Abrupt environmental changes can affect the population structures of living species and cause habitat loss and fragmentations in the ecosystem. During August–October 2020, remarkably high mortality events of avian species were reported across the western and central United States, likely resulting from winter storms and wildfires. However, the differences of mortality events among various species responding to the abrupt environmental changes remain poorly understood. In this study, we focused on three species, Wilson’s Warbler, Barn Owl, and Common Murre, with the highest mortality events that had been recorded by citizen scientists. We leveraged the citizen science data and multiple remotely sensed earth observations and employed the ensemble random forest models to disentangle the species responses to winter storm and wildfire. We found that the mortality events of Wilson’s Warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average daily snow cover. The Barn Owl’s mortalities were more identified in places with severe wildfire-induced air pollution. Both winter storms and wildfire had relatively mild effects on the mortality of Common Murre, which might be more related to anomalously warm water. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations. Additionally, the study emphasized the efficiency and effectiveness of monitoring large-scale abrupt environmental changes and conservation using remotely sensed and citizen science data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Interactions Enhance Ramp Reversal Memory in Locally Phase Separated Materials

The ramp-reversal memory (RRM) effect in metal–insulator transition metal oxides (TMOs), a non-volatile resistance change induced by repeated temperature cycling, has attracted considerable interest in neuromorphic computing and non-volatile memory devices. Our previous defect motion model successfully explained RRM in vanadium dioxide (VO 2 ), capturing observed critical temperature shifts and memory accumulation throughout the sample. However, this approach lacked interactions between metallic and insulating domains. Here, we extend our model by combining a correlated Random Field Ising Model with defect diffusion-segregation, enabling accurate hysteresis modeling while predicting the relationship between RRM and domain interactions. Our simulations demonstrate that the maximum RRM occurs when the turnaround temperature approaches the inflection point. This peak in RRM vs. turnaround temperature is consistent with prior transport measurements, as well as our own optical measurements reported here. Significantly, we find that increasing nearest-neighbor interactions enhances the maximum memory effect, thus providing a clear mechanism for optimizing RRM performance. Since our model employs minimal assumptions, we predict that RRM should be a widespread phenomenon in materials exhibiting patterned phase coexistence of electronic domains. This work not only advances fundamental understanding of memory behavior in TMOs but also establishes a much-needed theoretical framework for optimizing device applications.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Theoretical modelling and experimental data matching for active and passive microwave remote sensing of Earth terrain

Two theoretical models were developed to characterize terrain media: a random medium with a variance, a horizontal correlation length; and a homogeneous dielectric containing discrete scatterers. The earth terrain is modelled as layers of such scattering media bounded by air above and half-space below. Matching the theoretical results with experimental data collected from vegetation and snow-ice fields shows that: (1) for observation angles near nadir, rough surface effects are important; (2) for snow-ice field the horizontal correlation length is greater than the vertical correlation length whereas for vegetation field their relative sizes depend on the types of vegetation; (3) the vertically polarized backscattering cross-section is always larger than the horizontally polarized backscattering cross-section for half-space scattering media; (4) for snow field displaying diurnal change, a three-layer model including a thin top layer caused by sunlight illumination must be used; and (5) for a random medium with equal horizontal and vertical correlation lengths, the measured data can also be matched with a corresponding discrete scatterer model.

Kong, J. A.↗
NASA NTRS1973

The effect of multiple encounters on short-period comet orbits.

The observed orbital elements of short-period comets are found to be consistent with the hypothesis of derivation from long-period comets as long as two assumptions are made. First, the distribution of short-period comets has been randomized by multiple encounters with Jupiter and second, the short-period comets have low velocities of encounter with Jupiter. Some 16% of the observed short-period comets have lower encounter velocities than is allowed mathematically using Laplace's method. This may be due to double-encounter processes with Jupiter and Saturn. The distribution of unobservable short-period comets can be inferred in part from the observed comets. It is shown that there may be many short-period comets having orbits between Jupiter and Saturn with somewhat higher inclinations than those with perihelia near the earth.

Lowrey, B. E.↗
NASA NTRS1999

Snow Crystal Orientation Effects on the Scattering of Passive Microwave Radiation

For this study, consideration is given to the role crystal orientation plays in scattering and absorbing microwave radiation. A discrete dipole scattering model is used to measure the passive microwave radiation, at two polarizations (horizontal and vertical), scattered by snow crystals oriented in random and non random positions, having various sizes (ranging between 1 micrometers to 10,000 micrometers in radius), and shapes (including spheroids, cylinders, hexagons). The model results demonstrate that for the crystal sizes typically found in a snowpack, crystal orientation is insignificant compared to crystal size in terms of scattering microwave energy in the 8,100 gm (37 GHz) region of the spectrum. Therefore, the assumption used in radiative transfer approaches, where snow crystals are modeled as randomly oriented spheres, is adequate to account for the transfer of microwave energy emanating from the ground and passing through a snowpack.

Foster, J. L.↗
DOE OSTI2020

Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting

Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ~8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Radar Reflectivity of the Martian Polar Regions

Radar experiments provide a unique method of probing the surfaces and subsurfaces of planetary bodies. Information on surface and subsurface structures and properties can be extracted from radar data. There is a well developed history of radar investigations of the planet Mars, beginning with the first reports of variations of scattering properties as a function of martian longitude in the mid-1960's. Because of the rapid rotation of Mars, the standard technique of delay-doppler mapping cannot be used as effectively as it is on Mercury and Venus, making it a more difficult radar target. Techniques have been developed to overcome this difficulty, including an inversion technique to combine many doppler-only (CW) experiments from different viewing geometries into a map of surface reflectivity, and new random long-code techniques. These techniques still suffer from ambiguities, however. A third technique which does not suffer from these ambiguities is the combination of the powerful Goldstone transmitter with the VLA as the receiving instrument to create a combined radar imaging instrument. We have used this combined radar instrument to image the surface of Mars in 3.5-cm radar reflectivity during the 1988, 1992/93, and 1999 oppositions. During the 1988 experiments, the residual south polar ice cap (RSPIC) was the brightest radar reflector on the planet - intrinsically brighter than even the Tharsis lava flows. This was quite an unexpected result. In contrast, during the 1992/93 experiments, the residual north polar ice cap (RNPIC) was not nearly so bright, and in fact showed no enhancement at all, This was puzzling, given the 1988 results for the RSPIC. We attributed the lack of a radar reflection enhancement to a combination of three effects: the geometry was different; the season was different; and the intrinsic scattering from the residual ice caps was different. The 1999 experiments provided a chance to test the relative importance of these three effects, since the RNPIC would be visible in a geometry very similar to the RSPIC in 1988, and the season would also be very similar. Preliminary reduction of the data taken in 1999 shows that the RNPIC did in fact show a radar reflectivity enhancement, but that it was still not as bright as the RSPIC in 1988. Details regarding the radar reflectivity of the residual ice caps, as well as the surrounding polar layered terrains (in light of our radar reflectivity data) will be discussed. Additional information is contained in the original extended abstract.

Butler, B. J.↗
DOE OSTI2024

Comparison of interband related optical transitions and excitons in ZnGeN 2 and GaN

The optical dielectric function of ZnGeN 2 is calculated from the interband transitions using the energy bands calculated in the quasiparticle self-consistent (QS)GW^ method using two different levels of approximation: the independent particle approximation (IPA) and the Bethe-Salpeter Equation (BSE) approach. The first allows us to relate peaks in ε 2 to specific bands and k-points but does not include electron-hole interaction effects. The second includes electron-hole interaction or excitonic effects. The corresponding changes in the shape of ε 2 (ω) are found to be similar to those in GaN. The screened Coulomb interaction W^ is here calculated including electron-hole interactions in the polarization function and gives a band structure already going beyond the random phase approximation. The static dielectric constants including only electronic screening, commonly referred to as ε∞ , were calculated separately by extrapolating the wave vector dependent macroscopic dielectric function in the long-wavelength limit. Below the quasiparticle gap, we find three bound excitons optically active for different polarization. The convergence of these bound excitons with respect to the density of the k-mesh used in the BSE is studied and found to require a fine mesh. It is also found that these bound excitons originate from only the lowest conduction band and the top three valence bands. To incorporate the lattice screening, we include a scaling factor (ε∞/ε 0 ) 2 , which allows us to obtain exciton binding energies of the correct order of magnitude similar to those in GaN. Here, the excitons are related to each of the three fold split valence bands and the splittings of the latter are also studied as function of strain. Finally, a relation between the anisotropic effective masses and the valence band splitting is pointed out and explained.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Piston geometry and stroke optimization for high efficiency propane spark ignition engines

Propane has unique properties and offers interesting characteristics for high-efficiency spark ignition engines. Its high volatility reduces or completely eliminates fuel-wall wetting and facilitates fuel air mixing. Furthermore, propane has a research octane number of 112 and a high octane sensitivity of 15. Finally, its laminar flame speed is on the same order as that of conventional gasoline, and it exhibits high dilution tolerance. Modern spark ignition internal combustion engines rely on fast combustion rates and high dilution to achieve high brake thermal efficiencies. To accomplish this, high stroke-to-bore ratios and high geometric compression ratios have been used in new engine designs. Therefore, propane’s relatively high laminar flame speeds, high knock resistance, and dilution tolerance make it an excellent candidate fuel for modern spark ignition engines. The objective of this work is to co-optimize the piston geometry and the engine stroke to maximize the efficiency of a spark-ignition engine fueled with propane. 3D computational fluid dynamics (CFD) simulations employing the extended coherent flamelet model were used to study the parametric effects of piston shape and stroke length. A piston geometry based on high performing pistons was parameterized using four controlling parameters. The piston geometry and engine stroke design space was explored using deterministic and quasi-random sampling techniques. In conclusion, a Gaussian process regression model was built using the simulation data to explain the results observed.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Cross-domain digital twin architecture for predictive maintenance via machine learning and Large Language Models

This research introduces a comprehensive framework for creating and deploying a digital twin platform for continuous monitoring and predictive maintenance within industrial settings. Through utilizing advanced technologies, including Unreal Engine 5, Unity 3D, the Message Queue Telemetry Transport protocol, Random Forest machine learning algorithms, and Large Language Models (LLMs), we establish a platform that digitally reproduces physical equipment and translates digital controls into real-world actions. This facilitates preventive maintenance approaches and improves operational effectiveness. The digital twin platform gathers sensor data from operational equipment, analyzes it using machine learning, and delivers practical insights to prevent potential malfunctions and enhance equipment performance. Furthermore, the incorporation of a web portal enables efficient monitoring and access to historical data, educational materials, and equipment status information. Preliminary findings indicate that digital twins can transform industrial equipment management and maintenance methodologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A multi-dimensional dataset for two-phase instability in low pressure natural circulation based on direct transient local measurement

Thermal hydraulic instability can challenge the control and the safety of nuclear reactors. This experimental research captures and investigates the instability in a low-pressure (1–10 atmospheres) natural circulation loop with an adiabatic chimney installed above the heated section. Direct transient local measurement of two-phase flow is performed at multiple axial locations along the flow channel with thermocouples and conductivity probes traversed by customized high-precision mechanisms. Based on the periodicity of oscillations, a post-processing method is proposed to align the data captured non-simultaneously to reconstruct the instability behavior in one representative period. The averaging operation over multiple oscillation cycles is performed to reduce the random errors. Cyclic phenomena are clearly demonstrated by the post-processed data, and rapid void fraction change induced by the flashing is observed to be significant in the driving mechanism for low pressure oscillations. Parametric effects of inlet subcooling, heat flux, inlet flow restriction and system pressure are studied within the experiment range, and qualitative results are explained and compared with existing literature. The ratio between the oscillation period and the travelling time of the fluid particle through the test section is compared with the theoretical value for the density wave oscillation. The new dataset contains directly measured transient multi-dimensional two-phase data along the flow channel for natural circulation under density wave oscillations. The generated dataset is valuable for the validation of system analysis codes and CFD codes in the simulation of natural circulation phenomena and dynamic two-phase flow.

42 ENGINEERING↗
DOE OSTI2020

Seismic Anisotropy of Temperate Ice in Polar Ice Sheets

Here we present a series of simple shear numerical simulations of dynamic recrystallization of two-phase nonlinear viscous materials that represent temperate ice. First, we investigate the effect of the presence of water on the resulting microstructures and, second, how water influences on P wave (V p ) and fast S wave (V s ) velocities. Regardless the water percentage, all simulations evolve from a random fabric to a vertical single maximum. For a purely solid aggregate, the highest V p quickly aligns with the maximum c-axis orientation. At the same time, the maximum c-axis development reduces V s in this orientation. When water is present, the developed maximum c-axis orientation is less intense, which results in lower V p and V s . At high percentage of water, V p does not align with the maximum c-axis orientation. If the bulk modulus of ice is assumed for the water phase (i.e., implying that water is at high pressure), we find a remarkable decrease of V s while V p remains close to the value for purely solid ice. These results suggest that the decrease in V s observed at the base of the ice sheets could be explained by the presence of water at elevated pressure, which would reside in isolated pockets at grain triple junctions. Under these conditions water would not favor sliding between ice grains. However, if we consider that deformation dominates over recrystallization, water pockets get continuously stretched, allowing water films to be located at grain boundaries. This configuration would modify and even overprint the maximum c-axis-dependent orientation and the magnitude of seismic anisotropy.

58 GEOSCIENCES↗
DOE OSTI2020

Evaluating diffusion and the thermodynamic factor for binary ionic mixtures

Molecular dynamics (MD) simulations are a powerful tool for the calculation of transport properties in mixtures. Not only are MD simulations capable of treating multicomponent systems, they are also applicable over a wide range of temperatures and densities. In plasma physics, this is particularly important for applications such as inertial confinement fusion. While many studies have focused on the effect of plasma coupling on transport properties, here we focus on the effects of mixing. We compute the thermodynamic factor, a measure of ideal/non-ideal mixing, for three binary ionic mixtures. Here, we consider mixtures of hydrogen and carbon, hydrogen and argon, and argon and carbon, each at 500 randomly generated state points in the warm dense matter and plasma regimes. The calculated thermodynamic factors indicate different mixing behavior across phase space, which can significantly affect the corresponding mutual diffusion coefficients. As MD simulations are still computationally expensive, we apply modern data science tools to predict the thermodynamic factor over a large phase space. Further, we propose a more accurate approximation to the mutual diffusion coefficient than the commonly applied Darken relation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Quantum mixed state compiling

The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansätze for compiling the target state; the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be used to study hardware noise-induced states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Floquet dynamics of disordered bands with isolated critical energies

We investigate localization properties of driven models which exhibit a subextensive number of extended states in the static setting. We consider instances where the extended modes are or are not protected by topological considerations. To this end, we contrast the strongly driven, disordered, lowest Landau level, which we refer to as the random Landau model (RLM), with the random dimer model (RDM); the RDM also has a subextensive set of delocalized modes in the middle of the spectrum whose origin is not topological. We map the driven models on to a higher-dimensional effective model and numerically compute the localization length as a function of disorder strength, drive amplitude, and frequency using the recursive Green's function method. Our numerical results indicate that, in the presence of a strong drive (low frequency and/or large drive amplitude), the topologically protected RLM continues to exhibit a spectrum with both localized and delocalized (or critical) modes, but the spectral range of delocalized modes is enhanced by the driving. This occurs due to an admixture of the localized modes with extended modes arising due to the topologically protected critical energy in the middle of the spectrum. On the other hand, in the RDM, a weak drive immediately localizes the entire spectrum. This occurs in contrast to the naive expectation from perturbation theory that mixing between localized and delocalized modes generically enhances the delocalization of all modes. Our work highlights the importance of the origin of the delocalized modes in the localization properties of the corresponding Floquet model.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

An Efficient Three-Dimensional CFD-Based Numerical Wave Tank for a Wave Energy Converter in Extreme Irregular Waves

A numerical wave tank approach for computational fluid dynamics (CFD) modelling of an extreme irregular seastate is presented. The technique couples a potential flow solution with a CFD solver for more efficient numerical predictions. This method has recently become attractive both for the research community and the industry working with offshore structures. The model is used to determine the response of a submerged pressure differential wave energy converter (WEC) in a fully nonlinear irregular wave condition using the high-fidelity CFD code, STAR-CCM+. Potential flow based numerical models are commonly used to predict motions and performance of wave energy converters. Wave kinematics can deviate from potential flow predictions for extreme wave conditions; the excitation loads on an absorber can also be increasingly influenced by viscous effects, not predicted by potential flow engineering level models. In these extreme conditions, a Reynolds-averaged Navier-Stokes CFD model can better predict motions and loads for a WEC. Long time series with varying random seed numbers can be used to identify singular extreme wave events from a stochastic irregular sea state. This approach simulates a more realistic wave series for a given sea state than a regular wave or a focused wave. However, it is computationally infeasible to run these long time series for three-dimensional (3D) CFD simulations. In this work, two-dimensional (2D) CFD simulations with a long domain allow the full development of an extreme nonlinear wave condition. The results are used to identify extreme events from a 50-year storm condition for the PacWave site off the coast of Oregon. A relatively short time window including this extreme event is then mapped to a 3D simulation using a user defined wave methodology. Convergence studies for domain length, wave forcing lengths, and time before the extreme event were conducted.

CFD↗