Impedance analysis of semiconductor electrodes in the accumulation region
General physical model for impedance of semiconductor electrodes from accumulation to depletion to inversion.
Chemistry↗
SEARCH · Engineering Papers
Results for “model physics”
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.
Predicting polymer solubility from phase diagrams to compatibility: a perspective on challenges and opportunities
Advances in physical models and data science are improving predictions of polymer–solvent phase behavior and we discuss the different approaches taken today and the remaining barriers to making broadly useful predictions.
36 MATERIALS SCIENCE↗
Evaluation of Portable Acceleration Solutions for LArTPC Simulation Using Wire-Cell Toolkit
The Liquid Argon Time Projection Chamber (LArTPC) technology plays an essential role in many current and future neutrino experiments. Accurate and fast simulation is critical to developing efficient analysis algorithms and precise physics model projections. The speed of simulation becomes more important as Deep Learning algorithms are getting more widely used in LArTPC analysis and their training requires a large simulated dataset. Heterogeneous computing is an efficient way to delegate computationally intensive tasks to specialized hardware. However, as the landscape of compute accelerators quickly evolves, it becomes increasingly difficult to manually adapt the code to the latest hardware or software environments. A solution which is portable to multiple hardware architectures without substantially compromising performance would thus be very beneficial, especially for long-term projects such as the LArTPC simulations. In search of a portable, scalable and maintainable software solution for LArTPC simulations, we have started to explore high-level portable programming frameworks that support several hardware backends. In this paper, we present our experience porting the LArTPC simulation code in the Wire-Cell Toolkit to NVIDIA GPUs, first with the CUDA programming model and then with a portable library called Kokkos. Preliminary performance results on NVIDIA V100 GPUs and multi-core CPUs are presented, followed by a discussion of the factors affiecting the performance and plans for future improvements.
Yu, Haiwang↗
A next-generation inverse-geometry spallation-driven ultracold neutron source
The physics model of a next-generation spallation-driven high-current ultracold neutron (UCN) source capable of delivering an extracted UCN rate of around an order of magnitude higher than the strongest proposed sources, and around three orders of magnitude higher than existing sources, is presented. This UCN-current-optimized source would dramatically improve cutting-edge UCN measurements that are currently statistically limited. A novel “Inverse Geometry” design is used with 40 l of superfluid 4He (He-II), which acts as the converter of cold neutrons to UCNs, cooled with state-of-the-art subcooled cryogenic technology to ∼1.6K. Our source design is optimized for a 100 W maximum heat load constraint on the He-II and its vessel. In this paper, we first explore modifying the Lujan Center Mark-3 target for UCN production as a benchmark. In our Inverse Geometry, the spallation target is wrapped symmetrically around the cryogenic UCN converter to permit raster scanning the proton beam over a relatively large volume of tungsten spallation target to reduce the demand on the cooling requirements, which makes it reasonable to assume that water edge-cooling only is sufficient. Our design is refined in several steps to reach a UCN production rate PUCN=2.1×109s−1 under our other restriction of 1MW maximum available proton beam power. We then study the effects of the He-II scattering kernel used as well as reductions in PUCN due to pressurization to reach PUCN=1.8×109s−1. Finally, we provide a design for the UCN extraction system that takes into account the required He-II heat transport properties and implementation of a He-II containment foil that allows UCN transmission. We estimate a total useful UCN current from our source of Ruse≈5×108s−1 from an 18cm diameter guide ∼5m from the source. Under a conservative “no return” (or “single passage”) approximation, this rate can produce an extracted density of >1×104UCNcm−3 in <1000l external experimental volumes with a 58Ni (335neV) cutoff potential.
Leung, K. K. H. (ORCID:0000000203285326)↗
Reconstruction of the interatomic forces from dynamic scanning transmission electron microscopy data
We explore the possibility for reconstruction of the generative physical models describing interactions between atomic units in solids from observational electron microscopy data. In this work, scanning transmission electron microscopy (STEM) is used to observe the dynamic motion of Si atoms at the edge of monolayer graphene under continuous electron beam illumination. The resulting time-lapsed STEM images represent the snapshots of observed chemical states of the system. We use two approaches: potential of mean force calculation using a radial distribution function and a direct fitting of the graphene–Si interatomic pairwise potentials with force matching, to reconstruct the force fields in the materials. These studies lay the foundation for quantitative analysis of materials energetics from STEM data through the sampling of the metastable states in the chemical space of the system.
42 ENGINEERING↗
On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning
We demonstrate the first live, autonomous control over neutron diffraction experiments by developing and deploying ANDiE: the autonomous neutron diffraction explorer. Neutron scattering is a unique and versatile characterization technique for probing the magnetic structure and behavior of materials. However, instruments at neutron scattering facilities in the world is limited, and instruments at such facilities are perennially oversubscribed. We demonstrate a significant reduction in experimental time required for neutron diffraction experiments by implementation of autonomous navigation of measurement parameter space through machine learning. Prior scientific knowledge and Bayesian active learning are used to dynamically steer the sequence of measurements. We show that ANDiE can experimentally determine the magnetic ordering transition of both MnO and Fe 1.09 Te all while providing a fivefold enhancement in measurement efficiency. Furthermore, in a hypothesis testing post-processing step, ANDiE can determine transition behavior from a set of possible physical models. ANDiE's active learning approach is broadly applicable to a variety of neutron-based experiments and can open the door for neutron scattering as a tool of accelerated materials discovery.
36 MATERIALS SCIENCE↗
Robust unfolding of MeV x-ray spectra from filter stack spectrometer data
Here, we present an inversion method capable of robustly unfolding MeV x-ray spectra from filter stack spectrometer (FSS) data without requiring an a priori specification of a spectral shape or arbitrary termination of the algorithm. Our inversion method is based upon the perturbative minimization (PM) algorithm, which has previously been shown to be capable of unfolding x-ray transmission data, albeit for a limited regime in which the x-ray mass attenuation coefficient of the filter material increases monotonically with x-ray energy. Our inversion method improves upon the PM algorithm through regular smoothing of the candidate spectrum and by adding stochasticity to the search. With these additions, the inversion method does not require a physics model for an initial guess, fitting, or user-selected termination of the search. Instead, the only assumption made by the inversion method is that the x-ray spectrum should be near a smooth curve. Testing with synthetic data shows that the inversion method can successfully recover the primary large-scale features of MeV x-ray spectra, including the number of x-rays in energy bins of several-MeV widths to within 10%. Fine-scale features, however, are more difficult to recover accurately. Examples of unfolding experimental FSS data obtained at the Texas Petawatt Laser Facility and the OMEGA EP laser facility are also presented.
47 OTHER INSTRUMENTATION↗
Measurement of Thomson-scattering spectra with continuous angular resolution (invited)
Here, a novel Thomson-scattering diagnostic with continuous angular resolution over a span of 120° was developed for the characterization of plasmas produced at the Omega Laser Facility. Spectrally resolving light scattered from electron plasma wave features as a function of emission angle provides a means to efficiently probe a large range of plasma frequencies and k vectors. Together, these spectra contain critical constraints on the plasma-physics models used to interpret the data and enable experimental measurements of the electron-velocity distribution function over several orders of magnitude without assumptions about its mathematical form. Major components of the instrument include (1) a reflective collection objective that gathers light over a range of 120° × 12°; (2) a spatial-filter image relay for measurement localization; (3) cylindrical optics for producing a line image of the collection aperture; (4) a transmission grating spectrometer; and (5) a time-gated, image-intensified camera. Thomson-scattered light collected from an ~(50 – μM) 3 volume of plasma is recorded with 0.8-nm spectral and 1° angular resolution. Initial experiments examined the properties of the electron-velocity distribution in gas-jet-produced plasmas in the presence of heating via inverse bremsstrahlung absorption.
47 OTHER INSTRUMENTATION↗
X-ray diffraction strains in laser-ablated aluminum, nickel, sodium, and invar: Pressures to 475 GPa
Dynamically compressed materials in longitudinal waves are described by two physical models: hydrostatic pressure, with equal, normal, principal stresses or material uniaxially strained in the wave propagation direction. These models are disparate, so experimental comparisons and evaluations are important. Polycrystalline material in a state of hydrostatic pressure will have no eccentricity of x-ray-diffracted Debye–Scherrer rings. A general three-dimensional solution of Bragg diffracted x rays based on principal crystallographic strains in the compression wave was found. The distortion of x-ray diffraction beams has been used for strain measurements; the analysis developed incorporates a strained reciprocal lattice and the incident x-ray beam. Strain-distorted Polanyi surfaces form an annulus of compression with an ellipsoid of revolution in reciprocal space, which is intersected by Ewald's sphere for Bragg diffraction. The in situ measurements for strain describe nanosecond diffraction evaluated using two planes, (hkl) and (h′k′l′), both in the same crystallographic phase. Diffraction from Al, Ni, Na, and Invar quantifies the compression axial strains in these materials: The compression axial ratios are 0.65, 1.05, 0.88, and 1.58 at pressures of 291, 402, 409, and 367 GPa for the respective materials. Crystal structure transformations with homogeneous pressurized stresses, mandating equal normal strains, should not be anticipated to agree with heterogeneous, uniaxially strained, and sheared crystalline phases. Measurements support in-plane strains increasing with pressure, p , in fcc and hcp aluminum as ε 22 = −0.21 p /TPa.
Alloys↗
Quantifying the hydrodynamic contribution to electrical transport in non-Brownian suspensions
Electrical transport in semiconducting and metallic particle suspensions is an enabling feature of emerging grid-scale battery technologies. Although the physics of the transport process plays a key role in these technologies, no universal framework has yet emerged. Here, we examine the important contribution of shear flow to the electrical transport of non-Brownian suspensions. We find that these suspensions exhibit a strong dependence of the transport rate on the particle volume fraction and applied shear rate, which enables the conductivity to be dynamically changed by over 10 7 decades based on the applied shear rate. We combine experiments and simulations to conclude that the transport process relies on a combination of charge and particle diffusion with a rate that can be predicted using a quantitative physical model that incorporates the self-diffusion of the particles.
42 ENGINEERING↗
Corrosion rates measured under potentiostatic conditions to represent performance of a cermet nuclear waste form
Electrochemical tests were conducted to assess the corrosion performance of a developmental multiphase cermet nuclear waste form in different conditions. Corrosion currents were monitored as the surface equilibrated in an acidic brine solution during potentiostatic tests at different imposed potentials. Electrochemical impedance spectroscopy was used to quantify differences in the electrical properties of surfaces stabilised at different potentials. The passivating oxides that will stabilise the surface under disposal conditions are formed and maintained during testing. The measured corrosion currents were two orders of magnitude lower for passivated surfaces than for polished surfaces over a wide range of redox conditions. The electrochemical results were correlated with microscopic analyses of the corroded surfaces and physical models of the surface passivation and degradation processes are proposed. The measured corrosion rates are suitable for the long-term performance modeling of cermet nuclear waste forms.
Gattu, V. K.↗
Angular distribution of neutron production by proton and carbon-ion therapeutic beams
Carbon-ion beams are increasingly used in the clinical practice for external radiotherapy treatments of deep-seated tumors. At therapeutic energies, carbon ions yield significant secondary products, including neutrons, which may be of concern for the radiation protection of the patient and personnel. We simulated the neutron yield produced by proton and carbon-ion pencil beams impinging on a clinical phantom at three different angles: 15°, 45° and 90°, with respect to the beam axis. We validated the simulated results using the measured response of organic scintillation detectors. We compared the results obtained with FLUKA 2011.2 and MCNPX 2.7.0 based on three different physics models: Bertini, Isabel, and CEM. Over the different ions, energies, and angles, the FLUKA simulation results agree better with the measured data, compared to the MCNPX results. Simulations of carbon ions at low angles exhibit both the highest deviation from measured data and inter-model discrepancy, which is probably due to the different treatment of the pre-equilibrium stage. The reported neutron yield results could help in the comparison of carbon-ion and proton treatments in terms of secondary neutron production for radiation protection applications.
60 APPLIED LIFE SCIENCES↗
Larmor power limit for cyclotron radiation of relativistic particles in a waveguide
Cyclotron radiation emission spectroscopy (CRES) is a modern technique for high-precision energy spectroscopy, in which the energy of a charged particle in a magnetic field is measured via the frequency of the emitted cyclotron radiation. The He6-CRES collaboration aims to use CRES to probe beyond the standard model physics at the TeV scale by performing high-resolution and low-background beta-decay spectroscopy of 6 He and 19 Ne. Having demonstrated the first observation of individual, high-energy (0.1–2.5 MeV) positrons and electrons via their cyclotron radiation, the experiment provides a novel window into the radiation of relativistic charged particles in a waveguide via the time-derivative (slope) of the cyclotron radiation frequency, df c /dt. We show that analytic predictions for the total cyclotron radiation power emitted by a charged particle in circular and rectangular waveguides are approximately consistent with the Larmor formula, each scaling with the Lorentz factor of the underlying e ± as γ 4 . This hypothesis is corroborated with experimental CRES slope data.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
The Simons Observatory: impact of bandpass, polarization angle and calibration uncertainties on small-scale power spectrum analysis
We study the effects due to mismatches in passbands, polarization angles, and temperature and polarization calibrations in the context of the upcoming cosmic microwave background experiment Simons Observatory (SO). Using the SO multi-frequency likelihood, we estimate the bias and the degradation of constraining power in cosmological and astrophysical foreground parameters assuming different levels of knowledge of the instrumental effects. We find that incorrect but reasonable assumptions about the values ofall the systematics examined here can have significant effects on cosmologicalanalyses, hence requiring marginalization approaches at the likelihood level.When doing so, we find that the most relevant effect is due to bandpass shifts. When marginalizing over them, the posteriors of parameters describing astrophysical microwave foregrounds (such as radio point sources or dust) get degraded, while cosmological parameters constraints are not significantly affected.Marginalization over polarization angles with up to 0.25$^{°}$ uncertainty causes an irrelevant bias ≲ 0.05 σ in all parameters.Marginalization over calibration factors in polarization broadens the constraints on the effective number of relativistic degrees of freedom N$_{eff}$ by a factor 1.2, interpreted here as a proxy parameter for non standard model physics targeted by high-resolution CMB measurements.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
The effects of continuum fitting on Lyman-α forest correlations
Correlations of fluctuations of the flux in Lyman-α forests of high-redshift quasars have been observed by the Baryonic Acoustic Oscillation Spectroscopy Survey (BOSS) and the Dark Energy Spectroscopy Instrument (DESI) survey where they have revealed the effects of baryon acoustic oscillations (BAO). In order to fit the correlation functions to a physical model and thereby constrain cosmological parameters, it is necessary to take into account the effects of fitting the observed spectra to a template about which the fluctuations are measured. In this paper we use mock spectra to test the distortion matrix technique that has been used since the final BOSS data release to appropriately distort the models. We show that while percent-level effects on the derived forest bias parameters may be present, the technique works sufficiently well that the determination of the BAO peak position is not affected at the percent level. We introduce modifications in the technique used by DESI that were not in the original applications and suggest further possibilities for improvements.
Lyman alpha forest↗
Mesoscale to Microscale Coupling for Wind Energy Applications: Addressing the Challenges
The purpose of the US DOE's Mesoscale to Microscale Coupling (MMC) Project is to develop, verify, and validate physical models and modeling techniques that bridge the most important atmospheric scales that determine wind plant performance and reliability. The project seeks to create a new predictive numerical simulation capability that represents a range of dynamic atmospheric flow conditions impacting wind plant performance.
17 WIND ENERGY↗
Accuracy versus precision in boosted top tagging with the ATLAS detector
The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging , is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at $\sqrt{s}$ = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.
47 OTHER INSTRUMENTATION↗
Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme
Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.
97 MATHEMATICS AND COMPUTING↗