SEARCH · Engineering Papers
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.
We report the discovery of a long-sought-after phase of titanium nitride with stoichiometry Ti3N4 using diamond anvil cell experiments combined with in situ high-resolution x-ray diffraction and Raman spectroscopy techniques, supported by ab initio calculations.
The inverse calculation of burden distribution matrix (BDM) is one of the most important challenges in the blast furnace operation in iron-making processes. In general, blast furnace consumes 65% of the total energy for the whole steel-making. Focusing on this practical challenge, this article proposes a new burden distribution spatial model in calculating burden charging process, and develops a B-spline approximation-based probability density function (PDF) control algorithm to assign the expected thickness distribution of burden layer and, thus, develops a new method for the required inverse calculation of BDM. First, a novel method for the thickness distribution of burden layer is given using B-spline model to produce an expected distribution shape subjected to a desired tracking within a specific spatial constraint. Then, according to the coexistence of continuous and bounded discrete variables in BDM, a novel hybrid optimization control method by combining integer programming and PDF tracking is further established for the effective inverse calculation of BDM. Finally, the proposed PDF-based iterative inverse calculation of BDM using B-spline models are tested using various data from industrial examples. Furthermore, the simulation results show that the proposed method is well suited to solve the BDM inverse calculation problem in practice.
The problem of wave breaking during its propagation in the Bose–Einstein condensate to a stationary medium is considered for the case when the initial profile at the breaking instant can be approximated by a power function of the form (–x){sup 1/n}. The evolution of the wave is described by the Gross–Pitaevskii equation so that a dispersive shock wave is formed as a result of breaking; this wave can be represented using the Gurevich–Pitaevskii approach as a modulated periodic solution to the Gross–Pitaevskii equation, and the evolution of the modulation parameters is described by the Whitham equations obtained by averaging the conservation laws over fast oscillations in the wave. The solution to the Whitham modulation equations is obtained in closed form for n = 2, 3, and the velocities of the dispersion shock wave edges for asymptotically long evolution times are determined for arbitrary integers n > 1. The problem considered here can be applied for describing the generation of dispersion shock waves observed in experiments with the Bose–Einstein condensate.
The dependences of the electron mobility μ{sub eff} in the inversion layers of fully depleted double–gate silicon-on-insulator (SOI) metal–oxide–semiconductor (MOS) transistors on the density N{sub e} of induced charge carriers and temperature T are investigated at different states of the SOI film (inversion–accumulation) from the side of one of the gates. It is shown that at a high density of induced charge carriers of N{sub e} > 6 × 10{sup 12} cm{sup –2} the μeff(T) dependences allow the components of mobility μ{sub eff} that are related to scattering at surface phonons and from the film/insulator surface roughness to be distinguished. The μ{sub eff}(N{sub e}) dependences can be approximated by the power functions μ{sub eff}(N{sub e}) ∝ N{sub e}{sup −n}. The exponents n in the dependences and the dominant mechanisms of scattering of electrons induced near the interface between the SOI film and buried oxide are determined for different N{sub e} ranges and film states from the surface side.
The optical spectra of films composed of spherical silicon-dioxide particles coated with small-radius CdS quantum dots are recorded and analyzed. Large shifts of the absorption and photoluminescence bands are detected and studied in relation to the concentration of quantum dots and to the pumping density and wavelength. Analysis of the experimental data shows that the effects are due to electronic excitation energy transfer by particles through the mechanism of tunneling induced by a strong interaction between quantum dots. The results obtained at low pumping densities and different excitation wavelengths make it possible to describe the size distribution of CdS quantum dots. This distribution can be adequately approximated with a Gaussian function.
Abstract As accelerator-based neutrino oscillation experiments improve oscillation parameter constraints with more data, control over systematic uncertainties on the incoming neutrino flux and interaction models is increasingly important. The intense beams offered by modern experiments permit a variety of options to constrain the flux using in situ “standard candle” measurements. These standard candles must use very well understood interaction processes to avoid introducing additional interaction model dependence. One option often discussed in this context is the “low- $$\nu $$ ν ” method, which is designed to isolate neutrino interactions where there is low energy-transfer to the nucleus, such that the interaction cross section is expected to be approximately constant as a function of neutrino energy. The shape of the low-energy transfer event sample can then be used to extract the flux shape. Applications of the method at high neutrino energies (many tens of GeV) are well understood. However, the applicability of the method at the lower energies of current and future few-GeV accelerator neutrino experiments remains unclear due to the presence of nuclear and form-factor effects inherent in the interaction models.In this analysis we examine the prospects for improving constraints on the accelerator neutrino fluxes in situ with the low- $$\nu $$ ν method in an experiment-independent way, using (anti)neutrino interactions on argon and hydrocarbon targets from the GENIE, NEUT, NuWro and GiBUU event generators. We begin by investigating the extent to which deviations from the constant cross-section assumption are dependent on poorly understood aspects of the neutrino interaction model. We then assess whether a low energy-transfer event sample can be confidently identified using experimentally accessible observables. We finally consider how the practicalities of reconstructing the energy spectrum of interacting neutrinos in realistic detectors might further limit the utility of low- $$\nu $$ ν flux constraints. The results show that flux constraints from the low- $$\nu $$ ν method would be severely dependent on the interaction model assumptions used in an analysis of neutrinos with energies below 5 GeV, and anti-neutrinos below at least 15 GeV. The spread of model predictions show that a low- $$\nu $$ ν analysis is unlikely to offer much improvement on typical neutrino flux uncertainties, even with a perfect detector. Notably—running counter to the assumption inherent to the low- $$\nu $$ ν method—the model-dependence increases with decreasing energy transfer for experiments in the few-GeV region.
A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on noisy devices with limited fidelities. Thus, the evaluation of an objective function (e.g., the guiding function in the quantum approximate optimization algorithm (QAOA) or the expectation of the electronic Hamiltonian in variational quantum eigensolver (VQE)) required by a classical optimizer is subject not only to stochastic error from estimating an expected value but also to error resulting from intermittent hardware noise. Model-based derivative-free optimization methods have emerged as popular choices of a classical optimizer in the noisy VQA setting, based on empirical studies. However, these optimization methods were not explicitly designed with the consideration of noise. In this work we adapt recent developments from the “noise-aware numerical optimization” literature to these commonly used derivative-free model-based methods. We introduce the key defining characteristics of these novel noise-aware derivative-free model-based methods that separate them from standard model-based methods. In conclusion, we study an implementation of such noise-aware derivative-free model-based methods and compare its performance on demonstrative VQA simulations to classical solvers packaged in scikit-quant.
New path integral Monte Carlo simulation (PIMC) techniques will be developed and applied to derive the equation of state (EOS) for the regime of warm dense matter and dense plasmas where existing first-principles methods cannot be applied. While standard density functional theory has been used to accurately predict the structure of many solids and liquids up to temperatures on the order of 10,000 K, this method is not applicable at much higher temperature where electronic excitations become important because the number of partially occupied electronic orbitals reaches intractably large numbers and, more importantly, the use of zero-temperature exchange-correlation functionals introduces an uncontrolled approximation. Here we focus on PIMC methods that become more and more efficient with increasing temperatures and still include all electronic correlation effects. In this approach, electronic excitations increase the efficiency rather than reduce it. While it has commonly been assumed such methods can only be applied to elements without core electrons like hydrogen and helium, we recently showed how to extend PIMC to heavier elements by performing the first PIMC simulations of carbon and water plasmas [Driver, Militzer, Phys. Rev. Lett. 108 (2012) 115502]. Here we propose to continue this important development to extend the reach of PIMC simulations to yet heavier elements and also lower temperatures. The goal is to provide a robust first-principles simulation method that can accurately and efficiently study materials with excited electrons at solid-state densities in order to access parts of the phase diagram such the regime of warm dense matter and plasmas where so far only more approximate, semi-analytical methods could be applied.
This work proposes to optimize an additive manufacturing AM process to reduce energy and printing cost. The polymer AM process is inherently dependent on the time-temperature history of each layer to maintain geometric tolerances and mechanical integrity. Our preliminary study shows that regression-based layer time control model using thermal images could result in up to 30% build time reduction for simple geometries. This proposed work would use high-performance computing (HPC) to couple the data-driven model with thermal simulation for better predicting layer temperature profiles, improving throughput of large-scale additive manufacturing, and reducing its energy cost. We have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale AM via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreements with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation. In addition to the development of a layer time optimization method, we have developed a numerical framework for AM simulation with element activations in sync with toolpath, based on an open source finite element framework, DEAL.II. A major portion of this work was presented at SAMPE 2022 Conference and Exhibition on May 2022, and published in Proceedings of SAMPE 2022.
Two pellets of iridium salt were irradiated at the center of the Flattop critical assembly, one at low neutron flux and the other at high flux. After that two counting samples were prepared from each pellet following a given chemical procedure. Measurements of the four counting samples were carried out with two high-resolution silicon drift detectors for over a year. Pulse height spectra of the L→M X-rays from 193 m Ir and 192 Ir were approximated with a Voigt function and the ratio of produced 193 m Ir and 192 Ir nuclei obtained. The 193 m Ir/ 192 Ir ratios from each measured spectrum are compared to identify possible systematic deviations.
Abstract. From June to October, southern Africa produces one-third of the global biomass burning (BB) emissions by widespread fires. BB aerosols are transported westward over the south-eastern Atlantic with the mid-tropospheric winds, resulting in significant radiative effects. Ascension Island (ASI) is located midway between Africa and South America. From June 2016 to October 2017, a 17-month in situ observation campaign on ASI found a low single-scattering albedo (SSA) as well as a high mass absorption cross-section of black carbon (MACBC), demonstrating the strong absorbing marine boundary layer in the south-eastern Atlantic. Here we investigate the monthly variations of critical optical properties of BB aerosols, i.e. SSA and MACBC, during the BB seasons and the driving factors behind these variations. Both SSA and MACBC increase from June to August and decrease in September and October. The average SSA during the BB seasons is 0.81 at 529 nm wavelength, with the highest mean ∼ 0.85 in October and the lowest ∼ 0.78 in August. The absorption enhancement (Eabs) derived from the MACBC shows similar trends with SSA, with the average during the whole of the BB seasons at ∼ 1.96 and ∼ 2.07 in 2016 and 2017, respectively. As the Eabs is higher than the ∼ 1.5 commonly adopted value by climate models, this result suggests the marine boundary layer in the south-eastern Atlantic is more absorbing than model simulations. We find the enhanced ratio of BC to CO (ΔBC/ΔCO, equal to BC/ΔCO as the BC background concentration is considered to be 0) is well correlated with SSA and MACBC, providing a simple way to estimate the aerosol optical characteristics in the south-eastern Atlantic. The exponential function we proposed can approximate SSA and MACBC with BC/ΔCO, and when BC/ΔCO is small it can capture the rapid growth of SSA as BC/ΔCO decreases. BC/ΔCO is influenced by combustion conditions and aerosol scavenging. From the analysis of the location of BB, the primary source fuel, the water content in the fuel, combined with the mean cloud cover and precipitation in the transport areas of the BB plume, we conclude that the increase in BC/ΔCO from June to August is likely to be caused by burning becoming more flaming. The reduction in the water content of fuels may be responsible for the change in the burning conditions from June to August. The decrease in BC/ΔCO in September and October may be caused by two factors, one being a lower proportion of flaming conditions, possibly associated with a decrease in mean surface wind speed in the burning area, and the other being an increase in precipitation in the BB transport pathway, leading to enhanced aerosol scavenging, which ultimately results in an increase in SSA and MACBC.
This report describes the ongoing and planned development of the software package CT-Opt (Current/Tidal Optimization), a control co-design modeling tool for marine hydrokinetic turbines. The commercialization of these turbines has faced significant challenges due to the complex, multidisciplinary nature of their design and the extreme environmental conditions of their operation. This project aims to create a modeling tool that will enable the efficient design of robust, cost-competitive hydrokinetic turbine systems. Rather than using traditional optimization methods, CT-Opt combines multiple models across a range of fidelities to enable coupled optimization of the system design and system controller via a control co-design approach. With this method, the parameters that affect system performance are considered more comprehensively at every stage of the design process. The lowest-fidelity, frequency-domain model called by CT-Opt is RAFT (Response Amplitudes of Floating Turbines), which was originally developed by the National Renewable Energy Laboratory (NREL) to model response amplitudes of floating offshore wind turbines. The highest-fidelity, time-domain model is OpenFAST, which was developed by NREL for land-based and offshore wind turbines. As part of the CT-Opt project, new functionalities will be added to RAFT and OpenFAST to enable the accurate simulation of fixed and floating marine hydrokinetic turbines. In addition to expanding the capabilities of RAFT and OpenFAST, new midfidelity models will be developed. These models will be based on RAFT and OpenFAST and will consist of linearized, state-space models derived from the fully coupled, nonlinear OpenFAST equations and derivative function surrogate models that approximate the nonlinear system behavior. Each model will be coupled with controllers to allow control co-design methods to be applied both within models and across fidelity levels, enabling efficient system optimization.
In this study, we have developed a method to optimize a layer deposition time (a.k.a. layer time) for large-scale additive manufacturing (AM) via physics-based simulations. A long layer time leads to an over-cooled surface on which a new layer is deposited, and therefore, it may result in a weak bonding or debonding between layers, cracking, or warping. A short layer time leads to a high temperature of the structure due to insufficient cooling, and therefore, the structure may not be stiff enough and may collapse during manufacturing. Therefore, it is important to estimate the optimal layer time in additive manufacturing for a high-quality product. The temperature of a top layer right before deposition is recommended to be slightly higher than the glass temperature of the material. A temperature cooling was approximated to an exponential function of time, and the optimized layer time was obtained based on a target temperature while maintaining a minimal printing time. The material used is carbon fiber-reinforced polycarbonate (CF/PC), and the large-scale deposition system used is LSAM TM from Thermwood Corporation. Three different layer time cases were used for experiments, and a series of thermal images were obtained via an infra-red (IR) camera during the entire AM processes. AM process simulations were performed using a finite element method and the temperature profiles from the simulation were in good agreement with those from experiments. The layer time optimization was performed based on the temperature profiles from the simulations. A layer temperature with the optimal layer time was confirmed as the target temperature through simulation.
Many casualties of the Hiroshima and Nagasaki atomic bombs, and many of the firefighters who first responded to the Chernobyl nuclear power plant accident, became ill with acute radiation syndrome (ARS). Most of the people who do not recover from ARS will die within a few weeks to a few months after exposure, with the primary cause of death being the destruction of the body's bone marrow (BM). This is exemplified by the Chernobyl firefighters, the majority of whom received whole body doses between 0.8 and 10 Gy, and the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) final report on the disaster concluded that the underlying cause of death of all 28 firefighters who succumbed to ARS was from bone marrow failure. BM is comprised of hematopoietic stem cells (HSCs) which are responsible for the constant renewal of blood cells. Due to their high rate of proliferation, HSCs are especially vulnerable to ionizing radiation, but are endowed with remarkable regenerative potential. Owing to their central role in blood production, lethal irradiation of HSCs leads to death from severe anemia, infection and internal bleeding. This relationship between high doses of radiation and HSC apoptosis has led to the use of HSC transplantation as a life-saving intervention in cases of acute exposure. This is demonstrable in medical practice, where thousands of individuals have undergone supra-lethal Total Body Irradiation (TBI) for purposes of cancer therapy, and were rescued by well-matched BM transplantation. Indeed, life-threatening damage may be reversed by BM transplantation in individuals receiving doses of radiation of up to 10 Gy. At doses > 10 Gy, damage to gastrointestinal (GI) tissue may become a limiting factor in survival. Remarkably, in the common procedure of BM transplantation, the number of HSCs extracted from a single active BM site containing less than 5% of the donor's BM tissue is sufficient to support the complete reconstitution of the HSC compartment in a lethally irradiated recipient. This capacity of BM to expand and replenish is due to the high regenerative potential of the HSCs it harbors. In contrast to mature blood cells, which are dispersed throughout the body, HSCs are confined to the bones, allowing for effective targeted shielding. This has been confirmed in several animal models, where sparing the HSC-rich area of a subject receiving otherwise full-body irradiation is sufficient to support hematopoietic functions and allow survival. Approximately 48% of the human body's active BM is contained within the lumbar vertebrae and the pelvic region. This high concentration of BM makes the pelvic region an ideal area to shield for preventing the serious hematopoietic effects of radiation exposure. Stem-cell rich tissues like active BM are also more sensitive to the stochastic effects (i.e. cancer) of radiation exposure. The high amount of BM in the pelvic region, combined with the presence of the sensitive gastrointestinal system and female gonads in this same area of the body allows for the possibility of significantly reducing stochastic effects of radiation by limiting radiation exposure to this vital area. Thus, shielding this region holds great promise for both acute and protracted exposures. Thus, a specialized radiation shielding device for the protection of active BM concentrations is presented here. This belt-like selective shielding device focuses on the protection of BM that is present in the pelvis and hip bones, protecting the medullary volume from which BM is commonly extracted for transplantation (i.e. the iliac bones) while allowing relatively unhindered movement of the wearer. In order to optimize the use of shielding materials towards the protection of active BM, the shielding uniquely brings into account the natural shielding properties of human tissue, with thickness being inversely related to the thickness and radiation attenuation of the underlying tissue at each point surrounding the area being protected. The findings resulting from both experimental and simulation testing of this selective shielding device are presented here.
The accurate prediction of singlet and triplet excitation energies is an area of intense research of significant fundamental interest and critical for many applications. Most calculations of singlet and triplet energies use time-dependent density functional theory (TDDFT) in conjunction with an approximate exchange-correlation functional. In this work, we examine and critically assess an alternative method for predicting low-lying neutral excitations with similar computational cost, the ab initio Bethe-Salpeter equation (BSE) approach, and compare results against high-accuracy wavefunction-based methods. We consider singlet and triplet excitations of 27 prototypical organic molecules, including members of Thiel’s set, the acene series, and several aromatic hydrocarbons exhibiting charge-transfer-like excitations. Analogous to its impact in TDDFT, we find that the Tamm-Dancoff approximation (TDA) overcomes triplet instabilities in the BSE approach, improving both triplet and singlet energetics relative to higher level theories. Lastly, we find that BSE-TDA calculations built on effective DFT starting points, such as those utilizing optimally tuned range-separated hybrid functionals, can yield accurate singlet and triplet excitation energies for gas-phase organic molecules.
We describe a framework to reduce the computational effort to evaluate large deviation functions of time integrated observables within nonequilibrium steady states. We do this by incorporating an auxiliary dynamics into trajectory based Monte Carlo calculations, through a transformation of the system's propagator using an approximate guiding function. This procedure importance samples the trajectories that most contribute to the large deviation function, mitigating the exponential complexity of such calculations. Here, we illustrate the method by studying driven diffusion and interacting lattice models in one and two spatial dimensions. Finally, our work offers an avenue to calculate large deviation functions for high dimensional systems driven far from equilibrium.
The paper describes a certain class of homogeneous equations of convolution type in spaces of analytic functions on convex domains. We obtain sufficient conditions under which every solution of an equation from this class is approximated by its elementary solutions.
We perform excited-state variational Monte Carlo and diffusion Monte Carlo calculations using a simple and efficient wave function ansatz. This ansatz follows the recent variation-after-response formalism, accurately approximating a configuration interaction singles wave function as a sum of only two nonorthogonal Slater determinants and further including important orbital relaxation. The ansatz is used to perform diffusion Monte Carlo calculations with large augmented basis sets, comparing to benchmarks from near-exact quantum chemical methods. Here, the significance of orbital optimization in excited-state diffusion Monte Carlo is demonstrated, and the excited-state optimization procedure is discussed in detail. Diffuse excited states in water and formaldehyde are studied, in addition to the formaldimine and benzonitrile molecules.