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DOE OSTI2017

A degradation function consistent with Cocks–Ashby porosity kinetics

Here, the load carrying capacity of ductile materials degrades as a function of porosity, stress state and strain-rate. The effect of these variables on porosity kinetics is captured by the Cocks–Ashby model; however, the Cocks–Ashby model does not account for material degradation directly. This work uses a yield criteria to form a degradation function that is consistent with Cocks–Ashby porosity kinetics and is a function of porosity, stress state and strain-rate dependence. Approximations of this degradation function for pure hydrostatic stress states are also explored.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Ground state wave functions for single-band Hubbard models from the Gutzwiller conjugate gradient minimisation theory

The Gutzwiller conjugate gradient minimisation (GCGM) theory is an ab initio quantum many-body theory for computing the ground-state properties of infinite systems. Previous applications of GCGM provides satisfying accuracy of ground-state energy of Hubbard models. In the current work, we address the problem of whether the obtained wave function is a good approximation for the true ground state by comparing the correlation functions with the benchmark data. Additionally, our results confirms the accuracy of the reproduced ground state of the regular Hubbard model, but with some exception for the frustrated Hubbard model.

74 ATOMIC AND MOLECULAR PHYSICS↗

NASA NTRS1984

A new minimax algorithm

The representation min t s.t. F(I)(x). - t less than or equal to 0 for all i is examined. An active set strategy is designed of functions: active, semi-active, and non-active. This technique will help in preventing zigzagging which often occurs when an active set strategy is used. Some of the inequality constraints are handled with slack variables. Also a trust region strategy is used in which at each iteration there is a sphere around the current point in which the local approximation of the function is trusted. The algorithm is implemented into a successful computer program. Numerical results are provided.

Vardi, A.↗

DOE OSTI2016

Description of quasiparticle and satellite properties via cumulant expansions of the retarded one-particle Green's function

A cumulant-based GW approximation for the retarded one-particle Green's function is proposed, motivated by an exact relation between the improper Dyson self-energy and the cumulant generating function. We explore qualitative aspects of this method within a simple one-electron independent phonon model, where it is seen that the method preserves the energy moment of the spectral weight while also reproducing the exact Green's function in the weak-coupling limit. For the three-dimensional electron gas, this method predicts multiple satellites at the bottom of the band, albeit with inaccurate peak spacing. But, its quasiparticle properties and correlation energies are more accurate than both previous cumulant methods and standard G0W0. These results point to features that may be exploited within the framework of cumulant-based methods and suggest promising directions for future exploration and improvements of cumulant-based GW approaches.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Evaluating the Feasibility of Measuring and Analyzing Strain Caused by Changes in Barometric Pressure in the Vadose Zone

Changes in barometric pressure propagate into the subsurface where they can affect water level measurements in wells and cause vertical strain. Previous strain sensors were developed to measure vertical strain, but they were limited to measurements at a single depth, making it difficult to evaluate strains from a migrating pressure wave. Technology known as Coherence-length-gated Microwave Photonics Interferometry (CMPI) uses optical fiber sensors to measure strain at multiple locations. It has the potential to detect strain caused by variations in barometric pressure at multiple depths in the subsurface. The goal of this study is to evaluate the feasibility of using this technology to record the changes in vertical strain in the vadose zone from small fluctuations in air pressure. Propagation of these air pressure fluctuations is affected by hydrogeological properties such as water content and permeability. A second goal of this study is to evaluate how these properties affect the air pressure distribution, strain, and gas-phase diffusivity with a long-term goal of using strain to monitor the vadose zone. The focus of this study is on a suite of laboratory experiments that used a sand-filled column with an open head space. The CMPI fiber was packaged and embedded along the axis of the column to measure the strain at multiple locations while air pressure transducers were installed through the wall of the column. Small periodic fluctuations in air pressure, similar to barometric pressure fluctuations, were created in the head space of the column using an audio driven speaker driven at 4 Hz using a sinusoidal signal created by a function generator. Then, the fluctuations propagated along the column where they were measured by the transducers and recorded as functions of time. Initial tests were conducted using dry sand, but then additional tests were conducted after injecting water into the column, which changed the hydrologic and mechanical properties. Another experiment was conducted after creating a thin barrier to air flow, which was designed to simulate a thin layer of saturated ground during rainfall. A final experiment was conducted by changing the grain size of the material to create a heterogeneity in the upper half of the column. The suite of six experiments was conducted to highlight effects of variations in water content and heterogeneities on air pressure and vertical strain. Numerical simulations were also created to evaluate the laboratory data and to provide a baseline analysis for the results. The numerical simulations used the governing equations to linear poroelasticity and two-phase flow with boundary conditions representing the experiments. The conceptual model for strain caused by barometric pressure recognizes that a fluctuating barometric pressure causes fluctuations in the air pore pressure that decrease in amplitude and lag in time with increase depth. The pressure distribution results in two different loads that cause strain: 1.) a mechanical load as the barometric pressure acts on the ground surface; and 2.) pressure loading in the pore space. The results show that the total vertical strain is a contribution of both of these loads, and this is the strain that was observed in the laboratory. The lab experiments show that both air pressure and strain vary as sinusoids that propagate with depth. The amplitude of the air pressure and strain decrease as a function of depth in all the conditions that were evaluate. For example, in the dry sand, the amplitude of the air pressure decreases from 25 Pa at the head space to 5 Pa at a depth of 70 cm, whereas the amplitude of the strain decreases from 0.15 to 0.06 µε over the same depth interval. In many cases the amplitudes decrease is approximately negative exponential functions of depth. When water was injected into the sand and the saturation increased, the amplitude of the air pressure increased in the partially saturated sand but decreased sharply to zero where the soil was saturated. The strain decreased with depth, but the transition from partially to fully saturated conditions had little effect on the strain profile, even though it had a major effect on the air pressure. This apparently is because strain is caused by variations in both water and air pressure. The fluctuating air pressure caused the water pressure to fluctuate, which caused strain throughout the column. Phase delay of the air pressure and the strain both increase as linear to bi-linear functions of depth. The phase delays indicate that pressure propagates at a velocity of 7 to 8 m/s in dry or partially saturated sand, but it drops by more than an order of magnitude in the vicinity of the capillary fringe. The phase delays indicate the strain propagates faster than the pressure. The data indicate that the strain propagates from 1.2 to more than 4x faster than the pressure. Switching from sand to silt in the column caused the velocity of the pressure to drop by roughly half for both pressures, but curiously, it had little effect on the velocity of the strain.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Diffusion quantum Monte Carlo calculations of SrFeO 3 and LaFeO 3

The equations of state, formation energy, and migration energy barrier of the oxygen vacancy in SrFeO 3 and LaFeO 3 were calculated in this paper with the diffusion quantum Monte Carlo (DMC) method. Calculations were also performed with various Density Functional Theory (DFT) approximations for comparison. DMC reproduces the measured cohesive energies of these materials with errors below 0.23(5) eV and the structural properties within 1% of the experimental values. The DMC formation energies of the oxygen vacancy in SrFeO 3 and LaFeO 3 under oxygen-rich conditions are 1.3(1) and 6.24(7) eV, respectively. Similar calculations with semi-local DFT approximations for LaFeO 3 yielded vacancy formation energies 1.5 eV lower. Comparison of charge density evaluated with DMC and DFT approximations shows that DFT tends to overdelocalize the electrons in defected SrFeO 3 and LaFeO 3 . Finally, calculations with DMC and local density approximation yield similar vacancy migration energy barriers, indicating that steric/electrostatic effects mainly determine migration barriers in these materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Quasi-Stochastic Approximation and Off-Policy Reinforcement Learning: Preprint

The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic frameworks for reinforcement learning and often an efficient approach to solving (or approximating the solution to) complex optimal control problems. In many cases, however, practitioners are unable to apply these techniques because of an inherent high variance. This paper aims to provide a general foundation for 'quasi-stochastic approximation,' in which all of the processes under consideration are deterministic, much like quasi-Monte Carlo for variance reduction in simulation. The variance reduction can be substantial, subject to tuning of pertinent parameters in the algorithm. This paper introduces a new coupling argument to establish the optimal rate of convergence provided the gain is sufficiently large. These results are established for linear models and tested also in nonideal settings. A major application of these general results is a new class of reinforcement learning algorithms for deterministic state space models. In this setting, the main contribution is a class of algorithms for approximating the value function for a given policy, using a different policy designed to introduce exploration.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Single field double inflation and primordial black holes

Within the framework of scalar-tensor theories, we study the conditions that allow single field inflation dynamics on small cosmological scales to significantly differ from that of the large scales probed by the observations of cosmic microwave background. The resulting single field double inflation scenario is characterised by two consequent inflation eras, usually separated by a period where the slow-roll approximation fails. At large field values the dynamics of the inflaton is dominated by the interplay between its non-minimal coupling to gravity and the radiative corrections to the inflaton self-coupling. For small field values the potential is, instead, dominated by a polynomial that results in a hilltop inflation. Without relying on the slow-roll approximation, which is invalidated by the appearance of the intermediate stage, we propose a concrete model that matches the current measurements of inflationary observables and employs the freedom granted by the framework on small cosmological scales to give rise to a sizeable population of primordial black holes generated by large curvature fluctuations. We find that these features generally require a potential with a local minimum. We show that the associated primordial black hole mass function is only approximately lognormal.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1973

Aircraft Range Optimization Using Singular Perturbations

An approximate analytic solution is developed for the problem of maximizing the range of an aircraft for a fixed end state. The problem is formulated as a singular perturbation and solved by matched inner and outer asymptotic expansions and the minimum principle of Pontryagin. Cruise in the stratosphere, and on transition to and from cruise at constant Mach number are discussed. The state vector includes altitude, flight path angle, and mass. Specific fuel consumption becomes a linear function of power approximating that of the cruise values. Cruise represents the outer solution; altitude and flight path angle are constants, and only mass changes. Transitions between cruise and the specified initial and final conditions correspond to the inner solutions. The mass is constant and altitude and velocity vary. A solution is developed which is valid for cruise but which is not for the initial and final conditions. Transforming of the independent variable near the initial and final conditions result in solutions which are valid for the two inner solutions but not for cruise. The inner solutions can not be obtained without simplifying the state equations. The singular perturbation approach overcomes this difficulty. A quadratic approximation of the state equations is made. The resulting problem is solved analytically, and the two inner solutions are matched to the outer solution.

Oconnor, Joseph Taffe↗

NASA NTRS2013

Covalent Crosslinking of Carbon Nanotube Materials for Improved Tensile Strength

Carbon nanotubes have attracted much interest in recent years due to their exceptional mechanical properties. Currently, the tensile properties of bulk carbon nanotube-based materials (yarns, sheets, etc.) fall far short of those of the individual nanotube elements. The premature failure in these materials under tensile load has been attributed to inter-tube sliding, which requires far less force than that needed to fracture individual nanotubes.1,2 In order for nanotube materials to achieve their full potential, methods are needed to restrict this tube-tube shear and increase inter-tube forces.Our group is examining covalent crosslinking between the nanotubes as a means to increase the tensile properties of carbon nanotube materials. We are working with multi-walled carbon nanotube (MWCNT) sheet and yarn materials obtained from commercial sources. Several routes to functionalize the nanotubes have been examined including nitrene, aryl diazonium, and epoxide chemistries. The functional nanotubes were crosslinked through small molecule or polymeric bridges. Additionally, electron beam irradiation induced crosslinking of the non-functional and functional nanotube materials was conducted. For example, a nanotube sheet material containing approximately 3.5 mol amine functional groups exhibited a tensile strength of 75 MPa and a tensile modulus of 1.16 GPa, compared to 49 MPa and 0.57 GPa, respectively, for the as-received material. Electron beam irradiation (2.2x 1017 ecm2) of the same amine-functional sheet material further increased the tensile strength to 120 MPa and the modulus to 2.61 GPa. This represents approximately a 150 increase in tensile strength and a 360 increase in tensile modulus over the as-received material with only a 25 increase in material mass. Once we have optimized the nanotube crosslinking methods, the performance of these materials in polymer matrix composites will be evaluated.

polymer matrix composites↗

DOE OSTI2017

Accelerated Time-Domain Modeling of Electromagnetic Pulse Excitation of Finite-Length Dissipative Conductors over a Ground Plane via Function Fitting and Recursive Convolution

In this report we overview the fundamental concepts for a pair of techniques which together greatly hasten computational predictions of electromagnetic pulse (EMP) excitation of finite-length dissipative conductors over a ground plane. In a time- domain, transmission line (TL) model implementation, predictions are computationally bottlenecked time-wise, either for late-time predictions (about 100ns-10000ns range) or predictions concerning EMP excitation of long TLs (order of kilometers or more ). This is because the method requires a temporal convolution to account for the losses in the ground. Addressing this to facilitate practical simulation of EMP excitation of TLs, we first apply a technique to extract an (approximate) complex exponential function basis-fit to the ground/Earth's impedance function, followed by incorporating this into a recursion-based convolution acceleration technique. Because the recursion-based method only requires the evaluation of the most recent voltage history data (versus the entire history in a "brute-force" convolution evaluation), we achieve necessary time speed- ups across a variety of TL/Earth geometry/material scenarios. Intentionally Left Blank

36 MATERIALS SCIENCE↗

NASA NTRS2015

CALIPSO Observations of Near-Cloud Aerosol Properties as a Function of Cloud Fraction

This paper uses spaceborne lidar data to study how near-cloud aerosol statistics of attenuated backscatter depend on cloud fraction. The results for a large region around the Azores show that: (1) far-from-cloud aerosol statistics are dominated by samples from scenes with lower cloud fractions, while near-cloud aerosol statistics are dominated by samples from scenes with higher cloud fractions; (2) near-cloud enhancements of attenuated backscatter occur for any cloud fraction but are most pronounced for higher cloud fractions; (3) the difference in the enhancements for different cloud fractions is most significant within 5km from clouds; (4) near-cloud enhancements can be well approximated by logarithmic functions of cloud fraction and distance to clouds. These findings demonstrate that if variability in cloud fraction across the scenes used to composite aerosol statistics are not considered, a sampling artifact will affect these statistics calculated as a function of distance to clouds. For the Azores-region dataset examined here, this artifact occurs mostly within 5 km from clouds, and exaggerates the near-cloud enhancements of lidar backscatter and color ratio by about 30. This shows that for accurate characterization of the changes in aerosol properties with distance to clouds, it is important to account for the impact of changes in cloud fraction.

near-cloud aerosol↗

DOE OSTI2021

Injection data analysis using material balance time for CO 2 storage capacity estimation in deep closed saline aquifers

Estimating the ultimate storage capacity of deep saline aquifers is important to address the formation potential to store the envisioned large volumes of CO 2 . Injection data (i.e. injection rate, bottomhole pressure, and cumulative injected volume of CO 2 ) are routinely recorded during storage operations. These data contain valuable information on the subsurface (e.g. the reservoir pore volume and the formation storage capacity) that can be extracted. In this paper, we present a two-step graphical technique to infer the pore volume and the ultimate storage capacity of closed saline aquifers by analyzing the available injection data. First, the pore volume is inferred through adapting the concept of the material balance time. Material balance time is an approximate superposition time function developed to interpret production data from oil and gas wells operating at variable pressure/rate conditions during the boundary-dominated flow period. Using material balance techniques, the ultimate storage capacity is then estimated through linear extrapolation of the average pressure trend to the maximum allowable pressure the formation can withstand. The average pressure is not available in practice, but is can be obtained from the injection data. Two approaches are presented in this study to calculate the average pressure; namely the rigorous and the approximate approaches. Unlike the rigorous approach, the approximate approach does not require a prior knowledge of some reservoir properties (e.g. relative permeability, absolute permeability, formation porosity and thickness) to calculate the average pressure. To investigate its potential and reliability in analyzing CO 2 injection data, the proposed technique is applied to four synthetic cases representing different well operating conditions. Results indicate that the approximate approach consistently overestimates the actual (simulated) storage capacity as compared to the rigorous approach. The agreement - between the inferred and the simulated reservoir pore volume, and between the analytical and numerical estimates of storage capacity - validates the potential application of the technique to CO 2 storage in closed saline aquifers. The technique is further substantiated through application to a field data set utilized from a commercial-scale geological storage (CGS) project. Finally, field data interpretation shows that the proposed technique can be utilized to identify the degree of hydraulic continuity and reservoir compartmentalization within a target formation by interpreting the corresponding pressure and rate responses.

02 PETROLEUM↗

NASA NTRS1990

Verification of floating-point software

Floating point computation presents a number of problems for formal verification. Should one treat the actual details of floating point operations, or accept them as imprecisely defined, or should one ignore round-off error altogether and behave as if floating point operations are perfectly accurate. There is the further problem that a numerical algorithm usually only approximately computes some mathematical function, and we often do not know just how good the approximation is, even in the absence of round-off error. ORA has developed a theory of asymptotic correctness which allows one to verify floating point software with a minimum entanglement in these problems. This theory and its implementation in the Ariel C verification system are described. The theory is illustrated using a simple program which finds a zero of a given function by bisection. This paper is presented in viewgraph form.

Hoover, Doug N.↗

DOE OSTI2019

Neutrino-Nucleus Cross Section within the Extended Factorization Scheme

The factorization scheme, based on the impulse approximation and the spectral function formalism, has been recently generalized to allow the description of electromagnetic nuclear interactions driven by two-nucleon currents. We have extended this framework to the case of weakly charged and neutral currents, and carried out calculations of the double-differential neutrino-carbon and neutrino-oxygen cross sections using two different models of the target spectral functions. The results, showing a moderate dependence on the input spectral function, confirm that our approach provides a consistent treatment of all reaction mechanisms contributing to the signals detected by accelerator-based neutrino experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2022

HomPINNs: Homotopy physics-informed neural networks for learning multiple solutions of nonlinear elliptic differential equations

Physics-informed neural networks (PINNs) based machine learning is an emerging framework for solving nonlinear differential equations. However, due to the implicit regularity of neural network structure, PINNs can only find the flattest solution in most cases by minimizing the loss functions. In this paper, we combine PINNs with the homotopy continuation method, a classical numerical method to compute isolated roots of polynomial systems, and propose a new deep learning framework, named homotopy physics-informed neural networks (HomPINNs), for solving multiple solutions of nonlinear elliptic differential equations. The implementation of an HomPINN is a homotopy process that is composed of the training of a fully connected neural network, named the starting neural network, and training processes of several PINNs with different tracking parameters. The starting neural network is to approximate a starting function constructed by the trivial solutions, while other PINNs are to minimize the loss functions defined by boundary condition and homotopy functions, varying with different tracking parameters. These training processes are regraded as different steps of a homotopy process, and a PINN is initialized by the well-trained neural network of the previous step, while the first starting neural network is initialized using the default initialization method. Finally, several numerical examples are presented to show the efficiency of our proposed HomPINNs, including reaction-diffusion equations with a heart-shaped domain.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Updates to R-matrix Evaluations of Fissile Actinides: 233,235 U, 239 Pu [Slides]

This presentation begins by discussing the importance and significance of cross section fluctuations. It also discusses the motivation and overarching goals which are to establish the highest fidelity nuclear evaluated data. In conclusion, a procedure to accurately model the measured cross section fluctuations for inclusion into evaluated nuclear data files was developed and tested on 239 Pu total cross sections. The procedure consisted of defining population of levels as well as related amplitudes extrapolated from the systematics of the resonance parameters in the resolved resonance region. By using realistic population of extrapolated levels, the fit of measured total cross section produced a continuous S-matrix function S( E ) in Reich-Moore approximation. The S-matrix function averaged over a suitable energy interval was used to obtain averaged cross sections consistent with the measured data. Transmittable formatted files (ENDF, GNDS) with LSSF=1 will consist of File 3 which are smoothed cross sections and File 2 which are average resonance parameters fitted to the energy bins defined in File 3. The capability to store probability tables is a way to show the fluctuating cross sections.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2019

Partition Density Functionals: Theory and Applications. Final Technical Report

Partition Density Functional Theory is a method to calculate the energy and density of a molecule via self-consistent calculations on isolated fragments. At the start of this project, Partition Density Functional Theory (PDFT) had only been applied to one-dimensional model systems of non-interacting electrons. Its most appealing feature (the reason why PDFT was born in the first place) was that it allowed one to construct an internally-consistent Density Functional Theory (DFT) valid for non-integer electron numbers to be used in the context of Chemical Reactivity Theory. Many formal properties of the quantities entering the theory, such as partition potentials, were unknown when the project started. The connections with other fragment-based electronic structure methods, and especially with embedding theories, were unclear, and the potential of PDFT for fixing problems of approximate exchange-correlation functionals was unanticipated. Supported by grant DE-SC0005031, we made progress in developing, understanding, implementing, and applying PDFT. We discovered its potential for applications in situations where approximate DFT fails, such as when stretching bonds or when calculating the energy of weakly-interacting systems, both cases of importance for energy research applications. We extended PDFT in several directions (spin-densities, current densities, external electric and magnetic fields), and carried out many calculations on diatomic molecules and small clusters to test ideas for approximate non-additive functionals. As a result, we were able to propose physically-motivated fragment-density approximations for both covalent and weak bonds. Because many applications will increasingly benefit from efficient and accurate electronic-structure calculations via density-embedding techniques, the research results enabled by this grant should prove useful for addressing complex problems in a wide variety of fields, from chemistry to molecular biology and materials engineering.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗