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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2011

Accurate Cross-section Calculations for Low-Energy Electron-Atom Collisions

We describe a recently developed fully relativistic B-spline R-matrix method for atomic structure as well as calculations for electron and photon collision with atoms and ions. The method is based on the solution of the many-electron Fock-Dirac equation and allows to employ non-orthogonal sets of atomic orbitals. A B-spline basis is used to generate both the target description and the R-matrix basis functions in the inner region. Employing B-splines of different orders for the large and small components prevents the appearance of spurious states in the spectrum of the Dirac equation. Using term-dependent and thus nonorthogonal sets of one-electron functions enables us to generate accurate and flexible representations of the target states and the scattering function. Our method is based upon the Dirac-Coulomb Hamiltonian and thus may be employed for any complex atom or ion, without the use of phenomenological core potentials. Example results from recent applications of the method for accurate calculations of low-energy electron scattering from noble gases are presented. In most cases we obtained a substantial improvement over results obtained in previous Breit-Pauli R-matrix calculations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2016

Coupled Calculations in Helical Steam Generator: Validation on Legacy Data

The NEAMS program aims to develop an integrated multi-physics simulation capability “pellet-to-plant” for the design and analysis of future generations of nuclear power plants. In particular, the Reactor Product Line code suite's multi-resolution hierarchy is being designed to ultimately span the full range of length and time scales present in relevant reactor design and safety analyses, as well as scale from desktop to petaflop computing platforms. Flow-induced vibration (FIV) is widespread problem in energy systems because they rely on fluid movement for energy conversion. Vibrating structures may be damaged as fatigue or wear occurs. Given the importance of reliable components in the nuclear industry, flow-induced vibration has long been a major concern in safety and operation of nuclear reactors. In particular, nuclear fuel rods and steam generators have been known to suffer from flow-induced vibration and related failures. Advanced reactors, such as integral Pressurized Water Reactors (PWRs) considered for Small Modular Reactors (SMR), often rely on innovative component designs to meet cost and safety targets. One component that is the subject of advanced designs is the steam generator, some designs of which forego the usual shell-and-tube architecture in order to fit within the primary vessel. In addition to being more cost- and space-efficient, such steam generators need to be more reliable, since failure of the primary vessel represents a potential loss of coolant and a safety concern. A significant amount of data exists on flow-induced vibration in shell-and-tube heat exchangers, and heuristic methods are available to predict their occurrence based on a set of given assumptions. In contrast, advanced designs have far less data available. Advanced modeling and simulation based on coupled structural and fluid simulations have the potential to predict flow-induced vibration in a variety of designs, reducing the need for expensive experimental programs, especially at the design stage. Over the past five years, the Reactor Product Line has developed the integrated multi-physics code suite SHARP. The goal of developing such a tool is to perform multi-physics neutronics, thermal/fluid, and structural mechanics modeling of the components inside the full reactor core or portions of it with a user-specified fidelity. In particular SHARP contains high-fidelity single-physics codes Diablo for structural mechanics and Nek5000 for fluid mechanics calculations. Both codes are state-of-the-art, highly scalable tools that have been extensively validated. These tools form a strong basis on which to build a flow-induced vibration modeling capability. In this report we discuss one-way coupled calculations performed with Nek5000 and Diablo aimed at simulating available FIV experiments in helical steam generators in the turbulent buffeting regime. In this regime one-way coupling is judged sufficient because the pressure loads do not cause substantial displacements. It is also the most common source of vibration in helical steam generators at the low flows expected in integral PWRs. The legacy data is obtained from two datasets developed at Argonne and B&W.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Demonstration of Fully Coupled Calculations in Helical Steam Generator: Toward Predictions of Fluid Elastic Instability

The NEAMS program aims to develop an integrated multi-physics simulation capability “pellet-to-plant” for the design and analysis of future generations of nuclear power plants. In particular, the Reactor Product Line code suite's multi-resolution hierarchy is being designed to ultimately span the full range of length and time scales present in relevant reactor design and safety analyses, as well as scale from desktop to petaflop computing platforms. Flow-induced vibration (FIV) is widespread problem in energy systems because they rely on fluid movement for energy conversion. Vibrating structures may be damaged as fatigue or wear occurs. Given the importance of reliable components in the nuclear industry, flow-induced vibration has long been a major concern in safety and operation of nuclear reactors. In particular, nuclear fuel rods and steam generators have been known to suffer from flow-induced vibration and related failures. Advanced reactors, such as integral Pressurized Water Reactors (PWRs) considered for Small Modular Reactors (SMR), often rely on innovative component designs to meet cost and safety targets. One component that is the subject of advanced designs is the steam generator, some designs of which forego the usual shell-and-tube architecture in order to fit within the primary vessel. In addition to being more cost- and space-efficient, such steam generators need to be more reliable, since failure of the primary vessel represents a potential loss of coolant and a safety concern. A significant amount of data exists on flow-induced vibration in shell-and-tube heat exchangers, and heuristic methods are available to predict their occurrence based on a set of given assumptions. In contrast, advanced designs have far less data available. Advanced modeling and simulation based on coupled structural and fluid simulations have the potential to predict flow-induced vibration in a variety of designs, reducing the need for expensive experimental programs, especially at the design stage. Over the past seven years, the Reactor Product Line has developed the integrated multi-physics code suite SHARP. The goal of developing such a tool is to perform multi-physics neutronics, thermal/fluid, and structural mechanics modeling of the components inside the full reactor core or portions of it with a user-specified fidelity. In particular SHARP contains high-fidelity single-physics codes Diablo for structural mechanics and Nek5000 for fluid mechanics calculations. Both codes are state-of-the-art, highly scalable tools that have been extensively validated. These tools form a strong basis on which to build a flow-induced vibration modeling capability. In the last two years a significant effort was devoted to 1) develop and validate a fully coupled capability in SHARP for Fluid-Structure calculations (FSI) and 2) validate the use of SHARP for one-way coupled calculations in helical steam generators on legacy data available as well as novel experiments. In this report we discuss an extension of previous one-way coupled calculations performed with Nek5000 and Diablo aimed at simulating available FIV experiments in helical steam generators in the turbulent buffeting regime. It was demonstrated that in the buffeting regime one-way coupling was judged sufficient because the pressure loads do not cause substantial displacements. However, higher speeds led to a reduction in accuracy and the need for a fully coupled capability for velocity or approaching the critical velocity and fluid elastic instability. In this report we demonstrate such fully coupled capability on the Argonne test case discussed in previous reports.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models

Understanding protein conformational variability remains a challenge in drug discovery. The issue arises in protein kinases, whose multiple conformational states can affect the binding of small-molecule inhibitors. To overcome this challenge, we propose a comprehensive computational framework based on Markov state models (MSMs). Our framework integrates the information from explicit-solvent molecular dynamics simulations to accurately rank-order the accessible conformational variants of a target protein. We tested the methodology using Abl kinase with a reference and blind-test set. Only half of the Abl conformational variants discovered by our approach are present in the disclosed X-ray structures. We report the approach successfully identified a protein conformational state not previously observed in public structures but evident in a retrospective analysis of Lilly in-house structures: the X-ray structure of Abl with WHI-P154. Using a MSM-derived model, the free energy landscape and kinetic profile of Abl was analyzed in detail highlighting opportunities for targeting the unique metastable states.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Phase Diagrams of Alloys and Their Hydrides via On-Lattice Graph Neural Networks and Limited Training Data

Efficient prediction of sampling-intensive thermodynamic properties is needed to evaluate material performance and permit high-throughput materials modeling for a diverse array of technology applications. To alleviate the prohibitive computational expense of high-throughput configurational sampling with density functional theory (DFT), surrogate modeling strategies like cluster expansion are many orders of magnitude more efficient but can be difficult to construct in systems with high compositional complexity. We therefore employ minimal-complexity graph neural network models that accurately predict and can even extrapolate to out-of-train distribution formation energies of DFT-relaxed structures from an ideal (unrelaxed) crystallographic representation. This enables the large-scale sampling necessary for various thermodynamic property predictions that may otherwise be intractable and can be achieved with small training data sets. Two exemplars, optimizing the thermodynamic stability of low-density high-entropy alloys and modulating the plateau pressure of hydrogen in metal alloys, demonstrate the power of this approach, which can be extended to a variety of materials discovery and modeling problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

COMBIgor: Data-Analysis Package for Combinatorial Materials Science

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially-resolved characterization of structure and properties. Here, due to maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment, designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data sets. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data analysis and visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

New Poly(dimethylsiloxane)/Poly(perfluorooctylethyl acrylate) Block Copolymers: Structure and Order Across Multiple Length Scales in Thin Films

Three sets of a new class of low surface tension block copolymers were synthesized consisting of a poly(dimethylsiloxane) (PDMS) block and a poly(perfluorooctylethyl acrylate) (AF8) block. The polymers were prepared using a bromo-terminated PDMS macroinitiator, to which was attached an AF8 block grown using atom transfer radical polymerization (ATRP) in such a designed way that the molecular weight and composition of the two polymer blocks were regularly varied. The interplay of both the phase separated microstructure and the mesomorphic character of the fluorinated domains with their effect on surface structure was evaluated using a suite of analytical tools. Surfaces of spin-coated and thermally annealed films were assessed using a combination of X-ray photoelectron spectroscopy (XPS) and near-edge X-ray absorption fine structure (NEXAFS) studies. Both atomic force microscopy (AFM) measurements and grazing incidence small angle X-ray scattering (GISAXS) studies were carried out to evaluate the microstructure of the thin films. Even in block copolymers in which the PDMS block was the majority component, a significant presence of the lower surface energy AF8 block was detected at the film surface. Moreover, the perfluorooctyl helices of the AF8 repeat units were highly oriented at the surface in an ordered, tilted smectic structure, which was compared with those of the bulk powder samples using wide-angle X-ray powder diffraction (WAXD) studies.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unlocking hidden information in sparse small-angle neutron scattering measurements

Hypothesis Small-Angle Neutron Scattering (SANS) is a powerful technique for studying soft matter systems such as colloids, polymers, and lyotropic phases, providing nanoscale structural insights. However, its effectiveness is limited by low neutron flux, leading to long acquisition times and noisy data. Here, we hypothesize that Bayesian statistical inference using Gaussian Process Regression (GPR) can reconstruct high-fidelity scattering data from sparse measurements by leveraging intensity smoothness and continuity. Experiments and Simulations The method was benchmarked computationally and validated through SANS experiments on various soft matter systems, including wormlike micelles, colloidal suspensions, polymeric structures, and lyotropic phases. GPR-based inference was applied to both experimental and synthetic data to evaluate its effectiveness in noise reduction and intensity reconstruction. Findings GPR significantly enhances SANS data quality and therefore reducing measurement times by up to two orders of magnitude. This cost-effective approach maximizes experimental efficiency, enabling high-throughput studies and real-time monitoring of dynamic systems. It is particularly beneficial for weakly scattering and time-sensitive studies. Beyond SANS, this framework applies to other low-SNR techniques, including laboratory-based small-angle X-ray scattering and various dynamical scattering methods. Furthermore, it offers transformative potential for compact neutron sources, enhancing their viability for structural analysis in resource-limited settings.

Small angle neutron scattering↗
DOE OSTI2012

THE GALACTIC PLANE INFRARED POLARIZATION SURVEY (GPIPS): DATA RELEASE 1

The Galactic Plane Infrared Polarization Survey (GPIPS) covers 76 deg{sup 2} of the first Galactic quadrant midplane, 18 Degree-Sign {<=} l {<=} 56 Degree-Sign and -1 Degree-Sign {<=} b {<=} +1 Degree-Sign , in H-band (1.6 {mu}m) linear polarimetry to reveal the plane-of-the-sky orientation of the magnetic field in diffuse and denser atomic and molecular clouds. The Survey consists of 3237 overlapping 10 Multiplication-Sign 10 arcmin fields observed using the Mimir instrument on the 1.8 m Perkins telescope. Here, the first community release of GPIPS data for 559 fields (17% of the Survey) is announced and basic characteristics are described. Data products consist of H-band stellar photometry and polarimetry as well as combined images. The formats and contents of the products are described and quality cuts are explored to provide insight into opportunities and limitations of the data. The Survey probes to distances as far as the Galactic bulge, revealing magnetic field properties that correlate with spiral arms and also show significant small-scale structure. The polarizations are classified into three 'usage' samples, based on stellar brightness and polarimetric uncertainty. The brightest, lowest uncertainty polarizations are suitable for individual use and direct magnetic field mapping. The next two fainter samples are useful, once averaged, for probing magnetic fields to greater distances, albeit with lower resolution. Based on this release, the full GPIPS data set will number about 5.6 million stars, with more than 1 million in the high-quality sample. This increases, by many orders of magnitude, the number of polarimetric probes of the Milky Way's magnetic field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

An improved calculation of the non-Gaussian halo mass function

The abundance of collapsed objects in the universe, or halo mass function, is an important theoretical tool in studying the effects of primordially generated non-Gaussianities on the large scale structure. The non-Gaussian mass function has been calculated by several authors in different ways, typically by exploiting the smallness of certain parameters which naturally appear in the calculation, to set up a perturbative expansion. We improve upon the existing results for the mass function by combining path integral methods and saddle point techniques (which have been separately applied in previous approaches). Additionally, we carefully account for the various scale dependent combinations of small parameters which appear. Some of these combinations in fact become of order unity for large mass scales and at high redshifts, and must therefore be treated non-perturbatively. Our approach allows us to do this, and also to account for multi-scale density correlations which appear in the calculation. We thus derive an accurate expression for the mass function which is based on approximations that are valid over a larger range of mass scales and redshifts than those of other authors. By tracking the terms ignored in the analysis, we estimate theoretical errors for our result and also for the results of others. We also discuss the complications introduced by the choice of smoothing filter function, which we take to be a top-hat in real space, and which leads to the dominant errors in our expression. Finally, we present a detailed comparison between the various expressions for the mass functions, exploring the accuracy and range of validity of each.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

ForSE: A GAN-based Algorithm for Extending CMB Foreground Models to Subdegree Angular Scales

We present ForSE (Foreground Scale Extender), a novel Python package that aims to overcome the current limitations in the simulation of diffuse Galactic radiation, in the context of cosmic microwave background (CMB) experiments. ForSE exploits the ability of generative adversarial neural networks (GANs) to learn and reproduce complex features present in a set of images, with the goal of simulating realistic and non-Gaussian foreground radiation at subdegree angular scales. This is of great importance in order to estimate the foreground contamination to lensing reconstruction, delensing, and primordial B-modes for future CMB experiments. We applied this algorithm to Galactic thermal dust emission in both total intensity and polarization. Our results show how ForSE is able to generate small-scale features (at 12') having as input the large-scale ones (80'). The injected structures have statistical properties, evaluated by means of the Minkowski functionals, in good agreement with those of the real sky and which show the correct amplitude scaling as a function of the angular dimension. Furthermore, the obtained thermal dust Stokes Q and U full-sky maps as well as the ForSE package are publicly available for download.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Artificially Structured Semiconductors to Model Novel Quantum Phenomena

Award Period: September 1st, 2013 through February 15th, 2017 Submitted to the USDOE Office of Basic Energy Sciences By Aron Pinczuk and Shalom J. Wind Department of Applied Physics and Applied Mathematics Columbia University New York, NY 10027 January 2017 Award # DE-SC0010695 ABSTRACT Research in this project seeks to design, create and study a class of tunable artificial quantum structures in order to extend the range and scope of new and exciting physical phenomena and to explore the potential for new applications. Advanced nanofabrication was used to create an external potential landscape that acts as a lattice of confinement sites for electrons (and/or holes) in a two-dimensional electron gas in a high perfection semiconductor in such a manner that quantum interactions between different sites dictate the significant physics. Our current focus is on ‘artificial graphene’ (AG) in which a set of quantum dots (or sites) are patterned in a honeycomb lattice. The combination of leading edge nanofabrication with ultra-pure semiconductor materials in this project extends the frontier for small period, low-disorder AG systems, enabling the exploration of graphene physics in a semiconductor platform. TECHNICAL DESCRIPTION Contemporary condensed matter science has entered an era of discovery of new low-dimensional materials, such as graphene and other atomically thin materials, that exhibit exciting new physical phenomena that were previously inaccessible. Concurrent with the discovery and development of these new materials are impressive advancements in nanofabrication, which offer an ever-expanding toolbox for creating a myriad of high quality patterns at nanoscale dimensions. This project started about four years ago. Among its major achievements are the realizations of very small period artificial lattices with honeycomb topology in GaAs quantum wells. In our most recent work the periods of the ‘artificial graphene’ (AG) lattices extend down to 40 nm. These small periods are about three times smaller than previously reported in GaAs quantum wells. This milestone establishes a new state-of-the-art in fields of research and nanofabrication. In experiments using optical scattering methods we uncovered evidence that free electrons in the small period AG lattices display novel features that arise from the symmetry of the honeycomb lattice. These achievements create semiconductor platforms for explorations of novel states and effects that offer opportunities to create quasiparticles with tunable character. The quest for the discovery of novel quantum physics by nanofabrication of ‘artificial structures’ in semiconductor quantum structures overlaps with the development of quantum simulators. Nanopatterns were created at Columbia University by the group of co-PI Shalom Wind using a 100keV e-beam nanolithography instrument (along with associated processing) that is part of the Columbia Nano Initiative. Optical experiments were carried out in the group of PI Aron Pinczuk. GaAs/AlGaAs quantum wells(QWs) of world-class perfection that serve as electron hosts are the starting material grown by molecular beam epitaxy (MBE) by our partners Dr. Loren Pfeiffer (Princeton Univ.) and Prof. Michael Manfra (Purdue Univ.). The inductively coupled plasma reactive ion etching (ICP-RIE) was carried out at the PRISM Micro/Nano Fabrication Laboratory of Princeton University. Dr. Vittorio Pellegrini (Istituto Italiano di Tecnologia, Genoa, Italy) has contributed critical insight on this research. Two graduate students in this project Sheng Wang and Diego Scarabelli, graduated in the summer/fall of 2016. Dr. Yuliya Kuznetsova has been a postdoc in the group. The current work is led by Dr. Lingjie Du, a postdoctoral scientist that joined the group of the PI on October 15th, 2016. Since the start of this project we have focused primarily on developing protocols towards the fabrication of the artificial lattices and in the implementation of characterization methods to identify the new electron states that are created. A major milestone has been the realization of the Dirac cones that emerge in honeycomb lattices. In the next phases of this project, and as we achieve milestones, we wish to expand our experimental platform so that we will be better positioned to access emergent novel quantum behavior and phases of electrons in artificial lattices designed and fabricated with ‘exquisite’ precision. The AG lattices with greatly reduced disorder from nano-processing that we have already created demonstrate that we are poised for the next stage exploration of graphene physics and applications in semiconductor quantum structures.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Fluctuating micro-heterogeneity in water–tert-butyl alcohol mixtures and lambda-type divergence of the mean cluster size with phase transition-like multiple anomalies

Water–tert-butyl alcohol (TBA) binary mixture exhibits a large number of thermodynamic and dynamic anomalies. These anomalies are observed at surprisingly low TBA mole fraction, with x{sub TBA} ≈ 0.03–0.07. We demonstrate here that the origin of the anomalies lies in the local structural changes that occur due to self-aggregation of TBA molecules. We observe a percolation transition of the TBA molecules at x{sub TBA} ≈ 0.05. We note that “islands” of TBA clusters form even below this mole fraction, while a large spanning cluster emerges above that mole fraction. At this percolation threshold, we observe a lambda-type divergence in the fluctuation of the size of the largest TBA cluster, reminiscent of a critical point. Alongside, the structure of water is also perturbed, albeit weakly, by the aggregation of TBA molecules. There is a monotonic decrease in the tetrahedral order parameter of water, while the dipole moment correlation shows a weak nonlinearity. Interestingly, water molecules themselves exhibit a reverse percolation transition at higher TBA concentration, x{sub TBA} ≈ 0.45, where large spanning water clusters now break-up into small clusters. This is accompanied by significant divergence of the fluctuations in the size of largest water cluster. This second transition gives rise to another set of anomalies around. Both the percolation transitions can be regarded as manifestations of Janus effect at small molecular level.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Improved renormalization group computation of likelihood functions for cosmological data sets

Evaluation of likelihood functions for cosmological large scale structure data sets (including CMB, galaxy redshift surveys, etc.) naturally involves marginalization, i.e., integration, over an unknown underlying random signal field. Recently, I showed how a renormalization group method can be used to carry out this integration efficiently by first integrating out the smallest scale structure, i.e., localized structure on the scale of differences between nearby data cells, then combining adjacent cells in a coarse graining step, then repeating this process over and over until all scales have been integrated. Here I extend the formulation in several ways in order to reduce the prefactor on the method's linear scaling with data set size. The key improvement is showing how to integrate out the difference between specific adjacent cells before summing them in the coarse graining step, compared to the original formulation in which small-scale fluctuations were integrated more generally. I suggest some other improvements in details of the scheme, including showing how to perform the integration around a maximum likelihood estimate for the underlying random field. In the end, an accurate likelihood computation for a million-cell Gaussian test data set runs in two minutes on my laptop, with room for further optimization and straightforward parallelization.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Mobility and coalescence of stacking fault tetrahedra in Cu

Stacking fault tetrahedra (SFTs) are ubiquitous defects in face-centered cubic metals. They are produced during cold work plastic deformation, quenching experiments or under irradiation. From a dislocation point of view, the SFTs are comprised of a set of stair-rod dislocations at the (110) edges of a tetrahedron bounding triangular stacking faults. These defects are extremely stable, increasing their energetic stability as they grow in size. At the sizes visible within transmission electron microscope they appear nearly immobile. Contrary to common belief, we show in this report, using a combination of molecular dynamics and temperature accelerated dynamics, how small SFTs can diffuse by temporarily disrupting their structure through activated thermal events. More over, we demonstrate that the diffusivity of defective SFTs is several orders of magnitude higher than perfect SFTs, and can be even higher than isolated vacancies. Finally, we show how SFTs can coalesce, forming a larger defect in what is a new mechanism for the growth of these omnipresent defects.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Interacting dark sector and precision cosmology

We consider a recently proposed model in which dark matter interacts with a thermal background of dark radiation. Dark radiation consists of relativistic degrees of freedom which allow larger values of the expansion rate of the universe today to be consistent with CMB data (H 0 -problem). Scattering between dark matter and radiation suppresses the matter power spectrum at small scales and can explain the apparent discrepancies between ΛCDM predictions of the matter power spectrum and direct measurements of Large Scale Structure LSS (σ 8 -problem). We go beyond previous work in two ways: 1. we enlarge the parameter space of our previous model and allow for an arbitrary fraction of the dark matter to be interacting and 2. we update the data sets used in our fits, most importantly we include LSS data with full k-dependence to explore the sensitivity of current data to the shape of the matter power spectrum. We find that LSS data prefer models with overall suppressed matter clustering due to dark matter - dark radiation interactions over ΛCDM at 3-4 σ. However recent weak lensing measurements of the power spectrum are not yet precise enough to clearly distinguish two limits of the model with different predicted shapes for the linear matter power spectrum. In two Appendices we give a derivation of the coupled dark matter and dark radiation perturbation equations from the Boltzmann equation in order to clarify a confusion in the recent literature, and we derive analytic approximations to the solutions of the perturbation equations in the two physically interesting limits of all dark matter weakly interacting or a small fraction of dark matter strongly interacting.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

Velocity structure of self-similar spherically collapsed halos

Using a generalized self-similar secondary infall model, which accounts for tidal torques acting on the halo, we analyze the velocity profiles of halos in order to gain intuition for N-body simulation results. We analytically calculate the asymptotic behavior of the internal radial and tangential kinetic energy profiles in different radial regimes. We then numerically compute the velocity anisotropy and pseudo-phase-space density profiles and compare them to recent N-body simulations. For cosmological initial conditions, we find both numerically and analytically that the anisotropy profile asymptotes at small radii to a constant set by model parameters. It rises on intermediate scales as the velocity dispersion becomes more radially dominated and then drops off at radii larger than the virial radius where the radial velocity dispersion vanishes in our model. The pseudo-phase-space density is universal on intermediate and large scales. However, its asymptotic slope on small scales depends on the halo mass and on how mass shells are torqued after turnaround. The results largely confirm N-body simulations but show some differences that are likely due to our assumption of a one-dimensional phase space manifold.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Systematic Parameterization of Lignin for the CHARMM Force Field

Plant cell walls have three primary components, cellulose, hemicellulose, and lignin, the latter of which is a recalcitrant, aromatic heteropolymer that provides structure to plants, water and nutrient transport through plant tissues, and a highly effective defense against pathogens. Overcoming the recalcitrance of lignin is key to effective biomass deconstruction, which would in turn enable the use of biomass as a feedstock for industrial processes. Our understanding of lignin structure in the plant cell wall is hampered by the limitations of the available lignin forcefields, which currently only account for a single linkage between lignins and lack explicit parameterization for emerging lignin structures both from natural variants and engineered lignin structures. Since polymerization of lignin occurs via radical intermediates, multiple C-O and C-C linkages have been isolated , and the current force field only represents a small subset of lignin the diverse lignin structures found in plants. In order to take into account the wide range of lignin polymerization chemistries, monomers and dimer combinations of C-, H-, G-, and S-lignins as well as with hydroxycinnamic acid linkages were subjected to extensive quantum mechanical calculations to establish target data from which to build a complete molecular mechanics force field tuned specifically for diverse lignins. This was carried out in a GPU-accelerated global optimization process, whereby all molecules were parameterized simultaneously using the same internal parameter set. By parameterizing lignin specifically, we are able to more accurately represent the interactions and conformations of lignin monomers and dimers relative to a general force field. This new force field will enables computational researchers to study the effects of different linkages on the structure of lignin, as well as construct more accurate plant cell wall models based on observed statistical distributions of lignin that differ between disparate feedstocks, and guide further lignin engineering efforts.

09 BIOMASS FUELS↗