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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Efficient Space–Time Reduced Order Model for Linear Dynamical Systems in Python Using Less than 120 Lines of Code

A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space–time ROM for linear dynamical problems has been developed [Choi et al., Space–tume reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems, Journal of Computational Physics, 2020], which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The authors show an order of a thousand speed-up with a relative error of less than 10−5 for a large-scale Boltzmann transport problem. In this work, we present for the first time the derivation of the space–time least-squares Petrov–Galerkin (LSPG) projection for linear dynamical systems and its corresponding block structures. Utilizing these block structures, we demonstrate the ease of construction of the space–time ROM method with two model problems: 2D diffusion and 2D convection diffusion, with and without a linear source term. For each problem, we demonstrate the entire process of generating the full order model (FOM) data, constructing the space–time ROM, and predicting the reduced-order solutions, all in less than 120 lines of Python code. We compare our LSPG method with the traditional Galerkin method and show that the space–time ROMs can achieve O(10−3) to O(10−4) relative errors for these problems. Depending on parameter–separability, online speed-ups may or may not be achieved. For the FOMs with parameter–separability, the space–time ROMs can achieve O(10) online speed-ups. Finally, we present an error analysis for the space–time LSPG projection and derive an error bound, which shows an improvement compared to traditional spatial Galerkin ROM methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Enhancing high-fidelity nonlinear solver with reduced order model

Abstract We propose the use of reduced order modeling (ROM) to reduce the computational cost and improve the convergence rate of nonlinear solvers of full order models (FOM) for solving partial differential equations. In this study, a novel ROM-assisted approach is developed to improve the computational efficiency of FOM nonlinear solvers by using ROM’s prediction as an initial guess. We hypothesize that the nonlinear solver will take fewer steps to the converged solutions with an initial guess that is closer to the real solutions. To evaluate our approach, four physical problems with varying degrees of nonlinearity in flow and mechanics have been tested: Richards’ equation of water flow in heterogeneous porous media, a contact problem in a hyperelastic material, two-phase flow in layered porous media, and fracture propagation in a homogeneous material. Overall, our approach maintains the FOM’s accuracy while speeding up nonlinear solver by 18–73% (through suitable ROM-assisted FOMs). More importantly, the proximity of ROM’s prediction to the solution space leads to the improved convergence of FOMs that would have otherwise diverged with default initial guesses. We demonstrate that the ROM’s accuracy can impact the computational efficiency with more accurate ROM solutions, resulting in a better cost reduction. We also illustrate that this approach could be used in many FOM discretizations (e.g., finite volume, finite element, or a combination of those). Since our ROMs are data-driven and non-intrusive, the proposed procedure can easily lend itself to any nonlinear physics-based problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Active learning emulators for nuclear two-body scattering in momentum space

In this work we extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scattering in momentum space. Our full-order model (FOM) solver is based on the Lippmann-Schwinger integral equation for the scattering t-matrix as opposed to the radial Schrödinger equation. We use (Petrov-)Galerkin projections and high-fidelity calculations at a few snapshots across the parameter space of the interaction to construct efficient reduced-order models (ROMs), trained by a greedy algorithm for locally optimal snapshot selection. Both the FOM solver and the corresponding ROMs are implemented efficiently in Python using Google's JAX library. We present results for emulating scattering phase shifts in coupled and uncoupled channels and cross sections, and assess the accuracy of the developed ROMs and their computational speedup factors. We also develop emulator error estimation for both the t-matrix and the total cross section. The software framework for reproducing and extending our results is publicly available. Together with our recent advances in developing active-learning emulators for three-body scattering, these emulator frameworks set the stage for full Bayesian calibrations of chiral nuclear interactions and optical models against scattering data with quantified emulator errors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes: Level II Milestone

The RAVEN code is becoming a comprehensive tool to perform Probabilistic Risk Assessment (PRA); Uncertainty Quantification (UQ) and Propagation; and Verification and Validation (V&V). The RAVEN code is being developed to support the Risk-Informed Safety Margin Characterization (RISMC) pathway by developing an advanced set of methodologies and algorithms for use in advanced risk analysis. The RISMC approach uses system simulator codes applied to stochastic analysis tools. The fundamental idea behind this coupling approach to perturb (by employing sampling strategies) timing and sequencing of events, internal parameters of the system codes (i.e., uncertain parameters of the physics model) and initial conditions to estimate values ranges and associated probabilities of figures of merit of interest for engineering and safety (e.g. core damage probability, etc.). This approach applied to complex systems such as nuclear power plants requires performing a series of computationally expensive simulation runs. The large computational burden is caused by the large set of (uncertain) parameters characterizing those systems. Consequently, exploring the uncertain/parametric domain, with a good level of confidence, is generally not affordable, considering the limited computational resources that are currently available. In addition, the recent tendency to develop newer tools, characterized by higher accuracy and larger computational resources (if compared with the presently used legacy codes, that have been developed decades ago), has made this issue even more compelling. In order to overcome to these limitations, the strategy for the exploration of the uncertain/parametric space needs to use at best the computational resources focusing the computational effort in those regions of the uncertain/parametric space that are “interesting” (e.g., risk-significant regions of the input space) with respect the targeted Figures Of Merit (FOM): for example, the failure of the system, subject of the analysis. These methodologies are named, in the RAVEN environment, adaptive sampling strategies. These methodologies infer system responses from surrogate models constructed from already existing samples (produced using high fidelity simulations) and suggest the most relevant location (coordinate in the input space) of the next sampling point to be explored in the uncertain/parametric domain. When using those methodologies, it is possible to understand features of the system response with a small number of carefully selected samples. This report focuses on the development and improvement of the limit surface search. The limit surface is an important concept in system reliability analysis. Without going into the details, which will be covered later in the report, the limit surface could be briefly described as an hyper-surface in the system uncertainty/parametric space separating the regions leading to a prescribed system outcome. For example, if the uncertainty/parametric space is the one generated by the reactor power level and the duration of the batteries, the system is a nuclear power plant and the system outcome discriminating variable is the clad failure in a station blackout scenario, then the limit surface separates the combinations of reactor power level and battery duration that lead to clad failure from the ones that do not.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Accelerating structural dynamics simulations with localised phenomena through matrix compression and projection‐based model order reduction

In this work, a novel approach is introduced for accelerating the solution of structural dynamics problems in the presence of localised phenomena, such as cracks. For this category of problems, conventional projection-based Model Order Reduction (MOR) methods are either limited with respect to the range of system configurations that can be represented or require frequent solutions of the Full Order Model (FOM) to update the low-dimensional spaces, in which solutions are represented. In the proposed approach, low-dimensional spaces, constructed for the healthy structure, are enriched with appropriately selected columns of the flexibility matrix of the system. It can be shown that these spaces contain the solution to the original problem for the static case, while their dimension is much smaller. In order to allow their online construction for arbitrary localised features, the full flexibility matrix of the system should be available. To this end, a hierarchical representation is used for the matrices involved, allowing to compute the flexibility matrix efficiently and with reduced memory requirements. The resulting method offers significant speedups, without sacrificing the flexibility and accuracy of the full order model. The performance and limitations of the approach are studied through a series of examples in structural dynamics.

fracture mechanics↗
DOE OSTI2019

Machine Learning Optimization of p-Type Transparent Conducting Films

p-Type transparent conducting materials (p-TCMs) are important components of optoelectronic devices including solar cells, photodetectors, displays, and flexible sensors. Cu–Zn–S thin films prepared by chemical bath deposition (CBD) can have both high transparency in the visible range (>80%) as well as excellent hole conductivity (>1000 S cm –1 ). However, the interplay between the deposition parameters in the CBD process (metal and sulfur precursor concentrations, temperature, pH, complexing agents, etc.) creates a multidimensional parameter space such that optimization for a specific application is challenging and time-consuming. Here we show that strategic design of experiment combined with machine learning (ML) allows for the efficient optimization of p-TCM performance. The approach is guided by a figure of merit (FOM) calculated from the film conductivity and optical transmission in the desired spectral range. A specific example is shown using two steps of optimization using a selected subset of four experimental CBD factors. The ML model is based on support vector regression employing a radial basis function as the kernel function. 10-fold cross-validation was performed to mitigate overfitting. After the first round of optimization, predicted areas in the parameter space with maximal FOMs were selected for a second round of optimization. Films with optimal FOMs were incorporated into heterojunction solar cells and transparent photodiodes. Here, the optimization approach shown here will be generally applicable to any materials synthesis process with multiple parameters.

14 SOLAR ENERGY↗
DOE OSTI2023

$\text{GPLaSDI}$: Gaussian Process-based interpretable Latent Space Dynamics Identification through deep autoencoder

Numerically solving partial differential equations (PDEs) can be challenging and computationally expensive. This has led to the development of reduced-order models (ROMs) that are accurate but faster than full order models (FOMs). Recently, machine learning advances have enabled the creation of non-linear projection methods, such as Latent Space Dynamics Identification (LaSDI). LaSDI maps full-order PDE solutions to a latent space using autoencoders and learns the system of ODEs governing the latent space dynamics. By interpolating and solving the ODE system in the reduced latent space, fast and accurate ROM predictions can be made by feeding the predicted latent space dynamics into the decoder. In this paper, we introduce GPLaSDI, a novel LaSDI-based framework that relies on Gaussian process (GP) for latent space ODE interpolations. Using GPs offers two significant advantages. First, it enables the quantification of uncertainty over the ROM predictions. Second, leveraging this prediction uncertainty allows for efficient adaptive training through a greedy selection of additional training data points. This approach does not require prior knowledge of the underlying PDEs. Consequently, GPLaSDI is inherently non-intrusive and can be applied to problems without a known PDE or its residual. Here we demonstrate the effectiveness of our approach on the Burgers equation, Vlasov equation for plasma physics, and a rising thermal bubble problem. Our proposed method achieves between 200 and 100,000 times speed-up, with up to 7% relative error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Projection-Based Model Reduction for Coupled Conduction—Enclosure Radiation Systems

We report a projection-based reduced order model (pROM) methodology has been developed for transient heat transfer problems involving coupled conduction and enclosure radiation. The approach was demonstrated on two test problems of varying complexity. The reduced order models demonstrated substantial speedups (up to 185×) relative to the full order model with good accuracy (less than 3% L ∞ error). An attractive feature of pROMs is that there is a natural error indicator for the ROM solution: the final residual norm at each time-step of the converged ROM solution. Using example test cases, we discuss how to interpret this error indicator to assess the accuracy of the ROM solution. The approach shows promise for many-query applications, such as uncertainty quantification and optimization. The reduced computational cost of the ROM relative to the full-order model (FOM) can enable the analysis of larger and more complex systems as well as the exploration of larger parameter spaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Online adaptive basis refinement and compression for reduced-order models via vector-space sieving

In many applications, projection-based reduced-order models (ROMs) have demonstrated the ability to provide rapid approximate solutions to high-fidelity full-order models (FOMs). However, there is no a priori assurance that these approximate solutions are accurate; their accuracy depends on the ability of the low-dimensional trial basis to represent the FOM solution. As a result, ROMs can generate inaccurate approximate solutions, e.g., when the FOM solution at the online prediction point is not well represented by training data used to construct the trial basis. To address this fundamental deficiency of standard model-reduction approaches, this work proposes a novel online-adaptive mechanism for efficiently enriching the trial basis in a manner that ensures convergence of the ROM to the FOM, yet does not incur any FOM solves. The mechanism is based on the previously proposed adaptive $h$-refinement method for ROMs, but improves upon this work in two crucial ways. First, the proposed method enables basis refinement with respect to any orthogonal basis (not just the Kronecker basis), thereby generalizing the refinement mechanism and enabling it to be tailored to the physics characterizing the problem at hand. Second, the proposed method provides a fast online algorithm for periodically compressing the enriched basis via an efficient proper orthogonal decomposition (POD) method, which does not incur any operations that scale with the FOM dimension. We report these two features allow the proposed method to serve as (1) a failsafe mechanism for ROMs, as the method enables the ROM to satisfy any prescribed error tolerance online (even in the case of inadequate training), and (2) an efficient online basis-adaptation mechanism, as the combination of basis enrichment and compression enables the basis to adapt online while controlling its dimension.

42 ENGINEERING↗
DOE OSTI2017

Assembling Multiple Models Within the RAVEN Framework

RAVEN (Risk Analysis Virtual Environment) is an INL-developed software tool that can be used to identify and increase the safety margin in complex systems. As a generic software framework, RAVEN is designed to perform parametric and probabilistic analysis based on the response of complex system codes. RAVEN is capable of investigating the system response as well as the input space using Monte Carlo, Grid, or Latin Hyper Cube sampling schemes, but its strength is focused toward system feature discovery, such as limit surfaces, separating regions of the input space leading to system failure, using dynamic supervised learning techniques. RAVEN is currently able to construct multi-targets Reduced Order Models (ROMs), which are aimed to represent the response of a system (in a fixed configuration) for multiple Figures of Merits (FOMs) and time-dependent ROMs. These capabilities represent the initial steps for a larger implementation about the interaction of multiple models. In fact, in several cases, multiple models need to interface with each other since the initial conditions of one are dependent on the outcomes of another. To better understand the problem that here is solved, it is useful to consider a simple example: The following problem is considered: a weather forecast simulation code 'a' is used to compute the external (i.e. ambient) temperature in a certain location. A second model 'b' is inquired to compute the average temperature in a room having as boundary condition, among several others, the external ambient temperature. The response of the model 'b' depends on the outcome of the model 'a'. The reported example is only aimed to illustrate the reason why the creation of a framework to make interact different models is a key development for the advancement of RAVEN as a comprehensive calculation flow driver. Before reporting how the ensemble-models have been implemented, it is necessary to briefly describe the representative Model 'entities' that are available in RAVEN.

42 ENGINEERING↗
DOE OSTI2012

PRECISION COSMOGRAPHY WITH STACKED VOIDS

We present a purely geometrical method for probing the expansion history of the universe from the observation of the shape of stacked voids in spectroscopic redshift surveys. Our method is an Alcock-Paczynski (AP) test based on the average sphericity of voids posited on the local isotropy of the universe. It works by comparing the temporal extent of cosmic voids along the line of sight with their angular, spatial extent. We describe the algorithm that we use to detect and stack voids in redshift shells on the light cone and test it on mock light cones produced from N-body simulations. We establish a robust statistical model for estimating the average stretching of voids in redshift space and quantify the contamination by peculiar velocities. Finally, assuming that the void statistics that we derive from N-body simulations is preserved when considering galaxy surveys, we assess the capability of this approach to constrain dark energy parameters. We report this assessment in terms of the figure of merit (FoM) of the dark energy task force and in particular of the proposed Euclid mission which is particularly suited for this technique since it is a spectroscopic survey. The FoM due to stacked voids from the Euclid wide survey may double that of all other dark energy probes derived from Euclid data alone (combined with Planck priors). In particular, voids seem to outperform baryon acoustic oscillations by an order of magnitude. This result is consistent with simple estimates based on mode counting. The AP test based on stacked voids may be a significant addition to the portfolio of major dark energy probes and its potentialities must be studied in detail.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Dark Energy Survey Year 3 results: Simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps. Validation on simulations

Beyond-two-point statistics contain additional information on cosmological as well as astrophysical and observational (systematics) parameters. In this methodology paper we provide an end-to-end simulationbased analysis of a set of Gaussian and non-Gaussian weak lensing statistics using detailed mock catalogs of the Dark Energy Survey (DES). Here, we implement: 1) second and third moments; 2) wavelet phase harmonics (WPH); 3) the scattering transform (ST). Our analysis is fully based on simulations, it spans a space of seven $νw$CDM cosmological parameters, and it forward models the most relevant sources of systematics of the data (masks, noise variations, clustering of the sources, intrinsic alignments, and shear and redshift calibration). We implement a neural network compression of the summary statistics, and we estimate the parameter posteriors using a likelihood-free-inference approach. We validate the pipeline extensively, and we find that WPH exhibits the strongest performance when combined with second moments, followed by ST, and then by third moments. The combination of all the different statistics further enhances constraints with respect to second moments, up to 25 percent, 15 percent, and 90 percent for S 8 , Ω m , and the figure-of-merit FoM S8;Ωm , respectively. We further find that non-Gaussian statistics improve constraints on w and on the amplitude of intrinsic alignment with respect to second moments constraints. The methodological advances presented here are suitable for application to Stage IV surveys from Euclid, Rubin-LSST, and Roman with additional validation on mock catalogs for each survey. In a companion paper we present an application to DES Year 3 data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

The BigBoss Experiment

BigBOSS is a Stage IV ground-based dark energy experiment to study baryon acoustic oscillations (BAO) and the growth of structure with a wide-area galaxy and quasar redshift survey over 14,000 square degrees. It has been conditionally accepted by NOAO in response to a call for major new instrumentation and a high-impact science program for the 4-m Mayall telescope at Kitt Peak. The BigBOSS instrument is a robotically-actuated, fiber-fed spectrograph capable of taking 5000 simultaneous spectra over a wavelength range from 340 nm to 1060 nm, with a resolution R = {lambda}/{Delta}{lambda} = 3000-4800. Using data from imaging surveys that are already underway, spectroscopic targets are selected that trace the underlying dark matter distribution. In particular, targets include luminous red galaxies (LRGs) up to z = 1.0, extending the BOSS LRG survey in both redshift and survey area. To probe the universe out to even higher redshift, BigBOSS will target bright [OII] emission line galaxies (ELGs) up to z = 1.7. In total, 20 million galaxy redshifts are obtained to measure the BAO feature, trace the matter power spectrum at smaller scales, and detect redshift space distortions. BigBOSS will provide additional constraints on early dark energy and on the curvature of the universe by measuring the Ly-alpha forest in the spectra of over 600,000 2.2 < z < 3.5 quasars. BigBOSS galaxy BAO measurements combined with an analysis of the broadband power, including the Ly-alpha forest in BigBOSS quasar spectra, achieves a FOM of 395 with Planck plus Stage III priors. This FOM is based on conservative assumptions for the analysis of broad band power (k{sub max} = 0.15), and could grow to over 600 if current work allows us to push the analysis to higher wave numbers (k{sub max} = 0.3). BigBOSS will also place constraints on theories of modified gravity and inflation, and will measure the sum of neutrino masses to 0.024 eV accuracy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Non-Nuclear Test Facility Development to Support Verification and Validation Needs

This study focuses on developing a non-nuclear test facility to help meet the verification and validation (V&V) requirements of a water-cooled small modular reactor (SMR) system. The scaling analysis necessary for reactor system experimentation and assessment model V&V considers various reactor system accident scenarios such as main steam line break (MSLB), steam generator tube rupture (SGTR), and containment condensation and heat transfer. The goal is to identify key physics based phenomena of interest (POI), specific figures of merit (FOMs), and the state of knowledge (SOK) by employing phenomena identification and ranking table (PIRT) studies. Important physics phenomena such as heat transfer, fluid dynamics, structural integrity, and material selection must be addressed when scaling a thermal-hydraulics system. Alongside these considerations, modifications to the facility may be needed to align the scaled system with the available footprint (height and space) of the facility. These modifications may include infrastructure upgrades, changes to the piping layout, implementation of safety measures, and installation of new instrumentation and control systems. The test facility aims to provide a scaled-down version of prototypic facilities for comprehensive V&V activities by overcoming these challenges and making the necessary modifications. This approach minimizes scaling distortion, simulates prototypic plant conditions, and accurately models accident event progressions. Thus, the facility will be capable of adequately modeling a scaled SMR system to support the design, development and licensing needs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Tailoring Thermal Radiative Properties with Doped-Silicon Nanowires

Aligned doped-silicon nanowire (D-SiNW) arrays form a hyperbolic metamaterial in the mid-infrared and have unique thermal radiative properties, such as broadband omnidirectional absorption, low-loss negative refraction, etc. A combined theoretical and experimental investigation will be performed to characterize D-SiNW arrays and other metamaterials for tailoring thermal radiative properties. Near-field thermal radiation between anisotropic materials with hyperbolic dispersions will also be predicted for potential application in energy harvesting. A new kind of anisotropic metamaterial with a hyperbolic dispersion in a broad infrared region has been proposed and demonstrated based on aligned doped-silicon nanowire (D-SiNW) arrays. D-SiNW-based metamaterials have unique thermal radiative properties, such as broadband omnidirectional absorption whose width and location can be tuned by varying the filling ratio and/or doping level. Furthermore, high figure of merit (FOM) can be achieved in a wide spectral region, suggesting that D-SiNW arrays may be used as a negative refraction material with much less loss than other structured materials, such as layered semiconductor materials. We have also shown that D-SiNWs and other nanostructures can significantly enhance near-field thermal radiation. The study of near-field radiative heat transfer between closely spaced objects and the electromagnetic wave interactions with micro/nanostructured materials has become an emerging multidisciplinary field due to its importance in advanced energy systems, manufacturing, local thermal management, and high spatial resolution thermal sensing and mapping. We have performed extensive study on the energy streamlines involving anisotropic metamaterials and the applicability of the effective medium theory for near-field thermal radiation. Graphene as a 2D material has attracted great attention in nanoelectronics, plasmonics, and energy harvesting. We have shown that graphene can be used to tailor the transmittance, reflectance, and absorptance of nanostructured materials. Furthermore, graphene can be used to enhance near-field coupling to increase the phonon tunneling probability. We have performed analysis of near-field thermophotovoltaic devices with backside reflecting mirror and with tungsten gratings. We have predicted a large enhancement of electroluminescent refrigeration at a separation distance down to 10 nm due to near-field thermal radiation effect. A heat flux measurement system is developed to measure the near-field radiation in vacuum. We have fabricated doped Si plates separated by sparsely distributed posts to create a 200-800 nm vacuum gap. Our measurement results demonstrate that 11 times enhancement of near-field thermal radiation between parallel doped-Si plates with a lateral dimension 1 cm by 1 cm.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Thermal Characterization of Tl 2 LiYCl 6 :Ce (TLYC)

Tl 2 LiYCl 6 :Ce (TLYC) is a new dual-detection elpasolite scintillator that can detect and distinguish between gamma rays and neutrons using pulse shape discrimination (PSD). It has a higher density and Z -number than the more mature and well-known elpasolite Cs 2 LiYCl 6 :Ce (CLYC), causing it to have a significantly better gamma-ray stopping power. These properties make TLYC an attractive alternative to CLYC for resource-constrained applications where size and weight are important, such as space or national security applications. Such applications may be subjected to a wide range of temperatures, and therefore, TLYC's performance was characterized for the first time over a temperature range of -20 °C to +50 °C in 10 °C increments. TLYC's thermal response effects on light-output linearity with energy, gamma-ray photopeak energy resolution, detected neutron energy, pulse shapes, and figure of merit (FOM) is analyzed and reported. The light output of TLYC was found to be linear with energy over the tested temperature range and was observed to decrease with increasing temperature. The decay time of the scintillation light output was observed to decrease with decreasing temperature at short times, leading to a decreasing PSD FOM. The gamma-ray photopeak energy resolution was also observed to degrade with decreasing temperature, due to an asymmetric broadening of the photopeak at low temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Cosmology from clustering, cosmic shear, CMB lensing, and cross correlations: combining Rubin observatory and Simons Observatory

ABSTRACT In the near future, the overlap of the Rubin Observatory Legacy Survey of Space and Time (LSST) and the Simons Observatory (SO) will present an ideal opportunity for joint cosmological data set analyses. In this paper, we simulate the joint likelihood analysis of these two experiments using six two-point functions derived from galaxy position, galaxy shear, and CMB lensing convergence fields. Our analysis focuses on realistic noise and systematics models and we find that the dark energy Figure of merit (FoM) increases by 53 per cent (92 per cent) from LSST-only to LSST+SO in Year 1 (Year 6). We also investigate the benefits of using the same galaxy sample for both clustering and lensing analyses, and find the choice improves the overall signal to noise by ${\sim}30{-}40{{\ \rm per\ cent}}$, which significantly improves the photo-z calibration and mildly improves the cosmological constraints. Finally, we explore the effects of catastrophic photo-z outliers finding that they cause significant parameter biases when ignored. We develop a new mitigation approach termed ‘island model’, which corrects a large fraction of the biases with only a few parameters while preserving the constraining power.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Acceleration Techniques using ADVANTG for the MDF Design and Optimization

The Spallation Neutron Source (SNS) facility at Oak Ridge National Laboratory (ORNL) is implementing a Moderator Demonstration Facility (MDF) to demonstrate the performance characteristics of advanced moderators central to the Second Target Station (STS) for SNS. The MDF will provide a small, short-pulsed neutron source for slow neutron production in which we intend to easily and quickly change from one moderator concept to another, characterizing the performance of each moderator in a reproducible and consistent fashion. The MDF will be unique and innovative amongst the world's moderator test facilities in that it will be intended to be very prototypic in terms of 'moderator illumination' - the spatial variation of the neutron flux entering the moderator itself - as well as capable of testing so-called high-brightness moderators in a wing configuration. During the design phase of the MDF, the Monte Carlo N-Particle transport code MCNPX v.2.7.0 is being used as the primary tool for the neutronic analysis of the facility and characterization of the moderator performance. The Automated Variance Reduction Generator (ADVANTG) code is being utilized to accelerate the MCNPX calculations. The ADVANTG code generates space- and energy-dependent weight window parameters for a successive MCNP run by calculating the adjoint flux with the Denovo discrete ordinates code based on the user defined MCNP tally. We have tested various strategies using ADVANTG to generate weight window parameters to efficiently calculate detailed moderator performance. Using point detector response functions for weight window parameter optimization with ADVANTG did not work well for improving the efficiency of Monte Carlo calculations of neutron emission into beam lines. However, using cell fluxes of the area of highest contributions to the point detector for the variance reduction parameter analysis worked very well and improved the FOM of the calculation of neutron emission into a beamline by a factor of 50 over the unbiased case. (authors)

42 ENGINEERING↗