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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Validated Finite Element Modeling Tool for Hydrodynamic Loading and Structural Analysis of Ocean Deployed Macroalgae Farms

The objective of this project was to develop and validate a fine-tuned, 3D computational modeling tool for high-fidelity simulations of macroalgae cultivation and harvest systems. To develop this tool, a full understanding of the geometric, material and hydrodynamic properties associated with farming kelp in exposed environments was necessary. Experiments and numerical modeling were done at increasing scales from 1) tank tests with a 1 m model, 2) to field tests with a 61 m culture line at a sheltered site, 3) to field tests with a 122 m culture line at an exposed site and to 4) a 5- line array at the exposed site with 186 m of culture length. The results of the experimental tank tests yielded a set of hydrodynamic drag-area coefficients for a 1 m aggregate based on densely grown kelp at 16 kg/m with a length up to 3 m as described in Fredriksson et al. This work provided the basis to model kelp as a 1 m aggregate based on in-situ geometric and material properties to include measurements of kelp blade length and width, number of blades, yield per m, material mass density and cantilever tests to obtain modulus of elasticity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

ROSE LCOM Tools

This project provides a tool to compute LCOM metrics for important Ada abstraction mechanisms (e.g., packages, protected objects). It analyzes data accesses and routine calls to compute an abstract representation of an Ada package (or some other abstraction). Different LCOM metrics (1-5) are computed from the abstract representation. The project also provides support for representing the abstraction graphically (via graphviz). The LCOM tool requires an existing ROSE installation configured with the GNAT 2019 compiler.

Lamar, KennethM↗
DOE OSTI2019

(U) SENSMG: First-Order Sensitivities of Neutron Reaction Rates, Reaction-Rate Ratios, Leakage, k eff , α, and Subcritical Multiplication Using PARTISN

SENSMG is a tool for computing first-order sensitivities of neutron reaction rates, reaction-rate ratios, leakage, keff, α, and subcritical multiplication using the PARTISN multigroup discrete-ordinates code.1 SENSMG computes sensitivities to all of the transport cross sections and data (total, fission,a nu, chi, and all scattering moments), four edit cross sections (absorption, capture, elastic scattering, and inelastic scattering), and the density for every isotope and energy group. It also computes sensitivities to the mass density for every material and derivatives with respect to all interface locations and outer boundaries. The tool can be used for one-dimensional spherical and slab (r) and two-dimensional cylindrical (r-z) geometries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Process Heating Assessments Using DOE’s Manufacturing Energy Assessment Software for Utility Reduction (MEASUR) Tool Suite

Process heating is the most energy-intensive manufacturing process for most sectors of industry. To quantify energy savings from various energy conservation measures, the Department of Energy (DOE) sponsored the development of the Process Heating Assessment and Survey Tool (PHAST) and similar tools for other industrial systems in the early 2000s. It has been used extensively in the Save Energy Now Program’s Energy Savings Assessments and the Better Plants Program’s In-Plant Trainings. Since the initial development of the legacy tools, both computer operating systems and software development have evolved significantly. Thus, DOE has invested in the modernization of PHAST and other legacy software tools to create the Manufacturing Energy Assessment Software for Utility Reduction (MEASUR) tool suite. MEASUR offers a collection of software tools that can aid manufacturing facilities in improving the efficiency of energy systems and equipment (specifically pumps, fans, steam, and process heating) and in conducting “Energy Treasure Hunts”. Eventually, the tool will also add compressed air and process cooling systems. The Process Heating Assessment (PHA) module of MEASUR is an upgrade of the PHAST tool. PHA provides the means to model fuel-fired, steam-based, and electric process heating systems, covering process heating for most industrial plants in manufacturing sector. It also includes several key upgrades, including the ability to consider multi-component charge loads and account for several different areas of energy losses. The new tool includes a comprehensive flue gas calculator to quantify available heat and heat loss for various gaseous, liquid, and solid fuels and new heat loss calculators. It generates a report and a dynamic Sankey diagram to show the energy consumption in various areas of energy use. MEASUR has significantly improved the user experience by adopting a modern software design. This paper details the structure and workflow of PHA and presents a real-world case study to demonstrate energy savings quantification and MEASUR’s outstanding reporting capabilities.

Nimbalkar, Sachin U.↗
DOE OSTI2016

UQTk (V. 3.0) User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

UQTk Version 3.0.4 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.4 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

UQTk Version 3.0.3 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.0.3 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sen- sitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

UQTk User Manual (V.3.1.0)

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.0 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

UQTk Version 3.1.2 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.2 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

UQTk Version 3.1.1 User Manual

The UQ Toolkit (UQTk) is a collection of libraries and tools for the quantification of uncertainty in numerical model predictions. Version 3.1.1 offers intrusive and non-intrusive methods for propagating input uncertainties through computational models, tools for sensitivity analysis, methods for sparse surrogate construction, and Bayesian inference tools for inferring parameters from experimental data. This manual discusses the download and installation process for UQTk, provides pointers to the UQ methods used in the toolkit, and describes some of the examples provided with the toolkit.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Theoretical framework for new magnetic materials for quantum computing and information storage. Final report for the Award No. DE-SC0018910

The focus of this grant was on molecular magnetic materials for information storage and quantum computing. We have been developing robust, first-principle methods for computing relevant electronic and magnetic properties of molecular building blocks (SMMs) of novel magnetic materials and quantum computers. These tools enable theoretical modeling of SMMs’ behavior, facilitating the interpretation of experimental studies and aiding the design of novel magnetic materials. Our strategy is based on the spin-flip (SF) approach, which extends the hierarchy of black-box single-reference methods to strongly correlated systems. Specifically, we developed general scalable algorithms and computer codes for calculating molecular properties, with an emphasis on spin-related properties, such as zero-field splittings, hyperfine couplings, and g-tensors. While our primary focus was on SF wave functions and SF-TDDFT, the underlying theory and computer codes were formulated using reduced density matrices, such that these tools are applicable to a broader class of methods. To extend the scope of applicability of wave-function-based SF methods to larger systems, we developed reduced-scaling approaches for the equation-of-motion coupled-cluster (EOM-CC) methods and continue developing libtensor (our open-source general tensor contraction library for many-body methods). We carried out extensive benchmarks and also carried out several applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An Investigation into the Approximations Used in Wave Packet Molecular Dynamics for the Study of Warm Dense Matter

Wave packet molecular dynamics (WPMD) has recently received a lot of attention as a computationally fast tool with which to study dynamical processes in warm dense matter beyond the Born–Oppenheimer approximation. These techniques, typically, employ many approximations to achieve computational efficiency while implementing semi-empirical scaling parameters to retain accuracy. We investigated three of the main approximations ubiquitous to WPMD: a restricted basis set, approximations to exchange, and the lack of correlation. We examined each of these approximations in regard to atomic and molecular hydrogen in addition to a dense hydrogen plasma. We found that the biggest improvement to WPMD comes from combining a two-Gaussian basis with a semi-empirical correction based on the valence-bond wave function. A single parameter scales this correction to match experimental pressures of dense hydrogen. Ultimately, we found that semi-empirical scaling parameters are necessary to correct for the main approximations in WPMD. However, reducing the scaling parameters for more ab-initio terms gives more accurate results and displays the underlying physics more readily.

Angermeier, William A. (ORCID:0000000177161564)↗
DOE OSTI2021

Lightweight Materials - Carbon Fiber and Polymer Composites, Integrated Computational Materials Engineering (ICME) Predictive Tools Development for Low-Cost Carbon Fiber for Lightweight Vehicles (University of Virginia) (Final Report)

This project aimed to develop low-cost alternative precursors and processing techniques through the implementation of an integrated computational materials engineering (ICME) framework to evaluate precursor conversion kinetics. Once established, this framework was then used to down-select precursors for laboratory- and pilot-scale production to validate resulting carbon fiber (CF) material properties and process cost-savings. The two most promising approaches to reduce the cost of CFs were to (1) use new low-cost precursors and (2) reduce the energy requirements of CF processing. These approaches were investigated over the course of three phases of work, spanning 3.5 years. This report describes the overall project objectives, approach, and the key accomplishments in each phase.

36 MATERIALS SCIENCE↗
DOE OSTI2018

MemSurfer

MemSurfer is a software tool to compute bilayer membrane surfaces. These surfaces are found in the biophysical simulations, and generally analyzed using a set of python packages. MemSurfer offers new robust functionalities to compute the undulations in the surfaces, and relevant properties of the lipids *along* the surface, rather than along the projections, as done in many existing tools.

Bremer, Peer-Timo↗
DOE OSTI2023

Spatiotemporal analysis of 3D human iPSC-derived neural networks using a 3D multi-electrode array

While there is a growing appreciation of three-dimensional (3D) neural tissues (i.e., hydrogel-based, organoids, and spheroids), shown to improve cellular health and network activity to mirror brain-like activity in vivo , functional assessment using current electrophysiology techniques (e.g., planar multi-electrode arrays or patch clamp) has been technically challenging and limited to surface measurements at the bottom or top of the 3D tissue. As next-generation MEAs, specifically 3D MEAs, are being developed to increase the spatial precision across all three dimensions (X, Y, Z), development of improved computational analytical tools to discern region-specific changes within the Z dimension of the 3D tissue is needed. In the present study, we introduce a novel computational analytical pipeline to analyze 3D neural network activity recorded from a “bottom-up” 3D MEA integrated with a 3D hydrogel-based tissue containing human iPSC-derived neurons and primary astrocytes. Over a period of ~6.5 weeks, we describe the development and maturation of 3D neural activity (i.e., features of spiking and bursting activity) within cross sections of the 3D tissue, based on the vertical position of the electrode on the 3D MEA probe, in addition to network activity (identified using synchrony analysis) within and between cross sections. Then, using the sequential addition of postsynaptic receptor antagonists, bicuculline (BIC), 2-amino-5-phosphonovaleric acid (AP-5), and 6-cyano-5-nitroquinoxaline-2,3-dione (CNQX), we demonstrate that networks within and between cross sections of the 3D hydrogel-based tissue show a preference for GABA and/or glutamate synaptic transmission, suggesting differences in the network composition throughout the neural tissue. The ability to monitor the functional dynamics of the entire 3D reconstructed neural tissue is a critical bottleneck; here we demonstrate a computational pipeline that can be implemented in studies to better interpret network activity within an engineered 3D neural tissue and have a better understanding of the modeled organ tissue.

3D culture↗
DOE OSTI2022

In Situ X-ray Radiography and Computational Modeling to Predict Grain Morphology in $\beta$-Titanium during Simulated Additive Manufacturing

The continued development of metal additive manufacturing (AM) has expanded the engineering metallic alloys for which these processes may be applied, including beta-titanium alloys with desirable strength-to-density ratios. To understand the response of beta-titanium alloys to AM processing, solidification and microstructure evolution needs to be investigated. In particular, thermal gradients (Gs) and solidification velocities (Vs) experienced during AM are needed to link processing to microstructure development, including the columnar-to-equiaxed transition (CET). In this work, in situ synchrotron X-ray radiography of the beta-titanium alloy Ti-10V-2Fe-3Al (wt.%) (Ti-1023) during simulated laser-powder bed fusion (L-PBF) was performed at the Advanced Photon Source at Argonne National Laboratory, allowing for direct determination of Vs. Two different computational modeling tools, SYSWELD and FLOW-3D, were utilized to investigate the solidification conditions of spot and raster melt scenarios. The predicted Vs obtained from both pieces of computational software exhibited good agreement with those obtained from in situ synchrotron X-ray radiography measurements. The model that accounted for fluid flow also showed the ability to predict trends unobservable in the in situ synchrotron X-ray radiography, but are known to occur during rapid solidification. A CET model for Ti-1023 was also developed using the Kurz–Giovanola–Trivedi model, which allowed modeled Gs and Vs to be compared in the context of predicted grain morphologies. Both pieces of software were in agreement for morphology predictions of spot-melts, but drastically differed for raster predictions. The discrepancy is attributable to the difference in accounting for fluid flow, resulting in magnitude-different values of Gs for similar Vs.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Toward real-time optimization through model reduction and model discrepancy sensitivities

Optimization problems arise in a range of scenarios, from optimal control to model parameter estimation. In many applications, such as the development of digital twins, it is essential to solve these optimization problems within wall-clock-time limitations. However, this is often unattainable for complex systems, such as those modeled by nonlinear partial differential equations. One strategy for mitigating this issue is to construct a reduced-order model (ROM) that enables more rapid optimization. In particular, the use of nonintrusive ROMs—those that do not require access to the full-order model at evaluation time—is popular because they facilitate the computation of optimization solutions within the wall-clock time requirements. However, the optimization solution will be unreliable if the iterates move outside the ROM training data. This article proposes the use of hyper-differential sensitivity analysis with respect to model discrepancy (HDSA-MD) as a computationally efficient tool to augment ROM-constrained optimization and improve its reliability. The proposed approach consists of two phases: (i) an offline phase where several full-order model evaluations are computed to train the ROM, and (ii) an online phase where a ROM-constrained optimization problem is solved, a limited number of full-order model evaluations are computed, and HDSA-MD is used to enhance the optimization solution. Numerical results are demonstrated for two examples, atmospheric contaminant control and wildfire ignition location estimation, in which a ROM is trained offline using inaccurate atmospheric data. In conclusion, the HDSA-MD update yields a significant improvement in the ROM-constrained optimization solution using only one full-order model evaluation online with corrected atmospheric data.

PDE-constrained optimization↗
DOE OSTI2018

Computing resonant modes of accelerator cavities by solving nonlinear eigenvalue problems via rational approximation

Here, we present an efficient and reliable algorithm for solving a class of nonlinear eigenvalue problems arising from the modeling of particle accelerator cavities. The eigenvalue nonlinearity in these problems results from the use of waveguides to couple external power sources or to allow certain excited electromagnetic modes to exit the cavity. We use a rational approximation to reduce the nonlinear eigenvalue problem first to a rational eigenvalue problem. We then apply a special linearization procedure to turn the rational eigenvalue problem into a larger linear eigenvalue problem with the same eigenvalues, which can be solved by existing iterative methods. By using a compact scheme to represent both the linearized operator and the eigenvectors to be computed, we obtain a numerical method that only involves solving linear systems of equations of the same dimension as the original nonlinear eigenvalue problem. We refer to this method as a compact rational Krylov (CORK) method. We implemented the CORK method in the Omega3P module of the Advanced Computational Electromagnetic 3D Parallel (ACE3P) simulation suite and validated it by comparing the computed cavity resonant frequencies and damping Q factors of a small model problem to those obtained from a fitting procedure that uses frequency responses computed by another ACE3P module called S3P. We also used the CORK method to compute trapped modes damped in an ideal eight 9-cell SRF cavity cryomodule. This was the first time it was possible to compute these modes directly. The damping Q factors of the computed modes match well with those measured in experiments and the difference in resonant frequencies is within the range introduced by cavity imperfection. Therefore, the CORK method is an extremely valuable tool for computational cavity design.

43 PARTICLE ACCELERATORS↗