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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Simulation of pellet ELM triggering in low-collisionality, ITER-like discharges

3D nonlinear, as well as 2D linear M3D-C1 simulations are used to model ELM triggering by small pellets in DIII-D discharges in the ITER relevant, peeling-limited pedestal stability regime. A critical pellet size threshold is found in both experiment and modeling depending on pedestal conditions, pellet velocity and injection direction. Using radial injection at the outboard midplane, the threshold is determined by M3D-C1 for multiple time slices of a DIII-D low-collisionality discharge that has pellet ELM triggering. Experimental observations show that a larger pellet size than the standard 1.3 mm diameter is necessary for ELM triggering; 1.8 mm pellets triggered several ELMs in cases where a smaller pellet failed. The M3D-C1 simulations are in good agreement with these observations. While the 2D linear simulations give insight into the change of growth rates for various toroidal modes with pellet size, the 3D nonlinear simulations apply a pellet ablation model that mimics the actual injection with good match to the experiment. The 3D nonlinear simulation confirms the pellet ELM triggering for a pellet size larger than the threshold found by the linear simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Improvements to the Griffin Transport Solvers

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor multiphysics analysis application jointly developed by Idaho National Laboratory and Argonne National Laboratory. The code includes a variety of steady-state solvers for fixed-source, k-eigenvalue, adjoint, and subcritical multiplication, as well as transient solvers for point-kinetics, improved quasi-static, and spatial dynamics. This document summarizes the transport solver development efforts pursued during Fiscal Year 2022. We added the multiphysics transient capability for the coarse-mesh finite difference accelerated Richardson iteration for discontinuous finite element method discrete ordinates (DFEM-SN) scheme to support high-order heterogeneous transport simulations. HFEM (hybrid finite element method) - PN (spherical harmonics expansion) was completed and red-black iteration was added for solving the HFEM-PN system with both preconditioned Jacobian-free Newton Krylov and Richardson iteration solvers. The HFEM-PN scheme, as one of the low-order transport schemes, is expected for supporting routine design simulations. Pin power reconstruction capability was also designed and implemented with the Griffin ISOXML module to enhance all the low-order transport solvers for more accurate multiphysics simulations. Numerical results are presented for demonstrating the capabilities and verifying their performance, and future works are discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Testing, Characterization, and Modeling of the Resonant Plate Test Environment

The resonant plate shock test is a dynamic test of a mid-field pyroshock environment where a projectile is struck against a plate. The structure undergoing the simulated field shock is mounted to the plate. The plate resonates when struck and provides a two sided shock that is representative of the shock observed in the field. This test environment shock simulates a shock in a single coordinate direction for components looking to provide evidence that they will survive a similar or less shock when deployed in their operating environment. However, testing in one axis at a time provides many challenges. The true environment is a multi-axis environment. The test environment exhibits strong off-axis motion when only motion in one axis is desired. Multiple fixtures are needed for a single test series. It would be advantageous if a single test could be developed that tests the multi-axis environment simultaneously. In order to design such a test, a model must be developed and validated. The model can be iterated in design and configuration until the specified multi-axis environment is met. The test can then execute the model driven test design. This report discusses the resonant plate model needed to design future tests and the steps and methods used to obtain the model. This report also details aspects of the resonant plate test discovered during the process of model development that aids in our understanding of the test.

42 ENGINEERING↗
DOE OSTI2021

Utilizing Additive Manufacturing to Create Prototype and Functional Beamline Instrumentation and Support Components

The world of beamline science is often fast-paced and dynamic. One of the major challenges in this environment is to be able to design, manufacture and then implement new items for use on the beamlines in a fast and accurate manner. Many times, this involves iterating the design to address unknown or new variables which were not pre- sent at the beginning of the project planning task. Through the use of additive manufacturing, I have been able to support the user programs of various (APS) Ad- vanced Photon Source beamlines* across multiple scien- tific disciplines. I will provide a few detailed examples of Items that were created for specific beamline applications and discuss what benefits they provided to the pertinent project. I will also talk about why choosing consumer- level printer options to produce the parts has been the direction I went and the pros and cons of this decision. Pri marily, this choice allowed for quicker turnaround times and the ability to make more frequent changes in an efficient manner. Currently, we are utilizing only the fused deposition modelling (FDM) type printers but I am exploring the addition of UV-activated resin printing, exotic materials that can be utilized using the current toolset, and the possibility of commercial metal printing systems. This technology has been a game-changer for the implementation of new support items and instrumentation over the last couple of years for the different disciplines I am sup- porting. I will discuss how the roadmap ahead and what the evolving technologies could potentially allow us to do.

Jensen, D.↗
DOE OSTI2025

Designing robust energy policy packages under deep uncertainty: A multi-metric decision support framework

The complexity of transitioning to sustainable energy systems requires policy frameworks capable of balancing multiple objectives while addressing deep uncertainty. However, existing approaches often lack systematic methods to identify combinations of policy levers that remain effective across a wide range of uncertain futures. This paper presents a novel decision support framework that guides the selection of robust policy packages based on their performance across multiple objectives under uncertainty. Our method leverages a large ensemble of scenarios and applies scenario discovery techniques to identify influential policy levers. Here, we introduce new indicators to assess the robustness of policies by evaluating their ability to mitigate adverse outcomes across metrics. These indicators support an iterative process to build a robust policy package. Finally, we map the technological and energy pathways associated with the robust policy package by leveraging an energy system optimization model. We illustrate the application of this framework to the Spanish energy system, providing insights into how specific combinations of policy levers shape decarbonization pathways under uncertainty.

Decision-support method↗
DOE OSTI2023

CRADA Number NFE-20-08283 with Actinic LLC (CRADA Final Report)

Significant advances have been made in the additive manufacturing (AM) of thermoplastics and optically cured thermosets; however, AM of thermally cured thermosets has lagged. Materials belonging to this class of thermosets, such as silicone rubbers and polyurethanes, are not well represented in the AM space despite their widespread use in medical, defense, and other fields. Ideally, a solution would enable AM of current commercially viable thermally cured thermosets with a speed that is comparable to those already realized for thermoplastics and optically cured thermosets. Realizing this goal will provide several benefits, including: 1. Make thermally cured thermoset designs impossible via casting. 2. Iteration of part designs without investment in new molds. 3. Devices and personal protection equipment, custom-fit to the end-user. 4. Ability to print multifunctional composite materials. 5. Rapid production ability for DoD personnel deployed at forward locations. 6. Simplify supply chains and replace multiple parts with a single raw material. These benefits motivate the development of a commercially viable 3D printer for thermally cured thermosets, based on the technique of photothermal curing. This project further developed a printer capable of printing composites containing commercially available thermally cured polydimethylsiloxane (PDMS), directly yielding cured parts such as boxes, walls, and free-standing overhangs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Super Resolving Unrolled Neural Networks for Remote Sensing

In remote sensing systems, the capabilities of the system are constrained by the complex interactions between size, weight, and power (SWAP) of potential designs. In electro-optical (EO) systems, examples of these critical parameters include the system’s sensitivity and resolution. Those parameters can be increased by ever larger optical apertures and focal planes but at the cost of more SWAP. Multi-image super resolution (MISR) techniques allow resolution to be enhanced via computation rather than more sophisticated optical hardware. These algorithms combine multiple images together into a single, higher resolution image, trading temporal resolution and computation for spatial resolution. Fielded MISR techniques, such as Drizzle, can require several hundred images to create a single super resolved image, implying reduced temporal resolution, increased data acquisition load, and limiting mission applications. Iterative techniques, such as model-based image reconstruction and compressive sensing, have been shown to create super resolved images using fewer images than Drizzle. They do this by posing an optimization problem that balances accuracy between a highly accurate physical model and an image model. In the case of super resolution, the physical model is defined by the relation between low resolution input images and the desired high resolution output image. The image model encodes some assumptions about the super resolved image. These assumptions are meant to suppress reconstruction artifacts that arise due to deterministic physical model error, stochastic measurement noise, and potential undersampling. In practice, the performance of iterative methods are limited by imaging models compatible with optimization. Deep learning-based methods can effectively learn image models of arbitrary complexity, but lack the theoretical explainability and robustness of iterative techniques. Consensus equilibrium (CE) generalizes the iterative techniques beyond optimization, enabling blackbox algorithms such as traditional and neural image denoisers to be used as the image model. CE-based approaches retain much of the explainability and robustness of iterative techniques while allowing the expressiveness of machine learning image models to be used. Additionally, by unrolling iterations of CE with an embedded image denoiser, the image denoiser can be further trained and specialized to the specific application with potentially higher quality reconstructions. Under this project, we demonstrated the feasibility of training an unrolled neural network based upon CE. While we didn’t train one, we showed that the CE process is differentiable and its gradient can be tractably computed. We also explored the usage of a variants of CE akin to generative neural works. Most importantly, we applied the CE framework to a number of problems including non-blind deconvolution, upsampling, single-image super resolution, MISR, event-based sensing, and saturated deconvolution. Our MISR prototype creates high quality reconstructions with an order of magnitude fewer images than previous approaches and, critically, produces these reconstructions fast enough for practical usage.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

“Multiagent” Screening Improves Directed Enzyme Evolution by Identifying Epistatic Mutations

Enzyme evolution has enabled numerous advances in biotechnology and synthetic biology, yet still requires many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which is caused by epistatic mutations that only offer improvements when combined with other mutations. We report an approach that incorporates diverse substrate analogues in the screening process, where multiple substrates act like multiple agents navigating the fitness landscape, identifying epistatic mutant residues without a need for testing the entire combinatorial search space. We initially validate this approach by engineering a malonyl-CoA synthetase and identify numerous epistatic mutations improving activity for several diverse substrates. The majority of these mutations would have been missed upon screening for a single substrate alone. We expect that this approach can accelerate a wide array of enzyme engineering programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Quantifying the impact of precision errors on quantum approximate optimization algorithms

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between intervals of controlled quantum evolution. Here, we examine the effect of analog precision errors on QAOA performance from the perspective of both algorithmic training and performance guarantees. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. We show using both analytical techniques and numerical simulations that fixed precision implementations of QAOA circuits are subject to an exponential degradation in performance dependent upon the number of optimal QAOA layers and magnitude of the precision error. Despite this significant reduction, we show that it is possible to mitigate precision errors in QAOA via digitization of the variational parameters at the cost of increasing circuit depth.

quantum algorithms↗
DOE OSTI2021

Multi-Area Model-Free State Estimation via Distributed Tensor Decomposition

This paper proposes a model-free method for distribution system state estimation based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A third-order tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. To demonstrate the efficacy of the developed algorithm, numerical simulations are carried out using an IEEE test system.

alternating direction method of multipliers↗
DOE OSTI2020

Deblending galaxies with variational autoencoders: A joint multiband, multi-instrument approach

ABSTRACT Blending of galaxies has a major contribution in the systematic error budget of weak-lensing studies, affecting photometric and shape measurements, particularly for ground-based, deep, photometric galaxy surveys, such as the Rubin Observatory Legacy Survey of Space and Time (LSST). Existing deblenders mostly rely on analytic modelling of galaxy profiles and suffer from the lack of flexible yet accurate models. We propose to use generative models based on deep neural networks, namely variational autoencoders (VAE), to learn probabilistic models directly from data. We train a VAE on images of centred, isolated galaxies, which we reuse, as a prior, in a second VAE-like neural network in charge of deblending galaxies. We train our networks on simulated images including six LSST bandpass filters and the visible and near-infrared bands of the Euclid satellite, as our method naturally generalizes to multiple bands and can incorporate data from multiple instruments. We obtain median reconstruction errors on ellipticities and r-band magnitude between ±0.01 and ±0.05, respectively, in most cases, and ellipticity multiplicative bias of 1.6 per cent for blended objects in the optimal configuration. We also study the impact of decentring and prove the method to be robust. This method only requires the approximate centre of each target galaxy, but no assumptions about the number of surrounding objects, pointing to an iterative detection/deblending procedure we leave for future work. Finally, we discuss future challenges about training on real data and obtain encouraging results when applying transfer learning.

Arcelin, Bastien↗
DOE OSTI2022

Multiarea Distribution System State Estimation via Distributed Tensor Completion

Here, this paper proposes a model-free distribution system state estimation method based on tensor completion using canonical polyadic decomposition. In particular, we consider a setting where the network is divided into multiple areas. The measured physical quantities at buses located in the same area are processed by an area controller. A three-way tensor is constructed to collect these measured quantities. The measurements are analyzed locally to recover the full state information of the network. A distributed closed-form iterative algorithm based on the alternating direction method of multipliers is developed to obtain the low-rank factors of the whole network state tensor where information exchange happens only between neighboring areas. The convergence properties of the distributed algorithm and the sufficient conditions on the number of samples for each smaller network that guarantee the identifiability of the factors of the state tensor are presented. To demonstrate the efficacy of the proposed algorithm and to check the identifiability conditions, numerical simulations are carried out using the IEEE 123-bus system and a large-scale real utility feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Dynamic Graph Sequence Data from Simulated Neutron Reflectometry Measurements

This dataset comprises dynamic graph sequences derived from simulated in-situ neutron reflectometry measurements, capturing the gradual evolution of a layer structure over time. Each graph sequence represents a synthetic sample, with node features detailing the scattering vector and corresponding reflectivity measurements, while adjacency matrices have corresponding reference material parameters attached as metadata. The dataset spans multiple sets, each with a different number of sequences, offering a comprehensive basis for training models that handle dynamic input sequences with embedded physics. This dataset is particularly suited for tackling inverse problems with hidden physical states that evolve over time, challenges that are typically difficult to address using conventional iterative fitting methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Progress on Demonstration of a MOOSE-Based Coupled Capability for Hot Channel Factors in Fast Reactors

Hot channel factors (HCFs) are computed values that account for the impact on predicted peak fuel, cladding, and coolant temperatures due to uncertainties in the as-built reactor’s material properties and geometry as well as uncertainties due to modeling approximations. Reduction in computed HCF values via reduction or elimination of modeling approximations may translate to significant economic savings if the reactor power can be raised due to the extra temperature margin gained. While limited historical datasets exist for sodium-cooled fast reactors (SFRs), there are no available HCF data for lead-cooled fast reactors (LFRs) outside of work generated previously within NEAMS. The computation of HCFs involves insights from reactor physics, thermal fluids and heat conduction calculations to determine how the peak temperatures respond to various uncertainties in the design. Due to the significant advantages for multi-physics coupling offered by the MOOSE framework, Griffin (MOOSE-based reactor physics code), MOOSE Heat Conduction Module, and Cardinal (MOOSE-wrapped multi-physics application which includes the NekRS thermal fluids code) are being coupled together using the MOOSE MultiApp System to develop a highfidelity multi-physics modeling capability for HCF simulations. This high-fidelity coupling workflow may also be beneficial for other fast reactor applications in the future. In previous work, Griffin and NekRS were individually assessed to ensure the necessary capabilities were in place. This work describes initial efforts to couple the codes (including folding in the MOOSE Heat Conduction Module) and determining the workflow for the perturbed calculations which will leverage the Stochastic Tools Module (STM). To our knowledge, this is the first coupling of Griffin and NekRS as well as the first exploratory use of Stochastic Tools Module for Cardinal. In this report, the neutronics code Griffin, the heat conduction solver in MOOSE, and the MOOSE-wrapped application containing NekRS (Cardinal) are linked together to demonstrate the coupled capability. Griffin and Cardinal are linked dynamically by specifying shared libraries. Different coupling hierarchies are tested for selecting the most appropriate coupling strategy. A coupling scheme is selected based on the efficiency of calculation and ease of data communication. Multiple tests are performed to choose suitable mesh structure, model configurations, scheme setup and boundary conditions to avoid loss of energy due to data interpolation between different modules or weak imposition of fluxes in finite element codes. Computational experiments are performed to study the tolerance control of each type of iteration to avoid false convergence. The coupled capability is demonstrated in both single pin and 7-pin models based on LFR materials and geometry. The study finds that the use of too large a time step size in the heat conduction module can lead to temperature oscillation even though the heat conduction equation does not have a time-derivative kernel, but only the time-dependent boundary condition. A 7-pin model without duct region achieved good convergence in the coupled calculation while a 7 pin model with duct region experienced data communication issues which need to be resolved.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Revisiting Dropout: Escaping Pressure for Training Neural Networks with Multiple Costs

A common approach to jointly learn multiple tasks with a shared structure is to optimize the model with a combined landscape of multiple sub-costs. However, gradients derived from each sub-cost often conflicts in cost plateaus, resulting in a subpar optimum. In this work, we shed light on such gradient conflict challenges and suggest a solution named Cost-Out, which randomly drops the sub-costs for each iteration. We provide the theoretical and empirical evidence of the existence of escaping pressure induced by the Cost-Out mechanism. While simple, the empirical results indicate that the proposed method can enhance the performance of multi-task learning problems, including two-digit image classification sampled from MNIST dataset and machine translation tasks for English from and to French, Spanish, and German WMT14 datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Mixed-precision iterative refinement using tensor cores on GPUs to accelerate solution of linear systems

Double-precision floating-point arithmetic (FP64) has been the de facto standard for engineering and scientific simulations for several decades. Problem complexity and the sheer volume of data coming from various instruments and sensors motivate researchers to mix and match various approaches to optimize compute resources, including different levels of floating-point precision. In recent years, machine learning has motivated hardware support for half-precision floating-point arithmetic. A primary challenge in high-performance computing is to leverage reduced-precision and mixed-precision hardware. We show how the FP16/FP32 Tensor Cores on NVIDIA GPUs can be exploited to accelerate the solution of linear systems of equations Ax = b without sacrificing numerical stability. The techniques we employ include multiprecision LU factorization, the preconditioned generalized minimal residual algorithm (GMRES), and scaling and auto-adaptive rounding to avoid overflow. We also show how to efficiently handle systems with multiple right-hand sides. On the NVIDIA Quadro GV100 (Volta) GPU, we achieve a 4×-5× performance increase and 5× better energy efficiency versus the standard FP64 implementation while maintaining an FP64 level of numerical stability.

GMRES↗
DOE OSTI2021

Bioinspired energy absorbing material designs using additive manufacturing

Nature provides many biological materials and structures with exceptional energy absorption capabilities. Few, relatively simple molecular building blocks (e.g., calcium carbonate), which have unremarkable intrinsic mechanical properties individually, are used to produce biopolymer-bioceramic composites with unique hierarchical architectures, thus producing biomaterial-architectures with extraordinary mechanical properties. Several biomaterials have inspired the design and manufacture of novel material architectures to address various engineering problems requiring high energy absorption capabilities. For example, the microarchitecture of seashell nacre has inspired multi-material 3D printed architectures that outperform the energy absorption capabilities of monolithic materials. Using the hierarchical architectural features of biological materials, iterative design approaches using simulation and experimentation are advancing the field of bioinspired material design. However, bioinspired architectures are still challenging to manufacture because of the size scale and architectural hierarchical complexity. Notwithstanding, additive manufacturing technologies are advancing rapidly, continually providing researchers improved abilities to fabricate sophisticated bioinspired, hierarchical designs using multiple materials. This review describes the use of additive manufacturing for producing innovative synthetic materials specifically for energy absorption applications inspired by nacre, conch shell, shrimp shell, horns, hooves, and beetle wings. Potential applications include athletic prosthetics, protective head gear, and automobile crush zones.

36 MATERIALS SCIENCE↗