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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

Synchronous and Concurrent Multidomain Computing Method for Cloud Computing Platforms

We present a numerical method for synchronous and concurrent solution of transient elastodynamics problem where the computational domain is divided into subdomains that may reside on separate computational platforms. Here, this work employs the variational multiscale discontinuous Galerkin (VMDG) method to develop interdomain transmission conditions for transient problems. The fine-scale modeling concept leads to variationally consistent coupling terms at the common interfaces. The method admits a large class of time discretization schemes, and decoupling of the solution for each subdomain is achieved by selecting any explicit algorithm. Numerical tests with a manufactured solution problem show optimal convergence rates. The energy history in a free vibration problem is in agreement with that of the solution from a monolithic computational domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Unconventional compute methods and future challenges for superconducting digital computing

Superconducting digital computing (SDC) based on Josephson junctions (JJs) offers significant potential for enhancing compute throughput and reducing energy consumption compared to conventional room-temperature CMOS-based approaches. Current superconducting logic families exhibit diverse characteristics in clocking strategies, power management, and information encoding techniques. This paper reviews recent advancements in unconventional computing methods specifically designed for superconducting digital circuits, emphasizing temporal computing and pulse-train representations. Notable techniques include race logic (RL), temporal pulse train computing (U-SFQ), and temporal multipliers, each offering unique performance and area advantages suited to superconducting implementations. Additionally, this paper reviews innovations in superconducting coarse-grain reconfigurable architectures (CGRA), superconducting-specific on-chip communication architectures, cryogenic sensor interfaces, and quantum computing control electronics. Finally, we highlight research challenges that should be addressed to facilitate the widespread adoption of superconducting digital computing.

EDA tools↗
DOE OSTI2024

Harnessing High‐Throughput Computational Methods to Accelerate the Discovery of Optimal Proton Conductors for High‐Performance and Durable Protonic Ceramic Electrochemical Cells

Abstract The pursuit of high‐performance and long‐lasting protonic ceramic electrochemical cells (PCECs) is impeded by the lack of efficient and enduring proton conductors. Conventional research approaches, predominantly based on a trial‐and‐error methodology, have proven to be demanding of resources and time‐consuming. Here, this work reports the findings in harnessing high‐throughput computational methods to expedite the discovery of optimal electrolytes for PCECs. This work methodically computes the oxygen vacancy formation energy (E V ), hydration energy (E H ), and the adsorption energies of H 2 O and CO 2 for a set of 932 oxide candidates. Notably, these findings highlight BaSn x Ce 0.8‐x Yb 0.2 O 3‐δ (BSCYb) as a prospective game‐changing contender, displaying superior proton conductivity and chemical resilience when compared to the well‐regarded BaZr x Ce 0.8‐x Y 0.1 Yb 0.1 O 3‐δ (BZCYYb) series. Experimental validations substantiate the computational predictions; PCECs incorporating BSCYb as the electrolyte achieved extraordinary peak power densities in the fuel cell mode (0.52 and 1.57 W cm −2 at 450 and 600 °C, respectively), a current density of 2.62 A cm −2 at 1.3 V and 600 °C in the electrolysis mode while demonstrating exceptional durability for over 1000‐h when exposed to 50% H 2 O. This research underscores the transformative potential of high‐throughput computational techniques in advancing the field of proton‐conducting oxides for sustainable power generation and hydrogen production.

08 HYDROGEN↗
DOE OSTI2024

Enhancing risk and crisis communication with computational methods: A systematic literature review

Abstract Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible.

Mathematical Methods In Social Sciences↗
DOE OSTI2024

Computational Methods for Modeling Electrospray Microdroplet Chemistry for Improved Quantitative Mass Spectrometry

This project aimed at enhancing the quantitative analysis capabilities of electrospray ionization mass spectrometry (ESI-MS) by developing advanced computational methods. The primary focus was to integrate continuum and molecular dynamics simulations to study the behavior of microdroplets in the ESI process, from formation to evaporation. Through this research, we sought to bridge significant length and time scales to provide a comprehensive understanding of how analyte concentrations evolve from bulk solutions into gas-phase ions. This understanding is crucial for addressing challenges such as ionization efficiency, solvent effects, and ion suppression, which currently limit the accuracy of quantitative ESI-MS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Aerodynamic Characterization of 3D Scanned Wind Turbine Blades Using Experimental and Computational Methods

This study presents an aerodynamic characterization of 3D scanned wind turbine blades using both experimental and computational methods. The research was conducted by Gulf Wind Technology and Sandia National Laboratories. The primary objective was to investigate the aerodynamic impacts of leading-edge manufacturing defects on wind turbine blades. The study utilized the Stratasys NEO 800 3D Printer for high-precision manufacturing and the GWT Accelerator Wind Tunnel for experimental testing. Computational simulations were performed using COMSOL Multiphysics to model the wind tunnel and analyze flow characteristics and OpenFOAM to study the aerodynamic impacts of leading-edge defects. OpenFAST was used to estimate how these defects can lead to revenue losses for wind farm operators as high as 6%. The results demonstrated significant aerodynamic performance variations due to defects, with detailed analysis provided through wind tunnel and CFD data. The findings contribute to the understanding of defect impacts on wind turbine blade performance and offer insights for future design improvements.

17 WIND ENERGY↗
DOE OSTI2026

Computational methods in solution-based plastics purification

Plastic waste can be recycled into resins with near-virgin properties by solution-based purification processes that selectively dissolve polymers, remove contaminants, or detach printing residues. Here, in this review, we examine computational methods for predicting the behavior governing solution-based plastic purification, motivated by the vast polymer–solvent–contaminant compositional space. We discuss thermodynamic and machine learning methods for predicting polymer–solvent and polymer–contaminant interaction and review physics-based molecular dynamics simulations that resolve molecular-scale phenomena within polymer matrices inaccessible to screening methods. We highlight how these methods have informed experimental design for dissolution-based recycling and solvent-based contaminant removal. Finally, we discuss the prospective role of agentic AI in integrating these computational tools with real-time sorting data to adapt purification conditions to the compositional variability of real post-consumer feedstocks. This review charts a path toward computationally guided solution-based purification workflows that can respond to the complexity inherent in plastic waste streams.

Altamimi, Ali [Univ. of Wisconsin, Madison, WI (Un↗
DOE OSTI2020

Computational Methods to Accelerate Development of Corrosion Resistant Coatings for Industrial Gas Turbines

Oxidation resistant overlay coatings protect the underlying superalloy component in industrial gas turbines from oxidation attack. Rate of depletion of the Al-rich β-phase in the bond coat governs the lifetime of these coatings. The applicability of a computational method in accelerating the development of corrosion resistant coatings and significantly reducing the extensive experimental effort to predict coating lifetimes and microstructural changes in three-coated Ni-based superalloys for real operational durations (20–40 kh) was undertaken in the present study. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and electron microprobe analysis (EPMA) were employed to characterize MCrAlY-coated superalloy substrates (1483, 247 and X4) after exposure at 900 °C in air + 10% H 2 O for up to 20,000 h. The model predicted the longest coating lifetime for the coating on X4 substrate. Precipitation of γ' in the coatings was correctly predicted for all three coating systems. Additionally, the model was able to predict the formation of topologically close packed (TCP)-phases in the investigated coating systems.

Pillai, Rishi R.↗
DOE OSTI2022

A review of computational methods for studying oscillating water columns – the Navier-Stokes based equation approach

This review evaluates the state-of-the-practice numerical tools used to predict the performance of Oscillating Water Column (OWC). The OWC is a widely studied Wave Energy Converter that provides a reliable form of renewable form of electricity that can potentially meet global energy needs. However, the fluid-flow phenomena affecting its hydrodynamic performance are not fully understood. While there has been the successful full-scale deployment of OWCs, various computational methods are being explored to optimize this technology. Potential flow theory is commonly used to evaluate the efficiency of OWCs; however, this assumption tends to over-predict the hydrodynamic performance. Recently, numerical studies using a diverse set of commercial, open-source, or in-house Computational Fluid Dynamics (CFD) using Reynolds Averaged Navier Stokes (RANS) and Large-Eddy Simulation codes show a better comparison to available experimental results but are computationally expensive. ANSYS Fluent was found to be the most widely used CFD code applied to the study of the OWCs, with a high degree of accuracy in terms of experimental validation of numerical results.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Computational Methods in Heterogeneous Catalysis

The unprecedented ability of computations to probe atomic-level details of catalytic systems holds immense promise for the fundamentals-based bottom-up design of novel heterogeneous catalysts, which are at the heart of the chemical and energy sectors of industry. Here, we critically analyze recent advances in computational heterogeneous catalysis. First, we will survey the progress in electronic structure methods and atomistic catalyst models employed, which have enabled the catalysis community to build increasingly intricate, realistic, and accurate models of the active sites of supported transition-metal catalysts. We then review developments in microkinetic modeling, specifically mean-field microkinetic models and kinetic Monte Carlo simulations, which bridge the gap between nanoscale computational insights and macroscale experimental kinetics data with increasing fidelity. Here, we finally review the advancements in theoretical methods for accelerating catalyst design and discovery. Throughout the review, we provide ample examples of applications, discuss remaining challenges, and provide our outlook for the near future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Facing the Challenges of Borderline Oxidation State Assignments Using State-of-the-Art Computational Methods.

The oxidation state (OS) of metals and ligands in inorganic complexes may be defined by carefully curated rules, such as from IUPAC, or by computational procedures such as the effective oxidation state (EOS) or localized orbital bonding analysis (LOBA). Such definitions typically agree for systems with simple ionic bonding and innocent ligands but may disagree as the boundary between ionic and covalent bonds is approached, or as the role of ligand noninnocence becomes nontrivial, or high oxidation states of metals are supported by heavy dative bonding, and so on. This work systematically compares IUPAC, EOS, and LOBA across a series of complexes where OS assignment is challenging. These systems include high-valent transition metal oxides, transition metal complexes with noninnocent ligands such as dithiolate and nitrosyl, metal sulfur dioxide adducts, and two transition metal carbene complexes. The differences in OS assignment by the three methods are carefully discussed, demonstrating the synergy between EOS and LOBA. In addition, a clarity index for LOBA OS assignments is introduced that provides an indication of whether or not its predictions are close to the ionic-covalent boundary.

Gimferrer, Martí↗
DOE OSTI2022

Computational Methods to Characterize Panel Loading Conditions for Accelerated Testing

Panel cracking and degradation due to wind loading are known to have a detrimental effect on power output. Recreating these damaging conditions in a controlled experimental setting requires an understanding of the loads being generated at the panel surface as a function of both wind speed, wind direction, and panel orientation. To better understand these relationships, a computational fluid dynamics (CFD) simulation package was constructed using an open-source Python library for solving partial differential equations with the finite element method. This CFD package allows the simulated wind speed and panel orientation of a multi-panel array to be easily changed and provides the capability to measure the traction forces at discrete points along the sun-facing and ground-facing surface of an interior panel residing in the wake of one or more upstream panels. A parameter exploration was performed in which the panel angle was varied in even increments from -70 deg to 70 deg and the wind speed was varied from 2 to 30 m/s. During post processing, the measured traction along the panel surface was averaged spatially and interpreted as a time-varying signal, where further processing of this signal yielded the root mean square amplitude and a characteristic frequency associated with the loading. This study found that higher wind speeds are generally associated with increased amplitude of loading and that the panel orientation angle can significantly exacerbate or mitigate this loading. These outcomes are presented along with current work on higher-fidelity verification simulations and recommendations for performing accelerated experimental testing.

loading↗
DOE OSTI2021

Evaluation of Attila and MCNP computational methods for dose and exposure estimation

Radiation transport calculations are often used to estimate dose or exposure to components and personnel surrounding a radiation source. The sources for these calculations are decaying radionuclides within various nuclear materials. Historically, dose calculations use MCNP (Monte Carlo N-Particle) transport code as the primary particle transport tool without a secondary computational tool to validate the results from the MCNP simulations [1]. The goal of this study is to make an independent check of the Monte Carlo solution from MCNP6 Version 6.2.1 with the discrete ordinates solution from Attila 10.2.0 Beta 3. As an example problem for this study, water-filled, stainless-steel vessels, modeled with an unstructured mesh (UM) with both MCNP and Attila [2], are exposed to 252Cf and 60Co point sources. This report also includes a discussion of the limitations of unstructured mesh in a MCNP calculation.

61 RADIATION PROTECTION AND DOSIMETRY↗