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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Use of Fisher's Ratio assisted multivariate curve resolution- alternating least squares for discovery-based analysis using ultrahigh pressure liquid chromatography-high resolution mass spectrometry

Non-targeted analysis of complex chemical mixtures can be difficult considering the convoluted nature of the matrix and the potential unknown chemical differences between samples or classes of samples. Ultrahigh pressure liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) is an ideal technique to probe chemical differences for a wide variety of samples. While UHPLC-QTOF can discover minute chemical differences down to low part per billion (ppb) concentrations with a high degree of confidence, the application of high-resolution mass spectrometry can yield massive amounts of information (∼ 10 gb per sample) that cannot be analyzed manually. Therefore, the application of chemometric techniques is mandatory for the interrogation of complex samples. Fisher's ratio (FR) assisted multivariate curve resolution-alternating least squares (MCR-ALS) was used to the discover and identify the chemical differences between two classes of materials: 1) a pond water matrix and 2) the matrix spiked with a pharmaceutical standard mix containing 17 compounds. Thirteen of the seventeen spiked compounds were discovered using FR analysis, and then five were successfully deconvoluted using MCR-ALS wherein the number of curves chosen were automatically determined using singular value decomposition (SVD). In conclusion, the use of an automated FR assisted MCR-ALS will aid in discovering trace levels of chemical components without the need for the researcher to provide potentially biased input which will aid in non-targeted workflow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Generative large language models for predictive maintenance planning

Maintenance planning and the generation of necessary components for tasks can prove time-consuming and complex. Automating the creation of recurring or similar tasks by leveraging previous planning packages and data, while uncovering insights to automate planning package generation, presents an opportunity to conserve valuable time and resources. This work aims to harness the textual and probabilistic capabilities of large language models (LLMs) to automate the generation of planning packages. Utilizing diverse data sources ranging from raw data to handwritten text, both singular and collaborative LLMs are trained and tested. Results demonstrate their capability to generate essential planning package components, effectively replicating the statistical patterns in the data. This demonstrates the use of these tools inside a digital asset for automated planning. This work outlines a methodology for constructing datasets, a training suite, and evaluation methods for LLM-based textual and conversational planning tools utilized in an asset digital twin. Results indicate that the fine-tuned models generate estimated planning information within the statistical ranges observed in real maintenance data. The models achieve high accuracy (>90%) in document question-answering and instruction generation tasks. Furthermore, the conversational retrieval-augmented generation (RAG) assistant system achieves 100% document retrieval accuracy, while conversational information capture exceeds 98% across the majority of work-package assistant modules.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The origin of CEMA and its relation to CSP

Currently, there exist two methods for analysing an explosive mode introduced by chemical kinetics in a reacting process: the Computational Singular Perturbation (CSP) algorithm and the Chemical Explosive Mode Analysis (CEMA). CSP was introduced in 1989 and addressed both dissipative and explosive modes encountered in the multi-scale dynamics that characterize the process, while CEMA was introduced in 2009 and addressed only the explosive modes. It is shown that (i) the algorithmic tools incorporated in CEMA were developed previously on the basis of CSP and (ii) the examination of explosive modes has been the subject of CSP-based works, reported before the introduction of CEMA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Time-dependent-bases with local CUR decomposition method for accelerating turbulent combustion simulations

Here, this study presents a novel reduced-order modeling framework, Time-Dependent Bases with Local CUR decomposition (TDB-L-CUR), designed to efficiently and accurately approximate the species transport equations in reacting flow simulations. The method extends the existing TDB-CUR approach for chemically reacting flows (Jung et al. Comput. Methods Appl. Mech. Engrg. 437 (2025) 117758), which leverages matrix decomposition techniques to form a global-in-space, time-dependent low-dimensional manifold. While TDB-CUR performs well in homogeneous systems, it may be less well-suited to spatially heterogeneous systems such as turbulent flames, where higher-rank approximations are typically required. The proposed TDB-L-CUR framework introduces two methodological extensions to the baseline approach. First, it applies unsupervised clustering to partition the physical domain into distinct regions, enabling spatially localized manifold construction, thereby reducing the rank required for the reduced-order representation. Second, it incorporates a computational singular perturbation (CSP)-based scheme for identifying and penalizing fast species, allowing for spatio-temporally adaptive mitigation of chemical stiffness. The proposed framework is validated on a hierarchy of test cases, including a one-dimensional premixed flame, a two-dimensional nonpremixed ignition case with vortex interaction, and a three-dimensional turbulent premixed flame. TDB-L-CUR significantly improves accuracy over TDB-CUR while further reducing computational cost. The fully on-the-fly formulation of TDB-L-CUR (i.e., requiring no offline training or prior knowledge) makes it a robust and scalable tool for reduced-order modeling of reactive flows.

Local manifold↗
DOE OSTI2024

Active learning for SNAP interatomic potentials via Bayesian predictive uncertainty

Bayesian inference with a simple Gaussian error model is used to efficiently compute prediction variances for energies, forces, and stresses in the linear SNAP interatomic potential. Here, the prediction variance is shown to have a strong correlation with the absolute error over approximately 24 orders of magnitude. Using this prediction variance, an active learning algorithm is constructed to iteratively train a potential by selecting the structures with the most uncertain properties from a pool of candidate structures. The relative importance of the energy, force, and stress errors in the objective function is shown to have a strong impact upon the trajectory of their respective net error metrics when running the active learning algorithm. Batched training of different batch sizes is also tested against singular structure updates, and it is found that batches can be used to significantly reduce the number of retraining steps required with only minor impact on the active learning trajectory.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Optimization strategies for produced water networks with integrated desalination facilities

Optimal management and desalination of produced water is a major challenge for U.S. oil and gas development. Integrating rigorous desalination models into multi-period produced water network optimization problems presents several hurdles, which need to be tackled using advanced optimization strategies. Here, in this work, a novel multi-period produced water network formulation with separate solid and liquid flows is introduced to avoid singularities at zero flows. Rigorous steady state desalination models based on mechanical vapor recompression are embedded at the desalination sites in the network model. An integrated optimization formulation is developed to co-optimize the design of desalination units along with the operation of the network. Furthermore, a more robust approach based on the trust region filter method is developed to efficiently integrate complex desalination models into the multi-period planning problem. Both optimization approaches are demonstrated on a produced water network from the PARETO library (Drouven et al., 2022) using thermal desalination units. Our results show that while the TRF and integrated approaches have comparable solve times, the TRF approach has better performance reliability in terms of solver convergence. Furthermore, the optimal solution obtained by embedding rigorous models into the network is significantly different than when desalination costs are approximated using simple cost models, which motivates further research in this field.

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

Jacobian-based model diagnostics and application to equation oriented modeling of a carbon capture system

It can be difficult to identify the specific variables or equations responsible for convergence issues in large mathematical programming models. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms. A singular value decomposition is then performed to identify degenerate sets of equations and remaining scaling issues. Here, this work presents a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. This work takes the reader through the entire process of model diagnostics and reformulation, from a basic introduction to the mathematics behind these model diagnostics to the reformulations necessary to make the model numerically robust, including a significantly modified enhancement factor model.

IDAES↗
DOE OSTI2021

Investigation of grid-based vorticity-velocity large eddy simulation off-body solvers for application to overset CFD

Accurately predicting unsteady wakes and vortex-dominated flows is essential to a wide range of engineering applications, including aircraft, rotorcraft, shipboard operations, bio-inspired unsteady flight and propulsion, wind turbines, and urban flows. While current CFD software can model the complete flow field and wake system, the computational costs incurred in high Reynolds number unsteady turbulent flow simulations often remain prohibitive for routine engineering use, particularly for applications involving moving components. Prior work has demonstrated that by adopting a vorticity-velocity formulation in a grid-based off-body flow solver (VorTran-M and VorTran-M2) one can lower these costs by several orders of magnitude when compared to conventional approaches. This paper describes the extensions made to VorTran-M2 to support turbulent flows, and associated benchmarking activity to assess its performance for problems involving strong stretching and diffusion processes, whose competing contributions to the vorticity field are core drivers of turbulent flow evolution. Predictions are presented for: (i) the Kida-Pelz problem whose inviscid form is of mathematical interest due to its apparent formation of singular flow in finite time; and (ii) the Taylor Green vortex arrangement, which has been extensively studied as a fundamental simulation challenge in the turbulent modeling community. Here, the results are used to evaluate the overall predictive ability and performance of two sub-grid scale models incorporated into VorTran-M2. Results indicate that the computational cost savings seen previously for inviscid and convection dominated problems extend to turbulent flow simulations supporting the viability of VorTran-M2 as a low cost means for accurately modeling the far-field and background flow, particularly when long duration vorticity evolution is of interest.

42 ENGINEERING↗
DOE OSTI2021

Practical algorithms for multivariate rational approximation

We present two approaches for computing rational approximations to multivariate functions, motivated by their effectiveness as surrogate models for high-energy physics (HEP) applications. Our first approach builds on the Stieltjes process to efficiently and robustly compute the coefficients of the rational approximation. Our second approach is based on an optimization formulation that allows us to include structural constraints on the rational approximation (in particular, constraints demanding the absence of singularities), resulting in a semi-infinite optimization problem that we solve using an outer approximation approach. We present results for synthetic and real-life HEP data, and we compare the approximation quality of our approaches with that of traditional polynomial approximations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

SpectrumSDT: A program for parallel calculation of coupled rotational-vibrational energies and lifetimes of bound states and scattering resonances in triatomic systems

In this work, we present SpectrumSDT – a program for calculations of energies and lifetimes of bound rotational-vibrational states below and scattering resonances above the dissociation threshold on a global potential energy surface of a triatomic system, which may include stable molecules, weekly-bound van-der-Waals complexes, and unbound atom + diatom scattering systems. Large-amplitude vibrational motion is treated explicitly using hyper-spherical coordinates. Three options for the rotational-vibrational interaction are supported: uncoupled (symmetric top rotor), partially coupled (to include interaction between several nearest states only) and full-coupled (vibrating asymmetric-top rotor). In addition to energies and lifetimes, SpectrumSDT is able to integrate ro-vibrational wave functions over the user-defined regions of potential energy surface, which helps to classify these states. In this release of the code, SpectrumSDT is limited to ABA-type molecules with wave functions that do not extend into the regions near Eckart singularities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

GenASiS Mathematics: Object-oriented manifolds, operations, and solvers for large-scale physics simulations (version 2)

We report GenASiS Mathematics provides modern Fortran classes furnishing extensible object-oriented functionality for the solution of fields governed by selected partial differential equations. The initial release included extensible object-oriented implementations of simple meshes and the evolution of generic conserved currents thereon. This revision - Version 2 of Mathematics - includes significant reorganization and streamlining of these classes, higher-order reconstruction by a different method, a Poisson solver, coarsening to avoid Courant time step limitations near coordinate singularities, and the offloading of computational kernels to GPUs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. For this work, we have implemented CSP in a C++ open-source library CSPlib using the Kokkos parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Building envelope anomaly characterization and simulation using drone time-lapse thermography

Defects in building envelopes deteriorate over time without being visible to the human eye, while significantly impacting energy performance due to unaccounted heat transfer. Defects can be characterized in the infrared (IR) spectrum. However, IR readings are typically recorded at singular points in time, when in several cases anomalies can only be revealed at specific times of the day, possibly in different seasons of the year. This paper presents a novel workflow for 3D envelope defect characterization and modeling using aerial time-lapse IR data collection using drones. A comprehensive envelope thermal profile is developed for a case study building employing the photogrammetry software Agisoft Photoscan, which generates temporal IR inspections of building skins using multiple thermography orthomosaics. Point-cloud data is then translated into a CAD model and thermal zones for whole Building Energy Modeling (BEM) using Honeybee as a frontend to EnergyPlus to showcase the potential of inclusion of detailed 4D data. Envelope contributions in this case study’s anomalies showed heat losses of 6447.6 kWh, and Energy Use Intensity (EUI) differences of ~2 kWh/m 2 /year from the baseline. Finally, why there is currently little translation of this work in BEM software is discussed, while identifying limitations and future research in the employment of time-lapse thermography using drones for more accurate building envelope inspection and modeling.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Who will be making wave energy? A community-driven design approach toward just and sustainable energy futures in Alaska

Renewable energy transition, once thought of as a singular, large-scale switch in industrial energy generating technologies from fossil fuels to renewables, have gained a broader set of requirements, including the need for justice and environmental protection. While discourse around policy and process has grown to consider these requirements, the focus of design of new renewable energy projects and technologies remains dominantly fixed on maximizing energy production, and renewables are falling short of meeting the requirements of just energy transitions. Meanwhile, in design studies, participatory and co-design approaches which explicitly consider justice in both process and outcomes have long been theorized and applied. However, applications of these approaches to renewable energy project design are under-theorized. In this paper, we explore the effectiveness of the adoption and adaptation of a co-design approach called Community-Driven Design (CDD) in enabling meaningful participation prior to technological maturity and bringing the attention of designers to a more diverse set of energy futures focused on justice and sustainability. Here, we do so by developing and testing the approach with community members in Sitka, AK, USA. We conclude that the approach has the potential to increase meaningful participation. Furthermore, we conclude that through diverse understandings of energy and the emergence of design narratives that engage with the complexity of vulnerability, accountability, and resilience, CDD led designers to consider justice, sustainability, and a diverse set of possible energy futures both within and beyond Sitka.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

A robust spectral element implementation of the $k - τ$ RANS model in Nek5000/NekRS

The $k - ω$ Reynolds Averaged Navier Stokes (RANS) model is one of the industry standard approaches for modeling of turbulent flows. It performs better than the $k - ϵ$ model for low Reynolds number flows and is also more suitable for boundary layers with adverse pressure gradients. Major drawback of the model, however, is that the asymptotic value of $ω$ at the walls is singular, necessitating the use of a contrived “sufficiently” large value for $ω$ as the boundary condition for its transport equation. Here, this invariably leads to the solution being sensitive to near wall grid spacing. While an acceptable solution for low order (finite volume) methods, the excessive near wall gradients lead to persistent numerical stability issues in high order codes. To alleviate the problem, specifically in the context of the high order spectral element code Nek5000, a regularized $k - ω$ approach was formulated in our prior work (Tomboulides et al., 2018). The formulation, however, relies on the use of wall distance and its gradients for modeling the closure terms and can pose problems for simulations in complex geometries. This work presents a novel implementation of the $k - τ$ RANS model in Nek5000, where $τ = 1/ω$, eliminating the need for regularization, owing to the asymptotically bounded behavior of the source terms in the $τ$ transport equation, and also eliminating dependence on wall distance. Robustness and stability of the $k - τ$ model is ensured through implicit treatment of the source terms and their careful numerical implementation and demonstrated through several cases aimed at verification and validation. Studies include both canonical and engineering relevant problems, viz., turbulent channel flow, pipe flow, backward facing step, flow over NACA 0012 airfoil and flow in a T-junction. Results from the $k - τ$ model are shown to be consistent with regularized $k - ω$ model and also with the $k - ω$ SST model in OpenFOAM (for select studies). Comparison with experimental data is also shown, where available, to bolster validation efforts for the $k - τ$ model implementation through prediction of key turbulent quantities of interest.

Nek5000↗
DOE OSTI2022

Dimensionless parameters for ballistic performance evaluation of ceramic-faced bicomponent targets against sharp-nosed projectiles

Two dimensionless parameters are proposed to give first-order approximations for the ballistic performance of ceramic-faced bicomponent armor plates impacted by conical-nosed steel projectiles. The current work collates expansive experimental data from Wilkins and Mayseless covering a broad range of material and mechanical properties, target construction, and projectile dimensions, the data is collapsed using the Hugoniot elastic limit of the ceramic strike face as a strength metric. We find that within datasets where experimental conditions are kept constant, a singular cubic curve can be generated, but variations in experimental conditions for the ballistic limit test between datasets can result in differences in the predictive curve coefficients.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Solid and gas thermal conductivity models improvement and validation in various porous insulation materials

In the past few decades, significant efforts have been made to improve the theoretical understanding of thermal transport mechanisms in thermal insulation materials and push the thermal conductivity's lower limits. However, most works focused singularly on specific types of materials, and the models used for thermal conductivity predictions are diverse - a model that fits one material might not fit others. Here, we improve and unify the gas and solid thermal conductivity models for porous materials. Through experimental characterization of several different materials as well as literature data for other materials, these models are validated. Further, we have also found that the pressure-dependent gas thermal conductivity of most materials can be well fitted by using one or two pore sizes without using a complex pore size distribution. With the refined models, we decompose the effective thermal conductivity of several thermal insulation materials into gas, solid, and radiation contributions. For cellular (polystyrene and polyurethane) foams, the relative contributions from air, solid, and radiation are 58–75%, 3–11%, 16–38%, respectively. For granular porous materials (polyurethane and silica in this work), the contributions from air, solid, and radiation are 45–66%, 34–46%, and 0–8%, respectively. This work is expected to provide guidance on the design and optimization of the next generation of thermal insulation materials, for example, through the effort of reducing gas conduction and radiation in foams and suppressing gas and solid conduction in aerogels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Molecular insights and future frontiers in cell photosensitization for solar-driven CO 2 conversion

The conversion of CO 2 to value-added products powered with solar energy is an ideal solution to establishing a closed carbon cycle. Combining microorganisms with light-harvesting nanomaterials into photosynthetic biohybrid systems (PBSs) presents an approach to reaching this solution. Metabolic pathways precisely evolved for CO 2 fixation selectively and reliably generate products. Nanomaterials harvest solar light and biocompatibly associate with microorganisms owing to similar lengths scales. Although this is a nascent field, a variety of approaches have been implemented encompassing different microorganisms and nanomaterials. To advance the field in an impactful manner, it is paramount to understand the molecular underpinnings of PBSs. In this perspective, we highlight studies inspecting charge uptake pathways and singularities in photosensitized cells. We discuss further analyses to more completely elucidate these constructs, and we focus on criteria to be met for designing photosensitizing nanomaterials. As a result, we advocate for the pairing of microorganisms with naturally occurring and highly biocompatible mineral-based semiconductor nanomaterials.

36 MATERIALS SCIENCE↗