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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

2.3.4.104 - Lignin Conversion to Sustainable Aviation Fuel Blendstocks

The Lignin Conversion to Sustainable Aviation Fuel Blendstocks (LigSAF) project focuses on the conversion of lignin-rich streams to deoxygenated aromatic and cycloalkane blendstocks in the jet fuel range. This work is done in close collaboration with the BETO-funded Lignin-First Biorefinery Development project and industrial scale-up partners, and the work is closely guided by analysis to develop cost-effective and sustainable routes to produce lignin-based SAF blendstocks. To date, we have demonstrated the continuous catalytic conversion of a lignin oil from poplar to deoxygenated aromatic products at -85% C-mol yield. This hydrodeoxygenation process uses a stable, earth-abundant catalyst and requires no solvent. We have also established a baseline process model and associated techno-economic analysis and life cycle assessment that together demonstrate the potential to achieve cost parity with fossil carbon-based jet fuel at -70% reduction in greenhouse gas emissions. Current work is focused on expanding the slate of feedstocks for hydrodeoxygenation to include lignin oils from softwoods, agricultural residues, and grasses as well as from hydrolysis lignin substrates from biochemical conversion and pulp-and-paper processes. We are also undertaking catalyst development efforts to tune the reaction selectivity from aromatic compounds to cycloalkanes. Lastly, we are investigating reaction engineering strategies to slurry solids for hydrodeoxygenation reactions.

aromatics↗
DOE OSTI2025

Direct Air Reactive Capture and Conversion for Utility-Scale Energy Storage (Final Report)

This final report for FEW0277 summarizes the work performed over the project performance period of October 2021 – March 2025. This project was funded under the “Reactive Capture and Conversion R&D” lab call released in FY2021. The goal of the project was to develop dual-function materials and process for capturing CO 2 from the atmosphere and converting it into CH 4 . The work was organized into four parallel tracks in 1) direct air capture materials synthesis and characterization, 2) catalysts for CO 2 conversion, 3) mechanistic investigations via ab initio simulations, and 4) process modeling, technoeconomic analysis, and lifecycle assessment. The project was split into two budget periods. The first budget period focused on development of amine-based materials, due to their known performance for CO 2 direct air capture and their potential to act synergistically with metal catalysts to enable a low-temperature methanation pathway. The second budget period focused on development of alkali-based materials and a simulated-moving-bed process for high conversion catalytic reduction of captured CO 2 to CH 4 . All project milestones were completed during the project performance period and are summarized in this report. Our work resulted in publication of eight peer-reviewed manuscripts, one patent application, and numerous presentations given at domestic and international conferences and invited academic department seminars.

03 NATURAL GAS↗
DOE OSTI2021

Investigation of Cycling Coal-Fired Power Plants Using High-Fidelity Models

The project delivers a well-integrated and validated simulation platform for cycling operation analysis in coal-fired power plant. Two critical mechanical components of the boiler island were analyzed through mechanical integrity assessment and economic benefit analysis. The current phase of the project focuses on the development of the integrated simulation infrastructure and prove its feasibility and effectiveness using two typical use cases. This integrated platform can help save a lot of engineering efforts for model development and simulation analysis. Through the real simulation scenarios in this document, it was demonstrated that using this platform, an analysis can be completed in approximately 2 days, while it could cost several weeks before using this platform. Going forward, the platform built in this project can be used for more boiler service applications, and it can be further enhanced with more functions/features to maximize its usage and benefits. 1) Extend component-level analysis with more use cases to cover all the major critical components of boiler island under cycling operations. A library of critical components can be developed and validated for typical pulverized coal-fired subcritical boiler units. 2) Develop predictive maintenance features based on the integrated models (Digital Twins) and engineering analysis procedures. Predictive maintenance enables each asset to be serviced based on forecast on life consumption and cost profile for replacing/welding the critical mechanical parts of the boiler. This minimizes the chance of unscheduled shutdowns and emergency services at much higher costs and prevent the fatal accidents in unit operations. 3) Develop and maintain a standard library for critical component analysis under flexible plant operations, which will include libraries of: process models, MI models for typical pressure parts, and economic models with typical plant operating data and ISO power trade data.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

CO 2 conversion to syngas via electrification of endothermal reactors: Process design and environmental impact analysis

CO 2 utilization via reverse water gas shift (rWGS) reaction has been proposed as a path to the sustainable utilization. Here this work presents a detailed process modelling study where steam methane reforming (SMR) generated hydrogen was combined with rWGS to produce syngas (CO + H 2 ) with various hydrogen-to-carbon oxide ratios. To further decrease CO 2 emissions that may offset the benefits of CO 2 converted in rWGS, electrification of endothermal reactors, both SMR and rWGS was considered where CO 2 emitting fuel burning in the furnace was replaced by the emerging ohmic (resistive) heating. Material and energy inventory obtained from process design calculations was used to perform Life Cycle Analysis (LCA) to calculate environmental impacts of CO 2 consumption and reactor electrification. The results showed that greenhouse gas emissions, in CO 2 kg equivalent, were the lowest when both SMR and rWGS were heated using wind-generated electricity, decreasing from 25 to 10 kg CO 2 equivalent for H 2 :CO = 2:1 while the conventional electricity mix used for furnace electrical heating across the board of scenarios generated highest environmental impacts, much higher than those that used natural gas as fuel. Process economics calculations suggested that, when both SMR and rWGS were electrically heated, the process only showed product syngas cost parity with the conventional fuel heated design when electricity cost was ~$0.008/kWh. This suggests that CO 2 utilization scenarios involving process electrification need to be carefully considered from the total design perspective so they do not produce more greenhouse gases than in conventional non-electrified scenarios.

42 ENGINEERING↗
DOE OSTI2021

Data-Driven Global Sensitivity Analysis of Three-Phase Distribution System with PVs

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency.

distribution system analysis↗
DOE OSTI2021

Keystone Solar Future Project (KSFP)

The Keystone Solar Future Project (KSFP, also referred to as “Keystone Project” in the report) implements a cost-effective, secure, reliable, and safe technology platform that paves the way for future interconnected distributed energy resources (DER), creates a framework that enables high penetration of DER, and transforms the DER interconnection process. The project is important and necessary in a few aspects. First, the industry definition of Distributed Energy Resource Management System (DERMS) is not yet validated against field deployments and mostly implemented in small pilots relying on third party integration. Keystone project implements a direct plug and play communication to inverter based DER systems. Secondly, most interconnection application portal is not yet fully integrated with back-end system, thus lacks the capability to drive a complete and accurate modeling of DER assets into the planning and operational systems. Keystone project rolled out a fully integrated modeling process of DERs from interconnection to operational management. Lastly, customers were given an opportunity to learn about the advanced functions of the inverters and take full advantages of their benefits through participation in the pilot program. During the project, the technical effectiveness and economic feasibility of DER management were investigated and demonstrated in real life events and simulation studies. The project highlighted the importance of coordinated DER interconnection, installation and management with the rest of the distribution system.

14 SOLAR ENERGY↗
DOE OSTI2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗
DOE OSTI2025

Denoising Autoencoder for Reconstructing Sensor Observation Data and Predicting Evapotranspiration: Noisy and Missing Values Repair and Uncertainty Quantification

Abstract Machine learning (ML) methods applied in scientific research often deal with interrelated features in high‐dimensional data. Reducing data noise and redundancy is needed to increase prediction accuracy and efficiency especially when dealing with data from field sensors. We explored an unsupervised learning method, the denoising autoencoder (DAE), to extract the underlying data structure from noisy raw data in the context of predicting hydrologic quantities from multiple field sensors. These sensors have intrinsic instrumental noise and occasional malfunctions that cause missing values. Our DAE neural network reconstructed meteorological sensor data containing noise and missing values to predict evapotranspiration in a mountainous watershed. The DAE reconstructed the sensor variables with a mean coefficient of determination value of 0.77 across 15 dimensions representing individual sensors. It reduced variance and bias uncertainties compared to a classical autoencoder model. The reconstruction quality varied across dimensions depending on their cross‐correlation and alignment with the underlying data structure. Uncertainties arising from the model structure were overall higher than those resulting from data corruption. We attached the DAE structure to a downstream ET‐prediction neural network in three formats and achieved reasonably accurate ET predictions . The use of the DAE notably reduced variance uncertainty in ET prediction. However, excessive variance reduction may be accompanied by an increase in bias due to the intrinsic bias‐variance tradeoff. Our method of evaluating and reducing uncertainties in aggregated data from different sources can be used to improve predictive models, process understanding, and uncertainty quantification for better water resource management. Plain Language Summary We present a machine learning method, namely the denoising autoencoder, which reduces the effects of data noise and missing values typically present in scientific data sets collected through sensor measurements. This method selects the most relevant information from noisy raw data collected by the instruments and fills in missing values. To demonstrate the effectiveness of our method, we applied it to predict evapotranspiration, a hydrologic variable that represents the water moved from the land surface to the atmosphere through a combination of evaporation and plant water use (transpiration). We also used a random sampling technique (the Monte Carlo method) to compare the uncertainty in the predictions when using the raw and noisy data versus the reconstructed data. The denoising process produced more accurate predictions of evapotranspiration with less uncertainty. Improved predictions of evapotranspiration can lead to a better understanding and accounting of water budgets. This ML approach is broadly suitable for a wide variety of applications that involve noisy sensor data with missing values. Key Points We used a denoising autoencoder (DAE) neural network to reduce noise in meteorological and soil sensor observations by on average We used Monte Carlo sampling to estimate the bias and variance of all model outputs, including uncertainty sources from data and the model We attached the DAE component to a downstream neural network to predict ET with the variance reduced by , compared to that without the DAE

denoising autoencoder↗
DOE OSTI2024

Techno-Economic Analysis and Life Cycle Assessment of the Production of Biodegradable Polyaliphatic–Polyaromatic Polyesters

Poly(butylene-adipate terephthalate) (PBAT) is a polyaliphatic–polyaromatic polyester that is biodegradable and has found application in several markets, making it a widely produced biodegradable polymer worldwide. However, the production of PBAT is carbon-intensive, as it relies on the use of petroleum-based monomers. There is, thus, significant interest in identifying polyesters that are biodegradable and less carbon-intensive (e.g., use of biomass- derived monomers). In this work, we develop a detailed process model (and an associated database) for the production of polyaliphatic–polyaromatic polyesters including petroleum-based PBAT and biomass-derived alternatives including poly(pentylene- adipate terephthalate) and poly(pentylene-adipate furandicarboxy- late). Techno-economic analysis (TEA) reveals that the production costs of these polyesters strongly depend on monomer costs (accounting for over 90% of the total production cost) and identifies market conditions under which biomass-based polyesters can be cost-competitive to petroleum-based PBAT. Life cycle assessment (LCA) shows that biomass-derived polyesters can reduce the global warming impact of PBAT by half. Altogether, the proposed TEA/LCA model aims to provide guidance into polyesters that are most promising and help assess their overall economic and environmental performance.

42 ENGINEERING↗
DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗
DOE OSTI2025

Essay: A Path for the Construction of a Muon Collider

Muons are elementary particles and provide cleaner collision events that can explore higher energies compared to composite particles like protons. Muons are also far heavier than their electron cousins, meaning that they emit less synchrotron radiation that effectively limits the energies of circular electron-positron colliders. These characteristics open up the possibility for a muon collider to surpass the direct energy reach of the Large Hadron Collider while achieving unprecedented precision measurements of standard model processes. In this Essay, after briefly summarizing the progress achieved so far, I identify important missing research and development steps and envision a compelling plan to bring a muon collider to reality in the next two decades. A muon collider could allow for the exploration of physics that is not available with current technologies. For example, it may provide a way to study the Higgs boson directly or probe new particles, including those related to dark matter or other phenomena beyond the standard model. . Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Phase-field model of precipitation processes with coherency loss

A phase-field model is proposed to simulate coherency loss coupled with microstructure evolution. A special field variable is employed to describe the degree of coherency loss of each particle and its evolution is governed by a Ginzburg-Landau type kinetic equation. For the sake of computational efficiency, a flood-fill algorithm is introduced that can drastically reduce the required number of field variables, which allows the model to efficiently simulate a large number of particles sufficient for characterizing their statistical features during Ostwald ripening. The model can incorporate size dependence of coherency loss, metastability of coherent particles, and effectively incorporate the underlying mechanisms of coherency loss by introducing a so-called differential energy criterion. The model is applied to simulate coarsening of Al 3 Sc precipitates in aluminum alloy and comprehensively compared with experiments. Our results clearly show how the particle size distribution is changed during coherency loss and affects the coarsening rate.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Characterizing soil water content variability across spatial scales from optimized high-resolution distributed temperature sensing technique

Fiber-optic Distributed Temperature Sensing, when combined with the Single-probe Heat-pulse technique can measure soil moisture (θ) across spatial scales. The key limitation of this system is in obtaining the relationship between soil thermal conductivity (λ) and θ for a specific field. Using the Department of Energy Atmospheric Radiation Measurement (ARM) site, this study tested a new methodology to account for the spatial variability in the λ-θ relationship using a Gaussian processes model. The resulting accurate θ measurements (RMSE = 0.03 m 3 m –3 ) were used to characterize the spatial variability of θ across scales and to develop an empirical equation that can correct for the changes in the θ spatial variability observed at different spatial resolutions. In addition, the number of required samples to accurately characterize θ and its variability over scales ranging from 5 m and 350 m were estimated. Finally, these findings provide key information to scale soil moisture from centimeters to hundreds of meters for process understanding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families

In previous work, we introduced process family design. The main idea is to design a platform of common elements, and, allowing us to capture additional cost savings, simultaneously design a family of processes, and reducing both engineering and deployment timelines. We formulate this as an optimization problem, specifically a nonlinear generalized disjunctive program (GDP). We have proposed two approaches for reformulating and solving this problem: one based on full-discretization of the design space and one that uses Machine Learning (ML) surrogates to replace the nonlinear process models. Using ML surrogates to predict required system costs and performance indicators allows us to reformulate the nonlinearities in the GDP generate an efficient MILP formulation. In this work, we apply the ML surrogate approach to two case studies. One case study involves designing a family of carbon capture systems to cover a set of different flue gas flow rates and inlet CO 2 concentrations, where we consider the absorber and stripper as common unit module types. The second case study focuses on a water-desalination process, where we design a family of these processes for a variety of salt concentrations and flow rates. In both of these case studies, we demonstrate a scalable optimization approach that enables the design of multiple processes simultaneously, reducing the time-to-market and overall costs by maximizing the cost savings due to both economies of scale and economies of numbers.

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

Deep Learning Methods for Symbolic Calculations in HEP

This project develops machine learning methods to accelerate symbolic calculations in high-energy physics. Using sequence-to-sequence transformer models, we construct frameworks to predict squared amplitudes and related quantities for Standard Model processes, including quantum electrodynamics, quantum chromodynamics, and electroweak interactions. The results demonstrate that deep learning can successfully learn complex symbolic relationships and provide a scalable approach to symbolic computation with potential applications in precision calculations and collider phenomenology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Simulation of 12-bed vacuum pressure-swing adsorption for hydrogen separation from methanol-steam reforming off-gas

This study focuses on analysis of a 12-bed vacuum pressure-swing adsorption (VPSA) process capable of purifying hydrogen from a ternary mixture (H 2 /CO 2 /CO 75/24/1 mol%) derived from methanol-steam reforming. The process produces 9 kmol H 2 /h with less than 2 ppm and 0.2 ppm of CO2 and CO, respectively, to supply a polymer electrolyte membrane fuel cell. The process model is developed in Aspen Adsorption® using the “uni-bed” approach. A parametric study of H 2 purity and recovery with respect to adsorption pressure, adsorbent height, activated carbon:zeolite ratio, feed composition, and number of beds is performed. Results show 12-bed VPSA can meet the H 2 purity goals, with H 2 recovery as high as 75.75%. Adsorption occurs at 7 bar, the column height is 1.2 m, and the adsorbent ratio is 70%:30%. Furthermore, a 4-bed VPSA can achieve the same purity goals as the 12-bed process, but H 2 recovery decreases to 61.34%.

03 NATURAL GAS↗
DOE OSTI2025

Fully Bayesian Analysis With Model Inadequacy Correction For Nuclear Graphite Property Models With Hierarchical Variance Structure

Nuclear-grade graphites are extensively utilized in the core designs of various advanced nuclear reactors. Within the reactor environment, graphite is subjected to prolonged exposure to extreme conditions, including high temperatures, radiation, and potentially molten salt and oxygen. Such exposure can induce several degradation mechanisms in graphite, such as nonuniform volumetric strains caused by irradiation and thermal expansion, leading to stresses that may compromise the performance of graphite components. Assessing component integrity, forecasting component performance over the reactor's lifespan, and developing design standards necessitate robust tools for predicting fracture initiation and propagation in graphite structural components within nuclear reactors. This code enables the Bayesian calibration of properties for nuclear-grade graphites. Using a hierarchical Bayesian approach, multiple experimental data sources are combined to develop Gaussian process models for the properties. Using the Kennedy O'Hagan framework, the uncertainties due inadequacies in the model and the inherent spread in the experimental data are quantified.

Dhulipala, Som Lakshmi NarasimhaLakshmi Narasimha ↗