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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Matrix-Based Process Modeling in Microsoft Excel

The purpose of this document is to describe the theory and methodology of matrix-based process modeling and demonstrate its use through a generic process model within Microsoft Excel. This type of model allows a modeler to quickly query various metrics, such as total production time, for a specified production run. These matrix models tend to be quick to build and use, but come with certain limitations and are more suited for smaller processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A modified model parametrization algorithm for solving a special type of heat and mass transfer systems

A new method for solving nonlinear heat and mass transfer design tasks was considered. Systems using the Number of Transfer Units (NTU) method are a special type of mathematical model of heat and mass exchangers. It was observed, that the NTU models in a form of differential-algebraic equations (DAEs) cannot be directly solved with higher values of NTU. The requirements for consistent initial conditions, as well as numerical limitations of DAEs solvers, result, that the solution to the considered design problems that cannot be obtained by a classical direct shooting procedure. To overcome the presented difficulties, the αDAE model optimization algorithm was adjusted for solving NTU-based models. The new approach consists of 3 main steps: 1) task discretization by a multiple-shooting approach, 2) design an appropriate function $f_{NTU}$(α) to effectively influence the variability of the state variables described by dynamical relations, 3) the iterative numerical optimization algorithm for the new parametrized system. Moreover, computations can be performed by a chosen numerical optimization approach, which can be communicated with an available outer procedure for solving differential-algebraic equations. The presented algorithm was implemented and applied to solve the design task with the NTU model of a counter-flow exchanger. Here, the new approach was used to modify the system dynamics to influence the difficulty of the considered problem. Finally, the presented method enabled failure-free numerical computations for the higher values of the NTU parameter.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2021

ESS-DIVE guidelines for archiving terrestrial model data

This dataset contains supporting documents and images for ESS-DIVE terrestrial model data archiving guidelines.Terrestrial models are broadly defined as numerical models that couple both land dynamics and energy, water, carbon, or nutrient fluxes. We created these guidelines based on input from the U.S. Department of Energy’s Biological and Environmental Research land modeling community. The guidelines are intended to help modelers determine which components of their terrestrial model data associated with publication should be archived. Based on input from the land modeling community, the guidelines recommend archiving both model input and testing data, as well as code, script, and metadata. The guidelines also recommend archiving model data output, depending on the limitations set by data repositories. Lastly, we provide recommendations for bundling data files for publication as well as a discussion about tools that can facilitate model data archiving and reuse.This dataset is an archive of the associated GitHub repository for our model archiving guidelines (https://github.com/ess-dive-community/essdive-model-data-archiving-guidelines). The ‘README.pdf’ file gives a general introduction to the guidelines, and the ‘instructions.pdf’ file provides more detailed steps for following the guidelines. We also provide 2 figures in this data package: 1) a decision tree (model_data_guidelines_decision_tree.png) that can help users determine which components of their model data to archive. and 2) the ‘model_data_guidelines_flmd.png’ file depicts the different files that can be archived in addition to the model data itself. Lastly, we include 3 digitized tables from our associated manuscript and 3 CSV files with anonymized input from DOE scientists about the importance of different aspects of model data archiving from which we developed the guidelines.Dataset updates for v1.1.0: We updated this data package on 2021-11-22 in response to review comments on our related manuscript. In this update we removed one figure so that the model archiving guidelines are conveyed in text rather than an image. We updated the file-level metadata (FLMD) figure to be in accord with the most recent FLMD recommendations. We made minor edits to the README file to update the recommended citation and added two co-authors. We also added 6 new data files (3 are anonymized input from DOE scientists that helped to inform guidelines, and 3 are digitized tables from our manuscript.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

First-Principles Cost Analysis of Advanced High-Temperature Nuclear Plants

Due to the vast number of recent nuclear reactor innovations, particularly those pertaining to generation IV reactor types like high-temperature gas cooled reactors (HTGRs) and sodium-cooled fast reactors (SFRs), and the newer deployment strategies envisioned, such as use of small modular reactors (SMRs) or even microreactors, reliable, detailed, and complete costs of these nuclear innovations are needed in wide availability. The types of models that generally achieve these objectives are those incorporating the fundamental nature of the real-world systems they aspire to predict, such as first-principles models. Furthermore, first principles models typically offer predictiveness that is not attained by most other types of individual-models. However, detailed, first-principles cost modeling of nuclear reactors and entire nuclear plants is relatively limited. To address this limitation in the availability of detailed, predictive models based on fundamentals, we recently developed a range of cost models, mostly based on first-principles methodologies, to project full lifecycle costs (LCCs) of nuclear power plants (NPPs) based on multiple parallel SM-HTG-pebble bed reactors (PBRs) and SM-SFRs.

Prosser, Jacob H. [Strategic Analysis, Inc., Arlin↗
DOE OSTI2025

First-Principles Cost Analysis of Advanced High-Temperature Nuclear Plants

Due to the vast number of recent nuclear reactor innovations, particularly those pertaining to generation IV reactor types like high-temperature gas cooled reactors (HTGRs) and sodium-cooled fast reactors (SFRs), and the newer deployment strategies envisioned, such as use of small modular reactors (SMRs) or even microreactors, reliable, detailed, and complete costs of these nuclear innovations are needed in wide availability. The types of models that generally achieve these objectives are those incorporating the fundamental nature of the real-world systems they aspire to predict, such as first-principles models. Furthermore, first principles models typically offer predictiveness that is not attained by most other types of individual-models. However, detailed, first-principles cost modeling of nuclear reactors and entire nuclear plants is relatively limited. To address this limitation in the availability of detailed, predictive models based on fundamentals, we recently developed a range of cost models, mostly based on first-principles methodologies, to project full lifecycle costs (LCCs) of nuclear power plants (NPPs) based on multiple parallel SM-HTG-pebble bed reactors (PBRs) and SM-SFRs.

Prosser, Jacob H. [Strategic Analysis, Inc., Arlin↗
DOE OSTI2021

Development of a kinetic model to describe six types of symbiotic interactions in a formate utilizing microalgae-bacteria cultivation system

This study investigated an algae/formate-utilizing-bacteria system that has been developed for carbon capture. This photomixotrophic consortium consumed formate to support bacterial growth so that the resulting respiration CO 2 could be simultaneously used by algae to avoid CO 2 gas-lipid mass transfer limitation. To understand biomass growth and population interactions in this unique system, a kinetic model has been developed to describe algae and bacteria multiplication, formate and nitrogen utilizations, CO 2 mass transfer, O 2 generation and consumption, lighting condition and shading effect. The simulation indicated that this ecosystem could form six types of interactions (mutualism, commensalism, parasitism, neutralism, amensalism, and competition) depending on the light intensity and nutrient availability. Furthermore, the simulation of both batch culture and chemostat, along with parameter sensitivity tests and experimental observations, offered insights into optimal applications of algae-bacteria consortium based photobiorefinery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

DEEPEN 3D PFA Index Models for Exploration Datasets at Newberry Volcano

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), index models needed to be developed to map values in geoscientific exploration datasets to favorability index values. This GDR submission includes those index models. Index models were created by binning values in exploration datasets into chunks based on their favorability, and then applying a number between 0 and 5 to each chunk, where 0 represents very unfavorable data values and 5 represents very favorable data values. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were created using histograms of the distributions of each exploration dataset in combination with literature and input from experts about what combinations of geophysical, geological, and geochemical signatures are considered favorable at Newberry. This is in attempt to create similar sized bins based on the current understanding of how different anomalies map to favorable areas for the different types of geothermal plays (i.e., conventional hydrothermal, superhot EGS, and supercritical). For example, an area of partial melt would likely appear as an area of low density, high conductivity, low vp, and high vp/vs. This means that these target anomalies would be given high (4 or 5) index values for the purpose of imaging the heat source. To account for differences in how exploration methods are used to detect each play component, separate index models are produced for each exploration method for each component of each play type. Index models were produced for the following datasets: - Geologic model - Alteration model - vp/vs - vp - vs - Temperature model - Seismicity (density*magnitude) - Density - Resistivity - Fault distance - Earthquake cutoff depth model

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2022

Double holography in string theory

We develop the notion of double holography in Type IIB string theory realizations of braneworld models. The Type IIB setups are based on the holographic duals of 4d BCFTs comprising 4d N = 4 SYM on a half space coupled to 3d N = 4 SCFTs on the boundary. Based on the concrete BCFTs and their brane construction, we provide microscopic realizations of the intermediate holographic description, obtained by dualizing only the 3d degrees of freedom. Triggered by recent observations in bottom-up models, we discuss the causal structures in the full BCFT duals and intermediate descriptions. This confirms qualitative features found in the bottom-up models but suggests a refinement of their interpretation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Integrated Computational Materials and Mechanical Modeling for Additive Manufacturing of Alloys with Graded Structure Used in Fossil Fuel Power Plants

Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures

The causal structure of a simulation is a major determinant of both its character and behavior, yet most methods we use to compare simulations focus only on simulation outputs. We introduce a method that combines graphical representation with information theoretic metrics to quantitatively compare the causal structures of models. The method applies to agent-based simulations as well as system dynamics models and facilitates comparison within and between types. Comparing models based on their causal structures can illuminate differences in assumptions made by the models, allowing modelers to (1) better situate their models in the context of existing work, including highlighting novelty, (2) explicitly compare conceptual theory and assumptions to simulated theory and assumptions, and (3) investigate potential causal drivers of divergent behavior between models. We demonstrate the method by comparing two epidemiology models at different levels of aggregation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Integrating Arctic Plant Functional Types in a Land Surface Model Using Above‐ and Belowground Field Observations

Abstract Accurate simulations of high‐latitude ecosystems are critical for confident Earth system model (ESM) projections of carbon cycle feedbacks to global climate change. Land surface model components of ESMs, including the E3SM Land Model (ELM), simulate vegetation growth and ecosystem responses to changing climate and atmospheric CO 2 concentrations by grouping heterogeneous vegetation into like sets of plant functional types (PFTs). Many such models represent high‐latitude vegetation using only two PFTs (shrub and grass), thereby missing the diversity of vegetation growth forms and functional traits in the Arctic. Here, we use field observations of biomass and leaf traits across a gradient of plant communities on the Seward Peninsula in northwest Alaska to replace the original ELM configuration for the first time with nine Arctic‐specific PFTs. The newly developed PFTs include: (1) nonvascular mosses and lichens, (2) deciduous and evergreen shrubs of various height classes, including an alder PFT, (3) graminoids, and (4) forbs. Improvements relative to the original model configuration included greater belowground biomass allocation, persistent fine roots and rhizomes of nonwoody plants, and better representation of variability in total plant biomass across sites with varying plant communities and depth to bedrock. Simulations through 2100 using the RCP8.5 climate scenario and constant PFT fractional areas showed alder‐dominated plant communities gaining more biomass and lichen‐dominated communities gaining less biomass compared to default PFTs. Our results highlight how representing the diversity of arctic vegetation and confronting models with measurements from varied plant communities improves the representation of arctic vegetation in terrestrial ecosystem models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Magnetized Winds of M-type Stars and Star–Planet Magnetic Interactions: Uncertainties and Modeling Strategy

M-type stars are the most common stars in the Universe. They are ideal hosts for the search of exoplanets in the habitable zone (HZ), as their small size and low temperature make the HZ much closer-in than their solar twins. Harboring very deep convective layers, they also usually exhibit very intense magnetic fields. Understanding their environment, in particular their coronal and wind properties, is thus very important, as they might be very different from what is observed in the solar system. The mass-loss rate of M-type stars is poorly known observationally, and recent attempts to estimate it for some of them (e.g., TRAPPIST-1 and Proxima Centauri) can vary by an order of magnitude. In this work, we revisit the stellar wind properties of M dwarfs in the light of the latest estimates of $\dot{M}$ through Lyα absorption at the astropause and slingshot prominences. We outline a modeling strategy to estimate the mass-loss rate, radiative loss, and wind speed, with uncertainties, based on an Alfvén-wave-driven stellar wind model. We find that it is very likely that several TRAPPIST-1 planets lie within the Alfvén surface, which implies that these planets experience star–planet magnetic interactions (SPMIs). We also find that SPMIs between Proxima Cen b and its host star could be the reason for recently observed radio emissions.

M stars↗
DOE OSTI2020

An Efficient 1-D Thermal Stratification Model for Pool-Type Sodium-Cooled Fast Reactors

Investigating thermal stratification in the upper plenum of a sodium fast reactor (SFR) is presently a technology gap in SFR safety analysis. Understanding thermal stratification will promote safe operation of the SFR before its commercial deployment. Stratified layers of liquid sodium with a large vertical temperature gradient could be established in the upper plenum of an SFR during a down-power or a loss-of-flow transient. These stratified layers are unstable and could result in uncertainties for the core safety of an SFR. In order to predict the occurrence of the thermal stratification efficiently, we developed a one-dimensional (1-D) transport model to estimate the temperature profile of the ambient fluid in the upper plenum. This model demands much less computational effort than computational fluid dynamics (CFD) codes and provides calculations with higher fidelity than historical system-level codes. Two flow conditions were considered separately in the current study depending on if in-vessel components are presented in the upper plenum. For the condition where in-vessel components, specifically the upper internal structure, are presented, we assumed that the impinging sodium was evenly dispersed in the ambient fluid within the distance between the bottom of the in-vessel component and the jet inlet surface. For the condition where no in-vessel components are presented, we assumed that the impinging sodium was evenly dispersed in the ambient fluid within the jet length, which was determined through data-driven trainings. The newly developed 1-D model showed similar performance with the CFD model in both cases. However, due to the assumption of flat profiles of the impinging jet axial dispersion rate, nonnegligible discrepancies between the 1-D prediction and the measured data were observed.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Demand Control Ventilation

This documentation focuses on a single end-use savings shape measure - Demand Control Ventilation (DCV). DCV can save energy by reducing the rate at which outdoor air (OA) is delivered during periods of less-than-design occupancy. This measure will enable DCV for air loops using applicable HVAC system types (all except dedicated outdoor air systems [DOAS], packaged systems, or that have an energy recovery ventilator [ERV]) and serving applicable space types (all except kitchens, dining areas, patient spaces, mechanical rooms, stairwells and corridors, or high exhaust space types) using model occupancy schedules to control the DCV. The measure is applicable to 72.7% of the stock floor area. As office buildings outside of California in ComStock are modeled using a single, whole-building space type, DCV is not applied to these building types. The DCV measure demonstrates 2.6% total site energy savings (119 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

“Lagrangian disks” in M-theory

While the study of bordered (pseudo-)holomorphic curves with boundary on Lagrangian submanifolds has a long history, a similar problem that involves (special) Lagrangian submanifolds with boundary on complex surfaces appears to be largely overlooked in both physics and math literature. We relate this problem to geometry of coassociative submanifolds in G 2 holonomy spaces and to Spin(7) metrics on 8-manifolds with T 2 fibrations. As an application to physics, we propose a large class of brane models in type IIA string theory that generalize brane brick models on the one hand and 2d theories T[M 4 ] on the other.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗