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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Mathematical Formulation used by MATLAB Code to Convert FTIR Interferograms to Calibrated Spectra

This report discusses the mathematical procedures used to convert raw interferograms from Fourier transform infrared (FTIR) sensors to calibrated spectra. The work discussed in this report was completed as part of the Helios project at Los Alamos National Laboratory. MATLAB code was developed to convert the raw interferograms to calibrated spectra. The report summarizes the developed MATLAB scripts and functions, along with a description of the mathematical methods used by the code. The first step in working with raw interferograms is to convert them to uncalibrated spectra by applying an apodization function to the raw data and then by performing a Fourier transform. The developed MATLAB code also addresses phase error correction by applying the Mertz method. This report provides documentation for the MATLAB scripts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Multiscale Mathematics for Biomass Conversion to Renewable Hydrogen

The overall objective of this project was to develop multiscale models for understanding and eventually designing complex processes for renewables. To the best of our knowledge, our work is the first attempt at modeling complex reacting systems, whose performance relies on underlying multiscale mathematics and developing rigorous mathematical techniques and computational algorithms to study such models. Our specific application lies at the heart of biofuels initiatives of DOE and entails modeling of catalytic systems, to enable economic, environmentally benign, and efficient conversion of biomass into either hydrogen or valuable chemicals.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Considerations for improving the biosphere assessment methodology and the associated conceptual and mathematical models for application to LLW/ILW radioactive wastes in Spain - 15194

We give consideration to future needs in assessment studies relating to the disposal of very low level radioactive waste (VLLW), low level radioactive waste (LLW) and intermediate level radioactive waste (ILW) in Spain. As these wastes are primarily disposed at El Cabril, particular attention is given to the characteristics of that site. The focus is on post-closure performance assessment studies and specifically on the biosphere aspect of those studies. In particular, it gives consideration to the assessment methodology that should be used, taking into account the recommendations made in the international BIOMASS and BIOCLIM programs, as well as more recent developments. It then goes on to consider the conceptual models of the biosphere that should be developed and comments on how these should be transformed into mathematical models. The issues arising in conceptual and mathematical model development are illustrated by reference to El Cabril, but several of these issues would also be applicable to other engineered facilities excavated from the surface in a variety of geographical and geological contexts. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2015

Cold-cap temperature profile comparison between the laboratory and mathematical model - 15591

The rate of waste vitrification in an electric melter is connected to the feed-to-glass conversion process, which occurs in the cold cap, a layer of reacting feed on top of molten glass. The cold cap consists of two layers: a low-temperature (∼100 deg. C - ∼800 deg. C) region of unconnected, reacting feed and a high-temperature (∼800 deg. C - ∼1100 deg. C) region of molten glass with gas bubbles and cavities. A recently developed mathematical model describes the effect of the cold cap on glass production. For verification of the mathematical model, a cold cap produced in a laboratory-scale melter was subsequently analyzed by correlating structural features of a polished region with those obtained in heat-treated feed samples to determine the temperature profile as a function of cold-cap position. After corrections were made for the diminished volume of bubbles and collapsed cavities that occurred in the cold-cap sample during cooling, the temperature gradients were alike in the model and the laboratory cold cap through the range from ∼500 deg. C to ∼1100 deg. C. The temperature profiles were different in the upper region of the cold cap because the radiant heat from the molten glass in the laboratory-scale melter resulted in dry and hot (∼400 deg. C) surface conditions whereas the model assumed a surface covered by boiling slurry. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Recent developments in mathematical aspects of relativistic fluids

Abstract We review some recent developments in mathematical aspects of relativistic fluids. The goal is to provide a quick entry point to some research topics of current interest that is accessible to graduate students and researchers from adjacent fields, as well as to researches working on broader aspects of relativistic fluid dynamics interested in its mathematical formalism. Instead of complete proofs, which can be found in the published literature, here we focus on the proofs’ main ideas and key concepts. After an introduction to the relativistic Euler equations, we cover the following topics: a new wave-transport formulation of the relativistic Euler equations tailored to applications; the problem of shock formation for relativistic Euler; rough (i.e., low-regularity) solutions to the relativistic Euler equations; the relativistic Euler equations with a physical vacuum boundary; relativistic fluids with viscosity. We finish with a discussion of open problems and future directions of research.

Disconzi, Marcelo (ORCID:0000000234497778)↗
DOE OSTI2021

Solving the puzzle of Fe homeostasis by integrating molecular, mathematical, and societal models

To ensure optimal utilization and bioavailability, iron uptake, transport, subcellular localization, and assimilation are tightly regulated in plants. In this work, we examine recent advances in our understanding of cellular responses to Fe deficiency. We then use intracellular mechanisms of Fe homeostasis to discuss how formalizing cell biology knowledge via a mathematical model can advance discovery even when quantitative data is limited. Using simulation-based inference to identify plausible systems mechanisms that conform to known emergent phenotypes can yield novel, testable hypotheses to guide targeted experiments. However, this approach relies on the accurate encoding of domain-expert knowledge in exploratory mathematical models. We argue that this would be facilitated by fostering more “systems thinking” life scientists and that diversifying your research team may be a practical path to achieve that goal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Understanding the Connection between Nanoparticle Uptake and Cancer Treatment Efficacy using Mathematical Modeling

Nanoparticles have shown great promise in improving cancer treatment efficacy while reducing toxicity and treatment side effects. Predicting the treatment outcome for nanoparticle systems by measuring nanoparticle biodistribution has been challenging due to the commonly unmatched, heterogeneous distribution of nanoparticles relative to free drug distribution. We here present a proof-of-concept study that uses mathematical modeling together with experimentation to address this challenge. Individual mice with 4T1 breast cancer were treated with either nanoparticle-delivered or free doxorubicin, with results demonstrating improved cancer kill efficacy of doxorubicin loaded nanoparticles in comparison to free doxorubicin. We then developed a mathematical theory to render model predictions from measured nanoparticle biodistribution, as determined using graphite furnace atomic absorption. Model analysis finds that treatment efficacy increased exponentially with increased nanoparticle accumulation within the tumor, emphasizing the significance of developing new ways to optimize the delivery efficiency of nanoparticles to the tumor microenvironment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

State of the Art in Time‐Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties

Abstract We present a state‐of‐the‐art report on time‐dependent flow topology. We survey representative papers in visualization and provide a taxonomy of existing approaches that generalize flow topology from time‐independent to time‐dependent settings. The approaches are classified based upon four categories: tracking of steady topology, reference frame adaption, pathline classification or clustering, and generalization of critical points. Our unique contributions include introducing a set of desirable mathematical properties to interpret physical meaningfulness for time‐dependent flow visualization, inferring mathematical properties associated with selective research papers, and utilizing such properties for classification. The five most important properties identified in the existing literature include coincidence with the steady case, induction of a partition within the domain, Lagrangian invariance, objectivity, and Galilean invariance.

Bujack, Roxana↗
DOE OSTI2022

Characteristics and mathematical modeling of apple slice drying in an electrohydrodynamic system with a needle‐plate electrode

Abstract The drying of apple slices in an electrohydrodynamic (EHD) system with a needle‐plate electrode was characterized and mathematically modeled at voltages of 30, 40, and 50 kV and with distances between needles of 5, 6, and 7 cm. The results showed that the drying rate, effective water diffusion coefficient, and rehydration ratio of the apple slices, and specific energy consumption of drying, all increased with increasing voltage. Initially, there was a period when the drying rate was approximately constant in the low‐voltage EHD, but not in the high voltage conditions. At the same voltage, the drying rate was highest when the distance between needles was 6 cm. The voltage and distance between needles had a significant ( p < .05) interactive effect on the effective water diffusion coefficient and specific energy consumption, but not on the rehydration ratio. General mathematical models for drying can be used to represent the drying behavior of apple slices. The Midilli and Kucuk model and the Logarithmic model had the best fits, with correlation coefficients of 0.998803 and 0.998267, respectively. Predictive functions for the moisture ratio of apple slices based on voltage and distance between needles were established and verified. The results indicated that the Logarithmic model could more accurately predict the moisture ratio of apple slices dried using the EHD method. Practical Applications As a new drying technology, electrohydrodynamic (EHD) drying has unequaled advantages. According to the results, using the appropriate voltage and distance between needles improves not only drying efficiency, but also the rehydration ratio of apple slices, which reflects the quality of dried products. The Logarithmic model can accurately predict the EHD drying behavior of apple slices, which is of great significance in optimizing actual production.

Li, Bowen↗
DOE OSTI2022

Applied Mathematics Challenge: Simulation of Power Electronics in Future Power Grid

There has been a buzz around increased grid modernization for more than a decade. Grid modernization includes, but is not limited to, upgrades to the grid to enhance reliability, resilience, security, and access to clean energy sources. One significant change that has been happening in this context is the increased penetration of computing power and controlled devices like power electronics in the power grid. As this change happens, the operation and characteristics of the power grid are set to undergo a significant change. The power grid moves from an older electric machine dominated grid that used analog electronics for controls to a power electronics dominated grid that uses digital computing for controls. As this transition happens, there are significant problems of applied mathematics that will need to be resolved in power electronics and power grid. The problems include the ability to simulate in different timescales, while also leveraging the significantly improved computing capabilities available today. In this paper, the challenges related to simulation of power electronics are discussed and the challenge problems laid down that applied mathematics may help resolve in future.

Debnath, Suman↗
DOE OSTI2023

Mathematical Framework Underlying the In Situ Electrochemical Diagnosis of Adsorbed Intermediates Formed during Redox Reactions at Electrode Surfaces

Previously, we have presented an electrochemical technique wherein an electroactive tracer species is employed to probe the rate-limiting factors governing redox reactions at an electrode surface. In this technique, the electrode is first held potentiostatically to facilitate a redox process (step 1), and then the potential is released to open circuit conditions (step 2) so as to monitor the time-dependent re-equilibration of the electrode potential in the presence of the tracer. The time-dependent potential response in step 2 has been shown to contain information about diffusion—limited or desorption—limited steps, enabling in situ probing of the electrochemistry at the electrode surface during step 1. In the present contribution, a theoretical model governing the transient response in step 2 is developed for two limiting cases: diffusion—limited and desorption—limited recovery of the electrode potential. Mathematical modeling shows that, during re-equilibration, the step 2 potential transient corresponding to a case where step 1 involves surface adsorbed species which undergo desorption in step 2 exhibits a much longer time constant than that when re-equilibration occurs under diffusion limitations. The mathematical framework presented herein provides a sound fundamental basis for applying the aforementioned technique to studying adsorption-desorption processes during electrochemistry. Also, technique limitations are presented in light of the modeling findings.

25 ENERGY STORAGE↗
DOE OSTI2026

Mathematical Modeling of the Potential and Time Dependence of Ir Dissolution from Hydrous Ir Oxide Oxygen Evolution Catalysts

One of the main degradation mechanisms of hydrous iridium oxide acidic oxygen evolution reaction (OER) catalysts is dissolution and loss into the acidic membrane. While degradation models have been proposed, there is a gap in understanding the potential and time dependence of the iridium dissolution reaction and its mechanistic underpinnings. In this work, Ir dissolution rates measured as a function of time and potential via time-resolved inductively-coupled plasma mass spectrometry (ICP-MS) in aqueous acidic electrolyte are used to establish a mathematical model for Ir dissolution. The mathematical model is generated using proposed formation and dissolution reactions for Ir species. Through comparison with the ICP-MS data and existing information on the potential-dependent Ir phase, we find that the potential and time-dependence of dissolution can be modeled as dissolution of an oxide phase, here represented as IrO 2 , with potential dependent kinetics and formation of a passivating species, a process with a rate-limiting step that is not potential dependent. This understanding of the potential dependence of dissolution and passivation kinetics using aqueous electrolyte half-cell measurements can be used to predict the degradation of Ir oxide in operating energy conversion devices relying on the OER, such as proton-exchange membrane water electrolyzers.

Kariuki, Nancy N. [Argonne National Laboratory (AN↗
DOE OSTI2024

Machine learning mathematical models for incidence estimation during pandemics

Accurate estimates of the incidence of infectious diseases are key for the control of epidemics. However, healthcare systems are often unable to test the population exhaustively, especially when asymptomatic and paucisymptomatic cases are widespread; this leads to significant and systematic under-reporting of the real incidence. Here, we propose a machine learning approach to estimate the incidence of a pandemic in real-time, using reported cases and the overall test rate. In particular, we use Bayesian symbolic regression to automatically learn the closed-form mathematical models that most parsimoniously describe incidence. We develop and validate our models using COVID-19 incidence values for nine different countries, confirming their ability to accurately predict daily incidence. Remarkably, despite the differences in epidemic trajectories and dynamics across countries, we find that a single model for all countries offers a more parsimonious description and is more predictive of actual incidence compared to separate models for each country. Our results show the potential to accurately model incidence in real-time using closed-form mathematical models, providing a valuable tool for public health decision-makers.

Fajardo-Fontiveros, Oscar (ORCID:0000000207058972)↗
DOE OSTI2021

AmeriFlux US-MWU Battle Creek Area Mathematics and Science Center

This is the AmeriFlux version of the carbon flux data for the site US-MWU Battle Creek Area Mathematics and Science Center. Site Description - Urban site located above the roof of the Battle Creek Area Mathematics and Science Center, a regional STEM magnet school. Site was chosen to represent urban areas within the Kalamazoo River watershed.

Chen, Jiquan↗
DOE OSTI2025

Mathematical Morphological Filtering with a Self-Adaptive Reconstruction Technique and Application to Local Seismic Data

Recorded seismic data are generally contaminated by noise from different sources, which masks the signals of interest. In the seismology community, frequency filtering (FF) is the standard method for noise suppression. However, when the signal of interest and noise share the same frequency band, the latter cannot be filtered out without infringing on the former. We implemented a noise suppression approach based on the mathematical morphology theorem. The method involves compound operations of dilation and erosion using structuring elements of varying lengths and decomposes an input noisy waveform into several time functions with differing characteristics. Further, the filtered waveform is constructed from the time functions using a self-adaptive reconstruction technique. Application to a data set of >4700 local waveforms suggests that the implemented mathematical morphological filtering (MMF) approach is efficient for data with low signal-to-noise ratio (SNR) and significantly outperforms FF in that SNR range. For most of the dataset, FF, machine learning (ML) denoising, and continuous wavelet transform (CWT) thresholding result in higher SNR values compared with the MMF method. However, for ~42% of the waveforms, MMF outperforms FF, and the SNR gain achieved with MMF is as large as ~23 dB. Compared to ML denoising and CWT thresholding, this proportion drops to only ~10%–14%. Our results suggests that in an operational setting, MMF cannot replace the other noise suppression methods; however, signal detection can be improved if MMF is used to supplement them in some scenarios. MMF could help detect signals in problematic low-SNR data, which are currently being missed particularly when using FF alone.

58 GEOSCIENCES↗
DOE OSTI2016

Development of Kinetics and Mathematical Models for High Pressure Gasification of Lignite-Switchgrass Blends

The overall objective of the current project was to investigate the high pressure gasification characteristics of a feed containing both coal and biomass. The two feed types differ in their ash contents and ash composition, particularly the alkali content. Gasification of a combined feed of coal and biomass has the potential for considerable synergies that might lead to a dramatic improvement in process economics and flexibility. The proposed study aimed to develop a detailed understanding of the chemistry, kinetics, and transport effects during high pressure gasification of coal-biomass blend feed. Specifically, we studied to develop: (a) an understanding of the catalytic effect of alkali and other inorganic species present in the biomass and coal, (b) an understanding of processing conditions under which synergistic effects of the blending of coal and biomass might be observed. This included the role of particle size, residence time, and proximity of the two feed types, (c) kinetics of high pressure gasification of individual feeds as well as the blends, and (d) development of mathematical models that incorporate kinetics and transport models to enable prediction of gasification rate at a given set of operating conditions, and (e) protocols to extend the results to other feed resources. The goal was to provide a fundamental understanding of the gasification process and guide in optimizing the configurations and design of the next generation of gasifiers. The approach undertaken was centered on two basic premises: (1) the gasification for small particles without internal mass transfer limitations can be treated as the sum of two processes in series (pyrolysis and char gasification) , and (2) the reactivity of the char generated during pyrolysis not only depends on the pressure and temperature but is also affected by the heating rates. Thus low heating rates (10-50 °C/min) typical of PTGA fail to produce char that would typically be formed at high heating rates (~10 4 °C/sec), encountered in entrained flow gasifiers. The char morphology, also a function of the heating rate, would influence the transport rates during the char gasification phase. Thus, heating rate plays a critical role through which both, pyrolysis and char gasification, are interconnected. We utilized two complementary gasification experiments: PEFR (pressurized entrained flow gasifier) and PTGA (pressurized thermo-gravimetric analyzer). The PEFR allowed us to study gasification at pressures, temperatures, and heating rates relevant for coal-biomass gasifiers. The PTGA work was useful in understanding the basic chemistry of the evolution of various gaseous species during pyrolysis. These results helped improved our understanding of the chemistry and chemical changes during pyrolysis. The role alkali metals and other inorganics in char gasification using steam and/or CO 2 was investigated. Finally, the mathematical models for char gasification without the transport effects were developed at commercial operating conditions.

09 BIOMASS FUELS↗
DOE OSTI2017

Mathematical Basis and Test Cases for Colloid-Facilitated Radionuclide Transport Modeling in GDSA-PFLOTRAN

This report provides documentation of the mathematical basis for a colloid-facilitated radionuclide transport modeling capability that can be incorporated into GDSA-PFLOTRAN. It also provides numerous test cases against which the modeling capability can be benchmarked once the model is implemented numerically in GDSA-PFLOTRAN. The test cases were run using a 1-D numerical model developed by the author, and the inputs and outputs from the 1-D model are provided in an electronic spreadsheet supplement to this report so that all cases can be reproduced in GDSA-PFLOTRAN, and the outputs can be directly compared with the 1-D model. The cases include examples of all potential scenarios in which colloid-facilitated transport could result in the accelerated transport of a radionuclide relative to its transport in the absence of colloids. Although it cannot be claimed that all the model features that are described in the mathematical basis were rigorously exercised in the test cases, the goal was to test the features that matter the most for colloid-facilitated transport; i.e., slow desorption of radionuclides from colloids, slow filtration of colloids, and equilibrium radionuclide partitioning to colloids that is strongly favored over partitioning to immobile surfaces, resulting in a substantial fraction of radionuclide mass being associated with mobile colloids.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Asymptotic preserving methods for fluid electron-fluid models in the large magnetic field limit with mathematically guaranteed properties (Final Report)

The current manuscript is a final report on the activities carried out under the Project LDRD-CIS #226834. In scientific terms, the work reported in this manuscript is a continuation of the efforts started with Project LDRD-express #223796 with final report of activities SAND2021-11481, see [83]. In this section we briefly explain what pre-existing developments motivated the current body of work and provide an overview of the activities developed with the funds provided. The overarching goal of the current project LDRD-CIS #226834 and the previous project LDRD-express #223796 is the development of numerical methods with mathematically guaranteed properties in order to solve the Euler-Maxwell system of plasma physics and generalizations thereof. Even though Project #223796 laid out general foundations of space and time discretization of Euler-Maxwell system, overall, it was focused on the development of numerical schemes for purely electrostatic fluid-plasma models. In particular, the project developed a family of schemes with mathematically guaranteed robustness in order to solve the Euler-Poisson model. This model is an asymptotic limit where only electrostatic response of the plasma is considered. Its primary feature is the presence of a non-local force, the electrostatic force, which introduces effects with infinite speed propagation into the problem. Even though instantaneous propagation of perturbations may be considered nonphysical, there are plenty of physical regimes of technical interest where such an approximation is perfectly valid.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗