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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Methodology For Determining The Effective Aircraft Impact Area For Irregularly Shaped Facilities

This report presents a computational methodology for determining the effective aircraft impact area for irregularly shaped facilities, extending the approach in Department of Energy (DOE) Standard 3014 by decomposing “critical areas” into multiple rectangular nodes rather than relying on a single bounding cuboid. This method reduces unnecessary conservatism that can arise when non-critical space is enveloped by a single footprint. The facility’s critical areas are partitioned into nodes of constant height, a baseline aircraft impact trajectory is established, and the facility perspective is rotated to accommodate arbitrary heading angles. For each node, analytic expressions are provided to construct the node-specific effective impact polygon that accounts for skid distance, structural footprint, fly-in (shadow) region driven by building height and descent angle, and wingspan offsets capturing wing-only strikes.

42 ENGINEERING↗
DOE OSTI2018

Non-adiabatic quantum reactive scattering in hyperspherical coordinates

A new electronically non-adiabatic quantum reactive scattering methodology is presented based on a time-independent coupled channel formalism and the adiabatically adjusting principal axis hyperspherical coordinates of Pack and Parker [J. Chem. Phys. 87, 3888 (1987)]. The methodology computes the full state-to-state scattering matrix for A + B 2 (v, j) ↔ AB(v', j') + B and A + AB(v, j) → A + AB(v', j') reactions that involve two coupled electronic states which exhibit a conical intersection. The methodology accurately treats all six degrees of freedom relative to the center-of-mass which includes non-zero total angular momentum J and identical particle exchange symmetry. The new methodology is applied to the ultracold hydrogen exchange reaction for which large geometric phase effects have been recently reported [B. K. Kendrick et al., Phys. Rev. Lett. 115, 153201 (2015)]. Rate coefficients for the H/D + HD(v = 4, j = 0) → H/D + HD(v', j') reactions are reported for collision energies between 1 μK and 100 K (total energy ≈1.9 eV). A new diabatic potential energy matrix is developed based on the Boothroyd, Keogh, Martin, and Peterson (BKMP2) and double many body expansion plus single-polynomial (DSP) adiabatic potential energy surfaces for the ground and first excited electronic states of H 3 , respectively. The rate coefficients computed using the new non-adiabatic methodology and diabatic potential matrix reproduce the recently reported rates that include the geometric phase and are computed using a single adiabatic ground electronic state potential energy surface (BKMP2). The dramatic enhancement and suppression of the ultracold rates due to the geometric phase are confirmed as well as its effects on several shape resonances near 1 K. In conclusion, the results reported here represent the first fully non-adiabatic quantum reactive scattering calculation for an ultracold reaction and validate the importance of the geometric phase on the Wigner threshold behavior.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Serpent and MCNP Calculations of the Energy Deposition in the Transformational Challenge Reactor

This paper focuses on the calculation of the energy deposition in the Transformational Challenge Reactor by two major Monte Carlo codes: Serpent and MCNP. The first software computation relies on Kinetic Energy Released per unit Mass (KERMA) factors while the second one relies on Q-values. The results from these two independent computation methodologies are in very good agreement; however, Serpent runs much faster than MCNP (for the same computational model) and allows for a detailed energy deposition distribution from a 1-mm-side square mesh with a relative statistical error between 0.5% and 1%. This detailed energy deposition is suitable for multiphysics analyses aimed at design optimizations. In order to calculate the energy deposition, Serpent needs enhanced ACE files (distributed by the software developers). Unlike other Monte Carlo software that uses inputs based on Python or Java languages, the Serpent input syntax is very similar to that of MCNP; a Python script can convert a MCNP input to a Serpent input in seconds. For simulations not requiring the calculation of the energy deposition, Serpent can also read nuclear data from MCNP ACE files, which eventually improves the comparison of the results of the two codes.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Computational materials assessment of the D/Li-stripping neutron source as a prototypical facility for fusion materials testing

As the US fusion materials community awaits the selection and design of a fusion prototypical neutron source (FPNS), a risk reduction exercise has been conducted to (i) provide an updated materials performance evaluation using state-of-the-art computational materials modeling, (ii) expand on legacy analysis based on pure Fe to other relevant fusion structural materials types, and (iii) ensure that materials response under FPNS operational conditions is consistent with referential fusion reactor conditions. The current paper describes the efforts undertaken to assemble a comprehensive computational methodology that includes neutronics, primary damage calculations, atomistic simulations of displacement cascades, chemical inventory evolution calculations, and a computational thermodynamic analysis of emerging phases during irradiation. Our work extends existing studies in pure Fe to reduced-activation ferritic/martensitic steels, tungsten, silicon carbide, and vanadium alloys. We focus on the single-beam deuteron/lithium-stripping neutron source behind the IFMIF-DONES concept, which we assess against ITER, two DEMO designs, and an ideal pure 14-MeV flux. Our analysis indicates that, within standard uncertainties inherent to the models employed, the DONES concept adequately captures fusion conditions in the four materials analyzed. Our work is intended as a comprehensive irradiation damage analysis of fusion-representative neutron sources, to be used for further neutron source evaluation and fusion facility operation.

Marian, Jaime [Univ. of California, Los Angeles, C↗
DOE OSTI2022

Rashba effects in lead-free ferroelectric semiconductor [CH 3 PH 3 ]SnBr 3

Here, first-principles density functional theory computations are used to predict Rashba effects cofunctional with ferroelectricity in a recently synthesized lead-free hybrid organic-inorganic perovskite MPSnBr 3 (MP=methylphosphonium, [CH 3 PH 3 ] + ). The ground state of the material is polar monoclinic with calculated spontaneous polarization of 3.01 µC/cm 2 . It exhibits near band edges spin-splitting of up to 3.3 meV and Rashba coefficient up to 0.62 eV Å. The spin textures have different topology in the conduction and valence band, which originates from the difference in the spin-momentum coupling strengths. They occur in two orthogonal planes of the Brillouin zone and are coupled to the direction of spontaneous polarization. These features persist at a finite temperature of 293 K. At 333 K, that is above monoclinic to orthorhombic phase transition, spontaneous polarization is reduced to 0.15 µC/cm 2 , while the maximum spin splitting and Rashba coefficient reduce slightly to the value of 2.9 meV and 0.41 eV Å, respectively. The spin textures remain coupled with the polarization direction. We investigate the dependence of the aforementioned properties on the choice of computational methodology to extend the first-principles predictions to include finite temperature effects. We find that the predictions are sensitive to the methodology. Our study reveals the potential of MPSnBr 3 for low-temperature applications in spintronics and quantum computing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

A cloud computing approach to massively-parallel distributed stochastic optimization with application to geomechanical reservoir characterization

Many search strategies used to optimize parameters during hydrologic model calibration are inherently limited in that simulations must be performed sequentially in order for the algorithm to converge. As a result, the overall time required to reach a solution increases linearly with the number of steps necessary. In contrast, sampling strategies that attempt to approximate the posterior distribution of the parameters do not require the model simulations to be run in sequence and therefore invite the use of a massively parallel approach across heterogeneous computing environments. We apply a cloud computing methodology to this problem where a head node running on Amazon Web Services continuously produces candidate models that are distributed to arbitrary compute environments to run the simulation, in our case Clemson’s Palmetto cluster computer. Thus the time required to obtain a sufficient number of samples to produce an adequate approximation of the posterior distribution of the parameters is limited by the number of available compute sites. Monte Carlo sampling of the posterior space would be duplicative and inefficient, thus we combine Latin hypercube methods with genetic algorithms to ensure that no large regions of the parameter space are left unexplored, and that high probability regions are investigated thoroughly. We independently assess the fit to different types of data sets without the need to construct a single objective function. Sets of Pareto optimal models are particularly valuable when the quality and information content of data is poorly known or conceptual model errors exist in our simulation. We illustrate our approach for a problem where in situ strain data are used to calibrate the parameters of an oil reservoir in the Avant oil field in Oklahoma. We use well injection and shut-in to stress a deep (500m) confined formation, and observe the geomechanical response using borehole strainmeters installed in the shallow subsurface (30m). These data were then used to calibrate the poroelastic properties of the reservoir and caprock along with the geometry of an elliptic channel associated with the formation. Using our approach we are able to identify issues with model error as well as substantial tradeoffs that occur within the parameters.

inversion, stochastic, geomechanics, poroelastic, ↗
DOE OSTI2024

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Quantifying the atomistic free-volume morphology of materials with graph theory

Here, we introduce a new computational methodology for the identification and characterization of free volume within/around atomistic configurations. This scheme employs a three-stage workflow, by which spheres are iteratively grown inside of voxels, and ultimately converted to planar graphs, which are then characterized via a graph-based order parameter. Our approach is computationally efficient, physically intuitive, and universally transferable to any material system. Validation of our methodology is performed on several sets of materials problems: (1) classification of unique free volumes in various crystal phases, (2) autonomous detection and classification of complex surface defects during epitaxial growth simulations, (3) characterization of free volume defects in metals/alloys, and (4) quantification of the spatio-temporal behavior of nano-scale free volume morphologies as a function of both temperature and free-volume size. Our method accurately identifies and characterizes unique free volumes over a multitude of systems and length scales, indicating its potential for future use in understanding the relationship between free volume morphology and material properties under both static and dynamic conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Charged Species Concentration in Combusting Mixtures Using Equilibrium Chemistry

Ionization in flames is of interest in the design and development of modern combustion devices. The identity and concentration of various charged species in reacting mixtures can play an important role in the diagnostics and control of such devices. Simplified chemistry computations that provide good estimates of ionic species in complex flow-fields can be used to model turbulent reacting flows in various combustion devices, greatly reducing the required computational resources for design and development studies. A critical assessment of the use of the equilibrium chemistry method to compute charged species concentration in combusting mixtures under various temperatures, pressures, and thermal disequilibrium conditions is presented. The use of equilibrium chemistry to compute charged species concentrations in propane-air mixtures performed by Calcote and King has been extended. A more accurate computational methodology that includes the effect of negative ions, chemi-ions (H 3 O + and CHO + ), and thermal nonequilibrium was investigated to evaluate the suitability of equilibrium computations for estimating charged species concentrations in reacting mixtures. The results show that equilibrium computations which include the effects of H 3 O + and elevated electron temperatures can indeed explain the levels of ion concentrations observed in laboratory flame experiments under lean and near-stoichiometric conditions. Furthermore, under engine-like conditions at higher temperatures and pressures, equilibrium computations can be used to obtain useful estimates of charged species concentrations in modern combustion devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

The Role of Electronic Excitations on Chemical Reaction Dynamics at Metal, Semiconductor and Nanoparticle Surfaces

Chemical reactions are often facilitated and steered when carried out on solid surfaces, essential for applications such as heterogeneous catalysis, solar energy conversion, corrosion, materials processing, and many others. A critical factor that can determine the rates and pathways of chemical reactions at surfaces is the efficiency and specificity of energy transfer; how fast does energy move around and where does it go? For reactions on insulator surfaces energy transfer generally moves in and out of vibrations of the adsorbed molecule and the underlying substrate. By contrast, on metal surfaces, metallic nanoparticles and semiconductors, another pathway for energy flow opens up, excitation and de-excitation of electrons. This so-called “nonadiabatic” mechanism often dominates the transfer of energy and can directly impact the course of a chemical reaction. Conventional computational methods such as molecular dynamics simulation do not account for this nonadiabatic behavior. The current DOE-BES funded project has focused on developing the underlying theoretical foundation and the computational methodology for the prediction of nonadiabatic chemical reaction dynamics at surfaces. The research has successfully opened up new methodology and new applications for molecular simulation. In particular, over the last three years, the “Electronic Friction” theory, pioneered by the PI, has now been developed into a stable and accurate computational method that is sufficiently practical to allow first principles “on-the-fly” simulation of chemical reaction dynamics at metal surfaces.

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

Enhancing the accuracy of XPS calculations: Exploring hybrid basis set schemes for CVS-EOMIP-CCSD calculations

Reliable computational methodologies and basis sets for modeling x-ray spectra are essential for extracting and interpreting electronic and structural information from experimental x-ray spectra. In particular, the trade-off between numerical accuracy and computational cost due to the size of the basis set is a major challenge, since molecular orbitals undergo extreme relaxation in the core-hole state. To gain clarity on the changes in electronic structure induced by the formation of a core-hole, the use of sufficiently flexible basis for expanding the orbitals, particularly for the core region, has been shown to be essential. This work focuses on the refinement of core-hole ionized state calculations using the equation-of-motion coupled cluster family of methods through an extensive analysis on the effectiveness of “hybrid” and mixed basis sets. In this investigation, we utilize the CVS-EOMIP-CCSD method in combination and construct hybrid basis sets piecewise from readily available Dunning’s correlation consistent basis sets in order to calculate x-ray ionization energies (IEs) for a set of small gas phase molecules. Our results provide insights into the impact of basis sets on the CVS-EOMIP-CCSD calculations of K-edge IEs of first-row p-block elements. Furthermore, these insights enable us to understand more about the basis set dependence of the core IEs computed and allow us to establish a protocol for deriving reliable and cost-effective theoretical estimates for computing IEs of small molecules containing such elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Computational Modeling of Fluctuations in Energy and Metabolic Pathways of Methanogenic Archaea

The methanogenic archaea, anaerobic microbes that convert CO2 and H2 and/or other small organic fermentation products into methane, play an unusually large role in the global carbon cycle. As they perform the final step in the anaerobic breakdown of biomass, methanogens are a biogenic source of an estimated one billion tons methane each year. Depending on the location, produced methane can be considered as either a greenhouse gas (agricultural byproduct), sequestered carbon storage (methane hydrate deposits), or a potential energy source (organic wastewater treatment). These microbes therefore represent an important target for biotechnology applications. Computational models of methanogens with predictive power are useful aids in the adaptation of methanogenic systems, but need to connect processes of wide-ranging time and length scales. In this project, we developed several computational methodologies for modeling the dynamic behavior of entire cells that connects stochastic reaction-diffusion dynamics of individual biochemical pathways with genome-scale modeling of metabolic networks. While each of these techniques were in the realm of well-defined computational methods, here we integrated them to develop several entirely new approaches to systems biology. The first scientific aim of the project was to model how noise in a biochemical pathway propagates into cellular phenotypes. Genetic circuits have been optimized by evolution to regulate molecular processes despite stochastic noise, but the effect of such noise on a cellular biochemical networks is currently unknown. An integrated stochastic/systems model of Escherichia coli species was created to analyze how noise in protein expression gives—and therefore noise in metabolic fluxes—gives rise to multiple cellular phenotype in isogenic population. After the initial work developing and validating methods that allow characterization of the heterogeneity in the model organism E. coli, the project shifted toward investigations of the methanogen Methanosarcina acetivorans. By integrating an unprecedented transcriptomics dataset for growth of the methanogen on many substrates with an in silico model, heterogeneity in metabolic pathway usage and methane production were examined. This lent insight into the physiological requirements of the organism under different environmental conditions and uncovered the unique regulatory role that mRNA half-life has in shaping metabolic flux distributions in this organism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Practical Considerations for Understanding Surface Reaction Mechanisms Involved in Heterogeneous Catalysis

Acquiring useful knowledge about the active site(s) of a catalyst, nature of reactant–catalyst interactions, nature of reactive intermediates, rate-determining step, reaction rate orders that affect various process parameters, and reaction mechanism as a whole is exceedingly challenging. This is especially true in the case of heterogeneous catalysts due to the complexity of the nature of surface active sites and their nonstatic behavior. Here, we present our perspective on differentiating between various surface reaction mechanisms in light of pioneering studies by leaders in the field, with the aim of clarifying some of the confusion associated with these complex mechanisms, especially the Eley–Rideal mechanism. Using bibliometric analysis, we identify and discuss the following four reactions that most commonly invoke the Eley– Rideal mechanism: H 2 activation, CO oxidation, esterification of alcohols by acids, and selective catalytic reduction (SCR) of NO x with NH 3 . Our analysis of studies utilizing well-suited experimental and computational methodologies for differentiating surface reaction mechanisms suggests that the above-mentioned four reactions do not occur via the Eley–Rideal mechanism. Instead, each reaction occurs via the Langmuir–Hinshelwood mechanism with nonidealities present. Lastly, we highlight practical considerations regarding select experimental (characterization methods and differential kinetics) and computational modeling that we believe can provide useful insights to accurately discern between the various possible reaction mechanisms in heterogeneous catalysis.

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

Revealing conductivity of p-type delta layer systems for novel computing applications

This project uses a quantum simulation technique to reveal the true conducting properties of novel atomic precision advanced manufacturing materials. With Moore's law approaching the limit of scaling for the CMOS technology, it is crucial to provide the best computing power and resources to National Security missions. Atomic precision advanced manufacturing-based computing systems can become the key to the design, use, and security of modern weapon systems, critical infrastructure, and communications. We will utilize the state-of-the-art computational methodology to create a predictive simulator for p-type atomic precision advanced manufacturing systems, which may also find applications in counterfeit detection and anti-tamper.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Uncertainty Quantification in CO2 Trapping Mechanisms: A Case Study of PUNQ-S3 Reservoir Model Using Representative Geological Realizations and Unsupervised Machine Learning

Evaluating uncertainty in CO2 injection projections often requires numerous high-resolution geological realizations (GRs) which, although effective, are computationally demanding. This study proposes the use of representative geological realizations (RGRs) as an efficient approach to capture the uncertainty range of the full set while reducing computational costs. A predetermined number of RGRs is selected using an integrated unsupervised machine learning (UML) framework, which includes Euclidean distance measurement, multidimensional scaling (MDS), and a deterministic K-means (DK-means) clustering algorithm. In the context of the intricate 3D aquifer CO2 storage model, PUNQ-S3, these algorithms are utilized. The UML methodology selects five RGRs from a pool of 25 possibilities (20% of the total), taking into account the reservoir quality index (RQI) as a static parameter of the reservoir. To determine the credibility of these RGRs, their simulation results are scrutinized through the application of the Kolmogorov–Smirnov (KS) test, which analyzes the distribution of the output. In this assessment, 40 CO2 injection wells cover the entire reservoir alongside the full set. The end-point simulation results indicate that the CO2 structural, residual, and solubility trapping within the RGRs and full set follow the same distribution. Simulating five RGRs alongside the full set of 25 GRs over 200 years, involving 10 years of CO2 injection, reveals consistently similar trapping distribution patterns, with an average value of Dmax of 0.21 remaining lower than Dcritical (0.66). Using this methodology, computational expenses related to scenario testing and development planning for CO2 storage reservoirs in the presence of geological uncertainties can be substantially reduced.

Mahjour, Seyed Kourosh↗
DOE OSTI2024

Mathematical methods for optimal polynomial recovery of high-dimensional systems from noisy data

The goal of our Early Career Research Project (ECRP) is to establish a modern mathematical foundation that will enable next-generation computational methods for polynomial approximation of high-dimensional systems, having a certain set of constraints, from a limited amount of noisy data. Such a foundation is critical to realizing the future potential of the DOE user facilities, and will ultimately empower scientists to address a fundamental question, namely, “how many realizations of a nonlinear manifold are required to recover the entire high-dimensional solution map, with optimal approximation guarantees and minimal computational cost?” The central theme of this effort aims to conquer this challenge by pioneering the development of extraordinarily innovative theoretical analysis and transformational non-intrusive computational methodologies. Such approaches will enable the reconstruction of the entire high-dimensional solution map, with accuracy comparable to the best approximation, while utilizing an optimal number of samples. During this reporting period we have made significant progress on four thrusts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond

The Materials Genome Initiative (MGI) has heralded a sea change in the philosophy of materials design. In an increasing number of applications, the successful deployment of novel materials has benefited from the use of computational methodologies, data descriptors, and machine learning. Polymers have long suffered from a lack of data on electronic, mechanical, and dielectric properties across large chemical spaces, causing a stagnation in the set of suitable candidates for various applications. Extensive efforts over the last few years have seen the fruitful application of MGI principles toward the accelerated discovery of attractive polymer dielectrics for capacitive energy storage. Here, we review these efforts, highlighting the importance of computational data generation and screening, targeted synthesis and characterization, polymer fingerprinting and machine-learning prediction models, and the creation of an online knowledgebase to guide ongoing and future polymer discovery and design. We lay special emphasis on the fingerprinting of polymers in terms of their genome or constituent atomic and molecular fragments, an idea that pays homage to the pioneers of the human genome project who identified the basic building blocks of the human DNA. As a result, by scoping the polymer genome, we present an essential roadmap for the design of polymer dielectrics, and provide future perspectives and directions for expansions to other polymer subclasses and properties.

36 MATERIALS SCIENCE↗