Search NASA⌕ Search

SEARCH · Search NASA

Results for “Multidimensional”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Multidimensional spectroscopy with attosecond extreme ultraviolet and shaped near-infrared pulses

Dynamics following excitation with attosecond extreme ultraviolet (XUV) pulses arise from enormous numbers of accessible excited states, complicating the retrieval of state-specific time evolutions. We develop attosecond XUV multidimensional spectroscopy here to separate interfering pathways on a near-infrared (NIR) energy axis, retrieving single state dynamics in argon atoms in a two-dimensional (2D) XUV-NIR spectrum. In this experiment, we measure four-wave mixing signal arising from the interaction of XUV attosecond pulses centered around 15 eV with two few-cycle NIR pulses. The 2D spectrum is created by measuring the emitted XUV signal field spectrum while applying narrowband amplitude and phase modulations to one of the NIR pulses. Application of such a technique to systems of high dimensionality will provide for the observation of state-resolved pure electronic dynamics, in direct analogy to phenomena unraveled by multidimensional spectroscopies at optical frequencies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Coupling Front-End Separations, Ion Mobility Spectrometry, and Mass Spectrometry For Enhanced Multidimensional Biological and Environmental Analyses

Ion mobility spectrometry (IMS) is a widely used analytical technique for rapid molecular separations in the gas phase. IMS alone is useful, but its coupling with mass spectrometry (MS) and front-end separations has been extremely beneficial for increasing measurement sensitivity, peak capacity of complex mixtures, and the scope of molecular information in biological and environmental sample analyses. Multiple studies in disease screening and environmental evaluations have even shown these IMS-based multidimensional separations extract information not possible with each technique individually. This review highlights 3-dimensional separations using IMS-MS in conjunction with a range of front-end techniques, such as gas chromatography (GC), supercritical fluid chromatography (SFC), liquid chromatography (LC), solid phase extractions (SPE), capillary electrophoresis (CE), field asymmetric ion mobility spectrometry (FAIMS), and microfluidic devices. The origination, current state, various applications, and future capabilities for these multidimensional approaches are described to provide insight into the utility and potential of each technique.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2026

Multidimensional scaling informed by F -statistic: Visualizing grouped microbiome data with inference

Multidimensional scaling (MDS) is a widely used dimensionality reduction technique in microbial ecology data analysis that captures the multivariate structure of the data while preserving pairwise distances between samples. While improvements in MDS have enhanced the ability to reveal group-specific data patterns, these MDS-based methods require prior assumptions for inference, limiting their application in general microbiome analysis. Here, in this study, we introduce a new MDS-based ordination method, “F-informed MDS,” which configures the data distribution based on the F-statistic, the ratio of dispersion between groups sharing common and different characteristics. Using semisynthetic datasets, we demonstrate that the proposed method is robust to hyperparameter selection while maintaining statistical significance throughout the ordination process. Various quality metrics for evaluating dimensionality reduction confirm that F-informed MDS is comparable to state-of-the-art methods in preserving both local and global data structures. Its application to a diatom-associated bacterial community suggests the role of this new method in interpreting the community’s response to the host. Our approach offers a well-founded refinement of MDS that aligns with statistical test results, which can be beneficial for broader multidimensional data analyses in microbiology and ecology. This new visualization tool can be incorporated into standard microbiome data analyses.

Biological and medical sciences↗
DOE OSTI2022

Multidimensional partial wave analysis of SIDIS dihadron beam spin asymmetries at CLAS12

Dihadron beam spin asymmetries provide a wide range of insights into nucleon structure and hadronization. Recent measurements at CLAS12 provide the first empirical evidence of nonzero G_1^\perp G 1 ⊥ , the parton helicity-dependent dihadron fragmentation function (DiFF) encoding spin-momentum correlations in hadronization. These measurements also allow for a point-by-point extraction of the subleading-twist PDF e(x) e ( x ) in a collinear framework. We observe different behavior of the asymmetries in different invariant mass regions, motivating a fully multidimensional study. The DiFFs also expand in terms of partial waves, each corresponding to the interference of dihadrons of particular polarizations. Altogether a fully multidimensional partial wave analysis is needed, and this presentation will summarize the efforts and results obtained thus far.

Dilks, Christopher↗
DOE OSTI2024

Exploring Multidimensional Spatial-Temporal Hydropower Operational Flexibilities by Modeling and Optimizing Water-Constrained Cascading Hydroelectric Systems

Because of unique characteristics such as clean and cost-competitive electricity as well as fast-ramping and storage abilities, the power industry continues to evolve its operation strategies for cascading hydroelectric (CHE) systems for providing enhanced values to the grid, especially under the deeper renewable resource integration. However, existing operation practices of CHEs predate the integration of renewables, which could prohibit the effective utilization of their inherent flexibilities in delivering maximum financial benefits and providing valuable grid services to the power system and electricity market operations. Indeed, modeling and optimizing these resource-limited while flexible CHE assets with uncertainties and imperfect information across multiple spatial-temporal dimensions present significant challenges. To facilitate CHE facility operators in effectively coordinating water usage and hydropower plant operations across multiple timescales, this project aims to fill the existing gaps by developing a suite of accurate water inflow (WI) forecast models as well as enhanced CHE modeling and optimization approaches with proper consideration of their unique characteristics, which would help explore their multidimensional spatial-temporal operational flexibility potentials. The developed approaches could better align reservoir operation strategies with variability and uncertainty of future water availability. They can also promote more effective utilization of multidimensional spatial-temporal hydropower operational flexibility potentials by designing long-term evacuation plans of reservoirs and short-term operation of CHEs, along with their coordination with other types of renewables. The project leverages various resources to facilitate the research and development activities, including actual characteristics data of CHE systems and a library of current and future cases of Portland General Electric (PGE). These realistic data enable the project team to study how to maximize the value of CHEs under current and future portfolios and evaluate opportunities to improve operation practices.

13 HYDRO ENERGY↗
DOE OSTI2025

Multidimensional Coherent Spectroscopy of van der Waals materials and heterostructures (Final Technical Report)

This project advanced multidimensional coherent spectroscopy (MDCS) and related spectroscopic-imaging methods as quantitative probes of many-body optical excitations in two-dimensional (2D) van der Waals (vdW) semiconductors and their heterostructures. Monolayer transition-metal dichalcogenides (TMDs) such as MoSe 2 and WSe 2 provide a uniquely strong platform for excitonic physics due to reduced dielectric screening, which enhances Coulomb interactions and makes higher-order correlated states (e.g., biexcitons and exciton–trion correlations) more prominent than in conventional bulk semiconductors. At the same time, the same sensitivity that enables strong interactions also increases susceptibility to spatial inhomogeneity—e.g., strain gradients, wrinkles, nanoscale disorder potentials, and charge puddling—which can broaden resonances, obscure interaction signatures, and complicate device-to-device reproducibility. The work reported here addressed this dual opportunity and challenge by: (i) developing and deploying coherent multidimensional methods (including double-quantum MDCS) capable of isolating interaction signals with reduced background, (ii) integrating electrostatic control to tune carrier populations and interaction strengths in situ, and (iii) introducing complementary spatially resolved photoluminescence (PL) techniques—hyperspectral PL imaging and polarization-/field-dependent PL lineshape analysis—to directly quantify disorder, strain, and localization that govern the coherent response.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multidimensional Parameter Study of Double Detonation Type Ia Supernovae Originating from Thin Helium Shell White Dwarfs

Despite the importance of Type Ia supernovae (SNe Ia) throughout astronomy, the precise progenitor systems and explosion mechanisms that drive SNe Ia are still unknown. An explosion scenario that has gained traction recently is double detonation, in which an accreted shell of He detonates and triggers a secondary detonation in the underlying white dwarf. Our research presents a number of high-resolution, multidimensional, full-star simulations of thin-He-shell, sub-Chandrasekhar-mass white dwarf progenitors that undergo a double detonation. This suite of thin-shell progenitors incorporates He shells that are thinner than those in previous multidimensional studies. We confirm the viability of the double detonation across a range of He-shell parameter space, as well as present bulk yields and ejecta profiles for each progenitor. The yields obtained are generally consistent with previous works and indicate the likelihood of producing observables that resemble SNe Ia. The dimensionality of our simulations allow us to examine features of the double detonation more closely, including the details of the off-center secondary ignition and asymmetric ejecta. We find considerable differences in the high-velocity extent of postdetonation products across different lines of sight. The data from this work will be used to generate predicted observables and may further support the viability of the double detonation scenario as an SN Ia channel, as well as show how the properties of the progenitor or viewing angle may influence trends in observable characteristics.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The Multidimensional Milky Way

Studying our Galaxy, the Milky Way (MW), gives us a close-up view of the interplay between cosmology, dark matter, and galaxy formation. In the next decade our understanding of the MW's dynamics, stellar populations, and structure will undergo a revolution thanks to planned and proposed astrometric, spectroscopic and photometric surveys, building on recent advances by the Gaia astrometric survey. Together, these new efforts will measure three-dimensional positions and velocities and numerous chemical abundances for stars to the MW's edge and well into the Local Group, leading to a complete multidimensional view of our Galaxy. Studies of the multidimensional Milky Way beyond the Gaia frontier---from the edge of the Galactic disk to the edge of our Galaxy's dark matter halo---will unlock new scientific advances across astrophysics, from constraints on dark matter to insights into galaxy formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics

Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross section, tandem mass spectra) against reference data obtained from measurements of authentic chemical standards. The breadth of annotatable compounds using this approach is necessarily limited by availability of authentic standards, analytical throughput, and resolving power of the separations that underly the measurements. The maturation of computational methods, both theory-driven and artificial intelligence/machine learning-based, for prediction of various molecular properties relevant to multidimensional mass spectrometry measurements has opened the door to a new “reference-free” paradigm of compound annotation. Through augmenting existing reference data for molecular properties with computational predictions, the universe of identifiable chemical species can be expanded significantly beyond its current limits. An unexplored aspect of this novel approach is understanding how to gauge confidence in resulting annotations, especially as the compound search space is expanded. Intuitively, the confidence of a compound annotation is related to the inherent discriminatory power of the molecular properties used for identification, as well as the precision with which the properties are measured or predicted. In this work, we characterize this relationship between measurement precision and identification probability in a systematic and quantitative fashion for a defined region of chemical space that includes organic small molecule metabolites. Importantly, this work establishes a framework for conducting metabolite identification probability analysis that enables others to quantify this relationship for their own compounds and properties of interest.

Metabolite Identification↗
DOE OSTI2023

Challenging the Curse of Dimensionality in Multidimensional Numerical Integration by Using a Low-Rank Tensor-Train Format

Numerical integration is a basic step in the implementation of more complex numerical algorithms suitable, for example, to solve ordinary and partial differential equations. The straightforward extension of a one-dimensional integration rule to a multidimensional grid by the tensor product of the spatial directions is deemed to be practically infeasible beyond a relatively small number of dimensions, e.g., three or four. In fact, the computational burden in terms of storage and floating point operations scales exponentially with the number of dimensions. This phenomenon is known as the curse of dimensionality and motivated the development of alternative methods such as the Monte Carlo method. The tensor product approach can be very effective for high-dimensional numerical integration if we can resort to an accurate low-rank tensor-train representation of the integrand function. In this work, we discuss this approach and present numerical evidence showing that it is very competitive with the Monte Carlo method in terms of accuracy and computational costs up to several hundredths of dimensions if the integrand function is regular enough and a sufficiently accurate low-rank approximation is available.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Revisiting the Fundamental Analytical Solutions of Heat and Mass Transfer: The Kernel of Multirate and Multidimensional Diffusion

There are two types of analytical solutions of temperature/concentration in and heat/mass transfer through boundaries of regularly shaped 1D, 2D, and 3D blocks. These infinite-series solutions with either error functions or exponentials exhibit highly irregular but complementary convergence at different dimensionless times, t d0 . In this paper, approximate solutions were developed by combining the error-function-series solutions for early times and the exponential-series solutions for late times and by using time partitioning at the switchover time, t d0 . The combined solutions contain either the leading term of both series for normal-accuracy approximations (with less than 0.003 relative error) or the first two terms for high-accuracy approximations (with less than 10-7 relative error) for 1D isotropic (spheres, cylinders, slabs) and 2D/3D rectangular blocks (squares, cubes, rectangles, and rectangular parallelepipeds). This rapid and uniform convergence for rectangular blocks was achieved by employing the same time partitioning with individual dimensionless times for different directions and the product of their combined 1D slab solutions. The switchover dimensionless time was determined to minimize the maximum approximation errors. Furthermore, the analytical solutions of first-order heat/mass flux for 2D/3D rectangular blocks were derived for normal-accuracy approximations. These flux equations contain the early-time solution with a three-term polynomial in √td and the late-time solution with the limited-term exponentials for rectangular blocks. The heat/mass flux equations and the combined temperature/concentration solutions form the ultimate kernel for fast simulations of multirate and multidimensional heat/mass transfer in porous/fractured media with millions of low-permeability blocks of varying shapes and sizes.

42 ENGINEERING↗
DOE OSTI2017

Modeling multidimensional and multispecies biofilms in porous media

ABSTRACT Modeling multidimensional and multispecies biofilm in porous media at the pore scale is challenging due to the need to simultaneously track the microbial community in the biofilms and the interfaces between the biofilms and the fluid. Therefore, researchers usually assume that the model has only one dimension in space or has only one microbial species. This work uses bioremediation of U(VI)‐contaminated groundwater as the context to develop a two‐dimensional and multispecies biofilm model. The model simulates the transverse mixing zone in which U(VI) is mixed with propionate, a nutrient externally supplied to stimulate the growth of microorganisms. The model considers multiple interactions among fluid flow, transport and reaction of chemical species, and growth of biofilm. The biofilm consists of two types of active biomass (syntrophs and dissimilatory metal reducing bacteria [DMBR]) and inert biomass. The two types of active biomass collaboratively remove U(VI). The model outputs biomass distribution, chemical species concentrations, and fluid flow at the pore scale to fundamentally study the multiple interactions. The model also outputs the contaminant removal rate that can be potentially used for up‐scaling studies. The simulated results are generally consistent with experimental observations from other studies in trend. The trend can be explained by the multiple interactions based on thermodynamics and microbial kinetics. Biotechnol. Bioeng. 2017;114: 1679–1687. © 2017 Wiley Periodicals, Inc.

Tang, Youneng↗
DOE OSTI2020

Application of multidimensional structural characterization to detect and describe moderate forest disturbance

The study of vegetation community and structural change has been central to ecology for over a century, yet the ways in which disturbances reshape the physical structure of forest canopies remain relatively unknown. Moderate severity disturbances affect different canopy strata and plant species, resulting in variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits (CSTs) with known links to ecosystem function. We used lidar-derived CSTs within a machine learning framework to detect and describe the structural changes that result from various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, senescence, pathogen, and insect infestation altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, counter to common assumptions regarding many moderate severity disturbances. While findings are limited in their generalizability due to lack of replication among disturbances, they do suggest that the current limitations of standard disturbance detection methods—such as optical-based remote sensing platforms, which are often above-canopy perspectives—limit our ability to understand the full ecological and structural impacts of disturbance, and to evaluate the consistency of structural patterns within and among disturbance agents. A more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structural change may potentially improve the modeling, detection, and prediction of functional implications of moderate severity disturbance as well as broaden our understanding of the ecological impacts of disturbance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Multidimensional Coherent Spectroscopy of Semiconductors

Abstract Optical multidimensional coherent spectroscopy (MDCS) is a nonlinear spectroscopy technique where a material is excited by a series of laser pulses to produce a spectrum as a function of multiple frequencies. The technique's ability to elucidate excited‐state structure and interactions has made MDCS a valuable tool in the study of excitons in semiconductors. This review introduces the method and describes progress it has fostered establishing a better understanding of dephasing rates, coherent coupling mechanisms, and many‐body interactions pertaining to optically generated electronic excitations in a variety of semiconductor material systems. Emphasis is placed on nanostructured gallium arsenide quantum wells and quantum dots, on quantum dots in other III–V and II–VI semiconductors, and on atomically thin transition metal dichalcogenides. Recent technical advances and potential future directions in the field are also discussed.

Smallwood, Christopher L.↗
DOE OSTI2019

Nanowell-mediated multidimensional separations combining nanoLC with SLIM IM-MS for rapid, high-peak-capacity proteomic analyses

Mass spectrometry (MS)–based analysis of complex biological samples is essential for biomedical research and clinical diagnostics. The separation prior to MS plays a key role in the overall analysis, with separations having larger peak capacities often leading to more identified species and improved confidence in those identifications. High-resolution ion mobility (IM) separations enabled by Structures for Lossless Ion Manipulation (SLIM) can provide extremely rapid, high-resolution separations and are well suited as a second dimension of separation following nanoscale liquid chromatography (nanoLC). However, existing sample handling approaches for offline coupling of separation modes require microliter-fraction volumes and are thus not well suited for analysis of trace biological samples. We have developed a novel nanowell-mediated fractionation system that enables nanoLC-separated samples to be efficiently preconcentrated and directly infused at nanoelectrospray flow rates for downstream analysis. When coupled with SLIM IM-MS, the platform enables rapid and high-peak-capacity multidimensional separations of small biological samples. In this study, peptides eluting from a 100 nL/min nanoLC separation were fractionated into ~?60 nanowells on a microfluidic glass chip using an in-house–developed robotic system. The dried samples on the chip were individually reconstituted and ionized by nanoelectrospray for SLIM IM-MS analysis. Using model peptides for characterization of the nanowell platform, we found that at least 80% of the peptide components of the fractionated samples were recovered from the nanowells, providing up to ~tenfold preconcentration for SLIM IM-MS analysis. The combined LC-SLIM IM separation peak capacities exceeded 3600 with a measurement throughput that is similar to current one-dimensional (1D) LC-MS proteomic analyses.

Dou, Maowei↗
DOE OSTI2018

Density Large Deviations for Multidimensional Stochastic Hyperbolic Conservation Laws

We investigate the density large deviation function for a multidimensional conservation law in the vanishing viscosity limit, when the probability concentrates on weak solutions of a hyperbolic conservation law. When the mobility and diffusivity matrices are proportional, i.e. an Einstein-like relation is satisfied, the problem has been solved in Bellettini and Mariani (Bull Greek Math Soc 57:31–45, 2010). When this proportionality does not hold, we compute explicitly the large deviation function for a step-like density profile, and we show that the associated optimal current has a non trivial structure. We also derive a lower bound for the large deviation function, valid for a more general weak solution, and leave the general large deviation function upper bound as a conjecture.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Multidimensional Analogs of the Cauchy–Riemann System and Representations of Their Solutions via Harmonic Functions

At present, there are numerous multidimensional generalizations of holomorphic vectors. The most general of these is the four-dimensional generalization of the Cauchy–Riemann system. In the present work, by introducing two quaternion functions and the notion of quaternion differentiation, we obtain, for the first time, a five-dimensional generalization of holomorphic vectors. By using the representation of holomorphic vectors via the quaternion harmonic function and its derivatives, we consider the Riemann–Hilbert problem and one problem in a layer. A new solution of the Riemann–Hilbert problem in the five-dimensional half space is obtained.

97 MATHEMATICS AND COMPUTING↗