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At least 343 records · Page 19

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DOE OSTI2020

Modular, multi-channel, interleaved power converters

A multi-phase power converter includes two or more multi-phase, bi-directional, multi-level, switching power converter subcircuits, connected in parallel at respective AC and DC sides, so as to provide a multi-channel, bi-directional, multi-level configuration. The AC sides of the switching converter subcircuits are directly coupled to one another and to a multi-phase AC input via series interface reactors, and the DC sides of the switching converter subcircuits are directly connected to one another and to a common split-capacitor bank at each level of the multi-level outputs of the switching converter subcircuits. A control circuit is configured to selectively control one or more switching semiconductor devices in each of the switching converter subcircuits. In some embodiments, the control circuit includes a closed-loop zero-sequence controller and a zero-sequence generator configured to eliminate circulating current among the switching converter subcircuits and to balance voltages across levels of the common split-capacitor bank.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Multi-material hydrodynamics with algebraic sharp interface capturing

A finite volume method for Eulerian multi-material hydrodynamics with sharp interface capturing is presented here. The pressure-temperature non-equilibrium multi-material system with finite-rate pressure relaxation in mixed-cells is considered here. This pressure closure facilitates material-property-dependent pressure relaxation, rather than instantaneous pressure equilibration, which in turn allows the use of unsplit high-order time-integrators. A modified tangent of hyperbola for interface capturing (THINC) method is used to reconstruct multi-material (> 2) interfaces, on three-dimensional unstructured meshes. A simple modification which extends the THINC reconstruction to interfaces between more than two materials is proposed. It is demonstrated that the modified THINC can capture multi-material interfaces within 2–4 tetrahedral cells. Since no geometric reconstructions are required by the THINC method, the presented multi-material method is algorithmically simple, and computationally efficient. Consistent reconstructions of conserved quantities at material interfaces ensure that conservation and closure laws are satisfied at the discrete level. Through a suite of test problems solved on unstructured meshes, it is demonstrated that the presented method is a promising candidate for accurate and efficient multi-material hydrodynamics computations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Multi-fidelity Fourier neural operator for fast modeling of large-scale geological carbon storage

Deep learning-based surrogate models have been widely applied in geological carbon storage (GCS) problems to accelerate the prediction of reservoir pressure and CO2 plume migration. Large amounts of data from physics-based numerical simulators are required to train a model to accurately predict the complex physical behaviors associated with this process. In practice, the available training data are always limited in large-scale 3D problems due to the high computational cost. Therefore, we propose to use a multi-fidelity Fourier neural operator (FNO) to solve large-scale GCS problems with more affordable multi-fidelity training datasets. FNO has a desirable grid-invariant property, which simplifies the transfer learning procedure between datasets with different discretization. Here, we first test the model efficacy on a GCS reservoir model being discretized into 110 k grid cells. The multi-fidelity model can predict with accuracy comparable to a high-fidelity model trained with the same amount of high-fidelity data with 81% less data generation costs. We further test the generalizability of the multi-fidelity model on a same reservoir model with a finer discretization of 1 million grid cells. This case was made more challenging by employing high-fidelity and low-fidelity datasets generated by different geostatistical models and reservoir simulators. We observe that the multi-fidelity FNO model can predict pressure fields with reasonable accuracy even when the high-fidelity data are extremely limited. The findings of this study can help for better understanding of the transferability of multi-fidelity deep learning surrogate models.

58 GEOSCIENCES↗

DOE OSTI2023

Review of multi-fidelity models

Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources. These models are especially beneficial when acquiring high-accuracy data is costly or computationally intensive. This review offers a comprehensive analysis of multi-fidelity models, focusing on their applications in scientific and engineering fields, particularly in optimization and uncertainty quantification. It classifies publications on multi-fidelity modeling according to several criteria, including application area, surrogate model selection, types of fidelity, combination methods and year of publication. The study investigates techniques for combining different fidelity levels, with an emphasis on multi-fidelity surrogate models. Here this work discusses reproducibility, open-sourcing methodologies and benchmarking procedures to promote transparency. The manuscript also includes educational toy problems to enhance understanding. Additionally, this paper outlines best practices for presenting multi-fidelity-related savings in a standardized, succinct and yet thorough manner. The review concludes by examining current trends in multi-fidelity modeling, including emerging techniques, recent advancements, and promising research directions.

42 ENGINEERING↗

NASA NTRS2016

An Assessment of the Icing Blade and the SEA Multi-Element Sensor for Liquid Water Content Calibration of the NASA GRC Icing Research Tunnel

The Icing Research Tunnel at NASA Glenn has recently switched from using the Icing Blade to using the SEA Multi-Element Sensor (also known as the multi-wire) for its calibration of cloud liquid water content. In order to peform this transition, tests were completed to compare the Multi-Element Sensor to the Icing Blade, particularly with respect to liquid water content, airspeed, and drop size. The two instruments were found to compare well for the majority of Appendix C conditions. However, it was discovered that the Icing Blade under-measures when the conditions approach the Ludlam Limit. This paper also describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, mounting underneath a splitter plate, and correcting for a jump in the compensation wire power. Further data is presented to describe the repeatability of the IRT with the Multi-Element Sensor, health-monitoring checks for the instrument, and a sensing-element configuration comparison. Ultimately these tests showed that in the IRT, the multi-wire is a better instrument for measuring cloud liquid water content than the blade.

aircraft icing↗

NASA NTRSDate not supplied

Structural Analysis, Modeling, and Testing of Multi-nut Joint for TriTruss Structure

The Multi-nut joint, developed at NASA Langley Research Center (LaRC), was designed to assemble TriTruss modules for a doubly curved 65.6-feet telescope support structure. Axial and bending tests for a multi-nut joint in the TriTruss structure were conducted to characterize the structural performance of the multi-nut joint and to obtain the installation torque value of the attachment fastener. This paper describes the summary of the axial and bending tests of the multi-nut joint, along with the test results. The equivalent axial stiffness (EA) and equivalent bending stiffness (EI) of the multi-nut joint were characterized based on these test results and are presented in this report. Additionally, finite element models (FEMs) of the axial and bending test specimens were developed, and the predicted axial and bending responses of the multi-nut joint were compared with the test results. Test-analysis comparison results show that the predicted axial load-displacement response of the test specimen was within 5 percent of the test results, while the predicted load-bending response of the test specimen exhibited a consistent trend with the test data.

TriTruss↗

NASA NTRSDate not supplied

Structural Analysis, Modeling, and Testing of Multi-nut Joint for TriTruss Structure

Structural analysis↗

NASA NTRS2026

Multi-Agent Swarm State of the Art Report

The Next-Generation Multi-Agent Swarm (NGS) Study conducted by NASA’s Ames Research Center for NASA’s Space Technology Mission Directorate (STMD) will develop a comprehensive understanding of emerging multi-agent swarm capabilities. The study aims to identify existing swarm capabilities and asses their potential for persistent lunar space situational awareness, surface monitoring, and distributed autonomy demonstrations. A key objective is to inform the design of a next-generation multi-agent swarm that can perform autonomous distributed remote sensing, position, navigation, and timing (PNT) services, automated deployment that leverages autonomy, edge computing, and interoperable networking to enable cooperative operations without the need for immediate human operation. This study will address specific shortfalls identified by STMD, including intelligent multi-agent constellations, autonomy, edge computation, position, navigation, and timing for small spacecraft, small spacecraft propulsion, and space situational awareness (1625, 1438, 1433, 1557, 1431, 1430, 1589). The NASA Ames Mission Design Center (MDC) will provide subject matter expertise to support systems engineering trades, while experts in autonomy and spacecraft swarms in NASA’s Intelligent Systems Division will lead the study and focus on identifying emerging next-generation swarm capabilities. The study objectives include: capturing the current state-of-the-art for multi-agent swarm capabilities, evaluating technologies and creating technology roadmaps, and developing at least one new technology demonstration mission concept. This initial NGS study report surveys the current state of the art in technology areas relevant for the next-generation multi-agent swarm design. Our primary focus is on surveying relevant deployed space systems1, supplemented with selective analysis of relevant proposed missions and technology developments that have yet to fly.

agent↗

DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles.

This paper develops a multi-objective eco-routing algorithm (combined eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Eco-routing is a technique that finds the most energy efficient route. ICEV and BEV energy consumption patterns are significantly different with regard to their sensitivity to driving cycles. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared to highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce the BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,” “moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared to the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗

DOE OSTI2026

Multi-material direct ink writing and co-sintering of gadolinium oxide – zirconium oxide components

Methods for fabrication of multi-material or functionally graded ceramic composite architectures are of interest for numerous applications. However, conventional co-sintering of multi-material ceramic parts is a challenge because differences in the sintering behavior of the two materials leads to interfacial strain and, ultimately, component failure. Direct ink writing (DIW) is an extrusion-based additive manufacturing process that excels at multi-material printing because multiple extrusion nozzles can be installed on the same gantry system. Furthermore, the use of DIW as a method to fabricate multi-material ceramic green bodies offers an additional variable for controlling and potentially matching sintering kinetics in the slurry formulation used for two dissimilar feedstocks. In the work documented in this manuscript, we explored two strategies to successfully co-sinter multi-material ceramic oxides: slurry optimization to match sintering kinetics and material gradients to step from one material to another. This manuscript also quantifies the allowable mismatch that avoids part cracking in solid solution forming multi-material systems and discusses best strategies to reduce mismatch during co-sintering. Inks composed of gadolinium oxide (Gd 2 O 3 ) and zirconium oxide (ZrO 2 ), a surrogate for uranium oxide (UO 2 ), were thermally matched, which resulted in a sintering mismatch reduction of over 10%. It was found that ~1% mismatch is tolerable during debind cycles and that ~5% mismatch is manageable during sintering cycles after slurry formulations are optimized to match the sintering behavior. Use of continuous gradients is shown to reduce sintering mismatch, although geometric resolution may be lost due to solid solution formation.

Snarr, Patrick L. [Oak Ridge National Laboratory (↗

DOE OSTI2021

Multi-objective Eco-Routing Model Development and Evaluation for Battery Electric Vehicles

This paper develops a multi-objective eco-routing algorithm (eco- and travel time-optimum routing) for battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs) and investigates the network-wide impacts of the proposed multi-objective Nash optimum (user equilibrium) traffic assignment on a large-scale network. Unlike ICEVs, BEVs are more energy efficient on low-speed arterial trips compared with highway trips. Different energy consumption patterns require different eco-routing strategies for ICEVs and BEVs. This study found that single-objective eco-routing could significantly reduce the energy consumption of BEVs but also significantly increase their average travel time. Consequently, the study developed a multi-objective routing model (eco- and travel time-routing) to improve both energy and travel time measures. The model introduced a link cost function that uses the specification of the value of time and the cost of fuel/energy. The simulation study found that multi-objective routing could reduce BEV energy consumption by 13.5%, 14.2%, 12.9%, and 10.7%, as well as ICEV fuel consumption by 0.1%, 4.3%, 3.4%, and 10.6% for “not congested, “slightly congested,”“moderately congested,” and “highly congested” conditions, respectively. The study also found that multi-objective user equilibrium routing reduced the average vehicle travel time by up to 10.1% compared with the standard user equilibrium traffic assignment for highly congested conditions, producing a solution closer to the system optimum traffic assignment. The results indicate that the proposed multi-objective eco-routing strategy can reduce vehicle fuel/energy consumption effectively with minimum impacts on travel times for both BEVs and ICEVs.

Engineering↗

DOE OSTI2023

Arrays of glass wedges for multi-dimensional optical diagnostics

There is a common need in the advancement of optical diagnostic techniques to increase the dimensionality of measurements. For example, point measurements could be improved to multi-point, line, planar, volumetric, or time-resolved volumetric measurements. In this work, a unique optical element is presented to enable multi-dimensional measurements, namely, an array of glass wedges. A light source is passed through the wedges, and different portions of the illumination are refracted by different amounts depending on the glass wedge angle. Subsequent optics can be used to focus the light to multiple points, lines, or planes. Basic characterization of a glass wedge array is presented. Additional wedge-array configurations are discussed, including the use of a periodic intensity mask for multi-planar measurements via structured illumination. The utility of this optical element is briefly demonstrated in (a) multi-planar flame particulate measurements, (b) multi-point femtosecond-laser electronic excitation tagging for flow velocimetry, and (c) multi-line nitric oxide molecular tagging velocimetry in a hypersonic shock-tunnel. One significant advantage of this optical component is its compatibility with high-energy laser sources, which may be a limiting factor with other beam-splitting or beam-forming elements such as some diffractive optics. Additionally, an array of glass wedges is simple and easily customizable compared to other methods for forming multiple closely spaced illumination patterns. In conclusion, suggestions for further development and applications are discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Ranking Biological Features in Soil-Based Microbial Multi-Omics Data with Integration Modeling

Distinguishing the most important features (e.g. proteins, metabolites, etc.) per group (e.g. control and treatment) is a critical challenge in feature-rich multi-omics experiments, especially in soil data. Traditional feature identification and ranking approaches, such as differential expression, are based on single omics and thus not directly translatable to multi-omics experiments. Here, 5 multi-omics integration models (DIABLO, JACA, MOFA, MultiMLP, and SLIDE) that were not explicitly built for soil data applications were tested using a soil-based multi-omics experiment. The data were obtained from an experimental setup of an autoclaved soil system inoculated with 8 bacteria and using chitin as the carbon source and including samples collected at 0- (control), 4-, 8-, and 12-weeks post-inoculation. The omics data included metaproteomics, 16S rRNA sequencing, and LC-MS/MS metabolomics (in positive and negative mode). Each multi-omics integration model was implemented, and top features were compared to differential univariate statistics per omic type, demonstrating that integration approaches cut the potential number of top features from 2957 identified by differential statistics to 13-224 (a 99.6% to 92.4% reduction). Interestingly, most top features across integration models were not shared; though, scaling and averaging ranks across models shared similar patterns. This work highlights the usefulness of multi-omics integration models in soil-based microbial studies and the power of using multiple integration models together to interpret results.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Detection of multi-modal Doppler spectra – Part 1: Establishing characteristic signals in radar moment data

Vertically pointing millimeter-wavelength radars provide a wealth of information about cloud and precipitation particle properties. Doppler spectral data can inform on how particles of varying vertical velocities contribute to the total backscattered power observed. It is more computationally cost effective to process moment data instead of spectra data, but doing so leaves valuable information on the cutting room floor. To confidently identify a multi-modal spectra event, in which two or more modes are present within a layer, Doppler spectral data are essential. This means long-term identification of layers featuring multi-modal spectra can be cost prohibitive. To address this, we explore three multi-modal spectra cases from winter precipitation events to determine characteristic signatures of these layers in the moment data averaged over short time periods (∼ 145 s) and explore how these layers differ from the rest of the vertical profiles. We find that the mean spectrum width and the standard deviation of mean Doppler velocity can be used to determine whether or not a layer is multi-modal. In particular, multi-modal layers in mixed-phase and ice clouds feature larger mean spectrum width (exceeding 0.17 m s −1 ) and smaller standard deviation of the mean Doppler velocity (below 0.1 m s −1 ). In Part 1 of this study, the identification criteria and methods are described. In Part 2 (Wugofski and Kumjian, 2025), we perform a verification of the method for three years of vertically pointing radar data, and explore the meteorological conditions associated with identified multi-modal spectral events.

Wugofski, Sarah [Pennsylvania State Univ., Univers↗

NASA NTRS1993

Multi-disciplinary coupling effects for integrated design of propulsion systems

Effective computational simulation procedures are described for modeling the inherent multi-disciplinary interactions which govern the accurate response of propulsion systems. Results are presented for propulsion system responses including multi-disciplinary coupling effects using coupled multi-discipline thermal, structural, and acoustic tailoring; an integrated system of multi-disciplinary simulators; coupled material behavior/fabrication process tailoring; sensitivities using a probabilistic simulator; and coupled materials, structures, fracture, and probabilistic behavior simulator. The results demonstrate that superior designs can be achieved if the analysis/tailoring methods account for the multi-disciplinary coupling effects. The coupling across disciplines can be used to develop an integrated coupled multi-discipline numerical propulsion system simulator.

Chamis, C. C.↗

NASA NTRS2001

Modeling of Multi-Tube Pulse Detonation Engine Operation

The present paper explores some preliminary issues concerning the operational characteristics of multiple-tube pulsed detonation engines (PDEs). The study is based on a two-dimensional analysis of the first-pulse operation of two detonation tubes exhausting through a common nozzle. Computations are first performed to assess isolated tube behavior followed by results for multi-tube flow phenomena. The computations are based on an eight-species, finite-rate transient flow-field model. The results serve as an important precursor to understanding appropriate propellant fill procedures and shock wave propagation in multi-tube, multi-dimensional simulations. Differences in behavior between single and multi-tube PDE models are discussed, The influence of multi-tube geometry and the preferred times for injecting the fresh propellant mixture during multi-tube PDE operation are studied.

Ebrahimi, Houshang B.↗

NASA NTRS2011

Microphysics in the Multi-Scale Modeling Systems with Unified Physics

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the microphysics developments of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the heavy precipitation processes will be presented.

Tao, Wei-Kuo↗