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Results for “Robustness analysis”

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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Probabilistic R&D Portfolio Management Applied to Energy Storage

Here this paper proposes a methodology to analyze the potential of several alternative technologies in advancing a research and development portfolio. The methodology considers technology maturation, use case requirements, performance goals, cost projections, and existing research and development (R&D) efforts to predict the impact of various R&D investment tracks across several energy storage technologies. Technology maturation and cost projections are derived from a probabilistic Monte Carlo simulation, yielding results that incorporate uncertainty in modeling the impact of future R&D. This framework allows the researcher to perform a robust sensitivity analysis to uncover critical pathways and areas where investment will have a high impact.

25 ENERGY STORAGE↗
DOE OSTI2020

Understanding effects of printhead geometry in aerosol jet printing

Aerosol jet printing offers a versatile, high-resolution digital patterning capability broadly relevant to flexible and printed electronic systems. Despite its promise and numerous demonstrations, the theoretical principles driving process outputs have not been thoroughly explored. In this study, a custom-built, modular printing system is developed to provide a head-to-head comparison of two print nozzle geometries to better understand the technology. Print resolution data from a range of process parameters are analyzed using a support vector machine framework. The linear deposition rate is identified as a key variable, which can confound careful studies of printing performance. Taking this into account, a clear difference is observed between the printheads, corresponding to a difference in resolution of 57% ± 11% under typical conditions. Models to understand differences in aerodynamic and mass transport effects identify enhanced drying within the NanoJet printhead as a likely cause of this difference. Overall, this study provides improved understanding of the aerosol jet printing process, including valuable insight to inform process optimization, robust data analysis, ink formulation, and printer geometric design.

42 ENGINEERING↗
DOE OSTI2022

Measuring the thermal and ionization state of the low- z IGM using likelihood free inference

ABSTRACT We present a new approach to measure the power-law temperature density relationship $T=T_0 (\rho/ \bar{\rho })^{\gamma -1}$ and the UV background photoionization rate $\Gamma _{{{{\rm H\, {\small I}}}}{}}$ of the intergalactic medium (IGM) based on the Voigt profile decomposition of the Ly α forest into a set of discrete absorption lines with Doppler parameter b and the neutral hydrogen column density $N_{\rm H\, {\small I}}$. Previous work demonstrated that the shape of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution is sensitive to the IGM thermal parameters T0 and γ, whereas our new inference algorithm also takes into account the normalization of the distribution, i.e. the line-density dN/dz, and we demonstrate that precise constraints can also be obtained on $\Gamma _{{{{\rm H\, {\small I}}}}{}}$. We use density-estimation likelihood-free inference (DELFI) to emulate the dependence of the $b-N_{{{{\rm H\, {\small I}}}}{}}$ distribution on IGM parameters trained on an ensemble of 624 nyx hydrodynamical simulations at z = 0.1, which we combine with a Gaussian process emulator of the normalization. To demonstrate the efficacy of this approach, we generate hundreds of realizations of realistic mock HST/COS data sets, each comprising 34 quasar sightlines, and forward model the noise and resolution to match the real data. We use this large ensemble of mocks to extensively test our inference and empirically demonstrate that our posterior distributions are robust. Our analysis shows that by applying our new approach to existing Ly α forest spectra at z ≃ 0.1, one can measure the thermal and ionization state of the IGM with very high precision ($\sigma _{\log T_0} \sim 0.08$ dex, σγ ∼ 0.06, and $\sigma _{\log \Gamma _{{{{\rm H\, {\small I}}}}{}}} \sim 0.07$ dex).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Megahertz-compatible angular streaking with few-femtosecond resolution at x-ray free-electron lasers

Highly brilliant, coherent, femtosecond x-ray pulses delivered by free-electron lasers (FELs) constitute one of the pillars of modern ultrafast science. Next generation FEL facilities provide up to megahertz repetition rates and pulse durations down to the attosecond regime utilizing self-amplification of spontaneous emission. However, the stochastic nature of this generation mechanism demands single-shot pulse characterization to perform meaningful experiments. Here we demonstrate a fast yet robust online analysis technique capable of megahertz-rate mapping of the temporal intensity structure and arrival time of x-ray FEL pulses with few-femtosecond resolution. We performed angular streaking measurements of both neon photo- and Auger electrons and show their applicability for a direct time-domain feedback system during ongoing experiments. Here, the fidelity of the real-time pulse characterization algorithm is corroborated by resolving isolated x-ray pulses and double pulse trains with few-femtosecond substructure, thus paving the way for x-ray-pump–x-ray-probe FEL science at repetition rates compatible with the demands of LCLS-II and European XFEL.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

OmniXAS: A universal deep-learning framework for materials x-ray absorption spectra

X-ray absorption spectroscopy (XAS) is a powerful characterization technique for probing the local chemical environment of absorbing atoms. However, analyzing XAS data presents significant challenges, often requiring extensive, computationally intensive simulations, as well as significant domain expertise. These limitations hinder the development of fast, robust XAS analysis pipelines that are essential in high-throughput studies and for autonomous experimentation. Here, we address these challenges with OmniXAS, a framework that contains a suite of transfer learning approaches for XAS prediction, each uniquely contributing to improved accuracy and efficiency, as demonstrated on the K-edge spectra database covering eight 3⁢d transition metals (Ti–Cu). The OmniXAS framework is built upon three distinct strategies. First, we use M3GNet [Nat. Comput. Sci. 2, 718 (2022)] to derive latent representations of the local chemical environment of absorption sites as input for XAS prediction, achieving significant improvements over conventional featurization techniques. Second, we employ a hierarchical transfer learning strategy, training a universal multitask model across elements before fine-tuning for element-specific predictions. Models based on this cascaded approach after elementwise fine-tuning outperform element-specific models by up to 69%. Third, we implement cross-fidelity transfer learning, adapting a universal model to predict spectra generated by simulation of a different fidelity with a much higher computational cost. This approach improves prediction accuracy by up to 11% over models trained on the target fidelity alone. Our approach significantly boosts the throughput of XAS modeling by orders of magnitude as compared to first-principles simulations and is extendable to XAS prediction for a broader range of elements. The proposed transfer learning framework is generalizable to enhance deep-learning models that target other properties in materials research.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Evaluating Text Analytic Frameworks for Mental Health Surveillance

Reducing suicide incidence among US veterans is one of the highest priorities for the US Department of Veterans Affairs (VA). We are implementing a suicide risk detection system, in collaboration with the VA, that would serve as a surveillance system for risk factors appearing in clinical text data. Primary requirements for this system are fast search capability, feature and information extraction, and delivery of data to up-stream natural language processing models. As such, we are evaluating scalable storage solutions on the basis of performance, fault tolerance, and scalability. In this paper we present our current approach to evaluation, preliminary findings, and the work in progress towards a more robust text analysis pipeline.

Mayer, Benjamin W.↗
DOE OSTI2017

Using the Charge-Discharge Cycling of Positive Electrode Symmetric Cells to Find Electrolyte/Electrode Combinations with Minimum Reactivity

The effects of solvents, salts, electrolyte additives and surface coatings on LiNi 0.4 Mn 0.4 Co 0.2 O 2 (NMC442) or LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) have been probed using positive electrode Li-ion symmetric cells coupled with dV/dQ analysis. A robust symmetric cell design is presented which prevents hardware corrosion for over at least 800 hours of testing to 4.5 V vs. Li/Li + at 40°C. Positive electrode symmetric cells using uncoated positive electrode materials and 1M LiPF 6 EC:EMC 3:7 or 1M LiPF 6 EMC electrolyte rapidly developed high impedance and showed poor capacity retention. However, if 1% pyridine boron trifluoride (PBF) was added to these electrolytes, cell performance was dramatically improved. Replacing LiPF 6 by LiBF 4 in the electrolytes above, with or without PBF, yielded positive electrode symmetric cells with good capacity retention. Two types of surface coatings were explored on NMC622 positive electrodes. Cells using surface-coated positive electrodes demonstrated better capacity retention for all electrolytes compared to cells without surface coatings. Finally, this work can be used as a guide by those attempting to find electrolyte/electrode pairs suitable for use in NMC/graphite cells that can operate with long lifetime to 4.5 V.

25 ENERGY STORAGE↗
DOE OSTI2017

Mercat

Mercat is a highly scalable property software package for robust of analysis of features in next-generation sequencing data and observed unique peptides from metaproteomic data. Mercat is offered in python 3.5/anaconda3, is multiparellel and is easily installed using bioconda/conda recipes. Mercat inputs include assembled contigs, raw sequence reads from any platform, and unique peptide files obtained from proteomics with feature abundance counts tables. Mercat is the only software available that allows for direct analysis of data properties without a data-dependent search tools such as BLAST or diamond for compositional analysis of whole community shotgun sequencing (e.g. metagenomes and/or metatranscriptomes) or metaproteomic data.

Source record↗
DOE OSTI2026

Detector Interface for Streaming, Control, and Open-source integration (DISCO) v1.0.0

This suite consists of a multi-package ecosystem featuring detector emulators, EPICS areaDetector drivers, and remote server frameworks designed for the Advanced Light Source (ALS). Engineered for high-bandwidth devices—including VFCCD, Timepix3, Timepix4, and related pixel detectors—the software simulates hardware, wraps vendor SDKs into remote-callable servers, and integrates with open-source control systems. Key Capabilities: Distributed SDK Architecture: Server packages wrap hardware-specific SDKs, allowing areaDetector drivers to execute remote framework calls. This isolates proprietary libraries from the EPICS IOC, enhancing stability and enabling distributed computing across beamline networks. Device Support: Custom drivers for VFCCD, the Timepix family, and similar sensors optimize the data path from hardware control to high-speed transport. Full-Stack Emulation: Sophisticated emulator packages allow end-to-end pipeline testing and software development without requiring physical hardware or beam time. Integrated Workflows: Supports high-bandwidth streaming for real-time analysis and robust, metadata-rich file-based workflows (e.g., HDF5/NeXus). By standardizing interfaces across heterogeneous hardware, this suite reduces technical debt. It provides the ALS with a scalable, open-source solution to manage massive data rates within a unified control environment.

Mahl, Johannes [Lawrence Berkeley National Laborat↗
DOE OSTI2018

CESM1(WACCM) Stratospheric Aerosol Geoengineering Large Ensemble Project

This paper describes the stratospheric aerosol geoengineering large ensemble (GLENS) project, which promotes the use of an unique model dataset, performed with Community Earth System Model, using the Whole Atmosphere Community Climate Model as its atmospheric component (CESM1(WACCM)), to investigate global and regional impacts of geoengineering. The performed simulations target multiple simultaneous climate goals unlike many earlier studies that targeted globally averaged surface temperature. This strategic approach is expected to reduce some of the previously found adverse effects of stratospheric aerosol geoengineering, including uneven cooling between the poles and equator and shifts in tropical precipitation. An ensemble of 20 members allows the identification of forced changes in global and regional climate variables in the coupled atmosphere, land, sea-ice, and ocean system in the presence of internal variability. Here, we invite the broader community to perform in-depth analysis of climate related impacts and to identify processes that lead to changes in the climate system as the result of a strategic application of stratospheric aerosol geoengineering. (Capsule Summary) A community project that enables analysis of robust global and regional changes resulting from strategic stratospheric aerosol geoengineering in the presence of internal climate variability.

58 GEOSCIENCES↗
DOE OSTI2016

Dominant Time Scales of Potentially Predictable Precipitation Variations across the Continental United States

While low-frequency variations in precipitation amount, occurrence counts (hereafter “occurrence”), and intensity can take place on seasonal to multidecadal time scales, it is often unclear at which time scales these precipitation variations can be ascribed to potentially predictable, climate-induced changes versus simple, stochastic (i.e., random) precipitation event evolutions. This paper seeks to isolate the dominant time scales at which potentially predictable changes in observed precipitation characteristics occur over the continental United States and analyze sources of revealed potentially predictable precipitation variations for particular regions. The results highlight that at interannual time scales (here defined as those shorter than 7 years), the potential for predicting annual precipitation amounts tends to be higher than for annual event occurrence or intensity, with interannual potential predictability highest in both relatively dry and wet locations and lowest in transition regions. By contrast, at time scales greater than 7 years the potential for predicting annual event occurrence tends to be higher than amount or intensity, with >20-yr time scale potential predictability highest in relatively wet locations and lowest in relatively dry locations. To highlight the utility of this type of analysis, two robust signals are selected for further investigation, including 1) approximately 10-yr time scale variations in potentially predictable annual amounts over the northwestern United States and 2) 20–60-yr time scale variations in potentially predictable annual event occurrence over the southwestern United States. While mechanistic drivers for these observed variations are still being investigated, concurrent and precursor climate-state estimates in the atmosphere and ocean—principally over the Pacific sector—are provided, the monitoring of which may help realize the potential for predicting precipitation variations in these regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗
DOE OSTI2023

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Multifidelity computing for coupling full and reduced order models

Hybrid physics-machine learning models are increasingly being used in simulations of transport processes. Many complex multiphysics systems relevant to scientific and engineering applications include multiple spatiotemporal scales and comprise a multifidelity problem sharing an interface between various formulations or heterogeneous computational entities. To this end, we present a robust hybrid analysis and modeling approach combining a physics-based full order model (FOM) and a data-driven reduced order model (ROM) to form the building blocks of an integrated approach among mixed fidelity descriptions toward predictive digital twin technologies. At the interface, we introduce a long short-term memory network to bridge these high and low-fidelity models in various forms of interfacial error correction or prolongation. The proposed interface learning approaches are tested as a new way to address ROM-FOM coupling problems solving nonlinear advection-diffusion flow situations with a bifidelity setup that captures the essence of a broad class of transport processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Echo: Data and Analysis Management [Slides]

Echo software capabilities are described. Echo provides a comprehensive suite of tools for data wrangling, management, and analysis. It is an object-oriented approach to data analysis designed for robust and scalable execution of complex analysis workflows written in MATLAB.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Frequency domain identification experiment on a large flexible structure

Recent experiences in the field of flexible structure control in space have indicated a need for on-orbit system identification to support robust control redesign to avoid in-flight instabilities and maintain high spacecraft performance. The authors highlight an automated frequency domain system identification methodology recently developed to fill this need. The methodology supports (1) the estimation of system quantities useful for robust control analysis and design, (2) experiment design tailored to performing system identification in a typically constrained on-orbit environment, and (3) the automation of operations to reduce human-in-the-loop requirements. A basic overview of the methodology is presented first, followed by an experimental verification of the approach performed on the JPL/AFAL testbed facility.

Bayard, D. S.↗