Engineering Papers⌕ Search

SEARCH · Engineering Papers

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.

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

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2016

Constraining white dwarf structure and neutrino physics in 47 Tucanae

We present a robust statistical analysis of the white dwarf cooling sequence in 47 Tucanae. We combine Hubble Space Telescope UV and optical data in the core of the cluster, Modules for Experiments in Stellar Evolution (MESA) white dwarf cooling models, white dwarf atmosphere models, artificial star tests, and a Markov Chain Monte Carlo sampling method to fit white dwarf cooling models to our data directly. We use a technique known as the unbinned maximum likelihood to fit these models to our data without binning. We use these data to constrain neutrino production and the thickness of the hydrogen layer in these white dwarfs. The data prefer thicker hydrogen layers (q{sub H}=3.2×10{sup −5}) and we can strongly rule out thin layers (q{sub H}=10{sup −6}). The neutrino rates currently in the models are consistent with the data. This analysis does not provide a constraint on the number of neutrino species.

79 ASTRONOMY AND ASTROPHYSICS↗
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.↗
NASA NTRS1991

Automated on-orbit frequency domain identification for large space structures

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. This paper highlights an automated frequency domain system identification methodology recently developed to fulfill this need. The methodology is focused to support (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.

Bayard, D. S.↗
NASA NTRS1997

Mixed H2/H-Infinity Control of a Flexible Space Structure

As theory progresses for design and analysis of robust multivariable control laws, synthesis procedures and to a larger extent, experimental verification generally lags behind. Recent developments in robust control theory have extended the H-infinity and mu-synthesis methods to incorporate H2 properties in the control synthesis. A major difficulty in implementing robust controllers is the order of the compensator and associated complexity of the computation required for synthesis, especially when order constraints are imposed. This paper presents results of system identification and robust control design for the Controls/Structures Interaction Ground Test Facility at NASA/Marshall Space Flight Center.

Whorton, Mark↗
NASA NTRS1995

DREAMS and IMAGE: A Model and Computer Implementation for Concurrent, Life-Cycle Design of Complex Systems

Computing architectures are being assembled that extend concurrent engineering practices by providing more efficient execution and collaboration on distributed, heterogeneous computing networks. Built on the successes of initial architectures, requirements for a next-generation design computing infrastructure can be developed. These requirements concentrate on those needed by a designer in decision-making processes from product conception to recycling and can be categorized in two areas: design process and design information management. A designer both designs and executes design processes throughout design time to achieve better product and process capabilities while expanding fewer resources. In order to accomplish this, information, or more appropriately design knowledge, needs to be adequately managed during product and process decomposition as well as recomposition. A foundation has been laid that captures these requirements in a design architecture called DREAMS (Developing Robust Engineering Analysis Models and Specifications). In addition, a computing infrastructure, called IMAGE (Intelligent Multidisciplinary Aircraft Generation Environment), is being developed that satisfies design requirements defined in DREAMS and incorporates enabling computational technologies.

Hale, Mark A.↗
NASA NTRS1999

Uncertainty Modeling Via Frequency Domain Model Validation

Abstract The majority of literature on robust control assumes that a design model is available and that the uncertainty model bounds the actual variations about the nominal model. However, methods for generating accurate design models have not received as much attention in the literature. The influence of the level of accuracy of the uncertainty model on closed loop performance has received even less attention. The research reported herein is an initial step in applying and extending the concept of model validation to the problem of obtaining practical uncertainty models for robust control analysis and design applications. An extension of model validation called 'sequential validation' is presented and applied to a simple spring-mass-damper system to establish the feasibility of the approach and demonstrate the benefits of the new developments.

Waszak, Martin R.↗