Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “unknown unknowns”

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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

shiftNMFk 1.1: Robust Nonnegative matrix factorization with kmeans clustering and signal shift, for allocation of unknown physical sources, toy version for open sourcing with publications

This code is a toy (short) version of CODE-2016-83. From a general perspective, the code represents an unsupervised adaptive machine learning algorithm that allows efficient and high performance de-mixing and feature extraction of a multitude of non-negative signals mixed and recorded by a network of uncorrelated sensor arrays. The code identifies the number of the mixed original signals and their locations. Further, the code also allows deciphering of signals that have been delayed in regards to the mixing process in each sensor. This code is high customizable and it can be efficiently used for a fast macro-analyses of data. The code is applicable to a plethora of distinct problems: chemical decomposition, pressure transient decomposition, unknown sources/signal allocation, EM signal decomposition. An additional procedure for allocation of the unknown sources is incorporated in the code.

Alexandrov, Boian S.↗
DOE OSTI2017

Solving differential equations with unknown constitutive relations as recurrent neural networks

We solve a system of ordinary differential equations with an unknown functional form of a sink (reaction rate) term. We assume that the measurements (time series) of state variables are partially available, and use a recurrent neural network to “learn” the reaction rate from this data. This is achieved by including discretized ordinary differential equations as part of a recurrent neural network training problem. We extend TensorFlow’s recurrent neural network architecture to create a simple but scalable and effective solver for the unknown functions, and apply it to a fedbatch bioreactor simulation problem. Use of techniques from recent deep learning literature enables training of functions with behavior manifesting over thousands of time steps. Our networks are structurally similar to recurrent neural networks, but differ in purpose, and require modified training strategies.

deep learning↗
DOE OSTI2024

Distribution Grid Incentive Design with Unknown Agent Behavior

Motivation: During extreme events, traditional grid regulation methods (e.g., energy prices, net power injection limits) may be insufficient. While system operators typically lack control over end-user grid interactions, (e.g., energy demand), incentives can influence behavior - for example, a user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. Problem: Optimize for the best incentive subject to system stability constraints. However, user behavior is unknown to the SO - i.e., for a given incentive, the amount of curtailed load or control variables exposed is unknown.

feedback based control↗
DOE OSTI2019

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

The Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation-based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theory with the classical CKF, which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviate from the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.

42 ENGINEERING↗
NASA NTRS2015

Developing Probabilistic Safety Performance Margins for Unknown and Underappreciated Risks

Probabilistic safety requirements currently formulated or proposed for space systems, nuclear reactor systems, nuclear weapon systems, and other types of systems that have a low-probability potential for high-consequence accidents depend on showing that the probability of such accidents is below a specified safety threshold or goal. Verification of compliance depends heavily upon synthetic modeling techniques such as PRA. To determine whether or not a system meets its probabilistic requirements, it is necessary to consider whether there are significant risks that are not fully considered in the PRA either because they are not known at the time or because their importance is not fully understood. The ultimate objective is to establish a reasonable margin to account for the difference between known risks and actual risks in attempting to validate compliance with a probabilistic safety threshold or goal. In this paper, we examine data accumulated over the past 60 years from the space program, from nuclear reactor experience, from aircraft systems, and from human reliability experience to formulate guidelines for estimating probabilistic margins to account for risks that are initially unknown or underappreciated. The formulation includes a review of the safety literature to identify the principal causes of such risks.

Safety Performance Margin↗
DOE OSTI2026

Open Set Recognition for Unknown Waveform Classification

This presentation applies open set recognition to classify unknown waveforms, enabling systems to not only identify known types but also reliably detect when waveforms fall outside the training distribution. This approach enhances robustness by avoiding forced misclassification of novel or anomalous signals.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Archaeal protein containing domain of unknown function 2193 undergoes oligomeric reconfiguration upon iron–sulfur cluster binding

Methanogenic archaea are particularly rich in iron–sulfur proteins, yet their roles remain largely enigmatic. Here, we characterized aMethanococcus voltae(Mvo) protein from the domain of unknown function (DUF) 2193 family, a group of proteins present primarily in archaea and characterized by a conserved cysteine‐rich C‐terminal motif.MvoDUF2193 was heterologously expressed and characterized by a range of spectroscopic and analytical methods. The results demonstrate thatMvoDUF2193 binds a single [4Fe–4S] cluster per subunit and that cluster occupancy regulates the transition from an apo tetramer to a [4Fe–4S] monomeric form. We hypothesize thatMvoDUF2193 serves a regulatory role in the cell, mediated by [Fe–S] cluster binding and changes in oligomeric state.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Environmental life cycle of fentanyl: From the cradle to an unknown grave

The lack of available information on the presence and persistence of fentanyl in the environment is a significant gap in the technical literature. Although the origins of the opioid in the environment are well-known because they follow the same pathways of other drug-related environmental contaminants, the downstream effects of fentanyl in the water supply and its retention in soil are less understood. The characterization of fentanyl and its potential degradation products in complex environmental samples such as soil is severely understudied. Very few articles are available that work to identify fentanyl and its degradation products in complex samples or name the possible hazards that may result from environmental exposure and degradation. Therefore, the objectives were to identify available articles focused on environmental fentanyl and its pathways and highlight quantifiable research or results that included specific degradation products or downstream effects. Research articles focused on fentanyl between 2000 and 2024 were identified and reviewed and then filtered using Boolean search terms for environmental parameters. Various studies have determined that trace levels of fentanyl can be found in a variety of environments, and additional data suggest preferential partitioning into soils from water and long-term persistence. Despite this knowledge, very little data exists on the long-term downstream effects of fentanyl or its analogs. As the chronic effects from low-level fentanyl exposure are currently unknown, this lack of insight brings to the forefront the need for further research to improve our understanding of fentanyl persistence, degradation, and toxicity within the environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

High-precision control of tracked field robots in the presence of unknown traction coefficients

Accurate steering through crop rows that avoids crop damage is one of the most important tasks for agricultural robots utilized in various field operations, such as monitoring, mechanical weeding, or spraying. In practice, varying soil conditions can result in off-track navigation due to unknown traction coefficients so that it can cause crop damage. To address this problem, this work presents the development, application, and experimental results of a real-time receding horizon estimation and control (RHEC) framework applied to a fully autonomous mobile robotic platform to increase its steering accuracy. Recent advances in cheap and fast microprocessors, as well as advances in solution methods for nonlinear optimization problems, have made nonlinear receding horizon control (RHC) and receding horizon estimation (RHE) methods suitable for field robots that require high-frequency (milliseconds) updates. A real-time RHEC framework is developed and applied to a fully autonomous mobile robotic platform designed by the authors for in-field phenotyping applications in sorghum fields. Nonlinear RHE is used to estimate constrained states and parameters, and nonlinear RHC is designed based on an adaptive system model that contains time-varying parameters. The capabilities of the real-time RHEC framework are verified experimentally, and the results show an accurate tracking performance on a bumpy and wet soil field. Finally, the mean values of the Euclidean error and required computation time of the RHEC framework are equal to 0.0423 m and 0.88 ms, respectively.

42 ENGINEERING↗
DOE OSTI2018

Determination of unknown thermal coefficients in a Stefan problem for Storm’s type materials

We consider a nonlinear one-dimensional Stefan problem for a semi-infinite material x>0, with phase change temperature T{sub f}. We assume that the heat capacity and the thermal conductivity satisfy a Storm’s condition. A convective boundary condition and a heat flux over-specified condition on the fixed face x=0 are considered. Unknown thermal coefficients are determined for the free boundary problem and for the associate moving boundary problem and we give sufficient conditions to obtain a parametric representation of a similarity type solution. Moreover, we give formulae for the thermal coefficients in both cases.

42 ENGINEERING↗
DOE OSTI2021

Transforming a Simple Structure Model to Represent a Complex Dynamic System with Unknown Boundary Restraints

Imposing a boundary condition on a structure can significantly alter its dynamic properties. However, sometimes the specifics of the new boundary conditions are not known. When the effects of a boundary condition are uncertain or there is not enough information, engineers need to excite the complex structure to obtain these modified properties. In order to experimentally obtain the new properties, engineers need multiple experiments and many outputs for interpolation in order to sufficiently represent the entire structure. In this study, the researchers attached a stinger to a cantilever beam, acting as a new transverse restraint of unknown properties. This paper presents a conversion expression that predicts the dynamic behavior of any point in the system with the new boundary condition. This expression relies only on one impact hammer experiment with one output and the model of the stinger-free cantilever beam, referred to as the simple structure. Researchers estimated the Transfer Function (TRF) of the beam and compared it with an experimentally measured TRF to validate the method. The mean absolute error of the estimated TRF compared to the experimental TRF is 1.99 dB. This demonstrates the use of the proposed method for estimating unmeasured TRFs in a system with an uncertain boundary condition using a single input, single output (SISO) test and a model of the simple structure.

42 ENGINEERING↗
DOE OSTI2021

Dynamic consensus and extended high gain observers as a tool to achieve practical frequency synchronization in power systems under unknown time-varying power demand

In this paper, we consider the frequency synchronization problem in a network of lossless, connected, and network-reduced power system. Frequency synchronization is an important problem in power systems as frequency deviation can lead to degraded power quality, tripping of generators, etc. We present a load-estimator-based consensus algorithm in a network of fourth-order generator models that achieves practical frequency synchronization in the presence of unknown time-varying power demand. We do not assume that the demand is generated by a known model. We follow a multiple time-scale approach for the stability analysis. It is shown that synchronization is achieved for the case of constant power demand, by showing that the system is exponentially stable. Furthermore, for the case of time-varying power demand, we show the error trajectories are ultimately bounded, and the synchronization error is proportional to an upper bound on the derivative of the time-varying power demand. We simulate the controller performance on a four area network which is equivalent to the IEEE New England 39-Bus system.

42 ENGINEERING↗
DOE OSTI2017

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

Metabolomics continues to make rapid progress through the development of new and better methods and their applications to gain insight into the metabolism of a wide range of different biological systems from a systems biology perspective. Customization of NMR databases and search tools allows the faster and more accurate identification of known metabolites, whereas the identification of unknowns, without a need for extensive purification, requires new strategies to integrate NMR with mass spectrometry, cheminformatics, and computational methods. For some applications, the use of covalent and non-covalent attachments in the form of labeled tags or nanoparticles can significantly reduce the complexity of these tasks.

Bingol, Kerem↗
DOE OSTI2014

Assembly of Robust Bacterial Microcompartment Shells Using Building Blocks from an Organelle of Unknown Function

Bacterial microconnpartnnents (BMCs) sequester enzymes from the cytoplasmic environment by encapsulation inside a selectively permeable protein shell. Bioinformatic analyses indicate that many bacteria encode BMC clusters of unknown function and with diverse combinations of shell proteins. The genome of the halophilic myxobacterium Haliangium ochraceum encodes one of the most atypical sets of shell proteins in terms of composition and primary structure. We found that microconnpartnnent shells could be purified in high yield when all seven H. ochraceum BMC shell genes were expressed from a synthetic operon in Escherichia coll. These shells differ substantially from previously isolated shell systems in that they are considerably smaller and more homogeneous, with measured diameters of 39 2 nm. The size and nearly uniform geometry allowed the development of a structural model for the shells composed of 260 hexagonal units and 13 hexagons per icosahedral face. We found that new proteins could be recruited to the shells by fusion to a predicted targeting peptide sequence, setting the stage for the use of these remarkably homogeneous shells for applications such as three-dimensional scaffolding and the construction of synthetic BMCs. Our results demonstrate the value of selecting from the diversity of BMC shell building blocks found in genomic sequence data for the construction of novel compartments. (C) 2014 Elsevier Ltd. All rights reserved.

Lassila, JK↗
DOE OSTI2018

Magnetic Barkhausen noise characterization of two pipeline steels with unknown history

Highlights: • The hardness, microstructure, texture and MBN of two pipeline steels were analyzed. • The angular MBN results revealed possible manufacturing histories of the two pipelines. • The MBN is closely related to the processing history, microstructure and stress state. • The use of MBN technique can help address the integrity issues in the pipeline industry. Steel samples cut from two line pipes with unknown processing history were analyzed using magnetic Barkhausen noise (MBN), electron backscatter diffraction (EBSD) and Vickers hardness techniques on the pipe surface and on sections through the wall thickness. The results show that the MBN responses vary significantly not only between the two pipe steels, but also among different thickness layers of each steel. Both steels show considerable variations in MBN at different locations on the surfaces. For one steel, the surface MBN illustrates an inverse linear relationship with respect to the hardness, while the other does not depict such a relation since the variation in hardness at different locations is very small. Across the thickness, the microstructure (phase and grain size) of both steels is quite similar, but the texture shows significant differences. Again, there is a large difference in hardness across the thickness for one steel, while the other only shows very small variation in hardness across the thickness. The variations in average MBN across the wall thickness for both steels are quite large. Angular MBN measurements on the sectioned samples revealed significant discrepancies in the magnetic anisotropy in the two steels. Based on the analysis of the angular MBN data with respect to the single and dual easy axis models, one pipe can be determined to have been manufactured through a seam welding process, while the other may have been formed by spiral welding. Due to the residual stresses in the steel samples, the effect of the crystallographic texture on the anisotropy in MBN response has essentially been suppressed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Characterization of Proteoforms with Unknown Post-translational Modifications Using the MIScore

Various proteoforms may be generated from a single gene due to primary structure alterations (PSAs) such as genetic variations, alternative splicing, and post-translational modifications (PTMs). Top-down mass spectrometry is capable of analyzing intact proteins and identifying patterns of multiple PSAs, making it the method of choice for studying complex proteoforms. In top-down proteomics, proteoform identification is often performed by searching tandem mass spectra against a protein sequence database that contains only one reference protein sequence for each gene or transcript variant in a proteome. Because of the incompleteness of the protein database, an identified proteoform may contain unknown PSAs compared with the reference sequence. Proteoform characterization is to identify and localize PSAs in a proteoform. Although many software tools have been proposed for proteoform identification by top-down mass spectrometry, the characterization of proteoforms in identified proteoform-spectrum matches still relies mainly on manual annotation. We propose to use the Modification Identification Score (MIScore), which is based on Bayesian models, to automatically identify and localize PTMs in proteoforms. Experiments showed that the MIScore is accurate in identifying and localizing one or two modifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

A highly abundant bacteriophage discovered in the unknown sequences of human faecal metagenomes

Metagenomics, or sequencing of the genetic material from a complete microbial community, is a promising tool to discover novel microbes and viruses. Viral metagenomes typically contain many unknown sequences. Here we describe the discovery of a previously unidentified bacteriophage present in the majority of published human faecal metagenomes, which we refer to as crAssphage. Its B97 kbp genome is six times more abundant in publicly available metagenomes than all other known phages together; it comprises up to 90% and 22% of all reads in virus-like particle (VLP)-derived metagenomes and total community metagenomes, respectively; and it totals 1.68% of all human faecal metagenomic sequencing reads in the public databases. The majority of crAssphage-encoded proteins match no known sequences in the database, which is why it was not detected before. Using a new cooccurrence profiling approach, we predict a Bacteroides host for this phage, consistent with Bacteroides-related protein homologues and a unique carbohydrate-binding domain encoded in the phage genome.

59 BASIC BIOLOGICAL SCIENCES↗