Characterizing Electrical Device Behavior with Functional Tolerance Bounds.
Abstract not provided.
Adams, Jason↗
SEARCH · Engineering Papers
Results for “functional tolerance bounds”
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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A geometric approach for computing tolerance bounds for elastic functional data
We design an approach for constructing tolerance bounds for functional data with random warping variability. In particular, we define a generative, probabilistic model for the amplitude and phase components of such observations, which parsimoniously characterizes variability in the baseline data. Based on the proposed model, we define two different types of tolerance bounds that are able to measure both types of variability, and as a result, identify when the data has gone beyond the bounds of amplitude and/or phase. The first functional tolerance bounds are computed via a bootstrap procedure on the geometric space of amplitude and phase functions. The second functional tolerance bounds utilize functional Principal Component Analysis to construct a tolerance factor. Our report is motivated by two main applications: process control and disease monitoring. The problem of statistical analysis and modeling of functional data in process control is important in determining when a production has moved beyond a baseline. Moreover, in biomedical applications, doctors use long, approximately periodic signals (such as the electrocardiogram) to diagnose and monitor diseases. In this context, it is desirable to identify abnormalities in these signals. We additionally consider a simulated example to assess our approach and compare it to two existing methods.
97 MATHEMATICS AND COMPUTING↗
Tolerance Bound Calculation for Compact Model Calibration Using Functional Data Analysis.
Abstract not provided.
Reza, Shahed↗
Bounding uncertainty in functional data: A case study
Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.
97 MATHEMATICS AND COMPUTING↗
Uncertainty Quantification and Comparison of Weld Residual Stress Measurements and Predictions.
In pressurized water reactors, the prevention, detection, and repair of cracks within dissimilar metal welds is essential to ensure proper plant functionality and safety. Weld residual stresses, which are difficult to model and cannot be directly measured, contribute to the formation and growth of cracks due to primary water stress corrosion cracking. Additionally, the uncertainty in weld residual stress measurements and modeling predictions is not well understood, further complicating the prediction of crack evolution. The purpose of this document is to develop methodology to quantify the uncertainty associated with weld residual stress that can be applied to modeling predictions and experimental measurements. Ultimately, the results can be used to assess the current state of uncertainty and to build confidence in both modeling and experimental procedures. The methodology consists of statistically modeling the variation in the weld residual stress profiles using functional data analysis techniques. Uncertainty is quantified using statistical bounds (e.g. confidence and tolerance bounds) constructed with a semi-parametric bootstrap procedure. Such bounds describe the range in which quantities of interest, such as means, are expected to lie as evidenced by the data. The methodology is extended to provide direct comparisons between experimental measurements and modeling predictions by constructing statistical confidence bounds for the average difference between the two quantities. The statistical bounds on the average difference can be used to assess the level of agreement between measurements and predictions. The methodology is applied to experimental measurements of residual stress obtained using two strain relief measurement methods and predictions from seven finite element models developed by different organizations during a round robin study.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Multi-parametric analysis for mixed integer linear programming: An application to transmission upgrade and congestion management
Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. Here, we compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Structural insights into protection against a SARS-CoV-2 spike variant by T cell receptor diversity
T cells play a crucial role in combatting SARS-CoV-2 and forming long-term memory responses to this coronavirus. The emergence of SARS-CoV-2 variants that can evade T cell immunity has raised concerns about vaccine efficacy and the risk of reinfection. Some SARS-CoV-2 T cell epitopes elicit clonally restricted CD8 + T cell responses characterized by T cell receptors (TCRs) that lack structural diversity. Mutations in such epitopes can lead to loss of recognition by most T cells specific for that epitope, facilitating viral escape. Here, we studied an HLA-A2–restricted spike protein epitope (RLQ) that elicits CD8 + T cell responses in COVID-19 convalescent patients characterized by highly diverse TCRs. We previously reported the structure of an RLQ-specific TCR (RLQ3) with greatly reduced recognition of the most common natural variant of the RLQ epitope (T1006I). Opposite to RLQ3, TCR RLQ7 recognizes T1006I with even higher functional avidity than the WT epitope. To explain the ability of RLQ7, but not RLQ3, to tolerate the T1006I mutation, we determined structures of RLQ7 bound to RLQ–HLA-A2 and T1006I–HLA-A2. These complexes show that there are multiple structural solutions to recognizing RLQ and thereby generating a clonally diverse T cell response to this epitope that assures protection against viral escape and T cell clonal loss.
60 APPLIED LIFE SCIENCES↗
Synchronization of Coupled Dynamical Systems: Tolerance to Weak Connectivity and Arbitrarily Bounded Time-Varying Delays
We study synchronization of coupled linear systems over networks with weak connectivity and time-varying delays. We focus on the case that the internal dynamics are time-varying but non-expansive. Both uniformly connected and infinitely connected communication topologies are considered. A new concept of P-synchronization is introduced and we first show that global asymptotic P-synchronization can be achieved over directed networks with uniform joint connectivity and arbitrarily bounded delays. We then study the case of the infinitely jointly connected communication topology. In particular, for the undirected communication topologies, it turns out that the existence of a uniform time interval for the communication topology is not necessary and P-synchronization can be achieved when the time varying delays are arbitrarily bounded. Simulations are given to validate the theoretical results.
Linear time-varying system, Synchronization, Switc↗
Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing (Final Report)
The objective of this project is to address challenges in the design and development of scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop parallel software consisting of a set of spatio-temporal data clustering and anomaly detection functions, both of which are very important for large-scale analysis and have wide applicability for in-situ runs as well as post-processing analysis. Our design principles for in-situ analysis consider the following: (1) identify parts of the computation can be done close to the data within the nodes, while it is still in memory; (2) extract analysis components can (and should) be performed in remote staging and analysis nodes; (3) develop error-bound approximation methods for applications tolerable for small errors; (4) identify the type of derived distributions and statistics, for spatio-temporal data, that can be kept locally in order to both accelerate computations and meet energy constraints in subsequent iterations and phases; (5) use a self-describing data format so that data can be consistent and understood among local storage (memory and SSDs) and at staging and analysis nodes, thereby providing portability and flexibility; (6) develop service-oriented functions that can schedule in-situ and post-hoc analysis tasks based on the dynamic requirements of applications. Our development focus is to produce the parallel data analysis software/library that will be scalable, reusable, extensible, and generic for applications in different disciplines. The software will be able to run in-situ with the simulations as well as post-hoc analysis. This approach will satisfy many synergistic requirements for data intensive applications executed on data coming from instruments and experiments. In particular, the proposed multilevel approach is directly applicable to perform design tradeoffs for running part of the algorithms near the instruments and the rest on remote (analysis) systems.
97 MATHEMATICS AND COMPUTING↗
NDE Technology Engineering Program for Hanford DST Non-Visual Volumetric Inspection Technology: Phase II RAVIS Radiation Tolerance Test Report
This test report provides the results of radiation tolerance robustness testing that was performed on samples of robotic components and an ultrasonic guided wave air-slot sensor that represent components/sub-systems of the Robotic Air-slot Volumetric Inspection System (RAVIS) that has been engineered for volumetric inspection of Hanford tank bottom plates via under-tank refractory pad air-slots. The specific components tested for 1) functionality during active irradiation and 2) tolerance to cumulative radiation dose (until failure or upon reaching a cumulative dose test limit) were: • four samples each of a printed circuit board (PCB) and direct current (DC) motor, which are robotic components, and • 26 ultrasonic piezoelectric elements (samples) inside an air-slot sensor. The robotic components are part of the RAVIS air-slot inspection crawler drive control system that is responsible for remote communication with and actuation of the air-slot inspection crawler. The failure of either of these components during under-tank deployment would require manual retrieval via the crawler’s tether, which risks damage to the robot/refractory/tank. Preemptive replacement of the components at appropriately conservative dose/time intervals informed by failure dose would reduce the likelihood of under-tank failure. The components were included in radiation tolerance testing to quantify their failure doses to inform replacement intervals. The air-slot sensor is responsible for collecting ultrasonic inspection data (scan images) for the tank bottom plates during under-tank deployment. Compromised signal quality due to elevated noise levels caused by gamma radiation would compromise inspection performance. The air-slot sensor was included in radiation tolerance testing to quantify the impact of active irradiation on sensor signal quality. The irradiation and in-situ functional tests of the PCBs, DC motors and air-slot sensor took place in June and July 2020 at the Pacific Northwest National Laboratory. Testing was performed at a gamma dose rate near 300 rad/hr., which, in the absence of under-tank dose rate data, has been conservatively estimated to be the upper-bound dose rate beneath the primary tanks at Hanford. Irradiation took place at elevated temperatures of 150-200°F to determine failure doses that reflect the compounding effects of gamma radiation and heat. The test results revealed: • The DC motors can tolerate being actively irradiated at the high dose rate at 200°F and can tolerate a cumulative dose of 300,000 rad, that which would be incurred after 5 years of service at the 300 rad/hr dose rate. The component therefore meets minimum and preferred radiation tolerance and lifecycle requirements for robotic components. • The air-slot sensor can tolerate being actively irradiated at the high dose rate at 150°F and can tolerate a cumulative dose of 60,000 rad, that which would be incurred after 1 year of service at the 300 rad/hr dose rate. The sensor therefore meets minimum radiation tolerance and lifecycle requirements. • The PCB can tolerate being actively irradiated at the high dose rate, but can only tolerate a cumulative dose of 19,000 rad at 150-200°F. The PCB does not meet minimum radiation tolerance and lifecycle requirements; however, because the component is considered replaceable, it can be replaced before a cumulative dose of 19,000 rad is reached, determined through either monitoring with a dosimeter or scheduled time intervals that are calculated based on conservative estimates of under-tank dose rates.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling
The increasing complexity of both scientific simulations and high-performance computing system architectures are driving the need for adaptive workflows, in which the composition and execution of computational and data manipulation steps dynamically depend on the evolutionary state of the simulation itself. Consider, for example, the frequency of data storage. Critical phases of the simulation should be captured with high frequency and with high fidelity for postanalysis; however, we cannot afford to retain the same frequency for the full simulation due to the high cost of data movement. We can instead look for triggers, indicators that the simulation will be entering a critical phase, and adapt the workflow accordingly. In this paper, we present a methodology for detecting triggers and demonstrate its use in the context of direct numerical simulations of turbulent combustion using S3D. We show that chemical explosive mode analysis (CEMA) can be used to devise a noise-tolerant indicator for rapid increase in heat release. However, exhaustive computation of CEMA values dominates the total simulation, and thus is prohibitively expensive. To overcome this computational bottleneck, we propose a quantile sampling approach. Our sampling-based algorithm comes with provable error/confidence bounds, as a function of the number of samples. Most importantly, the number of samples is independent of the problem size, and thus our proposed sampling algorithm offers perfect scalability. Our experiments on homogeneous charge compression ignition and reactivity controlled compression ignition simulations show that the proposed method can detect rapid increases in heat release, and its computational overhead is negligible. Our results will be used to make dynamic workflow decisions regarding data storage and mesh resolution in future combustion simulations.
97 MATHEMATICS AND COMPUTING↗
Even denominator fractional quantum Hall states in higher Landau levels of graphene
A remarkable development in the field of the fractional quantum Hall effect has been the proposal that the 5/2 state observed in the Landau level with orbital index n = 1 of two- dimensional electrons in a GaAs quantum well originates from a chiral p-wave paired state of composite fermions which are topological bound states of electrons and quantized vortices. This state is theoretically described by a “Pfaffian” wave function or its hole partner called the anti-Pfaffian, whose excitations are neither fermions nor bosons but Majorana quasiparticles obeying non-Abelian braid statistics. Here, this has inspired innovative ideas on fault-tolerant topological quantum computation and has also instigated a quest for other states with exotic quasi- particles. Here we report experiments on monolayer graphene that show clear evidence for unexpected even-denominator fractional quantum Hall physics in the n = 3 Landau level. We numerically investigate the known candidate states for even-denominator fractional quantum Hall effect, including the Pfaffian, the particle-hole symmetric Pfaffian, the 221-parton, and several valley / spin singlet states, and conclude that, among these, the 221-parton state is the most plausible candidate to explain the experimentally observed state. Like the Pfaffian, this state is also believed to harbour quasi-particles with non-Abelian braid statistics.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Exploiting Kronecker structure in exponential integrators: Fast approximation of the action of φ $-$functions of matrices via quadrature
Here, in this article, we propose an algorithm for approximating the action of φ $-$ functions of matrices against vectors, which is a key operation in exponential time integrators. In particular, we consider matrices with Kronecker sum structure, which arise from problems admitting a tensor product representation. The method is based on quadrature approximations of the integral form of the φ $-$ functions combined with a scaling and modified squaring method. Owing to the Kronecker sum representation, only actions of 1D matrix exponentials are needed at each quadrature node and assembly of the full matrix can be avoided. Additionally, we derive a priori bounds for the quadrature error, which show that, as expected by classical theory, the rate of convergence of our method is supergeometric. Guided by our analysis, we construct a fast and robust method for estimating the optimal scaling factor and number of quadrature nodes that minimizes the total cost for a prescribed error tolerance. We investigate the performance of our algorithm by solving several linear and semilinear time-dependent problems in 2D and 3D. The results show that our method is accurate and orders of magnitude faster than the current state-of-the-art.
97 MATHEMATICS AND COMPUTING↗
PARP-1 inhibition protects the diabetic heart through activation of SIRT1-PGC-1α axis
Type 2 diabetes mellitus (DM2) follows impaired glucose tolerance in obesity and is frequently associated with hypertension, causing adverse myocardial remodelling and leading to heart failure. The DNA bound protein PARP (poly ADP ribose) polymerase catalyses a post translational modification (polymerization of negatively charged ADP-ribose chains) of nuclear proteins. PARP-1 activation is NAD{sup +} dependent and takes part in DNA repair and in chromatin remodelling and has a function in transcriptional regulation, intracellular trafficking and energy metabolism. PARP-1 is activated in diabetic cardiomyopathy. We hypothesized that PARP-1 inhibition in diabetic mice may protect cardiomyocytes from inflammation and ROS production.
60 APPLIED LIFE SCIENCES↗
An Efficient, Globally Convergent Method for Optimization Under Uncertainty Using Adaptive Model Reduction and Sparse Grids
We present a new approach to efficiently solve optimization problems constrained by partial differential equations (PDEs) with uncertain coefficients. The method leverages two sources of inexactness that trade accuracy for speed: (1) stochastic collocation based on dimension-adaptive sparse grids (SGs), which approximates the stochastic objective function with a limited number of quadrature nodes, and (2) projection-based reduced-order models (ROMs), which generate efficient approximations to PDE solutions. These two sources of inexactness lead to inexact objective function and gradient evaluations, which are managed by a trust-region method that guarantees global convergence by adaptively refining the SG and ROM until a proposed error indicator drops below a tolerance specified by trust-region convergence theory. A key feature of the proposed method is that the error indicator---which accounts for errors incurred by both the SG and ROM---must be only an asymptotic error bound, i.e., a bound that holds up to an arbitrary constant that need not be computed. This allows the approach to be applicable to a wide range of problems, including those where sharp, computable error bounds are not available; this distinguishes the proposed method from previous works. Numerical experiments performed on a model problem from optimal flow control under uncertainty verify global convergence of the method and demonstrate the method's ability to outperform previously proposed alternatives.
97 MATHEMATICS AND COMPUTING↗
Structural and functional characterization of NEMO cleavage by SARS-CoV-2 3CLpro
Abstract In addition to its essential role in viral polyprotein processing, the SARS-CoV-2 3C-like protease (3CLpro) can cleave human immune signaling proteins, like NF-κB Essential Modulator (NEMO) and deregulate the host immune response. Here, in vitro assays show that SARS-CoV-2 3CLpro cleaves NEMO with fine-tuned efficiency. Analysis of the 2.50 Å resolution crystal structure of 3CLpro C145S bound to NEMO 226–234 reveals subsites that tolerate a range of viral and host substrates through main chain hydrogen bonds while also enforcing specificity using side chain hydrogen bonds and hydrophobic contacts. Machine learning- and physics-based computational methods predict that variation in key binding residues of 3CLpro-NEMO helps explain the high fitness of SARS-CoV-2 in humans. We posit that cleavage of NEMO is an important piece of information to be accounted for, in the pathology of COVID-19.
60 APPLIED LIFE SCIENCES↗
Modeling approaches for addressing unrelaxable bound constraints with unconstrained optimization methods
We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated. Our method reformulates the unrelaxable bound-constrained problem as an unconstrained optimization problem that is amenable to existing unconstrained optimization methods. The reformulation relies on a domain warping to form a merit function; the choice of the warping determines the level of exactness with which the unconstrained problem can be used to find solutions to the bound-constrained problem, as well as key properties of the unconstrained formulation such as smoothness. We develop theory when the domain warping is a multioutput sigmoidal warping, and we explore the practical elements of applying unconstrained optimization methods to the formulation. Here, we develop an algorithm that exploits the structure of the sigmoidal warping to guarantee that unconstrained optimization algorithms applied to the merit function will find a stationary point to the desired tolerance.
97 MATHEMATICS AND COMPUTING↗
Psb27, a photosystem II assembly protein, enables quenching of excess light energy during its participation in the PSII lifecycle
Photosystem II (PSII), the enzyme responsible for oxidizing water into molecular oxygen, undergoes a complex lifecycle during which multiple assembly proteins transiently bind to and depart from PSII assembly intermediate complexes. Psb27 is one such protein. It associates with the CP43 chlorophyll-binding subunit of PSII to form a Psb27-PSII sub-complex that constitutes 7–10% of the total PSII pool. Psb27 remains bound to PSII assembly intermediates and dissociates prior to the formation of fully functional PSII. In this study, we compared a series of Psb27 mutant strains in the cyanobacterium Synechocystis sp. PCC 6803 with varied expression levels of Psb27: wild type (WT); psb27 genetic deletion (Del27), genetically complemented psb27 (Com27); and over-expressed Psb27 (OE27). The Del27 strain demonstrated decreased non-photochemical fluorescence quenching, while the OE27 strain showed increased non-photochemical quenching and tolerance to fluctuating light conditions. Multiple flashes and fluorescence decay analysis indicated that OE27 has the least affected maximum PSII quantum yield of the mutants. OE27 also displayed a minimal impact on the half-life of the fast component of QA – reoxidation over multiple flashes, indicating robust PSII function. Further, we propose that the close association between Psb27 and CP43, and the absence of a fully functional manganese cluster in the Psb27-PSII complex create a PSII sub-population that dissipates excitation energy prior to its recruitment into the functional PSII pool. Efficient energy dissipation prevents damage to this pre-PSII pool and allows for efficient PSII repair and maturation. Participation of Psb27 in the PSII life cycle ensures high-quality PSII assembly.
59 BASIC BIOLOGICAL SCIENCES↗