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Results for “Reliability Prediction”

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 1,585 records · Page 88
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Accelerating Simulation for High-Fidelity PV Inverter System Reliability Assessment with High-Performance Computing

The overall cost of photovoltaic (PV) systems has shown a downward trend during the last decade; however, PV inverter failures account for the highest cost of operation and maintenance. To address this, reliability tools with powerful computation and better accuracy are required for the lifetime prediction and degradation evaluation of PV inverters. This paper proposes an event-driven parallel computing-based simulator. The proposed simulator applies high-performance computing techniques and other accessory optimization techniques-including cluster merging, adaptive model updates, and steady-state identification-to make reliability assessments for PV inverters under given input mission profiles and operating conditions with high efficiency and high fidelity. The main idea of the simulator and its workflow are introduced. Then, a demo PV inverter system simulator is implemented, and the speedup of the total simulations of the switching model reaches 123.03 times.

high-performance computing↗
NASA NTRS2003

CME Prediction from Magnetograms

We have found that active regions that are likely to be CME productive can be identified from measures of their nonpotentiality from magnetograms. We have developed four different measures from vector magnetograms and another that can be obtained from a line-of-sight magnetogram. We find that all five measures are strongly correlated with CME productivity to a similar degree. Hence, all five are roughly equally good predictors of active-region CME productivity. Since the measures all have similar predictive ability, the measures that are easiest to reliably measure are the best for operational forecasting of CMEs. The two best measures are the length of strong-shear main neutral line L(sub SS) (the length of the main neutral line with the magnetic shear angle greater than 45deg and observed transverse field greater than 150G) and the length of strong-gradient main neutral line L(sub G) (the length of the main neutral line with line-of-sight magnetic field greater than 50G/Mm and potential transverse field greater than 150G). As L(sub G) is measured from line-of-sight magnetograms it opens the larger data base of SOHO/MDI and Kitt Peak line-of-sight magnetograms for CME prediction study. This is especially important for evolutionary studies, with SOHO/MDI having no daylight, cloudy weather, or atmospheric seeing problems.

Falconer, D. A.↗
DOE OSTI2025

ON THE LANGUAGE OF RELIABILITY: A SYSTEM ENGINEER PERSPECTIVE

In its classical definition, risk is defined by three elements: what can go wrong, what are its consequences and how likely is it to occur. While this definition makes sense in a regulatory based framework to estimate risk associated to power plants (in terms of core damage frequency and large early release frequency), this approach does not provide a useful snapshot of the health of the plant. A possible alternate path can start by redefining the word “risk” to a broader meaning that better reflects the needs of a system health and asset management decision making process. Rather than asking how likely an event can occur (in probabilistic terms), we can ask how far this event is from occurring. We will show how, given the data available from plant equipment reliability and monitoring/diagnostic/prognostic centers, a margin can be described and determined for all type of maintenance approaches (e.g., corrective or predictive maintenance). We will show how to link SSC margin-based reliability models to system reliability models (i.e., fault trees) in order to assess system/plant health and how to perform margin-based system calculations. These calculations are not solved using classical probabilistic calculations applied to sets (as performed by any PRA code) but, instead, through metric spaces operations (i.e., distance/margin based approach).

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Lowering the computational barrier: Partially Bayesian neural networks for transparency in medical imaging AI

Deep Neural Networks (DNNs) can provide clinicians with fast and accurate predictions that are highly valuable for high-stakes medical decision-making, such as in brain tumor segmentation and treatment planning. However, these models largely lack transparency about the uncertainty in their predictions, potentially giving clinicians a false sense of reliability that may lead to grave consequences in patient care. Growing calls for Transparent and Responsible AI have promoted Uncertainty Quantification (UQ) to capture and communicate uncertainty in a systematic and principled manner. However, traditional Bayesian UQ methods remain prohibitively costly for large, million-dimensional tumor segmentation DNNs such as the U-Net. In this work, we discuss a computationally-efficient UQ approach via the partially Bayesian neural networks (pBNN). In pBNN, only a single layer, strategically selected based on gradient-based sensitivity analysis, is targeted for Bayesian inference. We illustrate the effectiveness of pBNN in capturing the full uncertainty for a 7.8-million parameter U-Net. We also demonstrate how practitioners and model developers can use the pBNN's predictions to better understand the model's capabilities and behavior.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Computational Study of Low-Speed Boundary-Layer Laminar-Turbulent Transition over a 30º Swept Plate in a Channel

Reliable boundary-layer laminar-turbulent transition predictions are critical for the design of aircraft that use natural or controlled laminar flow to achieve low drag and better fuel efficiency. During cruise flight through low turbulence, the three-dimensional boundary layer over swept surfaces can transition prematurely due to stationary crossflow instabilities induced by surface roughness. Parabolized stability equations analyses have been successfully used to explain the different phases of the transition process under well controlled perturbations such as steps or discrete roughness elements. However, methodologies are still lacking to model the boundary layer receptivity to distributed random roughness, and the following non-linear instability stages leading to transition. For engineering applications, several semi-empirical methods based on linear amplification factors or local helicity have found growing adoption, but their reliability is limited by their level of simplification and the sparsity of data they are based on. To address this gap, a new study of the effect of distributed random surface roughness on crossflow transition was started, using a 30◦ swept plate as a test vehicle. The plate, which is mounted in the 2 Foot × 3 Foot Low-Speed Boundary-Layer Channel at the NASA Langley Research Center, has been tested with micro-scale roughness of varied geometry near the leading edge. In order to analyze the flow in the test section and prepare Direct Numerical Simulations with resolved roughness, Reynolds-Averaged Navier-Stokes (RANS) simulations of the flow over the plate installed in the wind tunnel test section were performed using the Langtry-Menter transition model with stationary crossflow extension. This paper presents the results of these simulations, with comparison to the experiment.

ARMD↗
DOE OSTI2022

Phoebe: a high-performance framework for solving phonon and electron Boltzmann transport equations

Understanding the electrical and thermal transport properties of materials is critical to the design of electronics, sensors, and energy conversion devices. Computational modeling can accurately predict material properties but, in order to be reliable, requires accurate descriptions of electron and phonon states and their interactions. While first-principles methods are capable of describing the energy spectrum of each carrier, using them to compute transport properties is still a formidable task, both computationally demanding and memory intensive, requiring integration of fine microscopic scattering details for estimation of macroscopic transport properties. To address this challenge, we present Phoebe—a newly developed software package that includes the effects of electron–phonon, phonon–phonon, boundary, and isotope scattering in computations of electrical and thermal transport properties of materials with a variety of available methods and approximations. This open source C++ code combines MPI-OpenMP hybrid parallelization with GPU acceleration and distributed memory structures to manage computational cost, allowing Phoebe to effectively take advantage of contemporary computing infrastructures. We demonstrate that Phoebe accurately and efficiently predicts a wide range of transport properties, opening avenues for accelerated computational analysis of complex crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Prediction of the visual impact of motorways using GIS

Large scale transportation projects can adversely affect the visual perception of environmental quality and require adequate visual impact assessment. In this study, we investigated the effects of the characteristics of the road project and the character of the existing landscape on the perceived visual impact of motorways, and developed a GIS-based prediction model based on the findings. An online survey using computer-visualised scenes of different motorway and landscape scenarios was carried out to obtain perception-based judgements on the visual impact. Motorway scenarios simulated included the baseline scenario without road, original motorway, motorways with timber noise barriers, transparent noise barriers and tree screen; different landscape scenarios were created by changing land cover of buildings and trees in three distance zones. The landscape content of each scene was measured in GIS. The result shows that presence of a motorway especially with the timber barrier significantly decreases the visual quality of the view. The resulted visual impact tends to be lower where it is less visually pleasant with more buildings in the view, and can be slightly reduced by the visual absorption effect of the scattered trees between the motorway and the viewpoint. Based on the survey result, eleven predictors were identified for the visual impact prediction model which was applied in GIS to generate maps of visual impact of motorways in different scenarios. The proposed prediction model can be used to achieve efficient and reliable assessment of visual impact of motorways. - Highlights: • Motorways induce significant visual impact especially with timber noise barriers. • Visual impact is negatively correlated with amount of buildings in the view. • Visual impact is positively correlated with percentage of trees in the view. • Perception-based motorway visual impact prediction model using mapped predictors • Predicted visual impacts in different scenarios are mapped in GIS.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Comparison of Taxi Time Prediction Performance Using Different Taxi Speed Decision Trees

In the STBO modeler and tactical surface scheduler for ATD-2 project, taxi speed decision trees are used to calculate the unimpeded taxi times of flights taxiing on the airport surface. The initial taxi speed values in these decision trees did not show good prediction accuracy of taxi times. Using the more recent, reliable surveillance data, new taxi speed values in ramp area and movement area were computed. Before integrating these values into the STBO system, we performed test runs using live data from Charlotte airport, with different taxi speed settings: 1) initial taxi speed values and 2) new ones. Taxi time prediction performance was evaluated by comparing various metrics. The results show that the new taxi speed decision trees can calculate the unimpeded taxi-out times more accurately.

taxi time prediction↗
DOE OSTI2026

Active operator learning with predictive uncertainty quantification for partial differential equations

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. Here, we propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework’s uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1999

Skin Friction Drag Predictions: Summary of CFD Cross Checks, Wing/Body

The objective of the present study was to address the questions of: 1) how reliably or consistently the Navier-Stokes methods and processes used by the various organizations can predict integrated skin friction drag, and 2) how well the methods can predict trends within a family of optimized configurations. As a first step, all available skin friction drag predictions were accumulated to obtain a mean and standard deviation for the TCA (Technology Concept Airplane) baseline and each of the optimized configurations. It is observed that the optimization process has had little effect on the predicted skin friction drags. The variation in the mean that is observed is dwarfed by the standard deviations. In order to understand the reasons for the relatively large spreads in the computed results, a number of auxiliary computations have been performed using the UPS and OVERFLOW codes in an effort to identify and quantity potential sources of the variations.

Lawrence, Scott↗
DOE OSTI2026

Battery Life Prediction Using Reduced-Order Physics Models and Machine Learning (CRADA Final Report)

Phase 1 (Original CRADA, plus no-cost extension modifications #1-3, 6/1/2017 to 3/13/2021): The Australian Department of Defence (AUDoD) is performing accelerated aging tests of Li-ion batteries to benchmark their reliability and degradation characteristics. Using its previously developed battery lifetime predictive model framework, the National Laboratory of the Rockies (NLR) will develop analytical models based the AUDoD data to predict lifetime of the multiple Li-ion battery chemistries under real-world use scenarios of interest to AUDoD. The NLR model is based on physical degradation mechanisms encountered by Li-ion batteries and has been previously validated. Phase 2 (CRADA modification #4, plus no-cost extension modification #5, 2/22/2021 to 3/30/2025): Train and support Australian Department of Defence personnel to use NLR software for model-based estimation of Li-ion battery lifetime using accelerated battery aging data collected by the Australian Department of Defence. Under separate DOE funding from 2019 to 2021, NLR enhanced its battery life-prediction software using machine learning algorithms to automate portions of the model-fitting process, requiring significantly less labor and expert judgment and also adding uncertainty quantification, increasing statistical rigor. Under Phase 2, NLR will customize NLR Software and provide it to AuDoD. NLR will enhance its NLR Model to capture aging modes of AuDoD's multi-cell modules, including cell-balancing effects. NLR will develop example single-cell and multi-cell models based on one AuDoD battery aging dataset. NLR will train AuDoD personnel on NLR Software. By the conclusion of the project, NLR will have provided AuDoD the training materials, a user manual and software needed to perform their own analysis of additional and/or future battery aging datasets.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Four-Switch Buck-Boost Converter Based on Model Predictive Control with Smooth Mode Transition Capability

Four-switch buck-boost converter supports both voltage step-up and step-down functionalities, but it suffers from mode transfer challenge that would need reliable mode detection when designed to operate in multi different modes. In this paper, a novel control method based on model predictive current control is proposed for such converter with inherent smooth mode transfer capability without extra design on mode detection and transfer scheme. Modulator and mode detection are replaced by an optimization process through cost function. This largely simplifies the design and makes it easily implemented. Therefore, seamless transfer between buck and boost modes is achieved with many other system level benefits. Simulation and experimental results are provided to verify the effectiveness of proposed method for the four-switch buck-boost converter.

42 ENGINEERING↗
DOE OSTI2017

Using Conductive Firebrick Resistance Heating to Enable Very High-Temperature Heat Storage in Nuclear Gas Turbine Systems

In order to economically deploy renewable energy on large scales, affordable energy storage is a must. While the cost of current work storage technologies are too high to deploy in quantities that satisfy renewable energy deployment, heat storage options hold promise for dramatically cheaper storage, which can be used to (1) couple with industrial furnaces or refineries to provide high temperature heat to industry, or (2) couple with air-Brayton cycles to add heat for electricity production. The design space and preliminary performance evaluation for an affordable heat storage concept, called Firebrick Resistance-heated Energy Storage (FIRES), has shown promise for near-term deployment in industrial processes by using readily available materials: metallic resistance heaters for converting electricity to stored heat, and common ceramic firebricks used in furnaces for storing heat at temperatures as high as 1200 deg. C. There however remains serious interest in replacing traditional resistance heaters with electrically conductive firebricks that may serve as both the heating element and the storage medium. The work underway is the experimental development of a high-temperature low-cost electric resistance heater system using conductive firebrick, with the aims of (1) proving that conductive firebrick can be used to replace separate electrical heating system to reduce costs, increase operating temperatures, and increase reliability, and (2) developing methodology to enable design and prediction of performance of such systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Operation at Reduced Atmospheric Pressure and Concept of Reliability Redundancy for Optimized Design of Insulation Systems

Electrified transportation is calling for insulation design criteria that is adequate to provide elevated levels of power density, power dynamics and reliability. Increasing voltage levels are expected to cause accelerated intrinsic and extrinsic aging effects which will not be easily predictable at the design stage due to a lack of suitable modeling. Designing reliable insulation systems would require finding solutions able to control accelerated aging due to an unpredictable increase of intrinsic stresses and the onset of extrinsic stresses as partial discharges. This paper proposes the concept of reliability redundancy for the insulation design of aerospace electrical asset components, which is also validated at lower-than-standard atmospheric pressure. The principle is that extrinsic-aging-free design might be achieved upon determining the aging stress or abnormal service stresses distribution and being sure that aging will not generate conditions that can incept extrinsic aging (partial discharges) during operation life. However, such information is never, in practice, fully available to insulation system designers. Hence, especially in critical applications such as electrified aircraft, aerospace, and combat ships a further level of reliability should be added to a partial-discharge-free design, which can consist of the use of corona-resistant materials and/or of life models able to consider the accelerated aging effect of partial discharges (or any other type of extrinsic-accelerated aging factor). Innovative life modeling considering both extrinsic and intrinsic aging stresses, insulating material testing to estimate model parameters, and a metric for quantifying the extent of corona (or partial discharge) resistance can lead to establishing feasibility and limit conditions for optimized or fully reliability-redundant design. It is shown in the paper that if an extrinsic-aging-free design is not feasible, and it is therefore replaced by a redundant design, a further level of reliability redundancy can be provided by effective condition monitoring plans.

Montanari, Gian Carlo↗
NASA NTRS2004

COMSAC: Computational Methods for Stability and Control

The unprecedented advances being made in computational fluid dynamic (CFD) technology have demonstrated the powerful capabilities of codes in applications to civil and military aircraft. Used in conjunction with wind-tunnel and flight investigations, many codes are now routinely used by designers in diverse applications such as aerodynamic performance predictions and propulsion integration. Typically, these codes are most reliable for attached, steady, and predominantly turbulent flows. As a result of increasing reliability and confidence in CFD, wind-tunnel testing for some new configurations has been substantially reduced in key areas, such as wing trade studies for mission performance guarantees. Interest is now growing in the application of computational methods to other critical design challenges. One of the most important disciplinary elements for civil and military aircraft is prediction of stability and control characteristics. CFD offers the potential for significantly increasing the basic understanding, prediction, and control of flow phenomena associated with requirements for satisfactory aircraft handling characteristics.

Fremaux, C. Michael↗
DOE OSTI2020

Remaining Useful Strength (RUS) Prediction of SiCf-SiCm Composite Materials Using Deep Learning and Acoustic Emission

Prognosis techniques for prediction of remaining useful life (RUL) are of crucial importance to the management of complex systems for they can lead to appropriate maintenance interventions and improvements in reliability. While various data-driven methods have been introduced to predict the remaining useful life (RUL) of machinery systems or batteries, no research has been reported on the remaining useful strength (RUS) prediction of silicon carbide fiber reinforced silicon carbide matrix (SiCf-SiCm) materials with pivotal role in its potential usage as a structural material in nuclear reactors and turbine engines. Knowledge of its degradation process is of the utmost importance to the manufacturers. For this purpose, two approaches based on the machine-learning techniques of random-forest (RF) and convolutional neural network (CNN) are proposed to predict the RUS of SiCf-SiCm using only acoustic emission (AE) signals generated during the material’s stress applying process. Experimental results show that the CNN models achieved better predictive performance than the RF models but the latter with expert-engineered features achieves better prediction for AE signals in the early stage of degradation. Additionally, our results demonstrate that both models can correctly predict the SiCf-SiCm RUS as evaluated by our robust testing method from which the best average root mean square error (RMSE) and Pearson correlation coefficient of 3.55 ksi units and 0.85 were obtained.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds

Abstract Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1992

Recent developments of the NESSUS probabilistic structural analysis computer program

The NESSUS probabilistic structural analysis computer program combines state-of-the-art probabilistic algorithms with general purpose structural analysis methods to compute the probabilistic response and the reliability of engineering structures. Uncertainty in loading, material properties, geometry, boundary conditions and initial conditions can be simulated. The structural analysis methods include nonlinear finite element and boundary element methods. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. The scope of the code has recently been expanded to include probabilistic life and fatigue prediction of structures in terms of component and system reliability and risk analysis of structures considering cost of failure. The code is currently being extended to structural reliability considering progressive crack propagation. Several examples are presented to demonstrate the new capabilities.

Millwater, H.↗