Data-Driven Parsimonious Models for Virtual Diagnostics and Control
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Transfer learning for probabilistic localization of hidden cracks in concrete structures
Abstract The utility of discriminative supervised learning models built using multiple training-data sources is investigated for hidden crack localization in concrete. Feed-forward neural network (FFNN) is chosen as the model architecture, and transfer learning is used to assimilate the information obtained from different sources (computational physics simulations and laboratory experiments). The labeled training data consists of values of a damage index and the known locations of hidden cracks. The classification models need to learn how the presence of damage (hidden cracks) affects the damage index at different sensors for different test conditions. To this end, diagnostic FFNN models are built by sequentially adding and training new hidden layers to assimilate labeled information from computer models (different model geometries, test conditions, crack lengths, crack locations) and laboratory experiments on a plain cement slab. These transfer learning-based models are then used to localize damage in concrete specimens that reflect real-world conditions (i.e., specimens with steel reinforcement and randomly distributed aggregate). The actual damage state in these specimens is determined by extracting cores and performing petrographic studies on the extracted cores. The damage probability estimated by transfer learning-based models is compared with the petrographic damage rating index (DRI) to identify the most suitable approach to train the diagnostic models. The transfer learning-based diagnostic methodology shows promise and could be used in various structural health monitoring applications, where sufficient labeled data are typically not available from a single data source.
Miele, S.↗
Alpha1 LASSO data bundles Lamont, OK
A data bundle is a unified package consisting of LASSO LES input and output, observations, evaluation diagnostics, and model skill scores. LES input includes model configuration information and forcing data. LES output includes profile statistics and full domain fields of cloud and environmental variables. Model evaluation data consists of LES output and ARM observations co-registered on the same grid and sampling frequency. Model performance is quantified by skill scores and diagnostics in terms of cloud and environmental variables.
54 ENVIRONMENTAL SCIENCES↗
LASSO Data Bundles
The Alpha 2 release is the second release from the LASSO Pilot Phase that builds upon the Alpha 1 release. Alpha 2 contains additional diagnostics in the data bundles and focuses on cases from spring-summer 2016. A data bundle is a unified package consisting of LASSO LES input and output, observations, evaluation diagnostics, and model skill scores. LES input include model configuration information and forcing data. LES output includes profile statistics and full domain fields of cloud and environmental variables. Model evaluation data consists of LES output and ARM observations co-registered on the same grid and sampling frequency. Model performance is quantified by skill scores and diagnostics in terms of cloud and environmental variables.
54 ENVIRONMENTAL SCIENCES↗
Evaluating temperature and fuel stratification for heat-release rate control in a reactivity-controlled compression-ignition engine using optical diagnostics and chemical kinetics modeling
We investigated the combustion process in a dual-fuel, reactivity-controlled compression-ignition (RCCI) engine using a combination of optical diagnostics and chemical kinetics modeling to explain the role of equivalence ratio, temperature, and fuel reactivity stratification for heat-release rate control. An optically accessible engine is operated in the RCCI combustion mode using gasoline primary reference fuels (PRF). A well-mixed charge of iso-octane (PRF = 100) is created by injecting fuel into the engine cylinder during the intake stroke using a gasoline-type direct injector. Later in the cycle, n-heptane (PRF = 0) is delivered through a centrally mounted diesel-type common-rail injector. This injection strategy generates stratification in equivalence ratio, fuel blend, and temperature. The first part of this study uses a high-speed camera to image the injection events and record high-temperature combustion chemiluminescence. Moreover, the chemiluminescence imaging showed that, at the operating condition studied in the present work, mixtures in the squish region ignite first, and the reaction zone proceeds inward toward the center of the combustion chamber. The second part of this study investigates the charge preparation of the RCCI strategy using planar laser-induced fluorescence (PLIF) of a fuel tracer under non-reacting conditions to quantify fuel concentration distributions prior to ignition. The fuel-tracer PLIF data show that the combustion event proceeds down gradients in the n-heptane distribution. The third part of the study uses chemical kinetics modeling over a range of mixtures spanning the distributions observed from the fuel-tracer fluorescence imaging to isolate the roles of temperature, equivalence ratio, and PRF number stratification. The simulations predict that PRF number stratification is the dominant factor controlling the ignition location and growth rate of the reaction zone. Equivalence ratio has a smaller, but still significant, influence. Lastly, temperature stratification had a negligible influence due to the NTC behavior of the PRF mixtures.
42 ENGINEERING↗
ARM data-oriented metrics and diagnostics package for climate model evaluation (ARM-DIAGS-V3) version 3
A Python-based metrics and diagnostics package is currently being developed by the ARM Infrastructure Team at Lawrence Livermore National Laboratory to facilitate the use of long-term high frequency measurements from the ARM program in evaluating the regional climate simulation of clouds, radiation, precipitation, and aerosols. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The CMIP model data sets are also included in the package to enable model inter-comparison as demonstrated in Zhang et al. (2018) and Zhang et al. (2020). The mean of the CMIP model can be served as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, aerosol optical depth, and radiative fluxes, with plan to extend to more fields, such as the evaluation of model simulated aerosol physicochemical properties and cloud microphysics properties. Process-oriented diagnostics focusing on aerosol, cloud, and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. In addition to the Southern Great Plains (SGP), North Slope of Alaska (NSA) and Tropical Western Pacific (TWP) atmospheric observatories in the ARMDIAGS version 2.0, the version 3.0 package have extended to the data collected at the ARM Eastern North Atlantic (ENA) atmospheric observatory and the Observation and Modeling of the Green Ocean Amazon (GOAMAZON) field campaign. The metrics and diagnostics package are currently built upon standard Python libraries and additional Python packages developed by DOE (CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first provide an overview of major metrics in section 2. The input data, which constitutes the core content of the metrics and diagnostics package, is summarized in section 3. A user's guide documenting the workflow/structure of the version 3.0 codes and including step-by-step instruction for running the package is described in section 4.
54 ENVIRONMENTAL SCIENCES↗
The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data
The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Inter-comparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.
54 ENVIRONMENTAL SCIENCES↗
Improving Reliability of Large Language Models for Nuclear Power Plant Diagnostics Technical Presentation
Large Language Models (LLMs) struggle out of the box when answering factually about detailed questions, especially in domains that are sparsely represented in their training data. This causes hallucinations and reduces reliability making it difficult for them to be used in practice. This work shows that using RAG techniques can improve factual accuracy and reliability, allowing for the application of LLMs in specialized areas, even when those areas that aren’t extensively covered in their initial training.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Diagnostic evaluation of numerical model simulations using the tendency equation
A procedure is proposed to expand the diagnostic capabilities of the pressure tendency equation of a primitive equation NWP model by computing the pressure tendency in physical coordinates. The advantages of isolating the density advection as a diagnostic tool to understand pressure changes is shown. By simple thermodynamic arguments it is demonstrated that in areas of synoptic-scale cyclonic development, the vertically integrated density advection is more than sufficient to explain the depletion of mass over a growing depression. Consequently, the joint contribution of the net divergence and vertical motion opposes the pressure fall. This is illustrated for a case of rapid cyclogenesis in southern South America.
Jusem, Juan C.↗
Final Technical Report -- Bridging the PSI Knowledge Gap: A Multiscale Approach
The Plasma Surface Interactions (PSI) Science Center formed by the grant undertook a multidisciplinary set of studies on the complex interface between the plasma and solid states of matter. The strategy of the center was to combine and integrate the experimental, diagnostic and modeling toolkits from multiple institutions towards specific PSI problems. In this way the Center could tackle integrated science issues which were not addressable by single institutions, as well as evolve the underlying science of the PSI in a more general way than just for fusion applications. The overall strategy proved very successful. The research result and highlights of the MIT portion of the Center are primarily described. A particular highlight is the study of tungsten nano-tendril growth in the presence of helium plasmas. The Center research provided valuable new insights to the mechanisms controlling the nano-tendrils by developing coupled modeling and in situ diagnostic methods which could be directly compared. For example, the role of helium accumulation in tungsten distortion in the surface was followed with unique in situ helium concentration diagnostics developed. These depth-profiled, time-resolved helium concentration measurements continue to challenge the numerical models of nano-tendrils. The Center team also combined its expertise on tungsten nano-tendrils to demonstrate for the first time the growth of the tendrils in a fusion environment on the Alcator C-Mod fusion experiment, thus having significant impact on the broader fusion research effort. A new form of isolated nano-tendril “columns” were identified which are now being used to understand the underlying mechanisms controlling the tendril growth. The Center also advanced PSI science on a broader front with a particular emphasis on developing a wide range of in situ PSI diagnostic tools at the DIONISOS facility at MIT. For example the strong suppression of sputtering by the certain combination of light-species plasmas and metals was experimentally studied with independent measurement methods across the Center. This surprising result challenges the universal use of the binary-collision approximation in sputtering predictions and continues to be the subject of study. In order to address this issue MIT developed a new in situ erosion measurement technique based on ion beam analysis which can be used at elevated material temperatures. This exciting new technique is now being used to study material erosion in high performance plasma thrusters for space exploration and is being adopted to fusion experimental devices. This is an indicator of the positive synergies that arise from such a Center, with the research having impact beyond the initial area of study. The Center also served successfully as an organizing force for communication to the science community. The MIT members of the Center provided many high-profile overview presentations at prestigious international conferences and national workshops. The research resulted in three student theses and 24 peer-reviewed publications. PSI research continues to be identified as a critical area for fusion energy.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A multi-chamber model of combustion instabilities and its assessment using kilohertz laser diagnostics in a gas turbine model combustor
A multi-chamber model for the combustion instabilities manifested in a gas turbine model combustor was developed. Here the proposed model was used to explain the dependencies of instability frequency on burner geometry and other flow parameters, some of which could not be reconciled with previous models. The new model was built upon the Helmholtz analysis of two connected resonators. The instability frequency as well as the complex pressure ratio between two chambers were predicted by solving ordinary differential equations. To assess the assumptions and predictions of the proposed model, the spectra and magnitude of the oscillations of pressure, heat release rate, and velocity were measured for four different operating conditions: rich (R1), lean (L1), stoichiometric (S1), and reduced flow rate (R2) with a kilohertz laser diagnostic system. These measurements reconfirmed that the instability is of Helmholtz type. A global equivalence ratio that is consistently greater than unity was identified to be an enabling factor for combustion instability. This is also in agreement with the predictions made by the proposed model. Furthermore, the model was shown to be able to predict the right trend of instability frequency when multiple parameters were changed. It is concluded that the current model is an improvement over previous models, because the acoustic coupling between different chambers of the burner was considered.
42 ENGINEERING↗
Development of Fault Models for Hybrid Fault Detection and Diagnostics Algorithm
This report describes models of building faults created for OpenStudio to support the ongoing development of fault detection and diagnostic (FDD) algorithms at the National Renewable Energy Laboratory. Building faults are operating abnormalities that degrade building performance, such as using more energy than normal operation, failing to maintain building temperatures according to the thermostat set points, etc. Models of building faults in OpenStudio can be used to estimate fault impacts on building performance and to develop and evaluate FDD algorithms.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Transportation technology: Earth-to-orbit propulsion
The topics presented are covered in viewgraph form and include the following: (1) sensor autodiagnosis and autocalibration; (2) autodiagnostic and autocalibration program; (3) plume diagnostics; (4) plume diagnostics program; (5) failure modeling; (6) mass data storage and retrieval; (7) fiber optic instrumentation bus; (8) model based diagnostics; (9) safety monitoring system (SMS); (10) control and real time diagnostics; (11) current intelligent controls program; (12) augumented intelligent controls program; (13) current real time diagnostics program; (14) augumented RT diagnostics program; and (15) control and diagnostic system hardware.
Gorland, S.↗
An Overview of the NASA Aviation Safety Program Propulsion Health Monitoring Element
The NASA Aviation Safety Program (AvSP) has been initiated with aggressive goals to reduce the civil aviation accident rate, To meet these goals, several technology investment areas have been identified including a sub-element in propulsion health monitoring (PHM). Specific AvSP PHM objectives are to develop and validate propulsion system health monitoring technologies designed to prevent engine malfunctions from occurring in flight, and to mitigate detrimental effects in the event an in-flight malfunction does occur. A review of available propulsion system safety information was conducted to help prioritize PHM areas to focus on under the AvSP. It is noted that when a propulsion malfunction is involved in an aviation accident or incident, it is often a contributing factor rather than the sole cause for the event. Challenging aspects of the development and implementation of PHM technology such as cost, weight, robustness, and reliability are discussed. Specific technology plans are overviewed including vibration diagnostics, model-based controls and diagnostics, advanced instrumentation, and general aviation propulsion system health monitoring technology. Propulsion system health monitoring, in addition to engine design, inspection, maintenance, and pilot training and awareness, is intrinsic to enhancing aviation propulsion system safety.
Simon, Donald L.↗
A balanced diagnostic system compatible with a barotropic prognostic model
A system of diagnostic equations for the velocity field, or wind laws, for a barotropic primitive-equation model of large-scale atmospheric flow is derived. Attention is given to the classical balance equation and its ellipticity condition. Numerical solutions of the diagnostic system are presented, including examples of cases of the mixed elliptic-hyperbolic type and cases with non-zero divergence. Procedures for implementing such a system are outlined, along with a review of factors in using the technique for operational numerical weather prediction.
Ghil, M.↗
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics
In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
Kobayashi, Takahisa↗
Integrated Data Analysis to Improve Te and Zeff Measurement on NSTX-U
The multi-energy soft x-ray (ME-SXR) camera is a new concept for a fusion plasma diagnostic based on a pixelated detector with adjustable photon cutoff thresholds. It is a single, compact, solid-state detector which combines spatial and spectral sensitivity and can be configured remotely at run time, making it ideal for a fusion environment. The detector can be configured for application to a wide range of physics problems including impurity transport, electron temperature profiles, and non-thermal effects. The detector has been installed on the Madison Symmetric Torus (MST) as part of a collaboration between Princeton Plasma Physics Laboratory and the University of Wisconsin–Madison to develop the technology and analysis techniques. A full diagnostic forward model of the ME-SXR diagnostic has been developed to enable analysis of the raw photon count data. This model incorporates the atomic physics underlying the plasma emission, diagnostic geometry, and detector response characteristics. Employing this model, a Bayesian analysis approach is used to extract electron temperature from the raw data. This analysis approach uses all available information, clearly quantifies prior information, and handles uncertainties transparently. The ME-SXR camera is being used to study the 3D (helical, stellarator-like) state that forms in the MST device. This helical core structure has improved energy confinement. The transition to the helical state is accompanied by a rise in electron temperature in the central part of the helical structure, but then this enhancement fluctuates. The characteristics of this fluctuation are being compared to a magneto-hydrodynamics model.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A Model-based Health Monitoring and Diagnostic System for the UH-60 Helicopter
Model-based reasoning techniques hold much promise in providing comprehensive monitoring and diagnostics capabilities for complex systems. We are exploring the use of one of these techniques, which utilizes multi-signal modeling and the TEAMS-RT real-time diagnostic engine, on the UH-60 Rotorcraft Aircrew Systems Concepts Airborne Laboratory (RASCAL) flight research aircraft. We focus on the engine and transmission systems, and acquire sensor data across the 1553 bus as well as by direct analog-to-digital conversion from sensors to the QHuMS (Qualtech health and usage monitoring system) computer. The QHuMS computer uses commercially available components and is rack-mounted in the RASCAL facility. A multi-signal model of the transmission and engine subsystems enables studies of system testability and analysis of the degree of fault isolation available with various instrumentation suites. The model and examples of these analyses will be described and the data architectures enumerated. Flight tests of this system will validate the data architecture and provide real-time flight profiles to be further analyzed in the laboratory.
Patterson-Hine, Ann↗