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At least 1,639 records · Page 91

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DOE OSTI2013

Spectroscopy of the three-photon laser excitation of cold Rubidium Rydberg atoms in a magneto-optical trap

The spectra of the three-photon laser excitation 5S{sub 1/2} {yields} 5P{sub 3/2} {yields} 6S{sub 1/2}nP of cold Rb Rydberg atoms in an operating magneto-optical trap based on continuous single-frequency lasers at each stage are studied. These spectra contain two partly overlapping peaks of different amplitudes, which correspond to coherent three-photon excitation and incoherent three-step excitation due to the presence of two different ways of excitation through the dressed states of intermediate levels. A four-level theoretical model based on optical Bloch equations is developed to analyze these spectra. Good agreement between the experimental and calculated data is achieved by introducing additional decay of optical coherence induced by a finite laser line width and other broadening sources (stray electromagnetic fields, residual Doppler broadening, interatomic interactions) into the model.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Preface to the Focus Section on Machine Learning in Seismology

Machine learning (ML) is a collection of algorithms and statistical models that enable computers to extract relevant patterns and information from large data sets. Unlike physical modeling approaches, in which scientists develop theories based on physical laws to compare with real–world observations, ML approaches learn directly from data without explicitly reasoning about the underlying physical mechanisms. ML algorithms are often categorized into supervised and unsupervised learning (see fig. 2 in Kong et al., 2018). Supervised learning algorithms build a model from existing labeled input data with the goal of predicting the labels of new data.

58 GEOSCIENCES↗

DOE OSTI2023

High-Resolution PM2.5 Concentrations Estimation Based on Stacked Ensemble Learning Model Using Multi-Source Satellite TOA Data

Nepal has experienced severe fine particulate matter (PM2.5) pollution in recent years. However, few studies have focused on the distribution of PM2.5 and its variations in Nepal. Although many researchers have developed PM2.5 estimation models, these models have mainly focused on the kilometer scale, which cannot provide accurate spatial distribution of PM2.5 pollution. Based on Gaofen-1/6 and Landsat-8/9 satellite data, we developed a stacked ensemble learning model (named XGBLL) combined with meteorological data, ground PM2.5 concentrations, ground elevation, and population data. The model includes two layers: a XGBoost and Light GBM model in the first layer, and a linear regression model in the second layer. The accuracy of XGBLL model is better than that of a single model, and the fusion of multi-source satellite remote sensing data effectively improves the spatial coverage of PM2.5 concentrations. Besides, the spatial distribution of the daily mean PM2.5 concentrations in the Kathmandu region under different air conditions was analyzed. The validation results showed that the monthly averaged dataset was accurate (R2 = 0.80 and root mean square error = 7.07). In addition, compared to previous satellite PM2.5 datasets in Nepal, the dataset produced in this study achieved superior accuracy and spatial resolution.

Environmental Sciences & Ecology↗

DOE OSTI2024

Machine learning approaches for integrating multi-omics data to expand microbiome annotation

Preliminary: This final report corresponds to a grant (DE-SC0021216) that was awarded to the University of Montana. Mid-way through the grant period, I relocated from the University of Montana to the University of Arizona. The grant was ended at University of Montana in late 2022, with all efforts concluding on 08/26/22; the remaining funds supporting the project were relinquished by University of Montana, and were later awarded to University of Arizona under a new grant, with start date 04/01/23. This report focuses on results of research efforts at UMontana through 08/26/22. Results: We made progress in each of the three aims of the proposal. We released software that identifies and fills gaps in the annotation of metabolic proteins within bacterial genomes. We made substantial progress in developing software for alignment-based annotation of protein coding DNA, allowing for coding frameshifts caused by sequencing error. Finally, we made notable progress in developing AI methods (specifically: a neural embedding model) for identifying similarities between protein sequences based on amino-wise latent vectors. These efforts were supplemented by development of methods for protein modeling in support of predicting protein-drug binding activity, and by my leadership of a team in the NIH/DOE 2021 Petabyte-Scale Sequence Search hack-a-thon.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

Electrochemical model of polyaniline-based memristor with mass transfer step

The electrochemical organic memristor with polyaniline active layer is a stand-alone device designed and realized for reproduction of some synapse properties in the innovative electronic circuits, such as the new field-programmable gate arrays or the neuromorphic networks capable for learning. In this work a new theoretical model of the polyaniline memristor is presented. The developed model of organic memristor functioning was based on the detailed consideration of possible electrochemical processes occuring in the active zone of this device including the mass transfer step of ionic reactants. Results of the calculation have demonstrated not only the qualitative explanation of the characteristics observed in the experiment, but also quantitative similarities of the resultant current values. This model can establish a basis for the design and prediction of properties of more complicated circuits and systems (including stochastic ones) based on the organic memristive devices.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Investigation of Cycling Coal-Fired Power Plants Using High-Fidelity Models

The project delivers a well-integrated and validated simulation platform for cycling operation analysis in coal-fired power plant. Two critical mechanical components of the boiler island were analyzed through mechanical integrity assessment and economic benefit analysis. The current phase of the project focuses on the development of the integrated simulation infrastructure and prove its feasibility and effectiveness using two typical use cases. This integrated platform can help save a lot of engineering efforts for model development and simulation analysis. Through the real simulation scenarios in this document, it was demonstrated that using this platform, an analysis can be completed in approximately 2 days, while it could cost several weeks before using this platform. Going forward, the platform built in this project can be used for more boiler service applications, and it can be further enhanced with more functions/features to maximize its usage and benefits. 1) Extend component-level analysis with more use cases to cover all the major critical components of boiler island under cycling operations. A library of critical components can be developed and validated for typical pulverized coal-fired subcritical boiler units. 2) Develop predictive maintenance features based on the integrated models (Digital Twins) and engineering analysis procedures. Predictive maintenance enables each asset to be serviced based on forecast on life consumption and cost profile for replacing/welding the critical mechanical parts of the boiler. This minimizes the chance of unscheduled shutdowns and emergency services at much higher costs and prevent the fatal accidents in unit operations. 3) Develop and maintain a standard library for critical component analysis under flexible plant operations, which will include libraries of: process models, MI models for typical pressure parts, and economic models with typical plant operating data and ISO power trade data.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2023

High fidelity model of directed energy deposition: Laser-powder-melt pool interaction and effect of laser beam profile on solidification microstructure

Metal additive manufacturing technologies keep receiving a great deal of interest as well as strong requests to develop methods to link the process science to printed parts performance and understand how to overcome inherent limitations. Here, a high-fidelity model based on the multiphysics ALE3D code was developed to reproduce the directed energy deposition process down to the powder scale. This includes resolving the laser-powder-melt pool interactions (powder impingement and incorporation into melt pool, hydrodynamics flow condition and laser absorption inefficiencies) as well as the resulting solidification microstructure. This micrometer scale digital twin captured the effect of powder incorporation process and powder flow rate on porosity. Furthermore, it was used to explore how a ring laser beam profile instead of the standard Gaussian laser profile could decrease the thermal gradient along the solidification front in the melt pool, which in turn can increase propensity for more desirable equiaxed grains.

36 MATERIALS SCIENCE↗

DOE OSTI2014

SU-D-16A-07: Photobleaching Predicts Necrosis in Interstitial PDT

Purpose: Dosimetry for PDT has proven to be a challenge thus far, and for prediction of PDT outcome, a singlet oxygen model based on fundamental photophysical parameters has been developed. Previously, the photobleaching effect of photosensitizers was taken into account in the singlet oxygen explicit dosimetry model; here we report of direct measurements of photobleaching in the same model to assess the conditions under which implicit dosimetry using photobleaching can serve as an intermediate surrogate for PDT damage. Methods: Fluorescence spectra were measured interstitially in sensitized mouse tumors prior to after irradiation via a cylindrical diffuser. Photobleaching was determined by the relative decrease in fluorescence amplitude from the initial pre-treatment measurement. Spectra were analyzed by singular value decomposition to determine the photosensitizer concentration. Different photosensitizers were used to see the effect of photobleaching on PDT outcome and the impact of fluence on photobleaching. The drugs used were BPD (at two drug-light intervals), HPPH, and Photofrin. PDT outcome was determined by tumor necrosis radii measured upon sectioning and staining of treated tumors. Results: Post-PDT photosentizer concentrations were compared to initial pre-PDT photosensitizer concentrations, and the decrease was greater with a higher fluence measured during treatment. Furthermore, photobleaching and necrosis radius were found to be positively correlated. The relationship between photobleaching and necrosis radius is sensitizer-dependent, however the differences among sensitizers can be understood in terms of their respective photophysical parameters. Conclusions: Photobleaching is predictive of PDT outcome, but a comprehensive singlet oxygen model, has the potential to further improve the prediction of PDT outcome and the understanding of implicit dosimetry.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Large-Scale Structure of Chromatin: A Fractal Globule or a Logarithmic Fractal?

Two physical models are considered to describe the large-scale structure of chromatin in the nucleus of a biological cell in the interphase state: a fractal globule model and a logarithmic fractal model. Based on the classification of fractal objects developed by the small-angle neutron scattering (SANS) method, it is shown that the fractal globule model does not satisfy the experimental data on small-angle neutron scattering by the nuclei of biological cells. Conversely, the logarithmic fractal model well describes the experimental data on SANS and, hence, provides a good approximation to describe the large-scale structure of chromatin. The logarithmic fractal model predicts that the nuclear space is exactly half-filled with chromatin, and the second half consists of interchromatin voids filled with nucleoplasma in which various nuclear processes occur. Thus, two opposing trends are balanced in the structural organization of chromatin: an increase in the surface area of chromatin in the cell nucleus (accessibility to external agents) and a decrease in the volume occupied by chromatin (compactness of the nucleus).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Prototyping and Testing a New Volumetric Curvature Tool for Modeling Reservoir Compartments and Leakage Pathways in the Arbuckle Saline Aquifer: Reducing Uncertainty in CO 2 Storage and Permanence

This DOE-funded project evaluates the utility of seismic volumetric curvature (VC) for predicting stratal and structural architecture diagnostic of paleokarst reservoirs. Of special interest are applications geared toward carbon capture, utilization, and storage (CCUS). VC has been championed for identifying faults (offset <¼ λ) that cannot be imaged by conventional 3-D seismic attributes such as coherence. The objective of this research was to evaluate VC-techniques for reducing uncertainties in reservoir compartmentalization studies and seal risk assessments especially for saline aquifers. A 2000-ft horizontal lateral was purposefully drilled across VC-imaged lineaments—interpreted to record a fractured and a fault-bounded doline—to physically confirm their presence. The 15-mi² study area is located in southeastern Bemis-Shutts Field, which is situated along the crest of the Central Kansas Uplift (CKU) in Ellis County, Kansas. The uppermost Arbuckle (200+ ft) has extensive paleokarst including collapsed paleocaverns and dolines related to exceedingly prolonged pre-Simpson (Sauk–Tippecanoe) and/or pre-Pennsylvanian subaerial exposure. A lateral borehole was successfully drilled across the full extent (~1100 ft) of a VC-inferred paleokarst doline. Triple combo (GR-neutron/density-resistivity), full-wave sonic, and borehole micro-imager logs were successfully run to TD on drill-pipe. Results from the formation evaluation reveal breccias (e.g., crackle, mosaic, chaotic), fractures, faults, vugs (1-6"), and unaffected host strata consistent with the pre-spud interpretation. Well-rounded pebbles were also observed on the image log. VC-inferred lineaments coincide with 20–80-ft wide intervals of high GR values (100+ API), matrix-rich breccias, and faults. To further demonstrate their utility, VC attributes are integrated into a geocellular modeling workflow: 1) to constrain the structural model; 2) to generate facies probability grids, and; 3) to collocate petrophysical models to separate-vug rock fabrics along solution-enlarged fault and fracture systems. Simulation-based studies demonstrate a potential alternative field development model for developing CO 2 storage sites that target carbonate reservoirs overprinted by paleokarst. Simulation results for this complex reservoir indicate that individual fault blocks could function as discrete containers for CO 2 storage thereby reducing the risk of plume migration outside the legally defined extent of the permitted storage site. Vertically extensive, anastomosing, solution-enlarged fault/fracture systems — infilled by clay-rich sediments — would operate as non-to-low permeability vertical "curtains" that restrict CO 2 movement beyond the confines of the CO 2 storage site. Such a location could be developed in a checker-board fashion with CO 2 injection operations occurring in one block and surveillance operations occurring in the adjacent block. Such naturally partitioned reservoirs may be ideal candidates for reducing risks associated with CO 2 plume breakthrough.

02 PETROLEUM↗

DOE OSTI2016

TU-D-207B-01: A Prediction Model for Distinguishing Radiation Necrosis From Tumor Progression After Gamma Knife Radiosurgery Based On Radiomics Features From MR Images

Purpose: To develop and validate a prediction model using radiomics features extracted from MR images to distinguish radiation necrosis from tumor progression for brain metastases treated with Gamma knife radiosurgery. Methods: The images used to develop the model were T1 post-contrast MR scans from 71 patients who had had pathologic confirmation of necrosis or progression; 1 lesion was identified per patient (17 necrosis and 54 progression). Radiomics features were extracted from 2 images at 2 time points per patient, both obtained prior to resection. Each lesion was manually contoured on each image, and 282 radiomics features were calculated for each lesion. The correlation for each radiomics feature between two time points was calculated within each group to identify a subset of features with distinct values between two groups. The delta of this subset of radiomics features, characterizing changes from the earlier time to the later one, was included as a covariate to build a prediction model using support vector machines with a cubic polynomial kernel function. The model was evaluated with a 10-fold cross-validation. Results: Forty radiomics features were selected based on consistent correlation values of approximately 0 for the necrosis group and >0.2 for the progression group. In performing the 10-fold cross-validation, we narrowed this number down to 11 delta radiomics features for the model. This 11-delta-feature model showed an overall prediction accuracy of 83.1%, with a true positive rate of 58.8% in predicting necrosis and 90.7% for predicting tumor progression. The area under the curve for the prediction model was 0.79. Conclusion: These delta radiomics features extracted from MR scans showed potential for distinguishing radiation necrosis from tumor progression. This tool may be a useful, noninvasive means of determining the status of an enlarging lesion after radiosurgery, aiding decision-making regarding surgical resection versus conservative medical management.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Sorption Modeling and Verification for Off-Gas Treatment

This project was successfully executed to provide valuable adsorption data and improve a comprehensive model developed in previous work by the authors. Data obtained were used in an integrated computer program to predict the behavior of adsorption columns. The model is supported by experimental data and has been shown to predict capture of off gas similar to that evolving during the reprocessing of nuclear waste. The computer program structure contains (a) equilibrium models of off-gases with the adsorbate; (b) mass-transfer models to describe off-gas mass transfer to a particle, diffusion through the pores of the particle, and adsorption on the active sites of the particle; and (c) incorporation of these models into fixed bed adsorption modeling, which includes advection through the bed. These models are being connected with the MOOSE (Multiphysics Object-Oriented Simulation Environment) software developed at the Idaho National Laboratory through DGOSPREY (Discontinuous Galerkin Off-gas SeParation and REcoverY) computer codes developed in this project. Experiments for iodine and water adsorption have been conducted on reduced silver mordenite (Ag0Z) for single layered particles. Adsorption apparatuses have been constructed to execute these experiments over a useful range of conditions for temperatures ranging from ambient to 250°C and water dew points ranging from -69 to 19°C. Experimental results were analyzed to determine mass transfer and diffusion of these gases into the particles and to determine which models best describe the single and binary component mass transfer and diffusion processes. The experimental results were also used to demonstrate the capabilities of the comprehensive models developed to predict single-particle adsorption and transients of the adsorption-desorption processes in fixed beds. Models for adsorption and mass transfer have been developed to mathematically describe adsorption kinetics and transport via diffusion and advection processes. These models were built on a numerical framework for solving conservation law problems in one-dimensional geometries such as spheres, cylinders, and lines. Coupled with the framework are specific models for adsorption in commercial adsorbents, such as zeolites and mordenites. Utilizing this modeling approach, the authors were able to accurately describe and predict adsorption kinetic data obtained from experiments at a variety of different temperatures and gas phase concentrations. A demonstration of how these models, and framework, can be used to simulate adsorption in fixed- bed columns is provided. The CO 2 absorption work involved modeling with supportive experimental information. A dynamic model was developed to simulate CO 2 absorption using high alkaline content water solutions. The model is based upon transient mass and energy balances for chemical species commonly present in CO 2 absorption. A computer code was developed to implement CO 2 absorption with a chemical reaction model. Experiments were conducted in a laboratory scale column to determine the model parameters. The influence of geometric parameters and operating variables on CO 2 absorption was studied over a wide range of conditions. Continuing work could employ the model to control column operation and predict the absorption behavior under various input conditions and other prescribed experimental perturbations. The value of the validated models and numerical frameworks developed in this project is that they can be used to predict the sorption behavior of off-gas evolved during the reprocessing of nuclear waste and thus reduce the cost of the experiments. They can also be used to design sorption processes based on concentration limits and flow-rates determined at the plant level.

42 ENGINEERING↗

DOE OSTI2015

Agent-based model forecasts aging of the population of people who inject drugs in metropolitan Chicago and changing prevalence of hepatitis C infections

People who inject drugs (PWID) are at high risk for blood-borne pathogens transmitted during the sharing of contaminated injection equipment, particularly hepatitis C virus (HCV). HCV prevalence is influenced by a complex interplay of drug-use behaviors, social networks, and geography, as well as the availability of interventions, such as needle exchange programs. To adequately address this complexity in HCV epidemic forecasting, we have developed a computational model, the Agent-based Pathogen Kinetics model (APK). APK simulates the PWID population in metropolitan Chicago, including the social interactions that result in HCV infection. We used multiple empirical data sources on Chicago PWID to build a spatial distribution of an in silico PWID population and modeled networks among the PWID by considering the geography of the city and its suburbs. APK was validated against 2012 empirical data (the latest available) and shown to agree with network and epidemiological surveys to within 1%. For the period 2010–2020, APK forecasts a decline in HCV prevalence of 0.8% per year from 44(±2)% to 36(±5)%, although some sub-populations would continue to have relatively high prevalence, including Non-Hispanic Blacks, 48(±5)%. The rate of decline will be lowest in Non-Hispanic Whites and we find, in a reversal of historical trends, that incidence among non-Hispanic Whites would exceed incidence among Non-Hispanic Blacks (0.66 per 100 per years vs 0.17 per 100 person years). APK also forecasts an increase in PWID mean age from 35(±1) to 40(±2) with a corresponding increase from 59(±2)% to 80(±6)% in the proportion of the population >30 years old. Our research highlight the importance of analyzing sub-populations in disease predictions, the utility of computer simulation for analyzing demographic and health trends among PWID and serve as a tool for guiding intervention and prevention strategies in Chicago, and other major cities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Predicting oxidation damage in ultra high-temperature borides: A machine learning approach

Ultra-high temperature (UHT) borides are ceramics materials with melting points above 3000 °C for structural applications in extreme environments. However, at temperatures exceeding 1600 °C and under oxidizing conditions, the material suffers from detrimental degradation. Optimized design and performance of diboride materials under such extreme conditions requires filling the missing composition-microstructure-oxidation gap. This study proposes a computational data-driven framework to connect the processing and microstructure of Ultra-high temperature borides with the oxidation damage. Random Forest Regressor (RFR) model is adopted to forecast the oxide scale thickness developed after oxidation testing based on processing variables and microstructural features. The model trained on a dataset consisting of 107 samples of experimental data extracted from the literature aims to predict oxidation damage. With proper data manipulation and fine model tuning, the predictor could forecast the oxide scale thickness of UHT diborides with a Mean Absolute Error of 37.45 μm and an R-square of 0.83. This model could be used as a high-throughput scheme to design and test new UHT diborides materials computationally. Furthermore, a model with larger composition capabilities could also be developed in the future as more experimental data become available.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Multivariate analysis of gamma spectra to characterize used nuclear fuel

Abstract—The Multi-Isotope Process (MIP) Monitor provides an efficient approach to monitoring the process conditions in used nuclear fuel reprocessing facilities to support process verification and validation. The MIP Monitor applies multivariate analysis to gamma spectroscopy of reprocessing streams in order to detect small changes in the gamma spectrum, which may indicate changes in process conditions. This research extends the MIP Monitor by characterizing a used fuel sample after initial dissolution according to the type of reactor of origin (pressurized or boiling water reactor), initial enrichment, burn up, and cooling time. Simulated gamma spectra were used to develop and test three fuel characterization algorithms. The classification and estimation models employed are based on the partial least squares regression (PLS) algorithm. A PLS discriminate analysis model was developed which perfectly classified reactor type. Locally weighted PLS models were fitted on-the-fly to estimate continuous fuel characteristics. Burn up was predicted within 0.1% root mean squared percent error (RMSPE) and both cooling time and initial enrichment within approximately 2% RMSPE. This automated fuel characterization can be used to independently verify operator declarations of used fuel characteristics and inform the MIP Monitor anomaly detection routines at later stages of the fuel reprocessing stream to improve sensitivity to changes in operational parameters and material diversions.

Multivariate Analysis for Characterization of Used↗

DOE OSTI2015

Net energy ratio for the production of steam pretreated biomass-based pellets

In this study, a process model was developed to determine the net energy ratio (NER) for both regular and steam-pretreated pellet production from ligno-cellulosic biomass. NER is a ratio of the net energy output to the total net energy input from non-renewable energy source into the system. Scenarios were developed to measure the effect of temperature and level of steam pretreatment on the NER of both production processes. The NER for the base case at 6 kg h –1 is 1.29 and 5.0 for steam-pretreated and regular pellet production respectively. However, at the large scale NER would improve. The major factor for NER is energy for steam and drying unit. The sensitivity analysis for the model shows that the optimum temperature for steam pretreatment is 200 °C with 50% pretreatment (Steam pretreating 50% feed stock, while the rest is undergoing regular pelletization). Uncertainty result for steam pretreated and regular pellet is 1.35 ± 0.09 and 4.52 ± 0.34 respectively.

09 BIOMASS FUELS↗

DOE OSTI2018

Design Tool for Estimating Chemical Hydrogen Storage System Characteristics for Light-Duty Fuel Cell Vehicles

The U.S. Department of Energy (DOE) has developed a vehicle framework model to simulate fuel cell-based light-duty vehicle operation for various hydrogen storage systems. This transient model simulates the performance of the storage system, fuel cell, and vehicle for comparison to DOE’s Technical Targets using four drive cycles/profiles. Chemical hydrogen storage models have been developed for the Framework model for both exothermic and endothermic materials. Despite the utility of such models, they require that material researchers input system design specifications that cannot be easily estimated. To address this challenge, a design tool has been developed that allows researchers to directly enter kinetic and thermodynamic chemical hydrogen storage material properties into a simple sizing module that then estimates the systems parameters required to run the storage system model. Additionally, this design tool can be used as a standalone executable file to estimate the storage system mass and volume outside of the framework model and compare it to the DOE Technical Targets. These models will be explained and exercised with existing hydrogen storage materials.

hydrogen storage materials, Fuel Cell Vehicles, de↗

DOE OSTI2020

Coupling of CTF and TRANSFORM using the Functional Mockup Interface

An in-memory coupling between the sub-channel thermal hydraulics code COBRA-TF (CTF), which is included in the Virtual Environment for Reactor Applications (VERA), and the systems code Transient Simulation Framework of Reconfigurable Models (TRANSFORM) was developed in this work. Data exchange is accomplished by using the Functional Mock-Up Interface (FMI), an open standard for coupling dynamic systems models together. The FMI-based coupling necessitated the development of a novel FORTRAN wrapper for communicating with Functional Mock-Up Units. This wrapper will facilitate future FMI-based code couplings with other FORTRAN-based programs. CTF-TRANSFORM is exercised on a simplified Molten Salt Reactor Experiment (MSRE) model in steady-state and transient configurations. The coupled CTF-TRANSFORM model is shown to predict core temperature deltas similar to those available in historical MSRE operational data and is shown to be robust to fast power transients.The coupling paves the way to performing sensitivity and design studies impossible with VERA/CTF alone, in which secondary and tertiary loop operating conditions and design parameters may be perturbed and their impact on the core operating conditions studied.

97 MATHEMATICS AND COMPUTING↗