Uncertainty Analysis Research Articles

A comprehensive investigation of uncertainties in cavitating waterjet experiments

This paper systematically examines the sources of uncertainty in cavitating waterjet test setups and provides illustrative case studies to conduct thorough uncertainty analyses for erosion tests using cavitating waterjet method. Within this study, utilizing examples of uncertainty analysis presented by the International Towing Tank Conference (ITTC) for various testing techniques, a novel uncertainty analysis implementation suitable for cavitating waterjet tests has been conducted. Firstly, cavitation erosion experiments were carried out on samples aluminum (Al 6063) material under cavitation number conditions (σ = 0.02 to σ = 0.033 range) as part of the validation study. Subsequently, within the scope of uncertainty analysis calculations, five erosion tests were conducted for each of the two different cavitation numbers (σ = 0.03 and σ = 0.05). The study aims to examine the bias and precision uncertainty components separately for the test results of erosion rate and establish a reliable and repeatable procedure for predicting the uncertainty level of cavitating waterjet tests. For the first time in the literature, the uncertainty components of cavitating waterjet tests were investigated in detail and the uncertainty level in cavitating waterjet tests, including bias and precision errors, was predicted in accordance with specific ITTC guidelines and procedures. Based on the results, the primary bias error source was identified as the pressure inside the cavitation chamber, with the nozzle pressure being the second most significant bias error source. Consequently, it is recommended to perform the bias error prediction based on the ratio of these two, known as the cavitation number. Precision error, crucial for uncertainty, was estimated using five sets of erosion rate values, emphasizing its importance in cavitating waterjet tests. It is anticipated that the suggested methodology will serve as a benchmark for predicting uncertainty levels in cavitating waterjet tests.

Position-specific carbon stable isotope analysis of glyphosate: isotope fingerprinting of molecules within a mixture.

Glyphosate [N-(phosphonomethyl) glycine] is a widely used herbicide and a molecule of interest in the environmental sciences, due to its global use in agriculture and its potential impact on ecosystems. This study presents the first position-specific carbon isotope (13C/12C) analyses of glyphosates from multiple sources. In contrast to traditional isotope ratio mass spectrometry (IRMS), position-specific analysis provides 13C/12C ratios at individual carbon atom positions within a molecule, rather than an average carbon isotope ratio across a mixture or a specific compound. In this work, glyphosate in commercial herbicides was analyzed with only minimal purification, using a nuclear magnetic resonance (NMR) spectroscopy method that detects 1H nuclei with bonds to either 13C or 12C, and isolates the signals of interest from other signals in the mixture. Results demonstrate that glyphosate from different sources can have significantly different intramolecular 13C/12C distributions, which were found to be spread over a wide range, with δ13C Vienna Peedee Belemnite (VPDB) values of -28.7 to -57.9‰. In each glyphosate, the carbon with a bond to the phosphorus atom was found to be depleted in 13C compared to the carbon at the C2 position, by 4 to 10‰. Aminomethylphosphonic acid (AMPA) was analyzed for method validation; AMPA contains only a single carbon position, so the 13C/12C results provided by the NMR method could be directly compared with traditional isotope ratio mass spectrometry. The glyphosate mixtures were also analyzed by IRMS to obtain their average 13C/12C ratios, for comparison with our position-specific results. This comparison revealed that the IRMS results significantly disguise the intramolecular isotope distribution. Finally, we introduce a 31P NMR method that can provide a position-specific 13C/12C ratio for carbon positions with a C-P chemical bond, and the results obtained by 1H and 31P for C3 carbon agree with one another within their analytical uncertainty. These analytical tools for position-specific carbon isotope analysis permit the isotopic fingerprinting of target molecules within a mixture, with potential applications in a range of fields, including the environmental sciences and chemical forensics.

A comprehensive analysis of uncertainties in warm rain parameterizations in climate models based on in situ measurements

Abstract Because of the coarse grid size of Earth system models (ESM), representing warm-rain processes in ESMs is a challenging task involving multiple sources of uncertainty. Previous studies evaluated warm-rain parameterizations mainly according to their performance in emulating collision-coalescence rates for local droplet populations over a short period of a few seconds. The representativeness of these local process rates comes into question when applied in ESMs for grid sizes on the order of 100 kilometers and time steps on the order of 20-30 minutes. We evaluate several widely used warm-rain parameterizations in ESM application scenarios. In the comparison of local and instantaneous autoconversion rates, the two parameterization schemes based on numerical fitting to stochastic collection equation (SCE) results perform best. However, because of Jessen’s inequality, their performance deteriorates when grid-mean, instead of locally-resolved, cloud properties are used in their simulations. In contrast, the effect of Jessen’s inequality partly cancels the overestimation problem of two semi-analytical schemes, leading to an improvement in the ESM-like comparison. In the assessment of uncertainty due to the large time step of ESMs, it is found that the rain-water tendency simulated by the SCE is roughly linear for time steps smaller than 10 minutes, but the nonlinearity effect becomes significant for larger time steps, leading to errors up to a factor of 4 for a time step of 20 minutes. After considering all uncertainties, the grid-mean and time-averaged rain-water tendency based on the parameterization schemes are mostly within a factor of 4 of the local benchmark results simulated by SCE.

Unravelling uncertainty in trajectory prediction using a non-parametric approach

Predicting the trajectories of road agents is fundamental for self-driving cars. Trajectory prediction contains many sources of uncertainty in data and modelling. A thorough understanding of this uncertainty is crucial in a safety-critical task like auto-piloting a vehicle. In practice, it is necessary to distinguish between the uncertainty caused by partial observability of all factors that may affect a driver’s near-future decisions, the so-called aleatoric uncertainty, and the uncertainty of deploying a model in new scenarios that are possibly not present in the training set, the so-called epistemic uncertainty. They reflect the trade-off between data collection and model improvement In this paper, we propose a new framework to systematically quantify both sources of uncertainty. Specifically, to approximate the spatial distribution of an agent’s future position, we propose a 2D histogram-based deep learning model combined with deep ensemble techniques for measuring aleatoric and epistemic uncertainty by entropy-based quantities. The proposed Uncertainty Quantification Network (UQnet) employs a causal part to enhance its generalizability so rare driving behaviours can be effectively identified. Experiments on the INTERACTION dataset show that UQnet is able to give more robust predictions in generalizability tests compared to the correlation-based models. Further analysis presents that high aleatoric uncertainty cases are mainly caused by heterogeneous driving behaviours and unknown intended directions. Based on this aleatoric uncertainty component, we estimate the lower bounds of mean-square-error and final-displacement-error as indicators for the predictability of trajectories. Furthermore, the analysis of epistemic uncertainty illustrates that domain knowledge of speed-dependent driving behaviour is essential for adapting a model from low-speed to high-speed situations. Our paper contributes to motion forecasting with a new framework, that recasts the problem of accuracy improvement in a way that focuses on differentiating between unpredictable components and rare cases for which more and different data should be collected.

A new approach to predict pump transient phenomena in Molten salt reactor Experiment (MSRE) by missing data identification and regeneration

Molten Salt Reactor Experiment (MSRE) is being extensively used for validating the computational tools for Molten Salt Reactors (MSRs). The pump transient tests were performed during the operation of MSRE to obtain the reactivity response to flow perturbation. The transient flow rate during this set of tests is required as input to predict the reactivity response. Since the primary loop of MSRE was not equipped with a flow rate meter, the transient flow rate, which is the crucial factor for the reactivity change in MSRE, is missing. Achieving an accurate simulation of the reactivity response to the MSRE pump transient tests necessitates a precise estimation of the transient flow rate.This paper endeavors to reconstruct the missing flow rate with the aim of offering a valuable input for simulating reactivity responses in the pump transient test series. In order to obtain an accurate transient flow rate, we employed a centrifugal pump transient model based on the affinity laws and solved the model for the MSRE secondary pump to validate the underlying assumptions of the model. In addition, we constructed the homologous pump head curves based on the water test data for the same purpose. The affinity law approximation is proved to be adequate in predicting the MSRE pump startup transient with the root-mean-square error (RMSE) in the normalized flow rate to be 0.03. On the other hand, for the MSRE pump coastdown transient, the preliminary results indicate that neither the affinity law approximation nor the homologous head curve is sufficient to provide acceptable predictions. To overcome this noticeable modeling deficit, we propose an innovative approach based on a straightforward data mining technique to regenerate the MSRE pump coastdown homologous relations using the measured data of the secondary pump. These relations are transferred to the primary pump and used to simultaneously solve for the impeller speed and the flow rate of the pump in the primary pump coastdown. With the new approach, the estimated RMSE in the normalized pump speed for the primary pump coastdown is reduced 0.0052. This excellent agreement validates the accurate calculations for the flow rate during the pump coastdown transient. We also performed an uncertainty analysis to quantify the confidence interval of the predicted quantities, which further justifies the robustness of the proposed approach.

Uncertainty analysis for design of a graphene resonant gyroscope

Uncertainty analysis for design of a graphene resonant gyroscope

SpyderZ: An Efficient Support Vector Machine Library for Photometric Redshift Estimation and Redshift Probability Information

We present SpyderZ, a Python-based library for photometric redshift estimation using support vector machines (implemented with scikit-learn). Our approach discretizes redshift values into uniformly-sized bins and uses one-vs-one support vector classifiers with voting strategies to produce effective probability density functions (ePDFs) over redshift for each galaxy. These ePDFs, which are not constrained to be Gaussian or any other shape, allow for our model's predictions to be used quantitatively with uncertainty analysis methods, and have been shown to enable reliable catastrophic outlier detection. Adapted from the previous IDL package SpiderZ, SpyderZ offers training and evaluation speed optimizations on the order of 102, along with support for parallelization across CPU cores. Our library also offers in-built data sanity checks, result visualizations, metric calculations, cross validation, batch evaluations, and parallelized hyperparameter search (grid search and random search).

Report on Landsat 8 and Sentinel-2B observations of the Nord Stream 2 pipeline methane leak

Abstract. In late September 2022, explosions of the Nord Stream pipelines caused what could be the largest anthropogenic methane leak ever recorded. We report on Landsat 8 (L8) and Sentinel-2B (S-2B) observations of the sea-foam patch produced by the Nord Stream 2 (NS2) leak located close to Bornholm island, acquired on 29 and 30 September, respectively. Usually, reflected sunlight over sea is insufficient for these Earth imagers to observe any methane signal in nadir-viewing geometry. However, the NS2 foam patch observed here is bright enough to possibly allow the detection of methane above it. We apply the multi-band single-pass (MBSP) method to infer methane enhancement above the NS2 foam patch and then use the integrated mass enhancement (IME) method in a Monte Carlo ensemble approach to estimate methane leak rates and their uncertainties. This very specific NS2 observation case challenges some of MBSP and IME implicit assumptions and thus calls for customized calibrations: (1) for MBSP, we perform an empirical calibration of sea-foam albedo spectral dependence by using sea-foam observations in ship trails, and (2) for IME, we yield a tailored effective wind speed calibration that accounts for a partial plume observation, as methane enhancement may only be seen above the NS2 sea-foam patch. Our comprehensive uncertainty analysis yields large methane leak rate uncertainty ranges that include zero for single overpasses and, assuming they are independent, a best estimate of 502 ± 464 t h−1 for the combined averaged L8 and S-2B emission rate. Within all our Monte Carlo ensembles, positive methane leak rates have higher probabilities (80 %–88 %) than negative ones (12 %–20 %), thus indicating that L8 and S-2B likely captured a methane-related signal. Overall, we see our work both as a nuanced analysis of L8 and S-2B contributions to quantifying the NS2 leak emissions and as a methodological cautionary tale that builds insight into MBSP and IME sensitivities.

Uncertainty Quantification of Engineering Parameters for a Nuclear Reactor Loaded with Dispersed Fuel Particles

Owing to their high intrinsic safety, dispersed fuel particles are an important fuel pattern for fourth-generation nuclear reactors. Due to the unique cladding layers and the random dispersion characteristics, dispersed fuel particles significantly differ from pressurized water reactors regarding operation-induced uncertainty. This study quantitatively analyzed overall uncertainty while considering a random distribution of dispersed fuel particles, material thickness, and fuel enrichment. The results demonstrated that, for all packing fractions, the uncertainty induced by the random dispersion of dispersed fuel particles was below 0.03%. For every packing fraction, the differences between the results obtained by the regular and the random distribution models increased, and then decreased, until reaching its maximum (1.297%) at 15%. Keff decreased as the radius of the UO2 kernel increased; Keff increased as the thickness of the cladding layer increased; the uncertainty of Keff was 1.003% when a random distribution of particles, material thickness, and fuel enrichment were taken into consideration; the uncertainty of the power distribution of reactor core assemblies was maximized (1.495%) at the edge of the reactor core. Quantitative analysis of uncertainty provides references for the optimization of design and safety margin analysis for reactors.

Analysis of Uncertainties and Associated Convective Processes in Simulations of Extreme Precipitation Over Cities With a Regional Earth System Model: A Case Study

AbstractThis study utilizes the Weather Research and Forecasting model coupled with an atmospheric chemistry model, a multi‐layer urban canopy model (UCM), and a building energy model to simulate the extreme rainfall event influencing the Guangzhou city in South China on 7 May 2017. By employing small variations in the longwave emissivity of buildings within the UCM, 11 convective‐permitting experiments are conducted. The maximal 18‐hr and hourly rainfall accumulation vary from a 2‐year return period to as high as a 20 or 40‐year return period with notable spatial differences. Comparisons between the more accurate group (GOOD) and less accurate group (POOR) simulations highlight that some minor differences in the near‐surface air thermodynamic conditions in urban area could lead to substantial differences in local convection and its impacts on subsequent convective systems. With persistent transportation of warm, moist airflows from the northern South China Sea, formation of a slow‐moving mesoscale outflow boundary to the north of the urban agglomeration leads to the development of a quasi‐stationary, compactly structured meso‐γ‐scale rainstorm in the GOOD simulations. The stronger low‐level to near‐surface convergence and mid‐level cyclonic shear within this system substantially enhance low‐level updrafts, leading to increased microphysical production and stronger horizontal advection of rainwater within the system. These findings offer some process‐based understanding about the uncertainties in simulating urban extreme rainfall and underscore the need to develop ensemble forecasting methods for convection‐permitting numerical models that incorporate increasingly complicated representations of anthropogenic influences.

Syngas composition analysis for waste to methanol production: Techno-economic assessment using machine learning and Aspen plus

The yield of methanol is heavily influenced by the composition of the syngas which varies from waste type and gasification conditions. This study uniquely explores machine learning (ML) and artificial neural network (ANN) algorithms with shapley additive values (SHAP) analysis to predict syngas compositions and interpret the complexities of waste gasification. Aspen Plus is then utilized to investigate the impact of syngas compositions on the economics of methanol production. Among the different ML models, gradient boosting regression achieved remarkable accuracy in predicting carbon monoxide and carbon dioxide, while ANN excelled in predicting nitrogen and hydrogen based on the coefficient of determination (>0.95). The feature importance analysis demonstrated the gasification agent type as the most important feature. Steam gasification of waste sawdust, straw, and wood chips showed the highest methanol yield. Economic analysis indicated the lowest levelized cost of methanol (LCMeOH) to be 1195.79 USD/MT which further decreased to 933.97 USD/MT upon hydrogen addition. Uncertainty analysis using Monte Carlo method showed the possible minimum LCMeOH to be 254.05 USD/MT. This study provides a comprehensive picture of the economic viability of waste-to-methanol production and provides an important reference for stakeholders on a global scale to quickly assess the feasibility of waste-to-energy projects.

Mathematical analysis of transmission dynamics of Acute Bee Paralysis Virus within a hive

In this paper, a mechanistic model of Acute Bee Paralysis Virus (ABPV) transmission is designed and analyzed. The qualitative behavior of honeybee-pathogen and honeybee-pathogen-mite interactions sub-models revealed the existence of backward bifurcation. It is shown that whenever the survivability of a newly hatched egg is guaranteed, both the disease-free and endemic equilibria of the sub-models are globally asymptotically stable. Sensitivity and uncertainty analyses have shown that egg-laying, viral ingestion and deposition rates are the most influential parameters that drive ABPV infection. Suggesting that intervention strategies that focus on how to manage these rates are required for effective control. The combination of disinfection and fumigation of hives, using optimal control analysis, is shown to be the most effective strategy for controlling the spread of ABPV infection. In addition, disinfection alone is found to be the most cost-effective strategy. Finally, an extended model that incorporates mite migration and parasitism is formulated and simulated. The results have shown that mite migration weakens the colony and spacing hives to the farthest distance a forager can reach strengthen the colony. It is also shown that parasitism contributes towards colony collapse, however, fumigation strategy reduces honeybee mortality due to parasitism.

Physics‐reliable frugal local uncertainty analysis for full waveform inversion

AbstractFull waveform inversion stands at the forefront of seismic imaging technologies, pivotal in retrieving high‐resolution subsurface velocity models. Its application is especially profound when imaging complex geologies such as salt bodies, which are regions notoriously challenging, yet essential given their hydrocarbon potential. However, with the power of full waveform inversion comes the intrinsic challenge of estimating the associated uncertainties. Such uncertainties are crucial in understanding the reliability of subsurface models, particularly in terrains like subsalt regions. Addressing this, we advocate for a nuanced approach employing the Stein variational gradient descent algorithm. Through a judicious use of a limited number of velocity model particles and the integration of random field‐based perturbations, our methodology provides a local representation of the uncertainties inherent in full waveform inversion. Our evaluations, based on the Marmousi model, showcase the robustness of the proposed technique. Yet, it is our exploration into salt‐intensive terrains, leveraging data from the Sigsbee 2A synthetic model and the Gulf of Mexico, that emphasizes the method's versatility. Findings indicate pronounced uncertainties along salt boundaries and in the deeper subsalt sediments, contrasting the minimal uncertainties in non‐salt terrains. However, anomalies like salt canyons present unique challenges, potentially due to the interplay of multi‐scattering effects. Emphasizing the scalability and cost‐effectiveness of this approach, we highlight its potential for large‐scale industrial applications in full waveform inversion, while also underscoring the necessity for prudence when integrating these uncertainty insights into subsequent seismic‐driven geological and reservoir modelling.

Uncertainty analysis of SPI and SRI calculation using bootstrap in the Mediterranean regions of Algeria

Uncertainty analysis of SPI and SRI calculation using bootstrap in the Mediterranean regions of Algeria

Random Responses of Shield Tunnel to New Tunnel Undercrossing Considering Spatial Variability of Soil Elastic Modulus

This paper investigates the effect of spatial variability of soil elastic modulus on the longitudinal responses of the existing shield tunnel to the new tunnel undercrossing using a random two-stage analysis method (RTSAM). The Timoshenko–Winkler-based deterministic method considering longitudinal variation in the subgrade reaction coefficient and the random field of the soil elastic modulus discretized by the Karhunen–Loeve expansion method are combined to establish the RTSAM. Then, the proposed RTSAM is applied to carry out a random analysis based on an actual engineering case. Results show that the increases in the scale of fluctuation and the coefficient of variation of the soil elastic modulus lead to higher variabilities of tunnel responses. A decreasing pillar depth and mean value of the soil elastic modulus and an increasing skew angle strengthen the effect of the spatial variability of the soil elastic modulus on tunnel responses. The variabilities of tunnel responses under the random field of the soil elastic modulus are overestimated by the Euler–Bernoulli beam model. The results of this study provide references for the uncertainty analysis of the new tunneling-induced responses of the existing tunnel under the random field of soil properties.

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3-N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3-N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 107 CNY, and the treatment cost was 2.04 108 CNY to make NH3-N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19488.51 t/a and 8.24 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.

Optimal energy storage system selection for future cost-effective green hydrogen production: Integrating techno-economic, risk-based decision making, and cost projections

This study conducts technical, economic, and safety analysis of a green hydrogen production system consisting of a 1000 kWp photovoltaic cell, 3 options of energy storage namely lead carbon (PbC), lithium-ion (Li-ion), and repurposed lithium-ion (2nd Life Li-ion) battery, and an electrolyzer. Firstly, the system is optimized to maximum hydrogen production by adjusting equipment capacity and capacity factors, informed by a 1-h resolution solar energy generation profile. This optimization yields insights into the system's yearly operational dynamics. Techno-economic analysis was conducted to estimate the levelized cost of hydrogen (LCOH) from 2020 to 2050, additionally, uncertainty analysis using Monte-Carlo simulation to estimate the range of uncertainty in LCOH projection from 2020 to 2050 was performed. Furthermore, a quantitative risk analysis, utilizing the process route index metric, evaluates the risk of battery explosions. Finally, this study employs multi-criteria decision-making to choose the best energy storage technology to produce green hydrogen from economic and safety factors. The result indicates that in 2020, the PbC battery emerged as the most cost-effective option. However, over time, both lithium batteries became the cheapest option with almost similar trends. In terms of explosion hazard, the analysis shows that the PbC battery is a safer option for stationary energy storage. When both aspects are considered, the preferred battery option is almost certainly PbC over the years, but in extreme cases where the decision-maker places significant emphasis on economic factors both Li-ion options may become preferable.

A streamlined approach to uncertainty and sensitivity analysis for models with spatial outputs with an example from geodiversity assessment

The paper proposes a joint use of two global sensitivity analysis methods to evaluate the influence of model inputs on spatially explicit outputs. The analysis is conducted on spatial outputs of geodiversity assessment. First, the screening method is used to identify inputs with low importance and interactions, which can be discarded in later analysis. A simplified model is compared with a complete model using the mean and variance calculated on spatial outputs to evaluate outcome uncertainty. Finally, the variance decomposition method computes two sensitivity index maps per input. The results are mapped using bivariate classification, allowing for the simultaneous identification of regions with higher geodiversity scores and their corresponding variability. The research reveals that the streamlined approach preserves the overall patterns of outcome uncertainty and sensitivity observed in an original model while simultaneously reducing the number of outcome maps to interpret. Additionally, bivariate mapping proves efficient in concurrently depicting spatial sensitivity and interaction effects. The analytic efficiencies afforded by the presented approach and bivariate mapping enhance the operational knowledge of spatially explicit uncertainty and sensitivity analysis by simplifying the computational procedure and offering an alternative way of visualizing and interpreting input factors driving the sensitivity of model output.

Accurate machine-specific reference and small-field dosimetry for a self-shielded neuro-radiosurgical system.

The newly available ZAP-X stereotactic radiosurgical system is designed for the treatment of intracranial lesions, with several unique features that include a self-shielding, gyroscopic gantry, wheel collimation, non-orthogonal kV imaging, short source-axis distance, and low-energy megavoltage beam. Systematic characterization of its radiation as well as other properties is imperative to ensure its safe and effective clinical application. To accurately determine the radiation output of the ZAP-X with a special focus on the smaller diameter cones and an aim to provide useful recommendations on quantification of small field dosimetry. Six different types of detectors were used to measure relative output factors at field sizes ranging from 4to 25mm, including the PTW microSilicon and microdiamond diodes, Exradin W2 plastic scintillator, Exradin A16 and A1SL ionization chambers, and the alanine dosimeter. The 25mm cone served as the reference field size. Absolute dose was determined with both TG-51-based dosimetry using a calibrated PTW Semiflex ion chamber and measurements using alanine dosimeters. The average radiation output factors (maximum deviation from the average) measured with the microDiamond, microSilicon, and W2 detectors were: for the 4mm cone, 0.741 (1.0%); for the 5mm cone: 0.817 (1.0%); for the 7.5mm cone: 0.908 (1.0%); for the 10mm cone: 0.946 (0.4%); for the 12.5mm cone: 0.964 (0.2%); for the 15mm cone: 0.976 (0.1%); for the 20mm cone: 0.990 (0.1%). For field sizes larger than 10mm, the A1SL and A16 micro-chambers also yielded consistent output factors within 1.5% of those obtained using the microSilicon, microdiamond, and W2 detectors. The absolute dose measurement obtained with alanine was within 1.2%, consistent with combined uncertainties, compared to the PTW Semiflex chamber for the 25mm reference cone. For field sizes less than 10mm, the microSilicon diode, microDiamond detector, and W2 scintillator are suitable devices for accurate small field dosimetry of the ZAP-X system. For larger fields, the A1SL and A16 micro-chambers can also be used. Furthermore, alanine dosimetry can be an accurate verification of reference and absolute dose typically measured with ion chambers. Use of multiple suitable detectors and uncertainty analyses were recommended for reliable determination of small field radiation outputs.

Analysis of measurement uncertainty for verification/calibration of particulate matter measuring instruments using the gravimetric method

As demand for particulate matter (PM) monitoring increases for scientific and regulatory purposes, the need for an accurate calibration process for these instruments will also increase. This article focuses on studying the factors that cause measurement uncertainty during the calibration process with the calibration facility (ManDust) for reference gravimetric instruments. Measurement uncertainty in this article included type A and type B uncertainties. Type A uncertainty mostly included repeatability and reproducibility while volume uncertainty predominately contributed to type B uncertainty. To clearly examine how these factors contributed to the combined uncertainty, the calibration process was carried out at a high concentration range from 0.4 to 1.6 mg m−3. In general, the measurement uncertainty ranged from 1.95% to 6.65%. Further analysis showed that type A uncertainty contributed more to the total uncertainty than type B. In particular, uncertainty from volume calculation and measurement contributed over 80% of type B uncertainty. During the PM filter weighing process, the uncertainty during the conditioning process contributed the most to type B uncertainty. This article also conducted a sensitivity analysis and found that besides volume measurement, the filter weighing procedure was also sensitive to changes even though it only contributed less than 5% of type B uncertainty. Using high-quality devices and careful handling during these two procedures should be emphasized to reduce the uncertainty of the calibration process.