Error Model Research Articles

Control method for the ship track and speed in curved channels

Due to natural and external influences in curved channels, ships frequently require adjustments in course and speed, posing challenges for existing control methods. Particularly lacking are speed control methods suitable for curved channel navigation. This study initially developed a three-degree-of-freedom MMG model incorporating external interference. It introduced an OP-PID heading controller merging optimal control strategies with traditional PID, adaptable to both external conditions and ship speed, validated through heading control simulations. The study analysed the ship's speed change process, deriving a mathematical expression for advance distance, and proposed a dichotomy-based speed control method to determine speed change points, addressing differential equations with unknown integrands. To mitigate uncertainty errors like parameter inaccuracies in ship maneuvering models and dynamic environmental disturbances, the study proposed a comprehensive control approach. This approach integrates model predictive control, feedback compensation, segment identification, and an enhanced line-of-sight (LOS) guidance method alongside the OP-PID course controller and dichotomy-based speed control. Simulation experiments in the Dongboliao Channel compared the proposed and existing methods. Results demonstrate the proposed method's capability to handle frequent course and speed adjustments effectively, even under model errors and external interference, showcasing superior track deviation and course control accuracy over existing methods.

Ensemble-based sensitivity analysis of track forecasts of typhoon In-fa (2021) without and with model errors in the ECMWF, NCEP, and CMA ensemble prediction systems

Ensemble-based sensitivity analysis (ESA) has been widely applied to identifying and investigating the sources of forecast uncertainty in tropical cyclone (TC) track. The standard ESA used in most preceding studies involves calculating the time-lagged covariance of ensemble perturbations by removing the ensemble mean. This method primarily focuses on the influence of initial errors. However, such studies ignore two critical dimensions of ESA. One is how ensemble sensitivity is influenced by the varying forecast performance across different ensemble prediction systems (EPSs). Secondly, the impact of model errors on forecast uncertainties remains unaddressed. An in-depth examination of these two aspects provides a more comprehensive understanding of the factors contributing to TC track uncertainties.This study employed the standard ESA to analyze and compare the sources of uncertainty in the track forecast of Typhoon In-fa (2021) in three representative operational EPSs. Our findings reveal that the EPS's specific performance in ensemble spread markedly influence ensemble sensitivity. We identified that variations in the shape and location of key synoptic systems, such as the western Pacific subtropical high and monsoon trough, across ensemble members were notably distinct. These variations played a significant role in shaping the uncertainty for In-fa's track forecast within each system. Furthermore, we introduced a modified ESA to better account for the influence of model errors on TC track uncertainties. The modified ESA, when applied to ensembles with substantial systematic deviations, predominantly reflects the impact of model errors on track forecast inaccuracies, offering a notably different perspective compared to the standard ESA. Significance statementEnsemble-based sensitivity analysis (ESA) serves as an effective method for identifying the origins of forecast uncertainty in tropical cyclone (TC) tracks. Most preceding studies based on the standard ESA have predominantly concentrated on examining key physical processes within individual ensemble prediction system (EPS) and the impact of initial condition uncertainties. They often overlook the dependence of the ensemble sensitivity on the varying forecast performance of EPSs, as well as the impact of model errors on sensitivity assessment. This study revealed that the ensemble forecasts across different EPSs exhibit different performance in terms of ensemble spread in the shape and location of primary weather systems. This variability significantly contributes to the uncertainty in TC track forecasts through diverse processes. Furthermore, this research introduced a modified ESA, which effectively identifies the impact of model systematic deviations on TC track forecast errors. This approach along with the standard ESA provide a more comprehensive and nuanced analysis of the forecast error sensitivity associated with both initial condition and model related uncertainties.

Investigating Anode Potential Errors of Real-Time Capable DFN Type Models Induced by Inhomogeneity for Fast Charging of Cylindrical Lithium-Ion Batteries

The occurrence of lithium plating during fast charging poses a safety risk and can reduce the battery lifespan. To prevent plating during the application of model-based charging protocols, a safety margin is added to the lithium plating voltage criterion to compensate for unaccounted in-plane heterogeneities. This article investigates the value of this safety buffer when using real-time capable 1D-DFNs coupled with a 0D thermal model. Through comparison with a multi-scale model, the 1D-DFN error to the local minimum in the anode potential can be characterized. An adjusted cooling coefficient enables 0D temperature modeling with an average error of less than 1 ◦C, despite the inability to consider temperature gradients. For a high-energy NMC811/SiC parameterization of a 4680 format cell with tabless current collectors, the 1D-DFN error in the anode potential deviates by a maximum of 10 mV during charging up to 3C at 50 W m−2 K−1 convective mantle cooling. The anode potential error is influenced by the charging rate, cooling strategy, cell format, and current collector design.

Prediction model of aircraft hinge moment: Compressed sensing based on proper orthogonal decomposition

By hinge moment, we mean the aerodynamic torque exerted on the rudder shaft by the airflow passing through the aircraft control surface, with obtaining high-precision results often relying on wind tunnel tests. Due to the complex aerodynamic balance insulation and installation errors that must be considered in cryogenic wind tunnels, the main method for calculating hinge moments is to directly integrate surface pressure distribution information. However, it is usually difficult to arrange enough pressure taps, resulting in the accuracy failing to meet expectations. Combining the sparse wind tunnel test data and low-precision computational fluid dynamics results, this paper introduces the compressed sensing based on proper orthogonal decomposition (CS-POD) method and presents the sub-Ma model and the full-Ma model for predicting hinge moments. The number of sensors and sensor positions are determined based on the sparsity of the numerical simulations and basis functions. Then, the CS algorithm solves the basis coefficients. Finally, the hinge moments are obtained by integrating the reconstruction pressure distribution which is calculated by linearly combining the basis functions and basis coefficients. The result shows that the full-Ma model exhibits higher prediction accuracy with approximately five sensors under subsonic and transonic cases, reducing the relative error of the sub-Ma model by 2–10 times, even at high angles of attack. The mean reconstruction accuracy for the hinge moments is 97.6%, and for the normal forces, it is 94.3%. Therefore, adding relevant terms when the number of samples is small can effectively improve modeling accuracy.

Effects of agmatine on radial-arm maze memory performance and autistic-like behaviors in a male rat model of autism.

Autism spectrum disorder (ASD) is the fastest-growing child neuropsychiatric condition. Cognitive dysfunctions such as memory impairments are experienced by patients along with social disturbances and repetitive/stereotypic movements. We have used the radial arm maze (RAM), for measurement of working and reference memory errors in an animal model of autism. In addition, the potential effects of agmatine, an endogenous NMDA antagonist, on RAM performance and autistic-like behaviors were assessed. Autism was modeled by valproic acid (VPA) administration at gestational Day 12.5. Autism-associated behaviors in male offspring were examined in an open field test (OFT) and three-chambered test (TCT) on postnatal days 50-51. Thereafter, the animals were trained in the RAM (PND 55) until they attained the criteria of 80% correct choices during five consecutive trials. Forty-eight hours after the acquisition of criteria, agmatine was injected 30 min before subsequent behavioral testing, which included the retention phase of the RAM, OFT, and TCT. VPA-treated and intact rats showed the same performance in RAM, and acute injection of agmatine rescued social and anxiety-like behavior induced by VPA without the effect on RAM. In a rat model of autism, spatial learning, and memory did not change. Agmatine rescued social and anxiety-like behavior in autistic animals.

Translating strain to stress: a single-layer Bi-LSTM approach to predicting stress-strain curves in alloys during hot deformation

This study introduces a novel deep learning network that integrates a single-layer bidirectional long short-term memory (Bi-LSTM) network with a coding layer to analyze the hot deformation behavior of various alloys. The single-layer Bi-LSTM model adeptly predicts experimental stress–strain curves obtained under different deformation temperatures and strain rates, demonstrating superior effectiveness and excellent performance in modeling hot deformation behaviors of the FGH98 nickel-based alloy and TiAl intermetallic alloy. The present model achieves the coefficient of determination of 0.9051 for FGH98 and 0.9307 for TiAl alloys, whereas the corresponding values of 0.8105 and 0.8356 are obtained by the conventional strain-compensated Sellars constitutive equation (SCS model). Additionally, the mean absolute percentage error of the single-layer Bi-LSTM model are 11.37% for FGH98 and 7.16% for TiAl alloys, while the SCS model gains the corresponding error of 15.29% and 17.01%. These results show that the present model has enhances the predictive accuracy exceeding 10% for both FGH98 and TiAl alloys over the SCS model. Consequently, the proposed single-layer Bi-LSTM model provides substantial potential for optimizing manufacturing processes and improving material properties.

Dynamic performance of a polygonal thermoelectric generator using sickle-shaped fins for automotive application

About 40 % of automotive fuel energy is lost in the form of heat through exhaust gas, the utilization of thermoelectric power generation technology presents a direct conversion of thermal to electric energy. A thermoelectric generator (TEG) system was designed in this study, comprising a polygonal heat exchanger using sickle-shaped fins to enhance waste heat recycling. Additionally, a transient Computational Fluid Dynamics (CFD)-analysis model was introduced to investigate its dynamic response utilizing the instantaneous variations of exhaust gas and coolant under the China Light-Duty Vehicle Test Cycle (CLTC) as transient inputs. The TEG system exhibits an average power output of 24.48 W and a conversion efficiency of 1.12 % throughout the CLTC. After heating from ambient temperature to normal operating temperature, the power rises to 34.48 W and efficiency to 1.57 %, showing enhanced output performance. A comparison of the transient and steady-state analysis results indicates that the transient analysis reflects the hysteresis effect of heat transfer more accurately. Through experimental verification, the mean absolute percentage errors of the transient model are calculated to be 5.57 % and 8.28 % respectively, proving the credibility of the simulated results. The proposed dynamic model allows for evaluating the actual behavior of the TEG system under intricate driving cycles and offers a robust reference for TEG system optimization and its automotive applications in future.

High-performance real-time fire detection and forecasting framework for industrial cables

In industrial scenarios, cable fires have always been the most common threat, and traditional fire detection systems often rely on a large number of sensors, and the detection range is very limited, and it is impossible to effectively predict the fire situation in time. In this paper, we propose a detection and prediction scheme for industrial cable fire, which breaks the limitations of the previous research on multi-sensor signal input, and highly couples the detection and prediction modules to realize fire prediction based on video image input only. In fire detection, we design an object detection model using HSV for flame feature enhancement based on YOLOv8, and in fire prediction aspect, we use iTransformer as a time series prediction model to mine the correlation between various parameters to predict the spread of fire. In the experiments, the average absolute percentage error of the flame detection model for the detection of flame height, width and longitudinal position was 3.49%–10.64 %, 2.45%–8.89 % and 1.61%–9.31 %, respectively, and the MAPE of the time series prediction model for the above three parameters was 11.18%–15.06 % and 4.35%–8.18 %, 3.37%–6.62 %.The results of the above experiments verify that the proposed model has the ability to quantitatively analyze the fire spread trend in the actual fire and help firefighters make decisions.

Research and application on in-situ monitoring of strata movement in submarine cold seeps area based on three-dimensional MEMS sensor network

Accurate monitoring of strata movement is crucial for understanding the geomorphological evolution of the cold seeps. However, the complex environments and inherent challenges of deep-sea operations present significant obstacles for existing observation methods. In this paper, we present a strata movement monitoring system based on a three-dimensional micro-electromechanical system (MEMS) sensor network that includes multiple short and long sensor arrays. The movement vector is reconstructed using triaxial-acceleration and cubic Hermite interpolation. In laboratory simulations with vertical and horizontal displacement of 250 mm and 100 mm, respectively, the root mean square error of the reconstruction model is 3.74 mm. Two sensor arrays were deployed and operated for 4 days in cold seeps in South China Sea, and the observed displacement showed micro-tremors, and the displacement ranges in the x- and y- directions were (−0.4 mm, 0.18 mm) and (−0.43 mm, 0.19 mm), respectively. These results affirm the feasibility of the proposed system.

Modeling of Dynamic Control Error and Emergency Frequency Control for Direct-Drive PMSG-Based Wind Turbine Under Grid Fault

Modeling of Dynamic Control Error and Emergency Frequency Control for Direct-Drive PMSG-Based Wind Turbine Under Grid Fault

Recent Patents on Thermal Characteristic Analysis and Modeling of Machine Tools

Abstract: Modern machining machines are becoming more sophisticated and automated, but the problems of thermal deformation caused by machining have also come to the fore. CNC machine tools are used in high-speed, high-feed rate machining conditions, so the impact of thermal deformation factors will lead to a more prominent loss of accuracy. In this context, the design and manufacture of future CNC machine tools need to take further account of the errors caused by thermal deformation and the accompanying loss of accuracy. The principle of thermal characterisation and thermal error modeling of machine tools is to measure and reasonably analyse the machine tool at different temperature points and make corrections to the analysis data in order to get a value as close as possible to the real value and improve machining accuracy. Several representative patents and studies on thermal characterisation and thermal error modeling of machine tools at home and abroad are reviewed. This study presents a selection and summary of a large number of recent patents and studies on machine tool thermal characterisation and thermal error modeling, focusing on the selection of the machine tool thermal error measurement point area, thermal error modeling methods, spindle thermal displacement compensation, and other aspects, and discusses the future trends in the mainstream methods for reducing the thermal error of machine tools. The analysis and modeling of thermal errors in machine tools have an integral influence on further improvements in machining accuracy and efficiency in the future.

Investigating the influence of measurement uncertainty on chlorophyll-a predictions as an indicator of harmful algal blooms in machine learning models

Advancements in data availability, including high frequency, near real-time multiparameter sensors, laboratory analysis, and in-situ and remote observations, have driven the development of machine learning (ML) models for applications such as toxic Harmful Algal Bloom (HABs) monitoring. However, the performance of ML predictions is influenced by both model uncertainties due to inherent model structures and errors associated with input dataset measurements. For example, measurement uncertainty arises from sample collection, sensor drift and laboratory analysis and sample handling errors. While impacts of model uncertainty are commonly addressed using probabilistic approaches, the effect of measurement uncertainty is less studied due to the limited availability of detailed measurement information. This study focuses on assessing the impact of measurement uncertainty on the ML prediction of chlorophyll-a concentration as an index of HABs in a mesotrophic lake. Using randomized subsets of input measured datasets that mimic possible chlorophyll-a concentration distributions, the study built 1000 Random Forest (RF) and Support Vector Regression (SVR) models. An independent measured dataset was used to validate the ensemble models, allowing for model performance evaluation and the creation of prediction intervals to measure the propagated uncertainty. Our findings showed that the model predictions have MAE that ranged between 0.16 μg/l and 5.19 μg/l, and RMSE ranging between 0.20 μg/l and 7.39 μg/l. The highest uncertainty coverage of 0.71 was observed in the RF model without chlorophyll-a sensor values as a predictor. The study found that the training dataset sizes due to the high frequency and manually sampled nature influence how much measurement uncertainty is covered. The results of this study demonstrate how well ML models can capture various HABs patterns when given diverse measurement variables. Our findings will give researchers insightful information on how to lessen the impact of measurement uncertainty when using ML models as decision-support tools for HABs management.

The observation range adjusted method: a novel approach to accounting for observation uncertainty in model evaluation

Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.

Numerical modeling on wave–current flows and bed shear stresses over an algal reef

AbstractCurrents in coastal zones under multiple mechanisms in terms of tides, waves, wind, and high roughness are difficult to model; bed shear stresses under wave–current flows are particularly challenging yet not being well studied. Few studies reported the modeling and validation of the bed shear stress in reef environments. In this paper, we present the first direct assessment of numerical modeling on depth-averaged currents and bed shear stresses over an algal reef using a coupled wave–current model (Delft-3D). The modeled results were validated and compared to the field observed data. The model considers hydrodynamic forcing in terms of tides, waves, wind stresses, and bed friction. Results show that the model generally reproduces the depth-averaged currents and bed shear stresses when considering all the mechanisms. Two numerical cases with and without wind forcing were tested to examine the effects of the winds. We found that the tide is mostly the primary factor driving the current, even in shallow waters within a depth of 3 m; however, the currents are also significantly affected by wind speeds and wind directions during high-wind events. When the wind direction is in the same direction as the tidal current, the current speed increases, suggesting the importance of the wind stress on the coastal currents. In addition, two models were chosen to study the nonlinear enhancement of bed shear stress by waves. We found a significant difference between the two models in predicting the bed shear stresses compared to the observed data. Nonlinear contribution from wave enhances the magnitude of bed shear stresses, which reduces the model error. The results highlight the nonlinear interaction between waves and currents is meaningful in predicting the bed shear stresses during high-wave-orbital motions; improvement of the present wave-current nonlinear interaction model for predicting the bed shear stresses may be needed.

Transmembrane protein 53 craniotubular dysplasia (OMIM # 619727): The skeletal disease and consequent blindness of this new disorder

Craniotubular dysplasia, Ikegawa type (OMIM #619727) denotes the autosomal recessive skeletal disease identified in 2021 featuring blindness acquired in childhood. Five young members of four Indian families harbored a homozygous indel within TMEM53 (OMIM *619722), the gene that encodes transmembrane protein 53 (TMEM53). When intact, TMEM53 spans the nuclear envelope of osteoprogenitor cells, dampens BMP-SMAD signaling, and thereby slows bone formation. Consequently, defective TMEM53 accelerates osteogenesis. Herein, an American boy is compound heterozygous for a novel deletion and a novel missense mutation within TMEM53. His vision and sensorineural hearing became impaired. Radiographic survey revealed diploic thickening of his skull, broad calvarial and facial bones, skeletal modeling errors, vertebral body flattening, wide ribs, and osteopenia of expanded bones. DXA areal bone density (gm/cm2) Z-scores were low. His optic, auditory, and spinal canals were narrow. Mineral metabolism was intact. Serum alkaline phosphatase and osteocalcin levels were normal yet CTX was high. Iliac crest histomorphometry documented accelerated bone formation. His acute vision loss briefly improved following prednisone administration, optic canal decompression, and optic nerve sheath fenestration, but then progressed despite further surgeries and zoledronate treatment aimed to suppress bone turnover. Next generation sequencing of genes associated with elevated skeletal mass, including from high bone turnover, did not suggest an etiology. Whole genome sequencing then revealed within TMEM53: i) a paternally transmitted 54-base deletion, which included the mRNA splice acceptor site for exon 2 as well as 31 bases of exonic sequence (c. 62-23_92del), and ii) a maternally transmitted missense variant (c.650C > T, p.Ser217Leu: NM_024587.4/NP_078863.2) which is extremely rare in gnomAD (frequency = 0.000036), replaces Ser217 highly conserved across species, and is scored as damaging by SIFT and Mutation Taster. We call this new osteopathy TMEM53 craniotubular dysplasia.

Twinning quality sensors in wastewater treatment process via optimized echo state network-based soft sensors

The presence of a large amount of quality-related but hard-to-measure variables usually makes effective monitoring of industrial processes difficult, and even impossible. Soft computing techniques and digital twins can revolutionize standard approaches to solve this issue, even though industrial data are frustrated by nonlinearity, disturbances, and dynamic behaviors. In this light, this paper proposes a data-driven soft sensor with the help of the Fourier amplitude sensitivity test (FAST) and improved probabilistic regularized echo state network (IPRESN), then to twin the quality hardware sensors. Within this framework, the initial step is to rely on the FAST method to measure the collaborated importance of multiple process variables with respect to each quality variable. This quantification facilitates the selection of auxiliary variables for the training of IPRESN. Furthermore, a probabilistic regularization method is learned to improve the robust performance of the echo state network (ESN) by re-designing a new objective function to minimize the mean and variance of the modeling error. Additionally, the whale optimization algorithm (WOA) is improved and then used to optimize the critical hyper-parameters of ESN, thus mitigating the risks associated with suboptimal hyperparameter selection. The effectiveness of the proposed method is verified through a Benchmark Simulation Model 2 (BSM2) and a full-scale wastewater treatment plant data set. The findings show the potential of the proposed method in facilitating digital twin implementations for hardware sensors.

Modeling of deposition morphology and characteristic dimensions in material extrusion additive manufacturing

Developing prediction models for deposition strand morphology is critical in Material Extrusion Additive Manufacturing (MEAM) to optimize processes and guide innovative deposition strategies, while advancing our understanding of the flow mechanics during the deposition process.This study aimed to develop an accurate, rapid, and calibration-free model for predicting deposition strand morphology, height, and width. The relationship between the flow field constraints downstream of the nozzle and the deposition morphology was analyzed. By solving the flow front boundary in the region covered by the nozzle, a model for predicting the deposition height and width under constrained conditions was developed. Moreover, a quantitative method for predicting morphological features was offered. The established model is validated through fused deposition modeling (FDM) single-layer single-path experiments, demonstrating superior accuracy. An in-depth analysis of the model errors resulting from the assumptions in the model has been carried out.This work provided deeper insights by modeling the flow front boundary, which helps to understand the distribution of pressure flow and drag flow during the deposition process. The established model has better application potential due to its advantage of not requiring prior calibration of the machine.

Effect of Tripping and Domain Width on Transonic Buffet on Periodic NASA-CRM Airfoils

Transonic buffet is an instability characterized by shock oscillations and separated boundary layers. High-fidelity simulations have typically been limited to narrow domains to be computationally feasible, overly constraining the flow and introducing modeling errors. Depending on the boundary-layer state upstream of the interaction, different buffet features are observed. High-fidelity simulations (implicit large-eddy simulation) were performed on the periodic (infinite) NASA-CRM wing at a moderate Reynolds number to assess the sensitivity of the two-dimensional transonic buffet to boundary-layer state and domain width. Simulations were cross-validated against low-fidelity Reynolds-averaged Navier–Stokes (RANS)/unsteady RANS and global stability analysis, and excellent agreement was found near the onset. By varying the boundary-layer tripping amplitude, laminar, transitional, and turbulent buffet interactions were obtained. All cases consisted of a single shock and low-frequency oscillations (St≈0.07). The transitional interaction also exhibited reduced shock movement, a 15% increase in CL¯, and energy content at higher frequencies (St≈1.3). Spanwise domain studies showed sensitivity at the shock location and near the trailing edge. We conclude that the span width must be greater than the trailing-edge boundary-layer thickness to obtain span-independent solutions. For largely separated cases, the sensitivity to span width increased, and variations across the span were observed. This was found to be associated with a loss of two-dimensionality in the flow.

A data model to forecast the morphological evolution of multiple beach profiles

Beaches are a natural defense against extreme events, such as storms and hurricanes, whose intensity and frequency are expected to increase in the future due to climate change. In this context, models that forecast the morphological evolution of coastal areas can be used to anticipate the effects of future scenarios, allowing early action to mitigate the damage caused by extreme events. Hence, this study included data from three different monitoring programs in data models to simulate the seasonal morphological evolution of several Portuguese beaches. Two different data models were implemented using the Random Forest algorithm. One was fed with profile data and wave conditions while the other considered also sediment size data. Both models achieved suitable performances, but the inclusion of sediment data reduced the model errors and variance, and thus improved model performance. It was demonstrated that combining data from multidisciplinary campaigns can be a solution to generate reliable and robust morphological forecasting models.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.