Spot Rate Research Articles

Russia’s Invasion of Ukraine and Implications for the Ukrainian Hryvnia and the Russian Ruble

This paper examines the interest rates, expected spot rates, and inflation rates of the Ukrainian Hryvnia (₴) to the Russian Ruble (₽). Our methodology analyzes international parity relationships, including Purchasing Power Parity (PPP) and the International Fisher Effect (IFE). Focusing on the four years from two years before Russia’s initial invasion of Ukraine on February 24, 2022, i.e., February 2, 2020 to post-conflict up to December 2023, we hypothesize that the geopolitical tensions induced by the invasion have led to significant fluctuations and high volatility of these currencies. Additionally, we propose that the economic consequences of the invasion, such as disruptions to trade and food and supply shortages, may further affect these variables by increasing domestic and international money financing, therefore triggering higher inflation rates. Our analyses indicate that spot rates predicted using international parity relationships suggest the weakening of the Ukrainian Hryvnia after the Russia-Ukraine conflict. Our findings shed light on the magnitude and direction of currency movements, providing insights into the economic ramifications of the conflict. Specifically, we aim to elucidate how the invasion has impacted the region's exchange rates and economic stability.

Analysis and forecasting of the dry bulk shipping market: structural VAR models using FFA-spot-time charter rates

ABSTRACT This study investigates dynamic interactive patterns of the dry bulk shipping markets through the lens of structural vector autoregression (VAR) models. There are two distinct hypotheses in the extant literature. One is that asymmetry between spot and forward freight agreement (FFA) markets causes FFA prices to respond more rapidly to new information than spot prices, termed the Physical-Paper Hypothesis in this paper. The other hypothesis is that there are some diverse shocks with different propagation mechanisms, termed the Shock-Diversity Hypothesis. This paper combines these two seemingly separate hypotheses at one time in two illustrative structural VAR models, based on a dataset of weekly spot, FFA, and time charter (TC) rates in the dry bulk shipping markets. The two structural VAR models show that spot, FFA, and TC rates have different, particularly dynamic responses to identified permanent shocks. Furthermore, they show that the reduced-form tri-variate FFA-Spot-TC VAR model produces more accurate forecasts of future spot rates than the bi-variate FFA-Spot and TC-Spot VAR models. Therefore, market participants can use VAR models as an information-processing tool for understanding of (e.g. data descriptions and structural inferences), and forecasting in, dry bulk shipping markets.

Blind spots in medical education – International perspectives

Background All individuals and groups have blind spots that can create problems if unaddressed. The goal of this study was to examine blind spots in medical education from international perspectives. Methods From December 2022 to March 2023, we distributed an electronic survey through international networks of medical students, postgraduate trainees, and medical educators. Respondents named blind spots affecting their medical education system and then rated nine blind spot domains from a study of U.S. medical education along five-point Likert-type scales (1 = much less attention needed; 5 = much more attention needed). We tested for differences between blind spot ratings by respondent groups. We also analyzed the blind spots that respondents identified to determine those not previously described and performed content analysis on open-ended responses about blind spot domains. Results There were 356 respondents from 88 countries, including 127 (44%) educators, 80 (28%) medical students, and 33 (11%) postgraduate trainees. At least 80% of respondents rated each blind spot domain as needing ‘more’ or ‘much more’ attention; the highest was 88% for ‘Patient perspectives and voices that are not heard, valued, or understood.’ In analyses by gender, role in medical education, World Bank country income level, and region, a mean difference of 0.5 was seen in only five of the possible 279 statistical comparisons. Of 885 blind spots documented, new blind spot areas related to issues that crossed national boundaries (e.g. international standards) and the sufficiency of resources to support medical education. Comments about the nine blind spot domains illustrated that cultural, health system, and governmental elements influenced how blind spots are manifested across different settings. Discussion There may be general agreement throughout the world about blind spots in medical education that deserve more attention. This could establish a basis for coordinated international effort to allocate resources and tailor interventions that advance medical education.

Volatility forecasting and assessing risk of financial markets using multi-transformer neural network based architecture

This research introduces a more reliable model for predicting market volatility. The model incorporates Transformer and Multi-transformer layers with the GARCH and LSTM models and compares their performance to classic GARCH-type models. Euro-US Dollar FX Spot Rate (EURO-USD), Australian-US Dollar FX Spot Rate (AUD-USD), S&P 500 Index, FTSE 100 Index, Reliance Industries Ltd., and Samsung Electronics Co Ltd. are analyzed. The timeframe examined is January 2005 to December 2021, with training from 2005 to 2016 and testing from 2017 to 2021. Empirical evidence suggests that hybrid Neural-network models, notably Transformer-based models, outperform individual transformers, deep learning, neural networks, and traditional GARCH-type models, even in unpredictable conditions like the COVID-19 pandemic. Within the hybrid Neural-network models, MT-GARCH and MTL-GARCH showed lower validation error (RMSE) than the other models, showing that the bagging mechanism added to the attention mechanism of the Multi-Transformer architecture helped to lower the error in the variance in the noisy data of the daily returns of the assets, reducing the RMSE of the hybrid multi-Transformer models. In addition, three to four of the five hybrid neural-network models showed appropriate risk estimates for the entire test period, as observed from the Christoffersen test. Moreover, the lengthy sample period helps test whether hybrid models perform better than classic GARCH-type models in volatile conditions like the COVID-19 pandemic.

Use of Nasopharyngoscopy Severity Classification of Chronic Epipharyngitis and Its Application for Evaluating the Treatment Outcomes of Epipharyngeal Abrasive Therapy.

Background Chronic epipharyngitis causes postnasal drip, pharyngeal pain, pharyngeal discomfort, headache, and shoulder stiffness. Additionally, autonomic nervous system symptoms such as dizziness, general fatigue, and sleeplessness may occur. It can also contribute to the development of focal diseases. Although epipharyngeal abrasive therapy (EAT) is effective for chronic epipharyngitis involving the abrasion of the epipharynx with a zinc chloride solution, there is a lack of clear diagnostic criteria, and treatment outcomes are rarely reported. Methodology A classification of the severity of chronic epipharyngitis was attempted in 154 cases based on nasopharyngeal endoscopic findings, with a subsequent examination of treatment outcomes using EAT. Diagnosis of chronic epipharyngitis involved identifying redness, swelling, postnasal drip, and crusting of the epipharyngeal mucosa. Severity classification relied on a four-point scale measuring the degree of redness and swelling, with additional points assigned for the presence of postnasal drip and crusting. This classification also served as a criterion for judging treatment effectiveness. The prevalence and improvement rate of black spots and granular changes were assessed through nasopharyngeal endoscopy with narrow-band imaging. Subjective symptoms were evaluated using before and after treatment questionnaires, employing a four-point scale for symptoms commonly associated with chronic epipharyngitis (headache, postnasal drip, nasal obstruction, pharyngeal discomfort, pharyngeal pain, shoulder stiffness, tinnitus, ear fullness, dizziness, cough, and sputum). A 10-point numerical rating scale (NRS) was used to assess the physical condition. Results Following EAT, the severity of nasopharyngeal endoscopic findings notably improved, with a 76.0% (117/154) improvement rate (remarkable improvement: 19.5% (30), improvement: 56.5% (87)). The improvement rate for the chief complaint reached 85.7% (132/154), demonstrating significant enhancement in the score for each symptom. NRS scores also improved at a rate of 76.0% (117/154). A significant correlation was observed between the improvement in local findings and chief complaints. The prevalence of black spots and granular changes before EAT was 83.8% (129/154) and 64.3% (99/154), exhibiting improvement rates of 65.9% (87/132) and 54.8% (57/104), respectively. Conclusions Nasopharyngeal endoscopy proves valuable for diagnosing and assessing the severity of chronic epipharyngitis, as well as evaluating treatment effectiveness. The findings indicate that EAT is an effective treatment for chronic epipharyngitis, with improvements in local findings correlating with enhancements in the chief complaint. This underscores the importance of employing aggressive EAT in managing patients with chronic epipharyngitis.

The discrete-time arbitrage-free Nelson-Siegel model: a closed-form solution and applications to mixed funds representation

Abstract A closed-form solution for zero-coupon bonds is obtained for a version of the discrete-time arbitrage-free Nelson-Siegel model. An estimation procedure relying on a Kalman filter is provided. The model is shown to produce adequate fit when applied to historical Canadian spot rate data and to improve distributional predictive performance over benchmarks. An adaptation of the mixed fund return model from Augustyniak et al. ((2021). ASTIN Bulletin: The Journal of the IAA, 51(1), 131–159.) is also provided to include the discrete-time arbitrage-free Nelson-Siegel model as one of its building blocks.

Multi-regime foreign exchange rate model: Calibration and pricing

To price exotic foreign exchange (FX) options, a model needs to be selected for FX spot rate dynamics. The classic approach of modelling spot rates with Black–Scholes framework makes inappropriate assumptions of constant drift and volatility, resulting in mispricing. In this article, we investigate multi-regime Black–Scholes (MRBS) model for FX rate, with regime-switching behaviour of drift and volatility governed by a Markov chain. We derive an analytic formula for European FX call options via Fourier transform and present a Monte Carlo simulation algorithm for FX barrier option pricing. Further, we propose a calibration strategy with penalty. Finally, the empirical study shows that volatility smile can be recovered by MRBS model; calibrating MRBS with penalty gives stable calibrated parameters and small out-of-sample mean squared errors; combined use of our calibration methods and pricing algorithms provides reasonable prices for FX barrier options. Thus, MRBS model, together with proposed calibration-with-penalty strategy and pricing algorithm, provides a promising approach for FX option pricing.

Is there a relationship between the first-day results of anti-VEGF therapy for macular edema due to vascular diseases and longterm outcomes?

To evaluate early changes after the first antivascular endothelial growth factor injection for macular edema secondary to diabetic retinopathy and retinal vein occlusion and the relationship between longterm outcomes. The study enrolled patients who received anti-vascular endothelial growth factor injections for treatment-naive macular edema due to retinal vein occlusion and diabetic retinopathy. The central macular thickness was measured at baseline, post-injection day 1, week 2, and month 1, and at the last visit using spectral-domain optical coherence tomography. A good response was defined as a central macular thickness reduction of ≥10% on post-injection day 1. Patients were reassessed at the last visit with regard to treatment response on post-injection day 1 based on the favorable anatomic outcome defined as a central macular thickness <350 µm. In total, 26 (44.8%) patients had macular edema-retinal vein occlusion and 32 (55.2%) had macular edema-diabetic retinopathy. The mean follow-up time was 24.0 (SD 8.5) months. A statistically significant decrease in the central macular thickness was observed in both patients with macular edema-retinal vein occlusion and macular edema-diabetic retinopathy after antivascular endothelial growth factor injection therapy (p<0.001 for both). All patients with macular edema-retinal vein occlusion were good responders at post-injection day 1. All nongood responders at post-injection day 1 belong to the macular edema-diabetic retinopathy group (n=16.50%). The rate of hyperreflective spots was higher in nongood responders than in good responders of the macular edema-diabetic retinopathy group (p=0.03). Of 42 (2.4%) total good responders, one had a central macular thickness >350 µm, whereas 5 (31.2%) of 16 total nongood responders had a central macular thickness >350 µm at the last visit (p=0.003). The longterm anatomical outcomes of macular edema secondary to retinal vein occlusion and diabetic retinopathy may be predicted by treatment response 1 day after antivascular endothelial growth factor injection.

Probabilistic properties and estimation methods for periodic threshold autoregressive stochastic volatility

&lt;abstract&gt;&lt;p&gt;In an endeavor to encapsulate the dual aspects of volatility progression and periodicity inherent in autocorrelation frameworks demonstrated by various nonlinear time series, a novel conceptualization emerges—the periodic threshold autoregressive stochastic volatility (PTAR-SV) model. This model served as a viable alternative to the conventional periodic threshold generalized autoregressive conditional heteroskedasticity (TGARCH) process. The inherent probabilistic framework of the PTAR-SV model incorporated certain essential features, including strict periodic stationarity, enhancing its analytical robustness. Additionally, this study established the conditions for higher-order moments to exist within the PTAR-SV model. The autocovariance structure pertaining to the powers of the PTAR-SV process has been studied. The process of parameter estimation was scrutinized via the quasi-maximum likelihood technique. This estimation approach involved assessing likelihood using prediction error decomposition and Kalman filtering. Moreover, we extended our analysis to include a Bayesian Markov chain Monte Carlo (MCMC) method based on Griddy-Gibbs sampling, particularly suitable when the distribution of model innovations follows a standard Gaussian. Through a simulation study, we evaluated the performances of both the quasi-maximum likelihood (QML) and Bayesian Griddy Gibbs estimates, providing valuable insights into their respective strengths and weaknesses. Finally, we applied our newly developed methodology to model the spot rates of the euro against the Algerian dinar, demonstrating its applicability and efficacy in real-world financial modeling scenarios.&lt;/p&gt;&lt;/abstract&gt;

Evaluation of Spinach Cultivars for Resistance to Stemphylium Leaf Spot (Stemphylium vesicarium) and White Rust (Albugo occidentalis)

Stemphylium leaf spot, caused by Stemphylium vesicarium, and white rust, caused by Albugo occidentalis, can cause significant losses in spinach production. Management of these foliar diseases of spinach has become increasingly challenging with the development of fungicide resistance in some pathogen populations, high planting density and overhead irrigation used for baby leaf spinach production, and the fact that &gt;60% of fresh market spinach production in the United States is certified organic. To identify spinach cultivars with resistance to Stemphylium leaf spot and white rust, a field trial was performed near Crystal City, TX, USA, in 2021 (79 cultivars), 2022 (87 cultivars), and 2023 (63 cultivars). Each year, the plants were inoculated with S. vesicarium and rated for disease severity. Plants were also rated for white rust severity that resulted from natural infection during the 2021 and the 2022 trials. During each trial, 11% to 27% of the cultivars were identified as resistant to Stemphylium leaf spot, and another 29% to 48% had moderately resistant reactions. In contrast, only 5 of 79 cultivars (6%) in the 2021 trial did not develop symptoms of white rust, and all 87 cultivars evaluated in the 2022 trial had symptoms of white rust. Although there was no significant correlation between mean Stemphylium leaf spot ratings and mean white rust ratings during these trials, the cultivars Colusa, Kodiak, PV-1569, and PV-1664 displayed resistant or moderately resistant responses to both diseases in at least two trials. Therefore, processing and fresh market spinach growers have resistant cultivars from which to select to reduce the economic impacts of Stemphylium leaf spot and white rust.

FREIGHT RATE AND DEMAND FORECASTING IN ROAD FREIGHT TRANSPORTATION USING ECONOMETRIC AND ARTIFICIAL INTELLIGENCE METHODS

The digitisation of the transportation sector and data availability have opened up new opportunities to implement data-driven methods for improving company performance. This article analyses demand and freight rate forecasting techniques in the context of the road freight transportation company. The European market was analysed in this research, and direction from the Netherlands to Italy was selected for the case study. Performed investigation showed that econometric models such as Auto-Regressive Integrated Moving Average (ARIMA) used for demand prognosis provide good results. Freight rate forecasting is different; econometric models, including multivariate models ARIMA with exogenous variables (ARIMAX) and Seasonal ARIMAX (SARIMAX), do not perform satisfactorily under specified time intervals, therefore MultiLayer Perceptron (MLP) was used as a solution. It can be seen that Artificial Intelligence (AI) based methods provide better results. Despite its success, the AI-based approach alone is not recommended for practical implementation since forecasted input parameters are necessary. Lastly, the study uncovers a valuable insight. A strong correlation (0.86) between spot and contract rates was found, and the article shows how current spot rates can be used for contract rate forecasting.

The effect of COVID-19 in estimating the South African real spot-rate curve

This study investigates the dynamism of different mathematical term-structure models during COVID-19 to estimate the South African real spot-rate curve. The study is based on term-structure models following Nelson-Siegel framework; and further incorporates the recalibration process on static term-structure models over different economic environments. It was found that the recalibration methodology resulted in no improvements on Linear-parametric and Cubic-splines term-structure models. However, the recalibration effect on Nelson-Siegel and Svensson models resulted in an improved fit for South African inflation-indexed spot rates even in periods of heightened market risk.

Foreign exchange hedging using regime‐switching models: The case of pound sterling

AbstractWe develop a four‐state regime‐switching model for optimal foreign exchange (FX) hedging using forward contracts. The states correspond to four distinct market conditions, each defined by the direction and magnitude of deviation of the prevailing FX spot rate from its long‐term trends. The model's performance is evaluated for five currencies against the pound sterling for various horizons. Our examination compares the hedging outcomes of the proposed model to those of other commonly employed hedging methods. The empirical results suggest that our model demonstrates the highest level of risk reduction for the US dollar, euro, Japanese yen and Turkish lira and the second‐best performance for the Indian rupee. The risk reduction is significantly higher for lira compared with the other approaches, implying that the proposed model might be able to provide much more effective hedging for highly volatile currencies. The improved performance of the model can be attributed to the adjustability of the estimation horizon for the optimal hedge ratio based on the prevailing market conditions. This, in turn, allows it to better capture fat‐tail properties that are frequently observed in FX returns. Our findings suggest that FX investors tend to use short‐term memory during low market conditions (relative to trend) and long‐term memory in high ones. The results would be also useful to build a better understanding of how investor behaviour depends on market conditions and mitigate the adverse behavioural implications of short‐term memory, such as panic.

Forecasting the South African Real Spot-rate Curve During Covid-19 Stress

Purpose: The aim of this study is to investigate the performance of forecasting the South African real spot rate curve using different mathematical term-structure models during the Covid-19 period. This study follows previous studies by Reid (2009) and Mashoene et al., 2021) where dynamic term-structure models performed well compared to static term-structure models over normal market conditions. For high volatility, normality often breaks, which might result in a need for recalibration in the currently used methodology or a total change in the methodology.&#x0D; Methodology: This study explores the dynamism of both dynamic term-structure models, which follow the Nelson-Siegel framework, and static term-structure models with the option of recalibration of model parameters to account for a change in macro-economic dynamics brought by the effect of the Covid-19 pandemic. The forecasting exercise was applied to term-structure models following the Nelson-Siegel framework using AR(1) process.&#x0D; Findings: The incorporation of macroeconomic factors performed poorly in estimating future model parameters. This could be attributable to the effect of global stress, which resulted in poor linear relationships between macroeconomic and financial factors. Recalibration process on static Nelson-Siegel term-structure models improved the models' performance, especially during the high volatility/period of total economic shutdown due to the Covid-19 pandemic.&#x0D; Implications: The application of static Nelson-Siegel term-structure models with recalibrated parameters might improve bond pricing processes and risk management strategies in emerging markets, especially during market stress.

Potential Future Exposure of Target Redemption Forwards

We calculate the PFE of TARFs using both a smile aware Heston model and a smile unaware Garman-Kohlhagen (GK) model. Results show that the FX Heston model tends to produce significantly different PFEs than the GK model, highlighting the importance for a dealer's global simulation model to accurately capture 1) the distribution of the underlying spot rate, and 2) the joint distribution of the underlying and its implied volatility surface.

Exchange Rate Fluctuation and Management of Foreign Donor-Funded Program Budget

The study was about the effect of exchange rate fluctuation on the management of foreign donor-funded program budgets. Humanitarian and development agencies receive funding from foreign donors, who require the program budgets and financial reports in their respective country currencies although the actual utilization of the budget in the host country’s local currency during the program implementation. The study explored currency conversion methods and identified the best principles for effective management of the effect of exchange rate fluctuation. The research design used was both quantitative and qualitative where relevant literature related to exchange methods, donor requirements, and average monthly exchange rate data were reviewed. The sampling year used in this study is 3 years, 2021 to 2023 and the currency sample selected was Uganda Shillings (UGX) and United States Dollars (USD). First In and First Out (FIFO), Weighted average exchange rate methods, and daily spot rate methods were the most common currency conversion methods identified during the study. The study further noted the currency conversion method chosen should be documented in the written financial manual of the grant holder for reference. The study identified budget monitoring, requests for budget amendment, effective planning, provision for contingency reserves in the budget, and constant communication with the donors among the best ways to manage the effect of exchange rate fluctuation. The Study, therefore concluded that exchange rate fluctuation has either adverse or favorable effects on foreign donor-funded program budget and needed to be closely monitored and communicated to the donor for further guidance. Keywords: Exchange rate, Fluctuation, Foreign Donor- funded, Program and Budget.

Evaluating the Effect of Oil Price Volatility on Capital Expenditure of Federal Government in Nigeria

This study examined the effect of oil price volatility on federal government capital expenditure in Nigeria for the period 1993 to 2022 using secondary data from relevant government agencies. The study specifically examine the impact of Brent UK crude oil price volatility, OPEC spot rate crude oil volatility and West Texas Intermediate crude oil price volatility controlling the disruptions of oil subsidy, corruption and inflation on capital expenditure. The research employed an ex-post facto research design to produce results via Bounds test and Autoregressive Distributed Lag regression test. The long run estimate of the model report that Brent UK crude oil price volatility, OPEC spot rate crude oil volatility and West Texas Intermediate crude oil price volatility failed to report significant effect on the federal government capital expenditure in Nigeria. This shows that the oil price volatility is a short run phenomenon, that will fade out in short period, hence the reason for the high speed of adjustment of the error correction term. There is need for federal government in Nigeria to continually monitor crude oil international price and negotiate with OPEC on production output and quota. There is also need to monitor federal government’s capital expenditure pattern and revenues in critical revenue generating agencies. Federal Government should remain committed to sustaining fiscal adjustments by creating fiscal space for capital and infrastructural development.

The impact of the US employment report on the gold spot rate

Purpose- Considering the various financial markets, it can be observed that macroeconomic events such as announcement releases might affect the volatility and the direction of price movements in the related markets. While some announcements might play a substantial role in this subject, some might be categorized as unessential announcements in the economic calendars. Reports related to the employment situation, inflation, growth of the domestic product, and commodity reservations of a country are crucial points on the schedule of investors and traders all around the globe. However, reports coming from countries with a major economic share have a much more significant effect on the market. In that regard, researchers are more interested in the evaluation of economic events of countries like the United States, United Kingdom, Germany, and China. In that regard, this study focuses on the impact of the U.S. employment situation report on the XAU/USD spot exchange rate. Methodology- In the first part of the study, the significance of relevant factors of the announcement has been evaluated to specify the importance of the elements included in the employment report. In that interest, an OLS regression model has been developed in the first step. Furthermore, the face and statistical validity phases have been controlled to improve the efficiency of the model. The second part of the study focuses on the direction of the price movement respectively after specific periods from the report’s release. To satisfy the desired goal of the study, two various models have been applied to the data to evaluate the two models and their performances. The first model is based on logistic regression approaches while the second model benefits from XGboost regression. Accuracy metrics have been evaluated for both models to decide on the healthiness of the performances. Findings- Findings demonstrate that the gold spot exchange rate reacts strongly to the announced nonfarm payroll employment figure, while the market takes its revision of the prior month and unemployment rate as additional data around the release of the announcement. Results suggest that employment reports labeled as “bad news” for the U.S. economy caused an increase in the exchange rate of the gold spot. Price discovery for different time intervals after the announcement release shows that the first 10 minutes are the most crucial. Time intervals before the announcement release imply that exchange rate changes are regular and there is not any recognizable pattern for price movements before the announcement release, while abnormal returns start to show up just after the release of the announcement. Conclusion- To sum up, the impact of the announcement report on the price movement of the gold spot is undeniable. However, uncertainties increase before the announcement, and volatility increases after the announcement. Various statuses lead to specific movements in the market. While the uncertainties are lower before the announcement, the price movement of the gold spot would be diverse to the status of the announcement. Keywords: U.S. Nonfarm payrolls, employment report, macroeconomic event study, gold spot, machine learning, decision tree JEL Codes: F31, F62, G15

The effect of suture materials with different absorption times on isthmocele: a retrospective study.

With the increasing rate of cesarean operations, the formation of niches and related early and late complications have been observed more frequently. In this study, we examined the effects of using a suture material that can be absorbed faster than conventional sutures on the formation of niches. This study was designed as a retrospective study and completed with a total of 101 patients. During the cesarean operation, the uterus was closed with Rapide Vicryl® in 49 patients and Vicryl® in 52 patients. The uterine niche was measured with a sonohysterogram 6 months after the operation. The primary outcome of the study was determined as uterine niche formation and the secondary outcome was the post-menstrual spotting (PMS) rate. Duration of surgery, intraoperative/postoperative blood loss, and hospitalization time were similar between the two groups. Niche formation was significantly lower in the Rapide Vicryl group (22.4%) when compared to the Vicryl group (42.3%) (p = 0.046). Also, PMS was observed significantly lower in the Rapide Vicryl group (16.2% and 52.8% in Rapide Vicryl and Vicryl groups, respectively; p = 0.002). The formation of niches and associated PMS rates were less with suture materials that were absorbed faster.

The failure behavior analysis based on finite element simulation of PS-PVD (Gd0.9Yb0.1)2Zr2O7/YSZ coatings during burner rig tests

A double-ceramic-layer (DCL) thermal barrier coating consisting of quasi-columnar structured (Gd0.9Yb0.1)2Zr2O7 (GYbZ) on top and yttria-stabilized zirconia (YSZ) beneath was deposited by plasma spray-physical vapor deposition (PS-PVD). The as-deposited GYbZ/YSZ coating is composed of a dense continuous layer at the bottom and a columnar layer growing on it. The columnar layer comprises taller coarse columns, shorter tapered columns, and particles filling in the gaps between the columns. The thermal shock behavior of the coating is evaluated in a burner rig at 1300 °C, during which the coating mainly fails in the form of delamination. The coating initially delaminates in the regions where the particles are embedded beneath the taller GYbZ columns tips. Subsequently, delamination occurs in the lower region of GYbZ coating and eventually fails at the TGO/YSZ interface. A finite element model tailored to the microstructural characteristics of the DCL coating was established. Stress distributions of the coating indicate that the discontinuities of the strain tolerance caused by abrupt microstructural or componential changes lead to multiple out-of-plane stress (σ22) concentration spots. The position and procedure of the delamination of GYbZ coating obtained based on the energy release rate of each σ22 concentration spot are consistent with experimental results.