Error Propagation Method Research Articles

An Arctic sea ice concentration data record on a 6.25 km polar stereographic grid from 3 years of Landsat-8 imagery

Abstract. The decline in Arctic sea ice in the global warming era has received much attention as a contributing factor to the changes in the weather and climate in the Arctic and beyond. The coverage of Arctic sea ice (i.e. sea ice concentration (SIC)) has been monitored since 1972 using satellite passive microwave (PMW) measurements because of their extensive coverage and all-weather capability. However, the fundamental basis of algorithms for estimating SIC has not improved much since the early days due to the lack of reference SIC data, leading to discrepancies between existing PMW SIC algorithms. To overcome this issue, this study aims to construct data records of reference SIC over Arctic sea ice using 30 m resolution imagery from the Operational Land Imager (OLI) on board Landsat-8. In order to collect relatively bright and clear scenes, thresholds of solar elevation >15° and cloud cover <10 % were applied in this study. Clouds in each Landsat-8 scene were masked using the cloud-masking array provided in Landsat data. Due to the poor accuracy of the cloud-masking array over ice-covered surface types, an additional step of visually inspecting the state of the cloud mask using the true-colour image was designated in this study. Each Landsat-8 scene was sorted into four categories depending on the state of the cloud mask. The normalized difference snow index and OLI band-5 reflectivity were used to differentiate between ice and open water within each selected Landsat-8 pixel. The classified data were projected onto a 6.25 km polar stereographic grid, and SIC for each grid cell was obtained by counting ice-classified pixels within the grid. SIC was only computed for grid cells where more than 99 % of their area was covered with Landsat-8 pixels to limit the uncertainty in SIC arising from grids that are not fully concentrated with Landsat-8 pixels. Uncertainty in the produced SIC was 1 %–4 %, inferred using the Gaussian error propagation method. Out of 15 286 collected Landsat-8 images, 14 297 images were translated into SIC maps, and a total of 2 934 399 Landsat-8 SIC grid cells were generated. Evaluation of Landsat-8 SIC with SIC from ice charts revealed a good linear relationship (correlation coefficient of 0.96) between the two products, as well as a mean negative bias which fell within the uncertainty range of Landsat-8 SIC. SIC based on Landsat-8 can be used as reference SIC to evaluate existing SIC products, and, thus, one can improve SIC products, as well as use the improved SIC for other applications such as data assimilation and retrieval studies. The vast amount of Landsat-8 SIC generated in this study may also be used to train deep-learning models for the estimation of Arctic SIC coverage. The Landsat-8 SIC dataset can be publicly accessed at https://doi.org/10.5281/zenodo.10973297 (Jung et al., 2024), and the Python codes for the production and evaluation of the Landsat-8 SIC dataset are accessible at https://doi.org/10.5281/zenodo.12754602 (Jung, 2024).

Developing a high-fidelity reduced chemical kinetic mechanism for liquefied petroleum gas (LPG)

The current study aimed at developing an optimal and highly accurate reduced chemical kinetic mechanism for the Liquid Petroleum Gas (LPG). A three-stage reduction process including pre-processing, the main body reduction, and post-processing was utilized. In the main body reduction, a five-step reduction schematic was applied to the detailed mechanism including 980 species and 4972 reactions released by LLNL utilizing the Direct Relation Graph Error Propagation (DRGEP) method coupled with the isomer lumping and sensitivity analysis. Four different reduced mechanisms including 88, 73, 67, and 44 species were developed and compared with the experimental data. The laminar flame speed/equivalence ratio, ignition delay/temperature curves of the LPG combustion as well as 3D combustion parameters including in-cylinder pressure, mean temperature, heat release rate, and fuel consumption rate for an LPG-fueled opposed-piston engine were considered. It was seen that the 73 species mechanism results for the ignition delay/temperature and laminar flame speed/equivalence ratio matched with the experimental data with discrepancies of 3% and 5%, respectively. The 88 species and 73 species mechanisms showed good agreement in 3D modeling for all the considered parameters in comparison with the detailed mechanism with error values less than 2%. However, the 67 and 44 mechanisms results were not in accordance with the experimental data. Consequently, the developed 73-species mechanism was considered the optimal mechanism for LPG fuel.

Study on carbon emissions of a small hydropower plant in Southwest China

Hydropower plants with a small installed capacity, which are widely distributed in mountainous areas with abundant rainfall and steep rivers, play an important role in resolving energy problems in remote rural areas. These plants are a crucial source of clean electricity generated from water power. Harnessing local water resources not only helps alleviate energy shortages, but also reduces reliance on fossil fuels, contributing significantly to China’s national goals of achieving peak carbon emissions and carbon neutrality. This study investigates the carbon footprint of the Huangshadong Reservoir Project in Chongqing, China. The entire life cycle of the hydropower plant is assessed, including the preparation, construction, operation and maintenance, and demolition phases. The uncertainty was evaluated using the error propagation method. Following analysis, suggestions for carbon footprint reduction measures were proposed. Results showed that the total carbon footprint and the carbon intensity of the Huangshadong Reservoir Project over its entire life cycle are 33,148.29 t CO2e and 417.75 g CO2e/kWh, respectively. Of the total carbon footprint, the preparation phase, construction phase, operation and maintenance phase, and demolition phase account for 0.04%, 67.06%, 26.2%, and 6.7%, respectively. It means that the requirement for cement during the construction phase represents an important contribution to the entire life cycle carbon footprint of a small hydropower plant. As an integrated water conservancy project, the carbon intensity of the Huangshadong Reservoir Project is higher than that of medium-sized and large hydropower plants. However, its carbon intensity is lower than the emission factor of fossil power plants. The research results provide reference for both planning and construction of small hydropower plants and low-carbon development of rural hydraulic engineering.

A Monte Carlo Propagation of the Full Variance‐Covariance of GRACE‐Like Level‐2 Data With Applications in Hydrological Data Assimilation and Sea‐Level Budget Studies

AbstractUnderstanding mass (re‐)distribution within the Earth system, and addressing global challenges such as the impact of climate change on water resources requires global time‐variable terrestrial water storage (TWS) estimates along with reasonable uncertainty fields. The Gravity Recovery and Climate Experiment (GRACE) and GRACE‐FO satellite missions provide time‐variable gravity fields with full variance‐covariance information. A rigorous uncertainty propagation of these errors to TWS uncertainties is mathematically challenging and computationally inefficient. We propose a Monte Carlo Full Variance‐Covariance (MCFVC) error propagation approach to precisely compute TWS uncertainties. We also establish theoretical criteria to predict the actual convergence and accuracy of MCFVC, showing a convergence after 10,000 realizations with the relative error of 2.8% for variance and 4.7% for covariance at the confidence level of 95%. This can be achieved in few seconds using a single CPU to compute the uncertainties of each 1° resolution globally gridded TWS field. A validation against the rigorous error propagation method indicates relative differences of less than 0.8%. A global uncertainty assessment shows that neglecting the covariance of gravity coefficients can considerably bias the TWS uncertainties, that is, up to 60%, in some basins like Eyre. Flexibility of MCFVC allows the quantification of filtering impacts on the uncertainty of TWS fields, for example, up to 35% in the Tocantins River Basin. An empirical model is provided to reproduce GRACE‐like TWS uncertainty fields for hydrological studies. Finally, experiments of GRACE(‐FO) data assimilation for hydrological applications and sea‐level budget estimation are presented that indicate the importance of accounting for the full covariance information in these studies.

Application of static integrated skeletal reduction and tabulation of dynamic adaptive chemistry in the combustion simulation of ethylene-fueled scramjet combustor.

The high-fidelity reduced mechanism is one of the key elements in the combustion simulation of scramjet combustors to reveal their combustion and flow phenomena. In the present work, the hierarchically constructed NUIGMech1.2 (2857 species and 11 814 reactions) is applied to the combustion simulation of an ethylene-fueled scramjet combustor using the method of static integrated skeletal reduction and tabulation of dynamic adaptive chemistry (TDAC). The integrated skeletal reduction strategy successively consists of species elimination using the revised directed relation graph with error propagation method of fixed species scheme and improved sensitivity analysis method, and reactions elimination based on computational singular perturbation importance index. A preferred ethylene skeletal mechanism (26 species and 117 reactions) is obtained through the integrated skeletal reduction strategy under target working conditions of temperature range of 900-1800 K, pressure range of 1-4 atm, and equivalence ratio range of 0.25-5.0. The compact skeletal mechanism is comprehensively validated against the experimental results of ignition delay times, laminar flame speeds, and key species concentration profiles. Meanwhile, it shows consistent results with the detailed mechanism on the adiabatic flame temperature profiles and "S"-curves. When applying this skeletal mechanism to combustion simulations of ethylene-fueled scramjet combustor with double parallel cavities, the path flux analysis method and in situ adaptive tabulation algorithm of TDAC is further utilized to speed up the chemical reaction solution process at run-time. Under the scramjet and ramjet modes, the corresponding simulation results in terms of flame luminosity images, schlieren images, and static pressure distributions, coincide well with those of experimental measurements. The combustion and flow characteristics of the two modes are investigated and analyzed comparatively based on above results and combustion performance parameters. Present work contributes to the application of fuel kinetic mechanisms in scramjet combustor combustion simulation.

Error Analysis for Transport Properties of Lithium-Ion Battery Electrolytes

Accurate determination of electrolyte transport properties in lithium-ion batteries (LIBs) is pivotal for understanding battery performance and optimizing their design. Electrolyte transport properties, encompassing ionic conductivity, diffusion coefficients, and transference numbers, are critical parameters that influence charge and discharge rates, ion mobility, and energy efficiency.[1,2] However, experimental measurements are inevitably accompanied by uncertainties stemming from experimental conditions, instrument calibration, and data analysis methods. Error propagation in electrolyte transport properties has substantial ramifications for battery research and development. Inaccurate measurements can lead to flawed conclusions about battery behavior and hinder the design of optimal electrolyte formulations.[3] Based on the literature, measurements of concentration cells for the determination of electrolyte properties were conducted at different temperatures and concentrations to study its impact on transport properties, and the Gaussian error propagation method was used to analyze the data. The concentration cell experiment is essential to determine the transport factor a. [4] The equation to determine the transport factor is shown in Figure 1a and the exemplary results are represented in Figure 1b. The results show that the error bars are significantly higher at certain concentration pairs.The main aim of this research is to identify the sources that contribute to the prominent difference in the error bars across different concentrations and temperatures. It also discusses the significance of systematic errors, which arise due to calibration issues or instrumental biases, and random errors stemming from variations in experimental conditions.[5] Recognizing the nature of these errors is critical for accurate interpretation of experimental data. To determine the electrolyte transport properties and the corresponding error or uncertainty, experimental datasets from the literatureare studied and repeated in our laboratories.

Development of a new reduced n-pentane mechanism with NOX for combustion simulation at atmospheric pressure

Air-light hydrocarbon mixing gas is a new type of fuel with great potential, and its key component is n-pentane. In this study, a reduced n-pentane mechanism with NOX is developed for combustion simulation at atmospheric pressure, including 81 species and 348 elementary reactions. First, the dominant species and reactions for n-pentane combustion are recognized by the reaction pathway analysis based on the detailed mechanism. Then, a novel multistep reduction strategy with intersection thought is proposed to reduce the detailed mechanism of n-pentane. Ignition delay time and laminar flame speed are separately selected as the target parameters for validation. The two reduced mechanisms are developed by using the directed relation graphs (DRG) method, the directed relation graphs with error propagation (DRGEP) method, and the full species sensitivity analysis (FSSA) method. Species in the intersection mechanism of the above two mechanisms obtained with multistep reduction are treated as important species. Sensitivity analysis (SA) is further performed to remove the uncertain species, combined with the result of reaction pathway analysis, a reduced n-pentane mechanism including 77 species and 336 reactions is obtained. On this basis, a reduced NOX mechanism (4 species and 12 reactions) is incorporated. Then, the key kinetic parameters of the mechanism are optimized and adjusted according to the results of brute force sensitivity analysis. Finally, the proposed mechanism is validated by using the related data of ignition delay times, laminar flame speeds, premixed flame species profiles, and jet-stirred reactor (JSR) species profiles. The results indicate that the proposed mechanism shows good model performance. This study can provide significant assistance for subsequent research on air-light hydrocarbon mixing gas.

Measuring seismic attenuation in polar firn: method and application to Korff Ice Rise, West Antarctica

Abstract We present seismic measurements of the firn column at Korff Ice Rise, West Antarctica, including measurements of compressional-wave velocity and attenuation. We describe a modified spectral-ratio method of measuring the seismic quality factor (Q) based on analysis of diving waves, which, combined with a stochastic method of error propagation, enables us to characterise the attenuative structure of firn in greater detail than has previously been possible. Q increases from 56 ± 23 in the uppermost 12 m to 570 ± 450 between 55 and 77 m depth. We corroborate our method with consistent measurements obtained via primary reflection, multiple, source ghost, and critically refracted waves. Using the primary reflection and its ghost, we find Q = 53 ± 20 in the uppermost 20 m of firn. From the critical refraction, we find Q = 640 ± 400 at 90 m depth. Our method aids the understanding of the seismic structure of firn and benefits characterisation of deeper glaciological targets, providing an alternative means of correcting seismic reflection amplitudes in cases where conventional methods of Q correction may be impossible.

Recycling sludge in agriculture? Assessing sustainability of nutrient recovery in Italy

Abstract Using a hybrid multi-regional input–output approach, we traced sustainability footprints of a nutrient recovery strategy from sewage sludge applied in Italy. We then compared the results with the most common landfilling practice. Overall, accounting for indirect global upstream effects, using sewage sludge for organic fertiliser production generates more jobs and reduces more greenhouse gas emissions than landfilling. By contrast, landfilling stimulates the whole economy more, generating higher indirect turnover and reduces energy carrier use more. Finally, we accounted for uncertainties in these results using an error propagation method based on Monte Carlo simulations.

Integrating Commercial-Off-The-Shelf Components into Radiation-Hardened Drone Designs for Nuclear-Contaminated Search and Rescue Missions

This paper conducts a focused probabilistic risk assessment (PRA) on the reliability of commercial off-the-shelf (COTS) drones deployed for surveillance in areas with diverse radiation levels following a nuclear accident. The study employs the event tree/fault tree digraph approach, integrated with the dual-graph error propagation method (DEPM), to model sequences that could lead to loss of mission (LOM) scenarios due to combined hardware–software failures in the drone’s navigation system. The impact of radiation is simulated by a comparison of the total ionizing dose (TID) with the acceptable limit for each component. Errors are then propagated within the electronic hardware and software blocks to determine the navigation system’s reliability in different radiation zones. If the system is deemed unreliable, a strategy is suggested to identify the minimum radiation-hardening requirement for its subcomponents by reverse-engineering from the desired mission success criteria. The findings of this study can aid in the integration of COTS components into radiation-hardened (RAD-HARD) designs, optimizing the balance between cost, performance, and reliability in drone systems for nuclear-contaminated search and rescue missions.

Effect of the nature of uncertainty on the optimization of collision avoidance maneuvers

With the soaring number of space debris, collision avoidance maneuvers have become crucial to ensure the safety of any operating satellite. In real-world scenario, the problem gets a little more complicated due to the presence of uncertainty in the values of the states of the space objects. Also, the uncertainty distribution assigned to a given state variable evolves over time. This paper studies the effects of linear and nonlinear propagation of the uncertainty distributions on the collision avoidance maneuver optimization. These different methods for error propagation lead to differing shapes and sizes of the error distribution, resulting in differing regions to be avoided during the encounter, thus resulting in different optimal Δv requirements. In the method using nonlinear propagation of uncertainty distribution, the uncertainty of the passive object is propagated using Monte Carlo samples while that of the operating spacecraft is evolved using Unscented Transform. The sigma points from the Unscented Transform of the operating spacecraft are required to lie outside the stretch of most of the debris uncertainty while implementing cumulative density function. This also results in defining a new probabilistic measure which is like a modified Mahalanobis distance but for a non-Gaussian distribution. The other approach uses linearly propagating the error covariances and a combined error ellipsoid to ensure the maintenance of a desired Mahalanobis distance of the hard body from the uncertainty distribution to ascertain safety. Both the approaches consider nonlinear state dynamics while incorporating the perturbing forces to find the optimal thrust profile and the corresponding steer angles. The approach implementing nonlinear propagation of uncertainty and using cumulative density to ensure that the sigma points of the satellite avoid the debris’ uncertainty distribution is found to require lower Δv compared to the other method while also representing the error propagation better. However, the method implementing linear propagation of uncertainty and using Mahalanobis distance of the hard body from the combined error distribution to frame the avoidance constraint proves to be computationally much faster.

Development of a reduced combustion model for ammonia/hydrogen combustion

Ammonia/hydrogen receives increasing interest as a carbon-free fuel in novel combustion techniques. Simulation aided design often requires coupling of flow field and chemical kinetics, whose feasibility relies essentially on an affordable (reduced) reaction model. However, existing ammonia/hydrogen reduced models are scarce and cannot reasonably reproduce available experimental data over a comprehensive set of conditions. Hence, a reduced ammonia/hydrogen model was developed in the present work using a combined method: the directed relation graph with error propagation method was applied first followed by a contribution to species production/consumption screening method. The model reduction method substantially reduces the size of the model, from 37 species and 237 reactions to 25 species and 108 reactions. The reduced model was compared to available reduced models over a comprehensive set of experimental data, including ignition delay time measurements in shock-tube and rapid compression machine, laminar flame speed measurements and species profile data measured in jet stirred reactor and burner stabilized flame. The obtained model maintains comparable prediction accuracy compared to the detailed model and is superior to the other reduced models.

Principal force pattern and impulse response mode for structural equivalent force estimation and full-field response reconstruction

An impulse response mode-based approach is proposed in this study to estimate the equivalent force and achieve full-field monitoring under complex loading conditions. The structural impulse responses to quasi-static and dynamic forces are decomposed as the superposition of a set of predefined representative basis to provide the low-rank property of responses. The principal impulse response modes are calculated from the correlation matrix of the impulse responses in a time step using the principal component analysis. The principal relative force pattern is the eigenvector of the correlation matrix, and the truncation number is determined by the distribution of the eigenvalues. The equivalent force is subsequently defined and solved by the dynamic stiffness method. The full-field responses are then reconstructed by calculating the model responses to the estimated equivalent force. The systematic uncertainty is also quantified by an error propagation method for sensor placement optimization. Numerical examples and experimental tests demonstrate that the proposed methodology can estimate the equivalent force and reconstruct the structural responses accurately under various load scenarios.

Optimizing the Sulfates Content of Cement Using Neural Networks and Uncertainty Analysis

This study aims to approximate the optimum sulfate content of cement, applying maximization of compressive strength as a criterion for cement produced in industrial mills. The design includes tests on four types of cement containing up to three main components and belonging to three strength classes. We developed relationships correlating to 7- and 28-day strength with the sulfate and clinker content of the cement (CL), as well as the clinker mineral composition (tricalcium silicate, C3S, tricalcium aluminate, C3A). We correlated strength with the ratio %SO3/CL and the molecular ratios MSO3/C3S and MSO3/C3A. The data processing stage proved that artificial neural networks (ANNs) fit the results’ distribution better than a parabolic function, providing reliable models. The optimal %SO3/CL value for 7- and 28-day strength was 2.85 and 3.00, respectively. Concerning the ratios of SO3 at the mineral phases for 28-day strength, the best values were MSO3/C3S = 0.132–0.135 and MSO3/C3A = 1.55. We implemented some of the ANNs to gain a wide interval of input variables’ values. Thus, the approximations of SO3 optimum using ANNs had a relatively broad application in daily plant quality control, at least as a guide for experimental design. Finally, we investigated the impact of SO3 uncertainty on the 28-day strength variance using the error propagation method.

ANN-based prediction of ammonia nitrogen for wastewater discharge indicators under carbon neutral trend

IntroductionWith the rapid development of society and urbanization, greenhouse gas emissions have increased, leading to environmental problems such as global warming. The rise in urban water consumption has also resulted in increased sewage discharge, exacerbating freshwater scarcity and water pollution. Understanding the current status and spatial distribution of greenhouse gas emissions in China's sewage treatment industry is crucial for emission reduction measures and controlling ammonia nitrogen pollution.MethodsThis study comprehensively investigates greenhouse gas emissions from sewage treatment plants, analyzing influencing factors and predicting future spatial and temporal distributions. The uncertainty of ammonia nitrogen emissions is calculated using the IPCC's error propagation method, considering uncertainty ranges of variables. Additionally, an artificial neural network is employed to predict ammonia nitrogen content in sewage discharge, aiming to prevent excessive levels in wastewater.Results and discussionThe proposed model outperforms others with an R-Squared score of 0.926, demonstrating its superior accuracy in predicting ammonia content in wastewater. These findings contribute to better emission reduction strategies and control of ammonia nitrogen emissions. This model can effectively prevent excessive ammonia nitrogen content in discharged wastewater, contributing to water pollution control. In conclusion, this study highlights the importance of understanding greenhouse gas emissions from sewage treatment plants and their impact on water pollution. The research provides valuable insights into emission reduction measures, emission prediction, and technological innovations suitable for China's specific conditions. By effectively managing ammonia nitrogen emissions and adopting the proposed predictive model, the goals of carbon neutrality and environmental sustainability can be better achieved.

A Novel Surrogate Fuel Approach for the Numerical Simulation of Renewable Fuels for the Transport Sector

The use of fuels made from renewable resources can be beneficial in reducing the CO2 emission balance of the existing vehicle fleet. These fuels should be drop-in-capable, i.e., their properties should correspond as far as possible to those of fossil fuels. In addition, they should be able to be blended with conventional fuels. In order to determine the full potential of new renewable drop-in fuels, numerical investigations can make a significant contribution.A novel Surrogate Fuel Approach (SFA) was proposed to perform the numerical simulations for blends of paraffinic diesel fuel and alcohols. A set of modeling and mixing rules were used to estimate and extrapolate the required thermo-physical and physicochemical properties of renewable fuels. The detailed chemical mechanisms for n-dodecane and n-octanol were merged. A systematic reduction of the detailed chemical mechanisms was performed using the Directed Relation Graph with Error Propagation (DRGEP) method. For the selected target species, the reduced mechanism from the DRGEP and lumping method contains ∼31% species and ∼40% reactions as of the merged reaction mechanism. The blended fuel properties and the reduced reaction mechanism were incorporated into the numerical simulations. The surrogate fuel approach was validated for steady-state as well as transient operating conditions using the experimental results obtained from the optical high-pressure chamber (HPC) and a heavy-duty single-cylinder engine (HD-SCE). The HD-SCE testing shows that the 60% B0-Di/40% RF blend increases ITE by ∼2% in absolute values and lower ISCO2, ISCO, ISHC and smoke emissions as compared to Diesel fuel. This is due to the paraffinic fuel structure, the fuel-borne oxygen, the high calorific value, and the high cetane number. The calibrated 3D-CFD spray models show an excellent agreement with experimental data from the optical investigations of different spray characteristic values — Liquid and Gaseous penetration Length, Lift of Length and ignition delay for the 60% B0-Di/40% RF fuel. Further, the pressure traces, heat release rates, and nitrogen oxides (NOX) emissions from the numerical 3D-CFD simulations showed a good agreement with the results from the HD-SCE experiments. Overall, these results support the suitability of the proposed novel surrogate fuel approach for simulating different blends of renewable drop-in fuels.

Optimization of methane simplified chemical kinetic mechanism based on uncertainty quantitation analysis by sparse polynomial chaos expansions

A robust and compact simplified chemical kinetics mechanism for methane oxidation is newly developed based on the Directed Relation Graph with Error Propagation method (DRGEP) and the Full Species Sensitivity Analysis method (FSSA). It is constructed by adaptive self-tuning of reaction rates with a non-dominated sorting genetic algorithm II (NSGA-II) approach to satisfy ignition behaviors under varied engine conditions. Its high fidelity is achieved by uncertainty quantitative (UQ) analysis to retain the uncertainties caused by updated reaction rates. In UQ analysis, the sparse polynomial chaos expansions (SPCE) model based on the Least Angle regression (LAR) method is applied to the adaptive selection of significant polynomial bases, and the accuracy of the SPCE model is tested using a statistical methodology (e.g. leave-one-out cross-validation). Consequently, compared with the full PCE approximations, the UQ analysis scheme reduces the computational cost by more than one order of magnitude and effectively alleviates the “dimension disaster” dilemma of the classical PCE scheme. Therefore, the application of the adaptive SPCE scheme allows more reactions to be involved in quantifying the uncertainty propagation. Finally, the aforementioned new simplified methane mechanism is widely verified under engine-relevant conditions, and good auto-ignition performance is highlighted, especially in fuel-lean combustion.

Development of a reduced chemical mechanism for ammonia/n-heptane blends

A reduced chemical mechanism for ammonia/n-heptane blends, which consists of 495 reactions and 74 species, is proposed in this study. This reduced mechanism is developed using the direct relation graph with error propagation (DRGEP) method based on the latest detailed kinetic mechanism of ammonia/n-heptane blends, which includes the important interactive reactions between n-heptane and ammonia (n-C7H16 + NH2 <=> C7H15 + NH3). The reduced mechanism was first validated against experimental ignition delay times (IDTs) and laminar burning velocities (LBVs) for different ammonia/n-heptane blends and can capture well these data. Moreover, both the detailed and reduced mechanisms were used to simulate the combustion and emission characteristics of a homogeneous charge compression ignition (HCCI) engine fueled by different ammonia/n-heptane blends. The simulation results show that the predicted auto-ignition timings and peak pressure values of the reduced mechanism match reasonably well with those of the detailed mechanism for different ammonia/n-heptane blends. Also, the predicted time-history profiles of the important pollutants, including NO, NO2, N2O and HCN, show reasonably good agreement between the detailed and reduced mechanisms, and the maximum discrepancy of the peak concentration is within a factor of two. The important reaction pathways of pollutant formations at HCCI engine conditions are discussed in detail. Furthermore, the current reduced mechanism is also used to simulate the combustion process of an ammonia/diesel dual-fuel engine. Overall, the engine combustion phasing, peak pressure, and heat release can be well simulated using the current mechanism, and the computational efficiency is acceptable.

우리나라 산림지부문 입목바이오매스 탄소저장량의 불확도 평가

The Paris Agreement recommends its member parties to enhance transparency in Greenhouse Gas (GHG) inventory by sector. IPCC guidelines provide recommendations for reducing uncertainties of GHG emissions and removals for the accurate assessment. Thus, this study aimed to assess the uncertainties of estimated carbon stocks for living biomass in the forestry sector. Living biomass is an essential indicator for monitoring GHG removals in the LULUCF sector. This study made a comparative assessment of the uncertainty by applying error propagation methods (simple multiplication, addition and subtraction) and the Monte Carlo Simulation (MCS) suggested in the IPCC guidelines. The findings showed that the overall uncertainty for the simple multiplication in the error propagation was found to be as high as ±28.0% due to higher uncertainties of country-specific emission factors. In contrast, the overall uncertainty was as low as ±3.6% for the addition and subtraction considering the sensitiveness of activity data and emission factors as explanatory variables, while that for the MCS was at around ±6.0%. Despite the slightly higher uncertainty for MCS compared with addition and subtraction, it is reasonable to apply uncertainty assessment using MCS considering the international reliability and comparability. Additionally, since living biomass causes various error sources including measurement error and volume equations by tree species, there is a need for research to assessing different error sources for uncertainty.

Saltwater intrusion from an estuarine river: A field investigation

Estuarine rivers provide critical pathways for seawater to travel upstream of the coast and salinize adjacent aquifers. However, this salinization mechanism (forthwith termed riparian saltwater intrusion) has received relatively little attention compared to saltwater intrusion (SI) at the coast. Time series measurements of river and groundwater freshwater head and specific conductance (SC), as well as horizontal river-aquifer hydraulic gradient were collected at transects of piezometers perpendicular to an estuarine river in Ōtautahi Christchurch, Aotearoa New Zealand. The uncertainties of freshwater head and hydraulic gradient were estimated using error propagation methods. Discrete Fourier Transforms were applied to river and groundwater freshwater head and SC time series data, which confirmed the tidal influence in both systems. Cross–correlation analyses of river and groundwater freshwater heads showed very strong relationships with varying time lags. Hydraulic gradients and river SC fluctuated with tides, resulting in the alternation of SI (increase in groundwater SC) and saltwater retreat (decrease in groundwater SC) with various time delays that may be driven by cyclic flow processes. While riparian SI occurred in most piezometers, the hydraulic gradient on the outside of the river meander was steeper than on the inside of the river meander resulting in less SI. Although positive hydraulic gradient (aquifer to river flow direction) occurred most of the time at all sites, land subsidence and climate change conditions of sea–level rise, increased drought, and decreased river flows could increase the occurrence of negative hydraulic gradient, which may result in increased groundwater salinization.