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Conference paper titled, Comparing microclimate weather conditions from the weather research and forecasting (WRF) model with typical meteorological year (TMY) data for building energy simulations accepted for Building Simulation 2025 Conference in Brisbane, Australia in Aug 2025.
Lithium-sulfur (Li-S) battery is one of the most promising candidates for the next generation energy storage systems. However, several barriers, including polysulfide shuttle effect, the slow solid-solid surface reaction pathway in the lower discharge plateau, and corrosion of Li anode still limit its practical applications, especially under the lean electrolyte condition required for high energy density applications. Here, we propose a solution-mediated sulfur reduction pathway to improve the capacity and reversibility of the sulfur cathode and suppress dendrite growth on the Li metal anode simultaneously. With this method, a high coulombic efficiency (99%) and stable cycle life over 100 cycles were achieved under application-relevant conditions (S loading: 6.2 mg cm-2; electrolyte to sulfur ratio: 3 mLE gs-1; sulfur weigh ratio: 72 wt%). This result is enabled by a specially designed Li2S4-rich electrolyte, in which Li2S is formed through a chemical disproportionation reaction instead of electrochemical routes. A diglyme solvent was used to obtain electrolytes with the optimum range of Li2S4 concentration. Operando X-ray absorption spectroscopy confirms the solution pathway in a practical Li-S cell. This solution pathway not only introduces a new electrolyte regime for practical Li-S batteries, but also provides a new perspective for bypassing the inefficient surface pathway for other electrochemical processes.
Digitally enhanced services for wind power could reduce Operations and Maintenance costs as well as the Levelised Cost Of Energy. Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things and Big Data Analytics. The heart of wind turbines is an epicyclic gearbox and rolling element bearings are often responsible for machinery stops. The vibration signatures of bearings are rather weak compared to other components. As a result a number of signal processing techniques have been proposed, focusing towards accurate and early fault detection with limited false alarms and missed detections. In Envelope Analysis an envelope of the vibration signal is estimated usually after filtering around a frequency band. Different tools, such as Kurtogram, have been proposed to select the optimum filter parameters. Monitoring techniques have reached a mature level for steady speed and load operating conditions. On the other hand, under chaniging operating conditions, it is still unclear whether the change of the monitoring indicators is due to the change of the machinery's health or due to the change of operating parameters. Recently, the authors have proposed a new tool called IESFOgram, based on Cyclic Spectral Coherence for the automatic selection of the filtering band. In this paper, the performance of the tool is evaluated on the National Renewable Energy Laboratory wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.
Digitally enhanced services for wind power could reduce Operations and Maintenance (O&M) costs as well as the Levelised Cost Of Energy (LCOE). Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things (IoT) and Big Data Analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components which are often responsible for machinery stops are the rolling element bearings. The vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result an extended number of signal processing techniques and tools have been proposed during the last decades, focusing towards accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic Spectral Correlation and Cyclic Spectral Coherence, based on Cyclostationary Analysis, have been proved as very powerful tools for condition monitoring. The monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating conditions change, it is still unclear whether the change of the monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called IESFOgram, which is based on Cyclic Spectral Coherence and can automatically select the filtering band. Furthermore, the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.
Reducing the electrolyte amount is critical for high practical specific energy of Li-S batteries. The lean electrolyte condition raises a complex situation for sulfur redox reactions since they rely on the electrolyte mediation. The insulating nature of discharge product Li 2 S and its uncontrollable accumulation (passivation) at the cathode interface is one of the major challenges for stable cycling of a Li-S battery under lean electrolyte condition. Here, we present that the NH 4 TFSI additive in electrolyte solution greatly alleviates the passivation issue in Li-S batteries under lean electrolyte condition. The ammonium additive enhances the solvation between sulfide anion and NH 4 + cation, and greatly reduces the amount of large insoluble and insulting Li 2 S particles in the sulfur cathodes, which greatly facilitates the reversible and sustainable redox reactions of sulfur. Therefore, the cycle life of Li-S battery under lean electrolyte condition was greatly improved. In addition, we found that the morphology of Li anode is dependent on the cathode structures. Ammonium additive enables a homogeneous surface of cathode and Li anode and extended cycle life.
Abstract Catalytically active MnO x species have been reported to form in situ from various Mn‐complexes during electrocatalytic and solution‐based water oxidation when employing cerium(IV) ammonium ammonium nitrate (CAN) oxidant as a sacrificial reagent. The full structural characterization of these oxides may be complicated by the presence of support material and lack of a pure bulk phase. For the first time, we show that highly active MnO x catalysts form without supports in situ under photocatalytic conditions. Our most active 4 MnO x catalyst (∼0.84 mmol O 2 mol Mn −1 s −1 ) forms from a Mn 4 O 4 bearing a metal–organic framework. 4 MnO x is characterized by pair distribution function analysis (PDF), Raman spectroscopy, and HR‐TEM as a disordered, layered Mn‐oxide with high surface area (216 m 2 g −1 ) and small regions of crystallinity and layer flexibility. In contrast, the S MnO x formed from Mn 2+ salt gives an amorphous species of lower surface area (80 m 2 g −1 ) and lower activity (∼0.15 mmol O 2 mol Mn −1 s −1 ). We compare these catalysts to crystalline hexagonal birnessite, which activates under the same conditions. Full deconvolution of the XPS Mn2p 3/2 core levels detects enriched Mn 3+ and Mn 2+ content on the surfaces, which indicates possible disproportionation/comproportionation surface equilibria.
Ignition delay time (IDT) is a useful global metric for fuel performance screening and a major target for kinetic modeling. Measurements of IDT are conceptually straightforward, however interpretation could be complicated, especially for systems with changing temperature and pressure. Some experimental conditions in the high repetition rate miniature shock tube (HRRST) exhibit complex temperature and pressure state histories. To better interpret and correlate IDTs, especially those obtained in reactors with varying thermodynamic conditions, an inverse Livengood-Wu (L-W) integral technique is applied to de-convolve the constant condition IDTs from measured IDTs in the HRRST using information on the varying state history. In this paper, the approximate problem is demonstrated using only the measured pressure history leading up to ignition, where the temperature history is estimated based on an isentropic assumption. The IDTs of several fuels were first measured in the HRRST including isooctane and acetone to represent those fuels without strong low temperature chemistry effects. Based on the measured pressure and approximated temperature history, measurements of IDTs in the HRRST compare very favorably with those measured using more conventional techniques, including conventionally sized shock tubes, via the inverse L-W correlation with relaxed Newton iteration and Genetic Algorithm (GA). Furthermore, this study demonstrates the feasibility of using a high throughput ignition testing facility, like the HRRST to extract the constant state IDT measured in regular shock tubes, assisted by the inverse L-W correlation. It is expected with an independent temperature measurement available in the future, IDTs in a broader range of thermodynamic conditions and effects of heat loss could be better resolved.
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Remediation of uranium (U) contamination in the deep vadose zone (VZ) sediments abundant in calcite mineral is a challenging task considering the formation of highly stable and mobile uranyl complexes with carbonate and calcium in pore water composition. There is a concern that uranium contamination in the VZ can serve as a continued source for groundwater pollution, creating a risk to human health and the environment through the groundwater pathway. This requires in-situ remediation of the radionuclide-contaminated VZ to convert soluble U species to low solubility precipitates that are stable in the natural environment. Injection of reactive gasses (e.g., NH3) is a promising technology to decrease U mobility in the unsaturated zone without the addition of liquid amendments. The NH3 injection creates alkaline conditions that can alter the sediment pore water composition due to a release of elements from minerals (via desorption and dissolution) that are present in the sediment. However, it is not known how VZ pore water constituents (Si, Al3+, HCO3 -, and Ca2 +) would affect U(VI) removal/precipitation in alkaline conditions. This study quantified the role of major pore water constituents typically present in the arid and semi-arid environments of the western regions of the U.S and identified solid uranium-bearing phases that could potentially precipitate from solutions approximating pore water compositions after pH manipulations via ammonia gas injections. Triplicate samples were prepared using six Si (5, 50 100, 150, 200, and 250 mM), six HCO3 - (0, 3, 25, 50, 75, and 100 mM), and two Ca2+ (5 and 10 mM) concentrations. The concentration of aluminum and uranium was kept constant at 5mM and 0.0084 mM, respectively, in all synthetic formulations tested. Results showed that the percentage of U(VI) removal was controlled by the Si/Al molar ratios and Ca2+ concentrations. Regardless of the bicarbonate concentration tested, the percentage of U(VI) removed increased as the Si/Al ratios were increased. However, higher Ca concentrations correlated with higher U(VI) removal, ranging between 96% and 99%, at low Si/Al ratios. The SEM images of dried precipitates displayed dense amorphous regions high in silica content, where EDS elemental analysis unveiled higher U atomic percentages. The formation of uranyl silicate and carbonate minerals was also predicted by the speciation modeling. XRD analysis revealed the presence of uranyl carbonate mineral phases (andersonite, grimselite); however, uranyl silicates predicted (Naboltwoodite) were not identified experimentally, possibly due to the amorphous nature of the silica solid phases observed in our experiments.
In far-from-equilibrium conditions, the dissolution of borosilicate glasses used to immobilize nuclear waste is known to be a function of both temperature and pH. The aim of this paper is to study effects of these variables on three model waste glasses (SON68, ISG, AFCI). To do this, experiments were conducted at temperatures of 23, 40, 70, and 90 °C and pH(RT) values of 9, 10, 11, and 12 with the single-pass flow-through (SPFT) test method. The results from these tests were then used to parameterize a kinetic rate model based on transition state theory. Both the absolute dissolution rates and the rate model parameters are compared with previous results. Discrepancies in the absolute dissolution rates as compared to those obtained using other test methods are discussed. Rate model parameters for the three glasses studied here are nearly equivalent within error and in relative agreement with previous studies. The results were analyzed with a linear multivariate regression (LMR) and a nonlinear multivariate regression performed with the use of the Glass Corrosion Modeling Tool (GCMT), which is capable of providing a robust uncertainty analysis. This robust analysis highlights the high degree of correlation of various parameters in the kinetic rate model. As more data are obtained on borosilicate glasses with varying compositions, the effect of glass composition on the rate parameter values could possibly be obtained. This would allow for the possibility of predicting the forward dissolution rate of glass based solely on composition
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This study employs laser ablation (LA) to investigate mechanisms for U optical signal variation under various environmental conditions during laser absorption spectroscopy (LAS) and optical emission spectroscopy (OES). Potential explored mechanisms for signal quenching related to ambient conditions include plasma chemistry (e.g., uranium oxide formation), ambient gas confinement effects, and other collisional interactions between plasma constituents and the ambient gas. LA-LAS studies show that the persistence of the U ground state population is significantly reduced in the presence of air ambient compared to nitrogen. LA-OES results yield congested spectra from which the U I 356.18 nm transition is prominent and serves as the basis for signal tracking. LA-OES signal and persistence vary negligibly between the test gases (air and N2), unlike the LA-LAS results. The plume hydrodynamic features and plume fundamental properties showed similar results in both air and nitrogen ambient. Investigation of U oxide formation in the laser-produced plasma suggests that low U concentration in a sample hinders consistent detection of UO molecular spectra.
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In this work, we advance a quantitative description of the critical shear rate $\dot{\gamma}_{c}$ needed to dislodge a spherical particle resting on a surface with a model asperity in laminar and turbulent fluid flows. We have built a cone-plane experimental apparatus which enables measurement of $\dot{\gamma}_{c}$ over a wide range of particle Reynolds number Re p from 10–3 to 1.5 × 10 3 . The condition to dislodge the particle is found to be consistent with the torque balance condition after including the torque component due to drag about the particle center. The data for Re p < 0.5 is in good agreement with analytical calculations of the drag and lift coefficients in the Re p → 0 limit. For higher Rep, where analytical results are unavailable, the hydrodynamic coefficients are found to approach a constant for Re p > 1000. We show that a linear combination of the hydrodynamic coefficients found in the viscous and inertial limits can describe the observed $\dot{\gamma}_{c}$ as a function of the particle and fluid properties.
We report on a subtle global feature of the mass action kinetics equations for water radiolysis that results in predictions of a critical behavior in H2O2 and associated radical concentrations. While radiolysis kinetics has been studied extensively in the past, it is only in recent years that high speed computing has allowed the rapid exploration of the solution over widely varying dose and compositional conditions. We explore the radiolytic production of H2O2 under various externally fixed conditions of molecular H2 and O2 that have been regarded as problematic in the literature – specifically, “jumps” in predicted concentrations, and inconsistencies between predictions and experiments have been reported for alpha radiolysis. We computationally map-out a critical concentration behavior for alpha radiolysis kinetics using a comprehensive set of reactions. We then show that all features of interest are accurately reproduced with 15 reactions. An analytical solution for steady-state concentrations of the 15 reactions reveals regions in [H2] and [O2] where the H2O2 concentration is not unique – both stable and unstable concentrations exist. The boundary of this region can be characterized analytically as a function of G-values and rate constants independent of dose rate. Physically, the boundary can be understood as separating a region where a steady-state H2O2 concentration exists, from one where it does not exist without a direct decomposition reaction. We show that this behavior is consistent with reported alpha radiolysis data and that no such behavior should occur for gamma radiolysis. We suggest experiments that could verify or discredit a critical concentration behavior for alpha radiolysis and could place more restrictive ranges on G-values from derived relationships between them.
We present a generalized framework for multi-component liquid injections to understand and predict the breakdown of classic two-phase theory and spray atomization at engine-relevant conditions. The analysis focuses on the thermodynamic structure and the immiscibility state of representative gas-liquid interfaces. The most modern form of Helmholtz energy mixture state equation is utilized which exhibits a unique and physically-consistent behavior over the entire two-phase regime of fluid densities. It is combined with generalized models for non-linear Gradient Theory and for liquid injections to quantify multi-component two-phase interface structures in global thermal equilibrium. Then, the Helmholtz free energy is minimized which determines the interfacial species distribution as a consequence. This minimal free energy state is demonstrated to validate the underlying assumptions of classic two-phase theory and spray atomization. However, under certain engine-relevant conditions for which corroborating experimental data is presented, this requirement for interfacial thermal equilibrium becomes unsustainable. A rigorously derived probability density function quantifies the ability of the interface to develop internal spatial temperature gradients in the presence of significant temperature differences between injected liquid and ambient gas. Then, the interface can no longer be viewed as an isolated system at minimal free energy. Instead, the interfacial dynamics become intimately connected to those of the separated homogeneous phases. Hence, the interface transitions toward a state in local equilibrium whereupon it becomes a dense-fluid mixing layer. A new conceptual view of a transitional liquid injection process emerges from a transition time scale analysis. Close to the nozzle exit, the two-phase interface still remains largely intact and more classic two-phase processes prevail as a consequence. Further downstream, however, the transition to dense- fluid mixing generally occurs before the liquid length is reached. The significance of the presented modeling expressions is established by a direct comparison to a reduced model, which utilizes widely-applied approximations but fundamentally fails to capture the physical complexity discussed in this paper.
How to select and optimize the evaluation indicators from many characteristic information to characterize the internal insulation condition of GIS equipment, and establish a scientific technical and economic evaluation model of insulation condition is a huge challenge needs to be addressed urgently. Motived by this purpose, this study simulates the deterioration process of four typical insulation defects in GIS to obtain the information of UHF, ultrasound, and SF 6 decomposition components excited by different degradation stages of each insulating defect simultaneously. Moreover, the operating environment conditions, running time, overhaul times, and the residual value of equipment are taken into account. Using the improved minimum-redundancy-maximum-relevance (mRMR) criterion and genetic algorithm, a comprehensive evaluation index system including the 23 evaluation indicators in three categories is established, which covers the PD hazard, SF 6 insulation performance, equipment running time and environment, and residual value. The research lays a foundation for the later construction and application of multi-source information fusion evaluation model of GIS equipment insulation condition considering economic value.