Search Results (181)

If a CO₂ reservoir or transmission pipeline were to leak, both the surrounding ecology and maritime traffic safety could be put at risk. To better understand and prepare for this risk, multiphase Computational Fluid Dynamics (CFD) models were built in ANSYS Fluent to capture the behavior of a leak once it enters the water. A 3D Eulerian–Eulerian model was used for validation, while a simplified 2D model was applied to simulate conditions at a 50-m depth. The models integrate bubble dynamics, gas holdup, CO₂ dissolution, dissolved species transport, and seawater acidification into a unified CFD framework. Mass transfer was calculated using the Hughmark correlation, and local seawater temperature and salinity were factored in to determine dissociation behavior and the relevant Henry’s Law constant. To confirm the 3D model’s accuracy, results were checked against two experimental datasets: the QICS field study and the Hauser Tank experiments. The team also modeled a hypothetical release scenario at the High Island 10L site and compared the results with earlier published work. The results show that at a depth of 50 m, the surrounding water column can completely absorb a CO₂ release at a rate of 35 kg/s, since the gas dissolves into the seawater as it rises toward the surface. Beyond confirming this mitigation capacity, the simulations shed light on how a leak would actually unfold in the environment, including the shape and movement of the rising bubble plume, how much CO₂ dissolves along the way, and the resulting shifts in seawater pH and pCO₂. Together, this provides a practical framework for assessing how CO₂ leaks could affect marine environments in the Gulf of Mexico. Full article

Coal-bearing tight gas and deep coalbed methane (CBM) widely co-occur in China, and integrated commingled production outperforms separate development. Conventional separated simulation fails to capture coupled reservoir–wellbore gas–water flow. This study develops an integrated geomechanical coupled numerical model with multi-scale fractures and multi-phase wellbore flow: tight gas reservoirs use a stress-sensitive single-porosity model, deep CBM adopts a dual-porosity model for matrix desorption, and EDFM characterizes non-Darcy flow in hydraulic fractures. The Gray gas column and liquid column methods calculate layered bottomhole pressure according to reservoir vertical distribution, and matrix bordering solves the whole coupled system. Validated by field data of Well C-1 in Shanxi, the model yields average relative errors of 8.76% for daily gas output and 2.92% for daily water output. Sensitivity analysis on Well C-2 indicates vertical reservoir stacking controls interlayer pressure difference, and commingled gas curves show dual peaks with shifting dominant gas sources over production stages. A 3.9% rise in deep coalbed methane gas content significantly boosts mid-term peak production and cumulative gas output, making reservoir gas content the dominant geological factor governing commingled production performance. A 120.0% increase in tight gas saturation only delivers a slight uplift in cumulative production under low-porosity conditions. Elevated reservoir stress sensitivity triggers a cumulative gas production reduction of over 50%. Cumulative gas output varies proportionally with hydraulic fracture length, while fracture network width brings mismatched production improvement due to pressure drawdown funnel effects. Therefore, hydraulic fracturing operations should prioritize extending artificial fractures to expand the drainage area of commingled wells. Schemes with constant bottomhole flowing pressure and constant gas rate exert marginal influences on ultimate cumulative production and can be flexibly switched on site. To stabilize daily gas deliverability throughout the early, middle and late production stages, a bottomhole pressure drawdown rate of 0.05 MPa/d or a fixed daily gas rate of 4000 m³/d is recommended. This work provides theoretical support for optimizing commingled development of superimposed tight gas and deep CBM reservoirs. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

Severe fracturing-fluid retention and low post-fracturing flowback efficiency are common in the Lianggaoshan shale reservoirs of the Sichuan Basin. Liquid loading may also occur during late production. To address these problems, this study investigates a synergistic flowback technology that combines natural fracturing-fluid flowback with CO₂ gas lift. First, based on the complex fracture network characteristics of the Lianggaoshan shale reservoir, the interaction mechanisms between hydraulic fractures and natural fractures were investigated. An energy model for fracturing-fluid flowback under natural flowback conditions was established, revealing that reservoir gas expansion energy, hydromechanical energy, and rock elastic energy are the primary driving forces for fracturing-fluid flowback. Furthermore, considering fracture closure behavior, fluid leakoff, and wellbore flow dynamics, a calculation model for the natural flowback of fracturing fluid was developed, and a staged pressure-controlled flowback strategy was proposed. Subsequently, to address the decline in liquid unloading capacity caused by formation-energy depletion during the late stage of natural flowback, a gas-lift-assisted flowback multiphase flow model for the wellbore was established. The effects of the gas injection pressure, gas injection rate, and wellhead pressure on liquid unloading efficiency were systematically investigated. The results indicate that the liquid unloading rate increases with an increasing gas injection pressure and gas injection rate; however, a pronounced diminishing marginal effect is observed. For the Well H1 reference case, the central recommended gas injection pressure was 12 MPa, the gas injection rate was 8 × 10⁴–10 × 10⁴ m³/d, and the wellhead backpressure was maintained below 0.5 MPa. Furthermore, the CO₂-assisted flowback mechanisms were evaluated by distinguishing between the effects explicitly represented in the model and the potential reservoir-scale physicochemical effects. The reduction in wellbore mixture density and bottomhole flowing pressure was simulated directly, whereas CO₂–oil mass transfer, viscosity reduction, mineral dissolution, and changes in water-blocking behavior were interpreted with reference to published experimental studies. Based on these mechanisms, a three-stage synergistic optimized flowback scheme, consisting of “CO₂ soaking–natural flowback–CO₂ gas lift,” was established. A sequence of stagewise quasi-steady PIPESIM calculations was subsequently performed over the 30-day operating schedule. Under the adopted simulation conditions, the recommended soaking period is 5–7 days. The operation should be switched to gas lift when the wellhead pressure falls below 1.5 MPa or when daily liquid production declines continuously by more than 20%. Under the synergistic scheme, the 30-day cumulative flowback volume was predicted to reach 3492 m³. This value was substantially higher than those obtained by conventional natural flowback and standalone gas-lift processes. Moreover, the flowback curve exhibits a distinct “secondary surge” characteristic. These findings provide a theoretical basis and technical support for efficient fracturing-fluid flowback and stable long-term production in the Lianggaoshan Formation. They may also be applicable to other shale oil reservoirs with low porosity and ultra-low permeability. Full article

This study proposes a multiscale prediction method that couples the DTW-CEMHYD3D hydration kinetics model with Mori–Tanaka homogenization theory to establish a quantitative relationship between hydration microstructure evolution and macroscopic heat and mass transport properties of cement-based materials. First, dynamic time warping (DTW) is introduced to correct the mapping relationship between the hydration cycle number in CEMHYD3D and the real hydration time. Then, cement-based materials are regarded as multiphase composites, and a unified calculation method for the effective diffusion coefficient and thermal conductivity is established. The results show that the DTW-CEMHYD3D model can markedly improve the prediction accuracy of early-age hydration heat. The average relative error between the predicted effective diffusion coefficient and the N-phase sphere model is 0.67%, while the coefficients of determination for thermal conductivity prediction and relative diffusion coefficient prediction during hydration reach 0.9812 and 0.987, respectively. Parametric analysis indicates that a higher water-to-binder ratio significantly increases the relative diffusion coefficient, and this effect exhibits nonlinear enhancement with hydration age. The influence of fly ash content is age-dependent. An increase in the degree of saturation reduces the chloride diffusion coefficient but only slightly increases the thermal conductivity. The proposed method provides a reference for durability analysis and parameter determination in multiphysics coupling models of cement-based materials. Full article

The lubrication performance of high-speed transmission systems directly affects mechanical power consumption and operational reliability. During high-speed oil-stirring lubrication, strong gas–liquid interfacial shear, liquid-film deformation, droplet splashing, and oil-mist transport generate an unsteady multiphase turbulent flow field. Conventional continuum-based numerical methods often face difficulties in resolving interface breakup and transient turbulent dissipation under high-speed rotational excitation. To address this problem, this study develops a coupled Lattice Boltzmann–Large Eddy Simulation (LBM–LES) method for oil–air two-phase flow in a high-speed oil-stirring lubrication system. The D3Q27 discrete velocity model, cumulant collision operator, WALE subgrid-scale model, free-surface tracking, and local grid refinement are integrated to analyze free-surface deformation, oil-mist evolution, and power-loss characteristics. Taking a notched toothless oil-stirring disk as the reference configuration, the effects of oil immersion depth and disk topology on gas–liquid phase distribution, oil-mist coverage, power consumption, and vortex-induced energy dissipation are investigated. The results indicate that oil immersion depth has a nonlinear influence on lubrication performance and power loss. Among the investigated cases, an immersion depth of 20 mm provides a favorable balance between upper-region oil-mist coverage and lower-region oil-pool stability. At this depth, the notched disk exhibits directional oil delivery and relatively low power consumption, whereas the double-rhombus structure expands the oil-mist coverage but increases the average power consumption to approximately 175 W. These findings provide numerical support for balancing oil-mist coverage, mechanical power consumption, and disk topology design in high-speed transmission lubrication systems. Full article

Flake graphite is a natural non-metallic material with excellent electrical and thermal conductivity and good lubricity. This study proposed a multi-stage friction–shear cavitating waterjet method to enhance the liberation of flake graphite from gangue minerals. A corresponding nozzle was designed and fabricated by integrating liquid–solid two-phase transport, grinding kinetics, profile-based design, and similarity design. Fluent simulations were conducted with the Eulerian multiphase model to analyze the water–vapor–flake graphite three-phase flow field at different inlet pressures, focusing on vapor volume fraction, water phase flow, and flake graphite particle phase behavior. A distinct cavitation region appeared in the outlet diverging section, mainly near the wall. At inlet pressures of 25 MPa and above, the maximum vapor volume fraction was maintained above 99%, suggesting strong cavitation-inducing capability. Jet liberation experiments showed that the fixed carbon content increased from 49.11% in the feed sample to 78.77% in the waterjet-treated flotation concentrate, while D90 decreased from 121.36 to 103.33 μm and the average particle size decreased from 62.78 to 55.02 μm. These results indicate that multi-stage friction–shear cavitating waterjet treatment facilitates the liberation of flake graphite from gangue minerals, thereby improving the fixed carbon content of the flake graphite concentrate. Full article

At subsurface waste disposal sites, degradation of containment materials can cause leaks of chlorinated volatile organic compounds (Cl-VOCs) in the vadose zone. Material Disposal Area L (MDA L) is a heavily monitored waste site at Los Alamos National Laboratory in Northern New Mexico where a sharp increase in contaminant concentrations was measured in February 2019. Subsequently, soil vapor extraction (SVE) was performed as part of an ongoing interim measure. Here, we demonstrate a new method to introduce possible leakage within an existing numerical framework to bound possible leakage related to concentration increases seen in site monitoring data. A previously calibrated three-dimensional (3-D) model for SVE at MDA L is used to simulate the three conceptual stages from June 2017 to July 2024. The three conceptual stages based on the observed events are: leakage, passive diffusion, and soil vapor extraction. We use a 3-D multiphase flow simulator to introduce a simulated leak and attempt to approximately match monitoring data collected in February 2019, May 2024, and July 2024. After approximately matching the observed leak, outputs from the 3-D simulations were used to quantify the simulated mass of Cl-VOC leaked. Simulated results for a leak on the order of 40 kg of Cl-VOC showed general agreement with the monitoring data. Although the solution is non-unique, this paper presents a proof-of-concept addition to an existing case study, to show that a suspected subsurface container failure could create a signal consistent with the measured data and sets the stage for further analysis of future potential leak signals at the site. The work can also be adapted at other sites where changing subsurface conditions can require innovative modeling techniques to answer regulatory questions. Full article

The mineralogical characteristics of low-grade hard-rock uranium ore from the Zoujiashan deposit were systematically investigated via multiple analytical techniques, including chemical analysis, X-ray fluorescence (XRF) spectrometry, uranium occurrence analysis, 3D X-ray micro-computed tomography (CT), an automated mineral identification and characterization system (AMICS), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS). The results revealed that the uranium grade of the ore was only 0.202%, among which 65.87% existed in the form of independent uranium minerals, while the remaining 34.13% existed in a dispersed ionic state. Except for quartz, most uranium minerals and gangue minerals were finely disseminated and closely intergrown. The pre-concentration of the ore is therefore necessary to separate uranium-rich particles from barren particles at a coarse particle size. Ore density analysis demonstrated that the feed particle size exerted a significant impact on the separation performance, and the optimum feed particle size was determined to be 20 mm. Subsequently, dense medium cyclone (DMC) separation tests were conducted. The experimental results indicated that fine grains were prone to report to low-density products (tailings) during mixed-size beneficiation. Under a tailings yield of 54%, for the −20 + 8 mm coarse fraction, the tailings uranium grade was 0.025% and the uranium recovery of the concentrate was 88.05%. Therefore, classified separation can effectively promote separation efficiency. To reveal the density control mechanism of the particle separation behavior inside the DMC, computational fluid dynamics (CFD) simulations were implemented with the Eulerian–Eulerian multiphase model in ANSYS-Fluent (version 2020R2). The simulation results suggested that a density difference of 8.6% realized effective separation. This work achieved the effective treatment of low-grade hard-rock uranium ore via DMC separation, providing a novel technical route for uranium ore pre-concentration. Full article

In characterizing unconventional reservoirs, conventional Digital Rock Physics (DRP) has long been constrained by three fundamental bottlenecks: the trade-off between imaging resolution and field of view, challenges in reconstructing multiscale pore topology, and the prohibitive computational cost of direct numerical simulation (DNS) at the pore scale. The deep integration of artificial intelligence and rock physics has given rise to a new paradigm—Intelligent Digital Rock Physics (IDRP). This paper provides a systematic review of the evolutionary trajectory of IDRP, with a focus on how machine learning is reshaping the end-to-end workflow from imaging and segmentation to reconstruction and simulation. First, we survey image super-resolution and 3D pore structure generation techniques based on convolutional neural networks (CNNs), generative adversarial networks (GANs), and diffusion models, elucidating their mechanisms for surpassing optical diffraction limits and incorporating macroscopic petrophysical constraints. Second, we outline algorithmic strategies for fusing multi-source heterogeneous data (e.g., Micro-CT and SEM) and representing dual-porosity or multi-continuum systems. Third, we critically examine the application of machine learning surrogates in single- and multiphase flow prediction, highlighting how physics-informed machine learning (PIML) and reinforcement learning (RL)—by embedding governing equations such as Navier–Stokes or Muskat–Leverett into loss functions—achieve both computational acceleration and physical consistency. We further identify key limitations of current IDRP approaches, including insufficient validation of generated topological realism, narrow generalization across lithologies, inadequate representation of dynamic wettability, and limited model interpretability. Finally, we propose a forward-looking roadmap centered on multimodal foundation models for rocks, coupled with neural operators and uncertainty quantification frameworks, emphasizing the critical pathways for translating IDRP into engineering digital twins for unconventional hydrocarbon development, coalbed methane production enhancement, Enhanced Geothermal Systems, and geological CO₂ storage. This review offers a comprehensive reference for researchers at the intersection of geophysics, rock mechanics, and artificial intelligence. Full article

For the Vacuum Induction Gas Atomization (VIGA) powder preparation process, a multi-scale coupled numerical simulation and experimental validation were employed to systematically reveal the influence mechanisms of process parameters on the primary atomization flow field structure, secondary atomization droplet breakup behavior, and powder particle size distribution Using Computational Fluid Dynamics (CFD) methods combined with the VOF (Volume of Fluid) multiphase flow model, the fragmentation morphology of the melt during primary atomization was simulated, capturing the dynamic characteristics of liquid film thinning and the reduction in initial droplet area. Concurrently, the DPM (Discrete Phase Model) coupled with the TAB (Taylor Analogy Breakup) model was applied to predict the droplet size distribution in secondary atomization. The results indicate that increasing atomization pressure (2.5–4.5 MPa) significantly enhances secondary fragmentation intensity, reducing the median particle size (D50) from 42.1 μm to 37.5 μm. Experimental studies on Ni-based superalloys, validated by laser particle size analysis, confirmed that higher atomization pressure improves gas velocity and gas–liquid energy conversion efficiency, optimizes turbulent flow structures, and refines powder particles. The study concludes that the multi-scale coupled model effectively predicts atomization dynamics. By optimizing atomization pressure, powder particle size can be significantly refined, providing a theoretical basis for process control of high-performance spherical powders used in additive manufacturing. Full article

Hole cleaning ensures drilling safety and efficiency. Well inclination angle and drilling fluid density are important parameters affecting cuttings transport. To reveal their coupled interaction mechanism, this study employs the Euler–Euler multiphase flow model to conduct CFD simulations of cuttings transport in a 3D eccentric annulus with an eccentricity of 0.6 under various inclination angles (30°, 45°, 60°, 75°) and drilling fluid densities (1200~1800 kg/m³). Using the cuttings transport ratio (CTR), annulus cuttings volume concentration (CVT), outlet cuttings volume fraction, and annulus pressure drop as evaluation indicators, the influence mechanism of these parameters on hole cleaning efficiency is systematically analyzed. The results show that the effect of drilling fluid density on the CTR is regulated by inclination angle, with 45° being the critical angle for the extreme value of the CTR. Increasing density can significantly reduce cuttings deposition in the annulus, with a more pronounced improvement effect in high-inclination sections. Effective cuttings transport can be achieved by increasing the density to 1500, 1650, 1800, and 1800 kg/m³ for inclination angles of 30°, 45°, 60°, and 75°, respectively. The annulus pressure drop increases approximately linearly with density, and first rises then falls as the inclination angle increases from 30° to 75°, with 45° being the critical angle for peak pressure drop. This study clarifies the coupled regulation law of inclination angle and drilling fluid density, and determines the critical drilling fluid density under different inclinations, providing a numerical basis for optimizing hydraulic parameters and improving hole cleaning efficiency in directional drilling. Full article

This paper develops a novel coupled model to predict the thermal behavior of a high-temperature fast heat storage unit, integrating Power-to-Heat technology with steam generation. A phase change material (PCM) made of a ZnAl₆ metal alloy is used for heat storage. Electricity is used to charge the battery, and the stored energy is used to produce superheated steam during discharge. The coupled model was based on a 3D multiphase CFD model of the heat storage unit and a 1D multiphase water boiling model implemented in Python language. The CFD model solves the transient conservation equations of mass, momentum, and energy using the enthalpy–porosity method to describe phase change, while heat transfer to water is represented by a coupled 1D boiling model. The paper also presents a preliminary design, a computational strategy, and boundary conditions for the operating modes, providing an analytical foundation for detailed engineering, production, and implementation in real-world industrial environments. The presented results confirmed the correct operation of the model and enabled the evaluation of system performance, discharge behavior, and validation of the geometric assumptions required to achieve the target steam parameters. The proposed modular design allows for system scalability, while the entire system is a response to the daily variability of electricity prices resulting from periodic reductions in demand and overproduction of electricity from renewable sources. Estimated thermal behavior of the thermal storage unit for the discharging scenario allows reaching constant output power at the level of 200 kW for 85 min. Integration with a cooling reduction station allows constant system power output to be maintained by increasing the mass flow rate as the steam parameters decrease from over 400 °C to 200 °C with a lowering state of charge. Full article

Ammonia, as a hydrogen carrier and clean fuel, has an increasingly urgent demand for large-scale transportation. Utilizing the existing refined oil pipeline network for sequential transportation of ammonia and refined oil is an economically and efficiently feasible solution. However, the unique micro-solubility characteristics of ammonia and refined oil can cause significant differences in the mixing mechanism of the two substances during sequential transportation in the pipeline compared to traditional oil products. This study conducts transient flow numerical simulation and mechanism research on the mixing problem during the sequential transportation process of ammonia and refined oil under the influence of micro-solubility transfer. Using the ANSYS Fluent platform and combining it with the dynamic mesh technology, a sequential transportation pipeline model was constructed. In the VOF multiphase flow model framework, the Fick diffusion and convective transfer theories were coupled. Through the development of user-defined functions, a transfer model was established to describe the ammonia dissolution process in refined oil during sequential transportation. This model characterizes the axial transfer process of the two-phase flow and the dissolution transfer in the pipeline. Then, the correctness and accuracy of the transfer model were verified, proving that the model has reliable simulation capabilities. To evaluate the comprehensive influence of various engineering factors on the mixing law, this study selected seven key parameters. It then designed and simulated multiple sets of comparative conditions. The influence of each parameter on the development of the mixing section was analyzed, and a sensitivity analysis was conducted. Subsequently, using the growth rate of the mixing length (dL/dt) as the dependent variable to represent the dynamic development of the mixing process, and using the above seven parameters as independent variables, a semi-empirical fitting formula was established. This formula can comprehensively reflect the coupling effect of multiple factors. The results show that the model has good generalization ability and extrapolation robustness. It provides a prediction model and theoretical tool with certain engineering practical value. This can be used for predicting the amount of mixing and optimizing operating parameters in actual pipeline sequential transportation systems. Full article

A preliminary analysis of aircraft refueling using liquid hydrogen (LH2) for a future short–medium-range aircraft is presented. The focus is on how selected refueling parameters influence pressure buildup and the release of boil-off gas (BOG), in order to establishing guidelines towards efficient refueling. The flow physics uses a 0-D multi-phase lump model, which accounts for the effects of the injected LH2, BOG release, heat fluxes and phase changes. Refueling is controlled by volumetric compression during the filling, and relaxation afterwards. Mass-flow profile and refueling protocol have little influence on the amount of BOG vented (~1%), but control the duration of the process, with variations close to 50%. Low initial pressure can significantly reduce the amount of BOG. Full article