Test Cases Research Articles

An Explicit Algebraic Stress-Based Delayed Detached Eddy Simulation Model for Turbulent Flows

A new delayed detached eddy simulation (DDES) model under the ℓ2−ω DDES framework is proposed in this work, namely, the explicit algebraic stress DDES (EAS-DDES) model. The model inherently accounts for the modeled stress anisotropy, making it outperforms the traditional linear eddy viscosity (LEV) based DES models in complex turbulent flows, especially when near-wall stress anisotropy becomes pronounced. The switching between the Reynolds-averaged Navier-Stokes (RANS) and large eddy simulation (LES) branches is achieved based on the ℓ2−ω DDES framework. The explicit algebraic stress formula does not notably compromise the computational effort, stability, and robustness of the DDES model. Four test cases are simulated to assess the performance of the proposed EAS-DDES model. The first two cases are fully developed channel flows and flow over the backward-facing step, showing that the new EAS-DDES model has similar predictive performance to the LEV-based ℓ2−ω DDES model. The other two cases are square duct and rotating channel flows, where modeling the Reynolds stress anisotropy in secondary and rotating flows is crucial to predict the correct flow statistics. The accuracy of the EAS-DDES model compared with the LEV-based DDES models is significantly improved due to the reasonable prediction of near-wall modeled stress anisotropy. The new EAS-DDES model provides an advanced option for the DES of complex turbulent flows when the LEV assumption is problematic.

System Modeling-Based Investigation of SMR Operations Using Variable T-avg Control for Cost-Effective Electricity Generation in a Grid

Given the importance of nuclear power as low carbon energy source, this study conducted techno-economic evaluation of flexible use (baseload as well as load-following) of small modular reactors (SMRs) in an electric grid. The load-following operation of SMR (SMART-100) was restricted to the use of variable T-avg control, a least disruptive and cost minimizing method of load follow control. A system model was developed to describe the reactivity feedback effects of coolant temperature variations due to electric demand-based turbine load changes. Using the system model, conservative output ranges and maximum ramp rates that allow load-following operation without violating axial offset limits were derived. Assuming the introduction of SMR in Jeju Island in South Korea, a semi-independent grid region, with highly dependency on fossil fuel energy and power imports along with significant renewable energy generation (15–20 %), as a test case, this study evaluated the economic contribution of SMR using LPM to minimize the grid electricity generation costs. The results indicate that in regions with a high penetration of renewable energy in their grid systems, flexible energy generation of SMR can make a significant contribution in reducing electricity generation cost and carbon emissions.

Experiments in an enclosed Wave–Wind Flume environment to study the dynamics of a small-scale Floating Offshore Wind Turbine

The global floating offshore wind industry is experiencing rapid growth, but stability regulations are often limited. Reducing platform motions can increase turbine efficiency and support hybrid wind-wave energy systems. This paper presents an experimental study on the dynamics of a Floating Offshore Wind Turbine (FOWT) conducted in an enclosed Wave–Wind Flume (WWF) employing an image-based methodology. The study analyzes the effects of different sea states and the role of a Centerboard and Heave plate (CH) system in decreasing the platform’s motions. The study confirms a significant decrease in overall platform response, approximately 6%, along the longitudinal plane under various sea conditions due to the implemented system. Notable changes were also observed in the Response Amplitude Operators (RAO) at different periods, particularly pitch motion, which showed an average reduction of 27%. The surge, heave, and pitch responses consistently showed lower values when the system was installed, with a mean decrease of 25% across test cases. These results demonstrate the CH’s effectiveness in improving the platform’s dynamics and indicate that it can be integrated into other floating platforms’ designs to assess and improve their performance under distinct sea conditions.

GeoNet enables the accurate prediction of protein-ligand binding sites through interpretable geometric deep learning

The identification of protein binding residues is essential for understanding their functions invivo. However, it remains a computational challenge to accurately identify binding sites due to the lack of known residue binding patterns. Local residue spatial distribution and its interactive biophysical environment both determine binding patterns. Previous methods could not capture both information simultaneously, resulting in unsatisfactory performance. Here, we present GeoNet, an interpretable geometric deep learning model for predicting DNA, RNA, and protein binding sites by learning the latent residue binding patterns. GeoNet achieves this by introducing a coordinate-free geometric representation to characterize local residue distributions and generating an eigenspace to depict local interactive biophysical environments. Evaluation shows that GeoNet is superior compared to other leading predictors and it shows a strong interpretability of learned representations. We present three test cases, where interaction interfaces were successfully identified with GeoNet.

Comparing Discontinuous Galerkin Shock-Capturing Techniques Applied to Inviscid Three-Dimensional Hypersonic Flows

This paper considers the performance of various shock-capturing schemes when simulating three-dimensional, hypersonic flows using the nodal discontinuous Galerkin (DG) finite element method on unstructured, hexahedral meshes. Simulations use a new code, Cartablanca++, which is verified using the method of manufactured solutions. Three shock-capturing techniques are compared: artificial viscosity (AV), slope limiting, and subcell finite volume limiting. Three test cases are considered, including a shock tube (one-dimensional), a reflecting shock (two-dimensional), and an inclined cylinder with a hemispherical endcap (three-dimensional). The AV formulation was not robust in the sense that it could not maintain pressure positivity after initialization from freestream conditions in the final three-dimensional test case. The slope and subcell limiters performed well in all simulations. Both techniques robustly captured strong shock waves while still benefiting from the use of high-order polynomials. The targeted application of the slope limiter prevented residual convergence to machine precision, while the subcell limiter could achieve residual convergence. The mixed DG/finite volume formulation, inherent to the subcell limiting scheme, appears sensitive to the inviscid flux function. Future work will consider modifications to reduce this sensitivity. Additionally, modifications to the shock detection techniques would improve performance for both the slope and subcell limiters.

Heliostat Field Optical Efficiency Calculation

Abstract The solar power tower technology has the advantages of green pollution-free and stable power generation, which has gradually attracted extensive attention from researchers in recent years. The quick computation of the solar heliostat field’s optical efficiency carries substantial potential for refining the design of the heliostat field, thereby enhancing the efficiency of power generation. Based on the parallel light hypothesis, we propose a fast algorithm designed to compute the optical efficiency of heliostat field. The algorithm takes into account various optical loss terms such as mirror reflectivity, cosine efficiency, shadowing and blocking efficiency, atmospheric transmittance efficiency and truncation efficiency. When calculating the shadowing and blocking efficiency, the “judgment rectangle” is used to quickly screen the candidate heliostat that may produce shadowing and blocking to the target heliostat. When calculating the truncation efficiency of the receiver surface, the strip discrete method and geometric projection method are used to quickly judge whether the reflected light is intercepted by the receiver, thus improving the calculation speed of optical efficiency. Consider a surrounding heliostat field as a test case, the proposed algorithm is compared with the conventional ray tracing, and the effectiveness and efficiency of the new algorithm are verified. The algorithm proposed in this paper has the advantages of accurate results and high calculation efficiency, and provides valuable insights that can be utilized for optimizing the design of heliostat field and improving the efficiency of tower solar thermal power generation.

Influences in Experimental Studies on the Characteristics of Transonic Shock Buffet

This article examines the evolution of transonic shock buffet on the OAT15A supercritial airfoil, aspiring to elucidate some of the causes of disparate buffet-relevant settings reported in the literature. Experimental campaigns on three distinct chord lengths are conducted thus resulting in chord Reynolds numbers of 1.3×106, 2.0×106, and 2.7×106. Continuous variations in Mach and AoA allow mapping the shock wave behavior across a large parameter space and capturing the transition between prebuffet, onset, developed buffet, and offset. High-speed focusing schlieren imaging is employed to extract the temporal and spectral nature of buffet and to deduce key quantities that characterize the unsteadiness. Three-dimensional effects along the span due to interference with the side walls are assessed thoroughly, revealing that phases of upstream shock motion and large separation are accompanied by 3D distortion. Even though this effect gains relevance at reduced aspect ratio, a substantially 2D character of the shock surface around the centerplane is confirmed. Fully developed buffet governed by a 2D buffet mode is proven to exist across all test cases, although the buffet peak condition is gradually shifted toward reduced angle of attack, yet greater Mach number with increased Reynolds number. Conversely, accompanied by severely increased angles of attack, the buffet mechanism appears more volatile for small chord length and is consolidated with enlarged chord, which strongly suggests a dependence on the state of the turbulent boundary layer.

Safe Guidance Scheme for Proximity Operations Forced Motion

Autonomous spacecraft proximity operations represent a key enabler for future mission architectures such as in-orbit servicing, active debris removal, object inspection, and in-orbit assembly. This work addresses safety concepts for the relative trajectory guidance design applicable to challenging proximity operations in the close-range domain. The relative orbital element framework is used to formulate safety checks that improve the trajectory’s robustness in case of chaser’s malfunctions or loss of control. Particularly, concepts of passive abort safety and active collision safety are applied to the trajectory design to maintain the chaser outside keep-out zones. These definitions are included within a guidance algorithm that exploits sequential convex programming to efficiently solve the fixed final time safety-constrained close-range rendezvous problem. Test cases of reconfiguration between stable relative orbits and synchronization to a rotating hold point are presented, highlighting the advantages in using these new safety concepts in terms of safety, trajectory insight, and formulation efficiency.

Decomposition of nonlinear collision operator in quantum Lattice Boltzmann algorithm

We propose a quantum algorithm to tackle the quadratic nonlinearity in the Lattice Boltzmann (LB) collision operator. The key idea is to build the quantum gates based on the particle distribution functions (PDF) within the coherence time for qubits. Thus, both the operator and a state vector are linear functions of PDFs, and upon quantum state evolution, the resulting PDFs will have quadraticity. To this end, we decompose the collision operator for a DmQn lattice model into a product of operators, where n is the number of lattice velocity directions. After decomposition, the operators with constant entries remain unchanged throughout the simulation, whereas the remaining will be built based on the statevector of the previous time step. Also, we show that such a decomposition is not unique. Compared to the second-order Carleman-linearized LB, the present approach reduces the circuit width by half and circuit depth by exponential order. The proposed algorithm has been verified through the one-dimensional flow discontinuity and two-dimensional Kolmogrov-like flow test cases.

Generalized, Three-Dimensional Wind Tunnel Wall Correction Using Nonlinear Least-Squares Optimization

A novel three-dimensional technique for correcting wind tunnel measurements for wall interference has been developed. The flow around the test body and its wake is approximated as a series of inviscid singularities, while the wind tunnel walls and model support are approximated using a panel method. A nonlinear least-squares approach with trust-region reflective optimization is then used to obtain the strength and locations of singularities together with the domain boundary panel strengths by fitting to measured wall pressures. The technique is demonstrated using computational fluid dynamics for the test case of a well-documented slender delta wing body with high blockage and validated against both experiments and legacy data. The nonlinear least-squares wall correction method is shown to demonstrate a significant improvement in wall corrections compared to the classical two-variable technique.

Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA)

Assessing Differences in Groundwater Hydrology Dynamics Between In Situ Measurements and GRACE-Derived Estimates via Machine Learning: A Test Case of Consequences for Agroecological Relationships Within the Yazoo–Mississippi Delta (USA)

Identification crisis: a fauna-wide estimate of biodiversity expertise shows massive decline in a Central European country

Expertise in biodiversity research (taxonomy, faunistics, conservation with taxonomic background) appears to decline worldwide. While the “taxonomic impediment” is discussed extensively in the literature, much fewer papers focus on the identification crisis, i.e., the decreasing number of experts who can identify species, and the decline of species-based biodiversity research. As a test case to explore the gravity of the identification crisis, we chose Hungary, a Central European country with a strong history of comprehensive taxonomic expertise and research output. We set out to answer two main questions. (1) What proportion of the Hungarian fauna could currently be identified by Hungarian experts, and what factors determine which groups are covered; and (2) what are the trends of biodiversity research in Hungary, and what are the underlying reasons for these trends? We show that Hungary lacks active biodiversity experts for almost half of the nearly 36,000 animal species recorded in the country, and more than a quarter of the fauna have only one or two active experts available. We also show that faunistic research experienced a golden era between ca. 1990 and 2010. Since then, however, there has been a strong decline, with the number of active experts and published papers decreased to a level like that of the 1970s. Multiple factors are identified causing this trend, such as increased pressure to publish in high impact journals and increasing administrative duties of professional scientists. The next generation of biodiversity experts needs to be fluent in modern techniques and publication strategies but also maintain robust morphology-based knowledge to be equipped for identification tasks of difficult taxa. Despite being disadvantaged by exclusive application of citation-based evaluation, we do need more positions and focused grants for biodiversity researchers to maintain the country’s knowledge base and to avoid being increasingly dependent on—equally declining—foreign expertise.

A Fuzzer for Detecting Use-After-Free Vulnerabilities

Fuzzing is an extensively used automated vulnerability detection technique. Most existing fuzzers are guided by edge coverage, which makes them less effective in detecting specific vulnerabilities, especially use-after-free (UAF) vulnerabilities. This is because the triggering of a UAF vulnerability must not only cover a specific memory operation but also satisfy a specific sequence of operations. In this paper, we propose UAF-Fuzzer for detecting UAFs, which consists of static analysis and fuzzing stages. In the static analysis stage, UAF-Fuzzer first uses target identification to determine the basic blocks that may cause UAFs as the target basic blocks; subsequently, it then instruments these target basic blocks. Subsequently, we propose a memory operation evaluation method to assess the complexity of memory operations. In the fuzzing stage, UAF-Fuzzer assigns energy to seeds using a memory evaluation operation and employs a novel seed selection algorithm to prioritize the execution of test cases that are likely to trigger UAF vulnerabilities. We designed and implemented a UAF-Fuzzer to improve the detection of UAFs and compared it with AFL, AFLFast, FairFuzz, MOPT, EcoFuzz, and TortoiseFuzz in terms of UAF vulnerability detection, crash detection, and path discovery. The results showed that UAF-Fuzzer is more effective in terms of detecting UAF vulnerabilities. We have also discovered three UAF vulnerabilities, submitted them to the software maintainer for fixing, and obtained CVE IDs.

Offshore wind project installations: an advanced approach for weather risk assessments

Abstract This paper presents the development of a sequence-based simulation tool for the weather risk assessment of offshore work processes. The approach developed at the Fraunhofer Institute for Wind Energy Systems is the Advanced Weather Time Series Scheduling (Ad-WATSS) method. For statistical significance, this method combines hindcast weather time series of many years with the weather restrictions associated with offshore activities. The method is incorporated into the software tool COAST, where a project plan schedule is simulated by placing activities according to their operational limits, constraints and relationships, and minimum-required time for each operation. A scenario of an offshore wind installation project in the North Sea is used as reference test case for a weather risk assessment using the developed tool and ERA5 weather data. The assessment is then compared with the weather window statistics approach, based on the same weather data sets. The results from both approaches are analysed and compared on a project and task level detail. The results from the sequence-based simulations show that the method is able to capture the long-term variability and extreme events in weather conditions, making it a reliable tool for the simplification of the weather data-based planning, validation, and assessment of offshore projects.

Auto-contouring of cardiac substructures for Stereotactic arrhythmia radioablation (STAR): A STOPSTORM.eu consortium study

Background/PurposeHigh doses to healthy cardiac substructures (CS) in stereotactic arrhythmia radioablation (STAR) raise concerns regarding potential treatment-induced cardio-toxicity. However, CS contours are not routinely created, hindering the understanding of the CS dose–effect relationships. To address this issue, the alignment of CS contouring was initiated within the STOPSTORM consortium. In this study, we developed and evaluated auto-contouring models trained to delineate CS and major vessels in ventricular tachycardia (VT) patients. MethodsEight centres provided standard treatment planning computed tomography (CT) and/or contrast-enhanced CT datasets of 55 VT patients, each including 16 CS. Auto-contouring models were trained to contour either large structures or small structures. Dice Similarity Coefficient (DSC), 95 % Hausdorff distance (HD95) and volume ratio (VR) were used to evaluate model performance versus inter-observer variation (IOV) on seven VT patient test cases. Significant differences were tested using the Mann-Whitney U test. ResultsThe performance on the four chambers and the major vessels (median DSC: 0.88; HD95: 5.8–19.4 mm; VR: 1.09) was similar to the IOV (median DSC: 0.89; HD95: 4.8–14.0 mm; VR: 1.20).For the valves, model performance (median DSC: 0.37; HD95: 11.6 mm; VR: 1.63) was similar to the IOV (median DSC: 0.41; HD95: 12.4 mm; VR: 3.42), but slightly worse for the coronary arteries (median DSC: 0.33 vs 0.42; HD95: 24.4 mm vs 16.9 mm; VR: 1.93 vs 3.30). The IOV for these small structures remains large despite using contouring guidelines. ConclusionCS auto-contouring models trained on VT patient data perform similarly to IOV. This allows for time-efficient evaluation of CS as possible organs-at-risk.

Explainable and Reduced-Feature Machine learning models for shape and drag prediction of a freely moving drop in the sub-critical Weber number regime

Accurately predicting the shape and drag of a moving drop is crucial in many spray applications. However, due to the complex interaction between the drag force and drop shape deformation, accurate prediction of drop shape and drag from Computational Fluid Dynamics (CFD) simulation requires large computational resources and time. A novel data-driven approach using NARXNN (Non-linear Auto Regressive eXogenous input Neural Network) is proposed in this study, which recurrently predicts the drop shape and drag for a given Weber number (We) and Reynolds number (Re), in the sub-critical We regime where there is no drop breakup. The average error in radius and drag coefficient for the test cases, as low as 0.36% and 0.49%, respectively, is achieved. Most importantly, SHAP (SHapley Additive exPlanations) analysis is performed to identify important features for the predictions, hence enabling the explainability of the physics involved in the predictions of machine learning (ML) models for drop deformation and the interaction between the drop and gas flows. Global interpretation of SHAP values identifies We and Re as the most important features to predict the drop shape and drag. Based on the SHAP analysis, several reduced-feature models are developed. All the reduced-feature models can predict drop shape and drag coefficient within 4% average error in radius and drag coefficient for any We and Re combinations within the prescribed parameter space. The ML models developed in this study demonstrate a tremendous computational advantage over the traditional CFD simulations. The drop shape and drag coefficient evolution can be predicted within seconds for a single case, whereas the CFD simulation takes several days to a week on a single processor.

A mutual information statistic for assessing state space partitions of dynamical systems.

We propose a mutual information statistic to quantify the information encoded by a partition of the state space of a dynamical system. We measure the mutual information between each point's symbolic trajectory history under a coarse partition (one with few unique symbols) and its partition assignment under a fine partition (one with many unique symbols). When applied to a set of test cases, this statistic demonstrates predictable and consistent behavior. Empirical results and the statistic's formulation suggest that partitions based on trajectory history, such as the ordinal partition, perform best. As an application, we introduce the weighted ordinal partition, an extension of the popular ordinal partition with parameters that can be optimized using the mutual information statistic, and demonstrate improvements over the ordinal partition in time series analysis. We also demonstrate the weighted ordinal partition's applicability to real experimental datasets.

Applying triple collocation for verifying wind resource measurements and reanalysis data

Abstract Dual comparisons or dual collocation, respectively, is a standard approach in wind data analysis, and particularly in the wind energy resource assessment context, for comparing and relating two time series from different sources describing the same quantity as e.g. the wind speed at a specific height over a certain period of time. This comparison can be used to validate a data source, or to derive a correction for one of the two datasets. Though being widely used, dual collocation comes with inherent flaws which are partly unknown or ignored. A major problem is that dual collocation requires the definition of a reference in advance. Triple collocation may instead allow for a more objective and comprehensive evaluation, where (three) suitable data sources are available, and possibly help to identify which datasets are most suited to represent the relevant wind resource. In this study, we apply the triple collocation methodology to two typical wind resource assessment test cases. Results are promising in providing more information on the analysed datasets and improving the use of different types of data for wind resource assessment in the future.

A QSPR Model for Henry's Law Constants of Organic Compounds in Water and Ethanol for Distilled Spirits.

Henry's law describes the vapor-liquid equilibrium for dilute gases dissolved in a liquid solvent phase. Descriptions of vapor-liquid equilibrium allow the design of improved separations in the food and beverage industry. The consumer experience of taste and odor are greatly affected by the liquid and vapor phase behavior of organic compounds. This study presents a machine learning (ML) based model that allows quick, accurate predictions of Henry's law constants (kH) for many common organic compounds. Users input only a Simplified Molecular-Input Line-Entry System (SMILES) string or a common English name, and the model returns Henry's law estimates for compounds in water and ethanol. Training was performed on 5,690 compounds. Training data were gathered from an existing database and were supplemented with quantum mechanical (QM) calculations. An extra trees regression model was generated that predicts kH with a mean absolute error of 1.3 in log space and an R2 of 0.98. The model is applied to common flavor and odor compounds in bourbon whiskey as a test case for food and beverage applications.

Experimental performance verification of an intelligent detection and assessment scheme for disturbances and imbalances of three-phase synchronous machine output using coherence estimators

In this article, power quality disturbances, such as voltage and current unbalances, series and shunt faults, are monitored in three-phase synchronous machines. Because the imbalances affect machine efficiency and service life, it is essential to figure out whether the three-phase voltages/currents are balanced or unbalanced. An integrated detection algorithm is proposed to detect the unbalanced voltages/currents and various abnormal conditions precisely and quickly, which is based on the coherence estimator. Most existing detection methods require more than one cycle time to determine the contingency conditions, while the proposed technique detects these situations in a fast and discrete protection system. The suggested scheme acquires instantaneous measurements of the three-phase voltages and currents taken at the synchronous machine terminals in order to calculate fifteen coherence coefficients that are utilized to detect and assess any unbalance or trouble accurately and swiftly. A new proposal for imbalance and disturbance indicators based on the coherence estimators is developed in this study. Multiple test cases have been conducted to validate the proposed algorithm capabilities using a real power system, which includes a motor-generator set, a three-phase load and measurement transformers. The experimental results have been revealed that the relay reliability and accuracy percentages have been found to be 98.28% and 98.52%, respectively. The quantitative findings of the imbalance and disturbance assessment are recorded.