A community challenge for a pancancer drug mechanism of action inference from perturbational profile data

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with similar to 400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among similar to 1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action.Peer reviewe

Multi-Disciplinary Optimization Design of Axial-Flow Pump Impellers Based on the Approximation Model

This study adopts a multi-disciplinary optimization design method based on an approximation model to improve the comprehensive performance of axial-flow pump impellers and fully consider the interaction and mutual influences of the hydraulic and structural designs. The lightweight research on axial-flow pump impellers takes the blade mass and efficiency of the design condition as the objective functions and the head, efficiency, maximum stress value, and maximum deformation value under small flow condition as constraints. In the optimization process, the head of the design condition remains unchanged or varies in a small range. Results show that the mass of a single blade was reduced from 0.947 to 0.848 kg, reaching a decrease of 10.47%, and the efficiency of the design condition increased from 93.91% to 94.49%, with an increase rate of 0.61%. Accordingly, the optimization effect was evident. In addition, the error between the approximate model results and calculation results of each response was within 0.5%, except for the maximum stress value. This outcome shows that the accuracy of the approximate model was high, and the analysis result is reliable. The results provide guidance for the optimal design of axial-flow pump impellers

Experimental and Numerical Studies of Cloud Cavitation Behavior around a Reversible S-Shaped Hydrofoil

The S-shaped hydrofoil is often used in the design of reversible machinery due to its centrally symmetrical camber line. The objective of this paper is to study the influence of cloud cavitation on the flow structure and the unsteady characteristics of lift and drag around an S-shaped hydrofoil via experimental tests and numerical simulations. In the experimental component, the tests were carried out in a cavitation tunnel and a high-speed camera was used to record the cavitation details around the S-shaped hydrofoil with different cavitation numbers. The experimental results show that sheet cavitation gradually transforms into cloud cavitation with a decrease in the inlet cavitation number, the maximum cavity length increases faster after the occurrence of cloud cavitation, and the shedding cycle time of cloud cavitation gradually increases with a decrease in the inlet cavitation number. In the numerical component, the numerical results are in good agreement with the experimental data. The numerical results show that the movement of the re-entrant jet is the main factor for the formation of the cloud cavitation around the S-shaped hydrofoil. The shedding cloud cavity induces the U-shaped vortex structure around the S-shaped hydrofoil, and it produces a higher vorticity distribution around the cavity. The periodic motion of cloud cavity causes the unsteady fluctuation of the lift–drag coefficient of the S-shaped hydrofoil, and because of the unique pressure distribution characteristics of the S-shaped hydrofoil, the lift and drag coefficient appeared as two peaks in one typical cycle of cloud cavitation

Multi-Conditional Optimization of a High-Specific-Speed Axial Flow Pump Impeller Based on Machine Learning

In order to widen the range of high-efficiency area of a high-specific-speed axial flow pump and to improve the operating efficiency under non-design conditions, the parameters of the axial flow pump blades were optimized. An optimization system based on computational fluid dynamics (CFD), optimized Latin hypercube sampling (OLHS), machine learning (ML), and multi-island genetic algorithm (MIGA) was established. The prediction effects of three machine learning models based on Bayesian optimization, support vector machine regression (SVR), Gaussian process regression (GPR), and fully connected neural network (FNN) on the performance of the axial flow pump were compared. The results show that the GPR model has the highest prediction accuracy for the impeller head and weighted efficiency. Compared to the original impeller, the optimized impeller is forward skewed and backward swept, and the weighted efficiency of the impeller increases by 1.31 percentage points. The efficiency of the pump section at 0.8Qd, 1.0Qd, and 1.2Qd increases by about 1.1, 1.4, and 1.6 percentage points, respectively, which meets the optimization requirements. After optimization, the internal flow field of the impeller is more stable; the entropy production in the impeller reduces; the spanwise distribution of the total pressure coefficient and the axial velocity coefficient at the impeller outlet are more uniform; and the flow separation near the hub at the blade trailing edge is restrained. This research can provide a reference for the efficient operation of pumping stations and the optimal design of axial flow pumps under multiple working conditions

Multi-Conditional Optimization of a High-Specific-Speed Axial Flow Pump Impeller Based on Machine Learning

In order to widen the range of high-efficiency area of a high-specific-speed axial flow pump and to improve the operating efficiency under non-design conditions, the parameters of the axial flow pump blades were optimized. An optimization system based on computational fluid dynamics (CFD), optimized Latin hypercube sampling (OLHS), machine learning (ML), and multi-island genetic algorithm (MIGA) was established. The prediction effects of three machine learning models based on Bayesian optimization, support vector machine regression (SVR), Gaussian process regression (GPR), and fully connected neural network (FNN) on the performance of the axial flow pump were compared. The results show that the GPR model has the highest prediction accuracy for the impeller head and weighted efficiency. Compared to the original impeller, the optimized impeller is forward skewed and backward swept, and the weighted efficiency of the impeller increases by 1.31 percentage points. The efficiency of the pump section at 0.8Qd, 1.0Qd, and 1.2Qd increases by about 1.1, 1.4, and 1.6 percentage points, respectively, which meets the optimization requirements. After optimization, the internal flow field of the impeller is more stable; the entropy production in the impeller reduces; the spanwise distribution of the total pressure coefficient and the axial velocity coefficient at the impeller outlet are more uniform; and the flow separation near the hub at the blade trailing edge is restrained. This research can provide a reference for the efficient operation of pumping stations and the optimal design of axial flow pumps under multiple working conditions

ESDA2004-58565 THE PIV MEASUREMENTS ON THE FLOW FIELDS IN AN UNSHROUDED CENTRIFUGAL PUMP ESDA2004-58565

ABSTRACT PIV was applied to the measurements of flow field in an unshrouded centrifugal pump impeller. Three windows were selected for the measurements .Three operation points of the pump were taken during the measuring. The ratios (Q/QBEP) of the flow rate for measuring are 0.6, 1.0, and 1.4, respectively. The velocity distributions in blade-to-blade passages obtained at different windows give the evidence that the velocity distributions are asymmetric even under the design operation point. A lower velocity zone existed at middle of blade-to-blades passages near the pressure-side of the blade. INTRODUCTION The fluid motion in centrifugal pump is an unsteady motion. The inlet and outlet, the shape of the blade very affect its movement. Under the operation condition including design condition, the flow field may not coincident to the design consumption. The flow field in centrifugal pump impeller is more complicated as compared with other kinds of flow in pipes or valves since the motion of fluids is combined rotational and relative one. In the past decades many efforts were made in measuring the flow fields inside of pumps. However, only a small part of the blade to blade passage was measured due to existing of the suction pipe. On the other hand, the flow fields were measured point by point with a long time so cause the loss of synchronization of the measurements

Effects of an Inlet Vortex on the Performance of an Axial-Flow Pump

The formation of an inlet vortex seriously restricts axial-flow pump device performance and poses a great threat to the safe and stable operation of the entire system. In this study, the change trends of an inlet vortex and its influence on an axial-flow pump are investigated numerically and experimentally in a vertical axial-flow pump device. Four groups of fixed vortex generators (VGs) are installed in front of the impeller to create stable vortices at the impeller inlet. The vortex influence on the performance of pump device is qualitatively and quantitatively analyzed. The vortex patterns at different positions and moments in the pump device are explored to reveal the vortex shape change trend in the impeller and the pressure fluctuation induced by the vortex. The reliability and accuracy of steady and unsteady numerical results are verified by external characteristics and pressure fluctuation experimental results. Results show that it is feasible to install VGs before the impeller inlet to generate stable vortices. The vortex disturbs the inlet flow fields of the impeller, resulting in significant reductions of the axial velocity weighted average angle and the axial velocity uniformity. The vortex increases the inlet passage hydraulic loss and reduces the impeller efficiency, while it only slightly affects the guide vane and outlet passage performance. The vortex causes a low-frequency pressure pulsation and interacts with the impeller. The closer the vortex is to the impeller inlet, the more significant the impeller influence on the vortex. The blade cuts off the vortex in the impeller; afterwards, the vortex follows the blade rotation, and its strength weakens

Numerical Study for Flow Loss Characteristic of an Axial-Flow Pump as Turbine via Entropy Production Analysis

Low-head vertical axial-flow pump as turbine (PAT) devices play a vital part in the development of clean energy for hydropower in plain areas. The traditional method of evaluating the flow loss in hydraulic machinery is calculated by the pressure drop method, the limitation of which is that the location of the occurrence of large losses cannot be accurately determined. In this paper, entropy production theory is introduced to evaluate the irreversible losses in the axial-flow PAT from the perspective of the second law of thermodynamics. A three-dimensional model of the axial-flow PAT is established and solved numerically using the Reynolds time-averaged equation, and the turbulence model is adopted as Shear Stress Transport–Curvature Correction (SST-CC) model. The validity of the entropy production theory to evaluate the energy loss distribution of the axial-flow PAT is illustrated by comparing the flow loss calculated by the pressure drop and the entropy production theory, respectively. The entropy production by turbulent dissipative dominates the total entropy production in the whole flow conduit, and the turbulent dissipative entropy accounts for the smallest percentage of the whole conduit entropy production at the optimal working condition Qbep, which is 51%. The impeller and the dustpan-shaped conduit are the essential sources of hydraulic loss in the entire flow conduit of the axial-flow PAT, and most of the energy loss of the impeller occurs at the blade leading edge, the trailing edge, and the flow separation zone near the suction surface. The energy loss of the dustpan-shaped conduit results from the high-speed flow from the impeller outlet to dustpan-shaped conduit to form a vortex, backflow and other chaotic flow patterns. Flow impact, flow separation, vortex and backflow are the main causes of high entropy production and energy loss

Analysis of Energy Loss Characteristics of Vertical Axial Flow Pump Based on Entropy Production Method under Partial Conditions

The energy loss of the vertical axial flow pump device increases due to the unstable internal flow, which reduces the efficiency of the pump device and increases its energy consumption of the pump device. The research results of the flow loss characteristics of the total internal conduit are still unclear. Therefore, to show the internal energy loss mechanism of the axial flow pump, this paper used the entropy production method to calculate the energy loss of the total conduit of the pump device to clarify the internal energy loss mechanism of the pump device. The results show that the energy loss of the impeller is the largest under various flow conditions, accounting for more than 40% of the total energy loss of the pump device. The variation trend of the volume average entropy production and the energy loss is similar under various flow coefficients (KQ). The volume average entropy production rate (EPR) and the energy loss decrease first and then increase with the increase of flow, the minimum volume average entropy production is 378,000 W/m3 at KQ = 0.52, and the area average EPR of the impeller increases gradually with the increase of flow. Under various flow coefficient KQ, the energy loss of campaniform inlet conduit is the smallest, accounting for less than 1% of the total energy loss. Its maximum value is 63.58 W. The energy loss of the guide vane and elbow increases with the increase of flow coefficient KQ, and the maximum ratio of energy loss to the total energy loss of the pump device is 29% and 21%, respectively, at small flow condition KQ = 0.38. The energy loss of straight outlet conduit reduces first and then increases with the increase of flow coefficient KQ. When flow coefficient KQ = 0.62, it accounts for 27% of the total energy loss of the pump device, but its area average entropy production rate (EPR) and volume average entropy production rate (EPR) are small. The main entropy production loss in the pump device is dominated by entropy production by turbulent dissipation (EPTD), and the proportion of entropy production by direct dissipation (EPDD) is the smallest