Computational Model Research Articles

Recent advancements in flow control using plasma actuators and plasma vortex generators

AbstractFlow‐control techniques have attracted significant attention in many scientific areas due to their ability to improve the effectiveness and regulate the flow of aerodynamic devices. This study explores the latest developments in flow‐control techniques, specifically concentrating on the cutting‐edge technologies of plasma vortex generators (PVGs) and actuators. By taking advantage of the ionization of gases or air, plasma actuators have become a viable method for modifying an object's aerodynamic properties without needing physical moving parts. These actuators create localized plasma discharges that interact with the surrounding flow to provide accurate separation control, boundary‐layer dynamics, and aerodynamic forces on aircraft wings, wind turbine blades, and other surfaces. PVG, which produce controlled vortical structures, offer a novel way to manipulate airflow with plasma actuators. These generators create swirling motions through plasma discharges that can be used in various technical applications, such as automotive, marine, and aviation, to modify boundary layers, reduce drag, and improve lift characteristics. This study offers an overview of recent work, focusing on the theoretical underpinnings, experimental validations, and practical applications of plasma‐based flow‐control technologies. Advances in plasma‐generating techniques, computational modeling approaches, and experimental configurations to optimize and comprehend the intricate fluid–structure interactions are covered in the debate. Moreover, the study delves into incorporating plasma‐based flow management into cars, renewable energy systems, and next‐generation aerospace designs, highlighting the possibility of increased agility, decreased emissions, and efficiency. It also discusses the difficulties and potential paths for developing these technologies further for use in business and industry, highlighting the necessity of dependable, scalable, and durable solutions. Finally, this study summarizes the most recent advancements in vortex generators and plasma actuators for flow control. It demonstrates how they have the power to revolutionize fluid dynamics and aerodynamics in a variety of engineering fields.

Study of vortex dynamics in a solar tower vortex generator

Vortical flows, integral to fluid mechanics, significantly influence the performance and efficiency of various engineering applications. Utilizing a validated three-dimensional computational model, this study characterizes vortex structures and examines the impact of ambient conditions and the strategic positioning of vortex generators (swirler) at critical geometric locations, such as the throat, and regions above and below it, within a converging–diverging solar tower. The research explores the influence of swirler position on recirculation zone characteristics, focusing on center-to-center (C2C) distance, penetration length, and maximum width. When the swirler is positioned above or below the throat, C2C distance shows minimal change. However, placing the swirler exit at the throat significantly reduces C2C distance at the lowest velocity (0.13 m/s), due to a stable recirculation zone. This effect diminishes at higher velocities. Swirler position greatly affects penetration length, resulting in two distinct behaviors: Behavior A, dominated by swirler position, occurs when the swirler is above or below the throat, and Behavior B, which is velocity-dependent, is observed when the swirler exit is at the throat. The study also investigates the interaction between the recirculation zone and swirler exit flow, focusing on restriction and mixing ratios. The restriction ratio is higher below the throat (31 %) compared to the throat (27 %). The mixing ratio shows minimal variation with inlet velocity but significant variation with swirler position. Among the evaluated positions, placing the swirler below the throat results in the lowest swirl strength for most axial distances, except near the exit height, rendering it less suitable for water harvesting applications. These findings provide guidance for optimal swirler placement to achieve desired recirculation zone control and system performance.

Consilience of healthcare legislation, complexity science & computational analysis

Our US Health Care System (HCS) has evolved from simple to complex and needs reform. Thus far, all legislative initiatives have failed to result in establishing a friendly, cost-effective, quality healthcare system. The question becomes, can Complexity Science (CS) and computational analytic platform modeling be used to help create better Health Care Policies (HCP) and reform our all too complex HCS? Modeling has been used in many diverse disciplines but has yet to be utilized in preemptive evaluation of major US legislative HCPs. Review of US Health Care Policy History (HCPH) viewed in the context of a Complex Adaptive System(CAS) reveals how, unanticipated historical events, politics, social, and personal leadership have tangentially shaped our US HCPs. Future construction of HCPs with the help of CS, and preemptive computational modeling (CM) techniques, will hopefully yield stronger conclusions in legislative HCP construction and advance our US HCS into one with dynamic resilience.

Estimating Octanol-Water Partition Coefficients of Novel Brominated Flame Retardants by Reversed-Phase High-Performance Liquid Chromatography and Computational Models.

Legacy brominated flame retardants, including polybrominated diphenyl ethers (PBDEs), have been classified as persistent organic pollutants and replaced with novel brominated flame retardants (NBFRs). The octanol-water partition coefficients (log KOW) of NBFRs have been computationally estimated, but the log KOW values provided by these methods can differ by 1 to 3 orders of magnitude. Given the importance of this parameter in fate and toxicity models, we indirectly measured the log KOW values of eight NBFRs by their capacity factor (k') on a reversed-phase high-performance liquid chromatography (HPLC) C18 column by isocratic elution and compared these measured values with those estimated by nine computational models. Log KOW values were obtained for the NBFRs 1,2-bis(2,4,6-tribromophenoxy) ethane, pentabromobenzene, pentabromoethylbenzene, pentabromotoluene, 2-ethylhexyl 2,3,4,5-tetrabromobenzoate, allyl 2,4,6-tribromophenylether, 2,3-dibromopropyl-2,4,6-tribromophenyl ether, and bis(2-ethylhexyl) tetrabromophthalate. A training set of phthalates, polychlorinated biphenyls, PBDEs, and halogenated benzenes were chosen to obtain the log k'-log KOW calibration for the NBFRs. The computational models KowWIN, XLogP3, EAS-E Suite, COSMOtherm, DirectML, and Abraham polyparameter linear free energy relationships all predicted the log KOW values of the calibration compounds to within 1 order of magnitude without significant bias. The median of these models predicted log KOW values for the calibration compounds that were close to those known in the literature with root mean square error (RMSE) = 0.224 and for the NBFRs that were close to those measured by HPLC (RMSE = 0.334). Environ Toxicol Chem 2024;00:1-10. © 2024 SETAC.

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

Real-time prediction of ship motions based on the reservoir computing model

Real-time prediction of ship motions is crucial for ensuring the safety of offshore activities. In this study, we investigate the performance of the reservoir computing (RC) model in predicting the motions of a ship sailing in irregular waves, comparing it with the long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and gated recurrent unit (GRU) networks. The model tests are carried out in a towing tank to generate the datasets for training and testing the machine learning models. First, we explore the performance of machine learning models trained solely on motion data. It is found that the RC model outperforms the LSTM, BiLSTM, and GRU networks in both accuracy and efficiency for predicting ship motions. Besides, we investigate the performance of the RC model trained using the historical motion and wave elevation data. It is shown that, compared with the RC model trained solely on motion data, the RC model trained on the motion and wave elevation data can significantly improve the motion prediction accuracy. This study validates the effectiveness and efficiency of the RC model in ship motion prediction during sailing and highlights the utility of wave elevation data in enhancing the RC model’s prediction accuracy.

Simulation of an induction generator for a wind farm

A pressing problem in the modern economy is still the problem of generating a sufficient amount of electricity. The situation is aggravated by environmental problems when using so-called carbon energy, as well as when using hydro and nuclear energy. The development of alternative sources of electricity is now impossible to imagine without the use of wind power plants. Wind energy is practically free energy, but its use involves the use of electric generators. Currently these are mainly synchronous generators, but induction (asynchronous) generators are also used. Despite the relative cheapness and reliability, however, induction generators occupy a very modest place in percentage terms among wind generators. One of the problems with using induction wind generators is that they require very significant reactive power during operation, which either comes from the network or is compensated by a capacitor bank. The latter is typical for autonomous induction generators. Computer modeling of induction wind generators is complicated by significant nonlinearity, which is caused by the magnetization curve or, what is the same, the no-load characteristics of the generator. As a rule, in computer modeling or simulation the inductive reactance of the magnetization branch is assumed to be constant, which is not entirely correct, since it is reliably known that it depends on the rotor rotation speed. A number of models of an induction generator are known, but so far there is no one most reliable mathematical model that allows computer studies of an induction generator with a squirrel-cage rotor, taking into account all its features. The MATLAB 2023b software package contains a mathematical model of an induction wind generator, which is of scientific and practical interest. The article examines the capabilities of this model, its advantages and disadvantages. The dependences of the influence of machine parameters on the generated active power and consumed reactive power were obtained, the influence of the inductive resistance of the magnetization branch and the influence of the nature of the load on the generated energy by the generator were assessed

The thawing process in gentamicin and polyethylene glycol hydrogel: The impact of selected parameters on the calculation of activation energy using the methods of Belichmaer and Duswalt

AbstractThe subject of the research conducted by differential scanning calorimetry (DSC) was hydrated mixtures of polyethylene glycol and gentamicin (GEN) in a 1:1 (w/w) ratio. In this way, a system with a particularly high ability to form hydrogen bonds was created. It was postulated that studying the effective activation energy (EA) of the thawing process of diverse hydrated polymer mixtures will indirectly allow for an attempt to assess the distribution of water molecules in the examined material in future studies. This article presents a detailed analysis of the impact of selected parameters on the calculation of EA using the Belichmaer and Duswalt methods. The research focused on assessing how the interpolation method affects the accuracy of results obtained from these techniques. The interpolation method was analyzed in terms of its ability to accurately replicate experimental data. By evaluating the impact of the number of points from the DSC curve used in linear interpolation, a standard deviation from the average value of EA was obtained, respectively: 2.4% for the Belichmaer method and 1.9% for the Duswalt method. The article also shows that by using both methods simultaneously, it is possible to detect the moment when additional endothermic and exothermic reaction activity becomes present in the reaction curve. This allowed for the avoidance of the depletion error. Computer modeling and simulations were used to compare the effectiveness of both techniques in various scenarios. The results showed that the choice of interpolation method and taking into account the depletion error significantly affect the accuracy of calculating EA. The computed activation energy via Duswalt method was EA = 363.32 kJ mol−1 with a standard deviation of 7.07 kJ mol−1 and using the Belichmaer method the EA was 364.81 kJ mol−1, with a standard deviation of 9.10 kJ mol−1.

Scattering effect for relative blood volume monitoring during hemodialysis

The long-term survival rate of hemodialysis patients is limited by hemodialysis-related complications, which are attributed to the inappropriate level of relative blood volume (RBV) after treatment. For the conventional optical monitoring methods, the RBV is usually overestimated, especially when the RBV varies in a wide range. Here, we find that the scattering effect of blood on incident light is the dominant factor of nonlinear measurement errors according to the numerical simulation results. Then a computational model considering both the absorption and scattering effect of blood is proposed and applied to a relative blood volume monitoring system (RBVMS). Finally, the RBVMS is validated in a commercial hemodialysis machine, showing a relatively low measurement error of 6.9 %. This result indicates the potential of the system for RBV monitoring during hemodialysis.

Investigation of the influence of the number of poles on the external characteristic of a brushless DC machine in the generator mode

The article presents the results of a study on the generator mode of operation of a brushless DC machine (BLDC) when operating under active load. For analysis, two models were created with the number of permanent magnets on the rotor differing by a factor of two. The relevance of the research is due to the wide range of applications of BLDC in various fields. One of the key advantages of BLDC is the absence of a brush-commutator unit, which is replaced by a controller with semiconductor elements. This increases the reliability of BLDC and expands the range of operating modes. The main objective of this study is to determine the impact of the number of permanent magnets on the external characteristic of BLDC in generator mode. To achieve this goal, computer modeling was performed using ANSYS Maxwell software. This software is designed to study and calculate the magnetic field pattern using the finite element method in electromechanical devices, providing accurate results for analysis. For each BLDC model, the degree of saturation of the magnetic system elements was determined, and graphs of voltage, current, and braking torque at the generator shaft over time were obtained. Analysis of the calculation results for BLDC models with 46 and 92 permanent magnets demonstrated an impact on the voltage and current waveforms, leading to some slight distortion of their shape. The obtained calculation results allowed determining the efficiency and constructing the external characteristic for both BLDC models. Higher efficiency was obtained for the BLDC model with 92 permanent magnets on the rotor. The external characteristic for the generator mode of BLDC operation demonstrated a change in voltage when transitioning from idle to nominal current operation within the range of 15% and 21%, indicating the stiffness of the characteristic. The conclusions of the study indicate the potential for improving the efficiency of BLDC through optimization of the magnetic system design and cooling conditions, and can be useful for engineers and developers of electromechanical systems aiming to improve the performance of their products.

Evaluation of plant canopy microclimates with realistic plants in plant factories with artificial light using a computational fluid dynamics model

Airflow parameters play a vital role in regulating the microclimate of the plant canopy and significantly affect plant development and growth in plant factories with artificial light (PFALs). A computational fluid dynamics (CFD) model with a realistic soybean canopy was developed to predict the distribution of radiation (W m−2), leaf temperature (°C), air velocity (m s−1), air temperature (°C), and relative humidity (%) within a plant canopy. The accuracy of the CFD model was confirmed by comparing mean absolute percentage errors of these variables which were 9.9 %, 1.4 %, 11.4 %, 0.8 %, and 1.8 %, respectively. The validated CFD model was used to simulate the lamp temperature and microclimate distribution of the soybean canopy under different inflow parameter levels on a cultivation shelf. The results revealed that the microclimate was primarily influenced by lamp radiation rather than convective heat when the inflow velocity was below 0.57 m s−1. Inflow velocity, air temperature, and relative humidity exerted more significant effects on air velocity, air temperature, and relative humidity, respectively, than the other variables in the plant canopy. The increase of inflow velocity significantly improved the uniformity of air velocity, temperature, and relative humidity in the plant canopy. However, note that this study investigated the lamp temperature and microclimate distribution in plant canopies under different airflow parameters using the CFD model without coupling plant transpiration to the surrounding climate. This provides valuable insights into the control of the plant canopy microclimate in general and more particularly on the shelves of PFALs.

A novel oscillating flow cycle engine with characteristics of temperature glide heat addition and no regenerator for low-grade heat utilization

In oscillating flow cycle engines such as Stirling engines, nearly isothermal heat additions in heat exchangers will produce irreversibility losses during the heat transfer processes and the regenerator is indispensable and expensive. Thus, it reduces oscillating flow cycle engine’s economic attractions for low-grade heat utilization. In this work, three structures of one-way oscillating flow cycle engine are proposed for overcoming these problems. To assess the feasibility, one structure is chosen for analysis. Then, a one-dimensional computational fluid dynamic model considering various heat transfer and flow friction losses is developed. Based on the validated model, performance analysis is carried out. The temperature glide for heat absorption can reach up to 116 K and can be adjusted. The obtained maximum thermal efficiency is 7.79 %, 12 %, and 14.6 % respectively for low-grade heat’s inlet temperatures of 373 K, 423 K, and 473 K. Furthermore, the maximum specific work output for 1 kg/s hot water is 13.7 kW, 36.9 kW and 64.4 kW respectively at an inlet temperature of 373 K, 423 K, and 473 K, with corresponding maximum exergy efficiency of 42.8 %, 43.7 % and 41.1 % respectively. Thus, the results demonstrate that large temperature glide heat addition can be achieved, and a novel oscillating flow cycle engine without a regenerator is feasible and efficient.

Performance Analysis of Finned-Tube Heat Exchanger Charged with Phase Change Material for Space Cooling

The performance of a latent heat storage unit comprised of phase change material (PCM) enclosed in a finned-tube heat exchanger was evaluated experimentally and theoretically to determine its viability to condition a space during summer. The internal and external design conditions of a typical building were selected and analyzed to determine the type of PCM, and the phase change temperature required for space cooling. Subsequently, a PCM of Plus-ice A17 was selected and charged into a small-scale finned-tube heat exchanger. Extensive measurements were conducted on the PCM heat exchanger at different operating conditions. Meanwhile, a three-dimensional computational fluid dynamics model for the PCM heat exchanger was developed and validated with the experimental measurements and thus simulated. When the airflow velocity increases from 1.3 m/s to 6 m/s, the phase change periods decrease by 25% and 13% for the PCM charging and discharging processes respectively. When the PCM thermal conductivity increases from 1 W/(m·K) to 8 W/(m·K), the phase change periods reduce by 36.3% and 47.7% for the PCM charging and discharging processes respectively. In addition, for the same increased range of PCM thermal conductivity, the charging energy efficiency increases by 16.3%, and the discharging energy ratio drops by 7.1%.

Collective predictive coding hypothesis: symbol emergence as decentralized Bayesian inference.

Understanding the emergence of symbol systems, especially language, requires the construction of a computational model that reproduces both the developmental learning process in everyday life and the evolutionary dynamics of symbol emergence throughout history. This study introduces the collective predictive coding (CPC) hypothesis, which emphasizes and models the interdependence between forming internal representations through physical interactions with the environment and sharing and utilizing meanings through social semiotic interactions within a symbol emergence system. The total system dynamics is theorized from the perspective of predictive coding. The hypothesis draws inspiration from computational studies grounded in probabilistic generative models and language games, including the Metropolis-Hastings naming game. Thus, playing such games among agents in a distributed manner can be interpreted as a decentralized Bayesian inference of representations shared by a multi-agent system. Moreover, this study explores the potential link between the CPC hypothesis and the free-energy principle, positing that symbol emergence adheres to the society-wide free-energy principle. Furthermore, this paper provides a new explanation for why large language models appear to possess knowledge about the world based on experience, even though they have neither sensory organs nor bodies. This paper reviews past approaches to symbol emergence systems, offers a comprehensive survey of related prior studies, and presents a discussion on CPC-based generalizations. Future challenges and potential cross-disciplinary research avenues are highlighted.

Machine learning-based predictive control of an electrically-heated steam methane reforming process

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional–integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

System for monitoring building heat consumption

Relevance. Energy consumption and efficient use of district heating networks affect the environment, society and the economy. Solutions are needed that can significantly reduce specific thermal energy losses. Using a technically and economically feasible way to assess energy efficiency allows these decisions to be made. Currently, assessing the energy efficiency of buildings raises significant questions among specialists. There is not much modern specialized literature in this area. The problem of energy efficiency requires more rigorous attention to urban planning issues. Thus, refined methods for assessing the energy efficiency of buildings make it possible to simultaneously save capital investments and ensure efficient consumption of thermal energy. Aim. To develop a system for assessing the real heat consumption of buildings to ensure optimization of dispatching of centralized heat supply systems in real time. Methods. Computer modeling of the state of heat consumption of buildings with various characteristics and schemes for their connection to centralized heat supply networks; methods of geographic information analysis. Results. The authors have proposed the system for monitoring the heat consumption of buildings. This system allows assessing the energy efficiency of buildings through the maximum use of computer and software technologies. It is shown that the calculated heat flows of buildings used to predict savings when modernizing buildings do not always correspond to the actual heat loads, which can lead to an incorrect assessment of investment projects. The authors obtained graphical dependences of the quantities influencing specific heat losses on various primary independent factors. Conclusions. The constructed system for monitoring the heat consumption of buildings allows, together with the use of geoinformation analysis methods, obtaining a fairly complete picture of the state of heat consumption for city areas and an assessment of the energy characteristics of buildings.

Fracture modeling of CNT/epoxy nanocomposites based on phase-field method using multiscale strategy

The computational modeling of fracture, particularly in structures with complex crack topologies, remains challenging due to significant computational costs, especially in simulating two- and three-dimensional brittle fracture. This study presents an efficient phase-field model to address these challenges. By leveraging the user (UMAT) subroutine in ABAQUS and establishing an analogy between the phase-field evolution law and the heat transfer equation, the method efficiently tackles complex fracture problems. The model is verified through analysis of typical 2D and 3D fracture benchmarks with different failure modes, demonstrating accuracy and efficiency compared to experimental and numerical data. Additionally, the model is applied to explore brittle fracture in carbon nanotubes (CNTs)/epoxy nanocomposites, revealing insights into the impact of CNT weight fraction on fracture phenomena prediction. The incorporated CNTs in the matrix are considered uniformly dispersed and randomly oriented. Overall, the developed model and computational implementation show promise for meeting the requirements of structural-level engineering practices.

Quantum word embedding for machine learning

The accelerated progress in quantum computing has enabled a new form of machine intelligence that runs on quantum hardware, which holds great promise for more powerful computational models in various learning tasks. An emergent application of Quantum Machine Intelligence (QMI) is Quantum Natural Language Processing (QNLP). This paper proposes a multi-dimensional, finite automaton model for quantum word embedding (QWE) via the Galois field. We demonstrated the model to three applications: (1) English vocabulary, (2) amino acid-based genetic codes, and (3) DNA-based genetic codes. The numerical results obtained from the proposed algorithm for the English vocabulary indicate that it produces more representative word features than Word2Vec based on the word distance metric. Second, the proposed algorithm is also utilized to model RNA-Protein interaction based on the latent distance of a given molecule, which is demonstrated on three large datasets, namely RPI369, RPI1807, and RPI2241. Finally, two embedding techniques for DNA-based genetic codes are proposed in this work: Two-state Lackadaisical Encoding (TCE) and Topological-Cyclic Encoding (TLE). These techniques enable extracting relevant features for the efficacy score of gRNAs used in the CRISPR-Cas 9 system, demonstrated on 15 datasets, compared to 12 mathematical features. We make our implementation available at https://github.com/namnguyen0510/Quantum-Embedding-of-Word/tree/main.

Relation-Algebraic Verification of Disjoint-Set Forests

This paper studies how to use relation algebras, which are useful for high-level specification and verification, for proving the correctness of lower-level array-based implementations of algorithms. We give a simple relation-algebraic semantics of read and write operations on associative arrays. The array operations seamlessly integrate with assignments in computation models supporting while-programs. As a result, relation algebras can be used for verifying programs with associative arrays. We verify the correctness of an array-based implementation of disjoint-set forests using the union-by-rank strategy and find operations with path compression, path splitting and path halving. All results are formally proved in Isabelle/HOL. This paper is an extended version of [1].

Turbofan Performance Estimation Using Neural Network Component Maps and Genetic Algorithm-Least Squares Solvers

Computational models of turbofans that are oriented to assist the design and testing of innovative components are of fundamental importance in order to reduce their environmental impact. In this paper, we present an effective method for developing numerical turbofan models that allows reliable steady-state turbofan performance calculations. The main difference between the proposed method and those used in various commercial algorithms, such as GasTurb, GSP 12 and NPSS, is the use of neural networks as a multidimensional interpolation method for rotational component maps instead of classical β parameter. An additional aspect of fundamental importance lies in the simplicity of implementing this method in Matlab and the high degree of customization of the turbofan components without performing any manipulation of variables for the purpose of reducing the dimensionality of the problem, which would normally lead to a high condition number of the Jacobian matrix associated with the nonlinear turbofan system (and, thus, to significant error). In the proposed methodology, the component behavior can be modeled by analytical relationships and through the use of neural networks trained from component bench test data or data obtained from CFD simulations. Generalization of rotational component maps by feedforward neural networks leads to an average interpolation error up to around 1%, for all variables. The resulting nonlinear system is solved by a combined genetic algorithm and least squares algorithm approach, instead of the standard Newton’s method. The turbofan numerical model turns out to be convergent, and results suggest that the trend in overall turbofan performance, as flight conditions change, is in agreement with the outputs of the GSP 12 software.