In Silico Prediction of Physicochemical Properties

This report provides a critical review of computational models, and in particular(quantitative) structure-property relationship (QSPR) models, that are available for the prediction of physicochemical properties. The emphasis of the review is on the usefulness of the models for the regulatory assessment of chemicals, particularly for the purposes of the new European legislation for the Registration, Evaluation, Authorisation and Restriction of CHemicals (REACH), which entered into force in the European Union (EU) on 1 June 2007. It is estimated that some 30,000 chemicals will need to be further assessed under REACH. Clearly, the cost of determining the toxicological and ecotoxicological effects, the distribution and fate of 30,000 chemicals would be enormous. However, the legislation makes it clear that testing need not be carried out if adequate data can be obtained through information exchange between manufacturers, from in vitro testing, and from in silico predictions. The effects of a chemical on a living organism or on its distribution in the environment is controlled by the physicochemical properties of the chemical. Important physicochemical properties in this respect are, for example, partition coefficient, aqueous solubility, vapour pressure and dissociation constant. Whilst all of these properties can be measured, it is much quicker and cheaper, and in many cases just as accurate, to calculate them by using dedicated software packages or by using (QSPRs). These in silico approaches are critically reviewed in this report.JRC.I.3-Toxicology and chemical substance

Application of supervised learning algorithms for temperature prediction in nucleate flow boiling

This work investigates the use of supervised learning algorithms to predict temperatures in an experimental test bench, which was initially designed for studying nucleate boiling phenomena with ethylene glycol/water mixtures. The proposed predictive model consists of three stages of machine learning. In the first one, a supervised algorithm block is employed to determine whether the critical heat flux (CHF) will be reached within the test bench limits. This classification relies on input parameters including bulk temperature, tilt angle, pressure, and inlet velocity. Once the CHF condition is established, another machine learning algorithm predicts the specific heat flux at which CHF will occur. Subsequently, based on the classification generated by the first block, the evolution of temperature in response to increases in heat flux is predicted using either the previously estimated heat flux or the physical limits of the experimental facility as the stopping criterion. To accomplish all these predictions, the study compares the performance of various algorithms including artificial neural networks, random forest, support vector machine, AdaBoost, and XGBoost. These algorithms were specifically trained using cross-validation and grid search methods to optimize their effectiveness. Results for the CHF classification purpose demonstrate that the support vector machine algorithm performs the best, achieving an F1-score of 0.872 on the testing dataset, while the boosting methods (AdaBoost and XGBoost) exhibit signs of overfitting. In predicting the CHF value, the artificial neural network achieved the lower nMAE on the testing dataset (6.18%). Finally, the validation of the temperature forecasting models, trained on a dataset composed of 314,476 samples, reveals similar performances across all methods, with R2 values greater than 0.95.Agencia Estatal de Investigación | Ref. RTC2019-006955-4Agencia Estatal de Investigación | Ref. PID2020-114742RB-I00Universidade de Vigo/CISU

Mathematical Modelling of Energy Systems and Fluid Machinery

The ongoing digitalization of the energy sector, which will make a large amount of data available, should not be viewed as a passive ICT application for energy technology or a threat to thermodynamics and fluid dynamics, in the light of the competition triggered by data mining and machine learning techniques. These new technologies must be posed on solid bases for the representation of energy systems and fluid machinery. Therefore, mathematical modelling is still relevant and its importance cannot be underestimated. The aim of this Special Issue was to collect contributions about mathematical modelling of energy systems and fluid machinery in order to build and consolidate the base of this knowledge

Nuclear Power

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the "system-thinking" approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Nuclear Power - System Simulations and Operation

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the system-thinking approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Applications of artificial neural networks (ANNs) in several different materials research fields

PhDIn materials science, the traditional methodological framework is the identification of the composition-processing-structure-property causal pathways that link hierarchical structure to properties. However, all the properties of materials can be derived ultimately from structure and bonding, and so the properties of a material are interrelated to varying degrees. The work presented in this thesis, employed artificial neural networks (ANNs) to explore the correlations of different material properties with several examples in different fields. Those including 1) to verify and quantify known correlations between physical parameters and solid solubility of alloy systems, which were first discovered by Hume-Rothery in the 1930s. 2) To explore unknown crossproperty correlations without investigating complicated structure-property relationships, which is exemplified by i) predicting structural stability of perovskites from bond-valence based tolerance factors tBV, and predicting formability of perovskites by using A-O and B-O bond distances; ii) correlating polarizability with other properties, such as first ionization potential, melting point, heat of vaporization and specific heat capacity. 3) In the process of discovering unanticipated relationships between combination of properties of materials, ANNs were also found to be useful for highlighting unusual data points in handbooks, tables and databases that deserve to have their veracity inspected. By applying this method, massive errors in handbooks were found, and a systematic, intelligent and potentially automatic method to detect errors in handbooks is thus developed. Through presenting these four distinct examples from three aspects of ANN capability, different ways that ANNs can contribute to progress in materials science has been explored. These approaches are novel and deserve to be pursued as part of the newer methodologies that are beginning to underpin material research

Kritična svojstva i acentrični čimbenici modeliranja čistih spojeva primjenom modela QSPR-SVM i algoritma Dragonfly

This work aimed to model the critical pressure, temperature, volume properties, and acentric factors of 6700 pure compounds based on five relevant descriptors and two thermodynamic properties. To that end, four methods were used, namely, multi-linear regression (MLR), artificial neural networks (ANNs), support vector machines (SVMs) using sequential minimal optimisation (SMO), and hybrid SVM with Dragonfly optimisation algorithm (SVM-DA) to model each property. The results suggested that hybrid SVM-DA had better prediction performance compared to the other models in terms of average absolute relative deviation (AARD%) of {0.7551, 1.962, 1.929, and 2.173} and R2 of {0.9699, 0.9673, 0.9856, and 0.9766} for critical temperature, critical pressure, critical volume, and acentric factor, respectively. The developed models can be used to estimate the property of newly designed compounds only from their molecular structure.Cilj ovog rada bio je modeliranje kritičnog tlaka, temperature, volumnih svojstava i acentričnih čimbenika 6700 čistih spojeva na temelju pet relevantnih deskriptora i dva termodinamička svojstva. U tu svrhu primijenjene su četiri metode: višestruka linearna regresija (MLR), umjetna neuronska mreža (ANN), metoda potpornih vektora (SVM) i algoritam optimizacije Dragonfly (SVM-DA), koji se za modeliranje svakog svojstva koriste sekvencijalnom minimalnom optimizacijom (SMO) i hibridnim SVM-om. Rezultati su pokazali da hibridni SVM-DA daje bolje predviđanje u odnosu na ostale modele u smislu postotka prosječnog apsolutnog relativnog odstupanja (AARD%) od {0,7551, 1,962, 1,929 i 2,173} i R2 od {0,9699, 0,9673, 0,9856, i 0,9766} za kritičnu temperaturu, kritični tlak, kritični volumen i acentrični faktor. Razvijeni modeli mogu se primjenjivati za procjenu svojstava novodizajniranih spojeva samo iz njihove molekularne strukture

초임계유체의 미시적 기액 공존 현상과 거시적 보편 거동에 관한 전산 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2022.2. 이원보.임계점 이상의 온도와 압력에서는 기체와 액체 상의 경계가 사라지며 단일상 물질인 초임계유체가 관찰된다. 초임계유체는 기체와 액체의 중간 특성을 나타내어 독특한 용해 및 수송 물성을 갖기 때문에 분리· 추출·반응 매질로 산업 공정에서 널리 사용되고 있다. 초임계 상태의 분자들은 강한 열적 요동으로 인해 미시적 수준에서 비균질한 밀도 분포를 갖고 있으며, 잠열을 동반하는 1차 상전이가 나타나지 않으면서도 거시적 물성의 현저한 전이 현상을 보인다. 초임계유체의 거동을 효과적으로 이해하고 예측하기 위해서는 미시적 영역에서 거시적 수준에 이르는 모든 길이 규모에서의 독특한 현상을 통합적으로 기술하는 이론 틀이 필요하지만 전통적인 액체 계 물리학으로는 이를 다루기 어렵다. 또한 산업 현장에서 이산화탄소·물·메탄올 등 다양한 물질의 초임계유체가 활용되고 있기에 효율적인 응용을 위해서는 서로 다른 초임계 물질의 물성을 간단하게 예측하는 확장된 형태의 대응상태원리가 필요하다. 이 논문에서는 분자 시뮬레이션, 국부 구조 분석, 데이터기반 기계학습 알고리즘을 종합하여 초임계유체의 특이한 거동을 이해하고 예측하는 계산과학적 방법론을 보고한다. 이 논문의 주요 기여점은 두 가지이다. 첫째, 초임계유체가 거시적 수준에서 나타내는 변칙적인 물성을 미시적 수준에서의 기·액 공존으로 설명하는 통계역학적 이론을 제안하였다. 통계적 혼합 모델과 인공신경망 분류 알고리즘을 사용하여 초임계유체 상태의 분자를 기체·액체에 해당하는 미소상태로 분류함으로써 개별 미소상태의 구조적·열역학적 특성을 분석하는 방법론을 개발하였고, 분석 결과로부터 거시적 규모의 변칙 물성이 미소상태 간의 전이 확률과 밀접하게 연관되어 있으며 거시적 이상 현상은 두 미소상태의 비율이 1:1일 때 최대가 됨을 보였다. 전통적으로 초임계유체의 거시적 전이 현상은 열역학적 위덤 선(Widom line)에서 일어난다고 알려져 있었는데, 이상의 연구 결과를 종합하여 위덤 선을 미소상태 비율이 1:1인 지점으로 재정의하였으며, 위덤 선을 포함하고 기체·액체 미소상태가 유의한 비율로 공존하여 액상 및 기상과 구별되는 초임계유체의 존재 영역을 위덤 델타(Widom delta)로 정의하였다. 이상의 결과로부터 초임계유체의 미시적·거시적 이상 현상이 기·액 미소상태의 공존으로 인한 공간 분할 및 열적 요동에서 기원한다는 사실을 제안하여 서로 다른 길이 수준에서의 이론을 통합하였다. 둘째, 미소상태 간의 비율에 대한 미시적 분석으로부터 서로 다른 초임계 물질의 거동을 보편적으로 기술하는 눈금 바꿈 이론을 개발함으로써 새로운 대응상태원리를 제안하였다. 2차 상전이가 일어나는 임계점 근처에서는 자유에너지의 2계 미분이 발산하는데, 이와 유사하게 기체 미소상태에 속하는 분자의 개수비 역시 임계점 인근에서 그 미분계수가 발산하였다. 기체 미소상태의 개수비가 온도에 대해 보이는 멱급수 법칙을 이용하여 임계온도를 정확하게 추정하였으며, 온도가 서로 다른 등온 곡선에서의 미소상태 개수비를 예측하는 단일 곡선을 얻을 수 있었다. 또한, 각 미소상태의 평균 밀도를 미소상태 개수비의 함수로 전개하여 초임계유체의 거시적 밀도를 기술하는 근사적 관계식을 도출하고, 이를 바탕으로 아르곤, 이산화탄소, 물 등 초임계 상태의 여러 유체의 평균 밀도를 예측하는 보편적 방법론을 보고하였다. 이상의 결과를 종합하여 이 논문에서는 초임계 상태의 유체에는 기체·액체의 두 가지 미소상태가 존재하며, 미시적·거시적 규모의 변칙 물성이 모두 미소상태 간의 활발한 전이 현상에서 유래함을 주장한다. 미소상태 가설로부터 예측한 보편 거동이 시뮬레이션 결과 및 실험 결과와 상당히 일치하기에, 이 논문에서 제안한 통계역학적 이론은 초임계유체의 물성을 결정하는 물리적 원리를 상당한 수준에서 포착하고 있다고 논증할 수 있다. 또한 이 논문에서 사용된 국소 구조 분석 및 데이터기반 학습 방법론은 고압 유체의 동역학적 전이 현상, 과냉각수의 결빙 거동 예측, 연성물질의 상전이 현상 분석 등 비균질한 입자 분포를 보이는 제반 물리화학계에 확장 적용될 수 있을 것으로 기대된다.Supercritical fluids have a wide variety of applications in chemical industries as media for separation, extraction, and reaction, due to the anomalous blend of liquid-like and gas-like traits. Supercritical fluid simultaneously manifests microscopic density inhomogeneities and macroscopic crossover phenomena, yet the concrete relation between the anomalies at different length scales remains vague. Therefore, to understand and predict the behavior of supercritical fluids, it is crucial to develop a theoretical framework that can capture the physics of the supercritical fluid at all relevant length scales, which is beyond the scope of conventional fluid theories. Furthermore, for efficient utilization of supercritical fluids, a unifying framework is required to describe the properties of different supercritical fluids, including the substances of industrial importance. This thesis aims to develop statistical-mechanical theories and computational methodologies to understand and predict the anomalous behaviors of supercritical fluids, combining molecular simulation, local structure analysis, and data-driven machine learning algorithms. The main contribution of this thesis is two-fold. First, a statistical-mechanical theory is developed to explain the microscopic origin of the macroscopic anomalies in supercritical fluids. Motivated by the mixture model approach on the local density distribution of supercritical fluid into gas-like and liquid-like sub-distributions, an artificial neural network classifier is trained to label individual particles in a model supercritical fluid as liquid-like or gas-like. Supercritical anomalies are found to be strongly correlated to the frequency of the transitions between the microstates, which is maximized when the number fractions of the two categories are even. Summing up the results, the thermodynamic Widom line, the traditional loci of macroscopic crossover phenomena, is redefined as the line of equal microstate fraction. Since the Widom line is enclosed in the deltoid region of microstate coexistence, the domain of supercritical coexistence is suggested to be called the “Widom delta.” Second, a novel corresponding states law is proposed, where supercritical states of different substances are described by a scaling relation derived from microscopic analysis. Here, the scaling function is defined as the gradient of the microstate fraction, which shows a power-law divergence behavior in the vicinity of the critical point. The liquid-gas critical point can be accurately located from the exponent of the scaling function, indicating that the macroscopic physics is effectively encoded in the microscopic partitioning of microstates. Isothermal curves at a range of temperatures can be collapsed by assuming the self-similarity of the structures of the fluid, leading to a data collapse onto a single master curve. Expansion of the density equalities results in an approximate scaling relation on bulk density, which is universal among simple fluids with the same exponent acquired from machine learning analysis, providing an efficient method to predict the macroscopic properties of different fluids from a simple argument. Summing up the results, this thesis claims that two microstates of liquid-like and gas-like nature coexist in the supercritical fluid, and both the microscopic and the macroscopic anomalies originate from the vigorous fluctuations between the microstates. Predictions from the microstate hypothesis show substantial agreement with the computational and experimental results, implying that the statistical-mechanical theory suggested in this thesis accurately captures the governing physics of supercritical fluids. Furthermore, data-driven local structure analysis techniques employed in this thesis can be extended to a variety of systems with salient density inhomogeneities, including the dynamic transition in high-pressure fluids, freezing behavior of supercooled liquids, or microphase transitions in soft matter, to name a few. It is expected that the application of the analysis techniques reported in this thesis would open new research opportunities in various fields of physical chemistry and chemical physics.Abstract i List of Tables v List of Figures vi Chapter 1. Introduction 1 1.1. Background 1 1.2. Overview 4 Chapter 2. Methods 6 2.1. Simulation details 6 2.2. Machine learning 19 Chapter 3. Widom delta 28 3.1. Mixture model 30 3.2. Per-atom classification 44 3.3. Widom delta and the supercritical gas-liquid boundary 53 3.4 Summary 62 Chapter 4. Universality in supercritical fluids 63 4.1. Critical divergence 64 4.2. Scaling and universality 71 4.3. Isomorph theory 79 4.4. Summary 94 Chapter 5. Concluding remarks 96 5.1. Conclusion 96 5.2. Outlook 98 Bibliography 108 Abstract in Korean 119박

Surrogate Model Optimisation for PWR Fuel Management

Pressurised Water Reactor (PWR) fuel management is an operational problem for nuclear operators, requiring solutions on a regular basis throughout the life of the plant. A variety of conflicting factors and changing goals mean that fuel loading pattern design problems are multiobjective and, by design, have many input variables. This causes a combinatorial explosion, known as the ‘curse of dimensionality’, which makes these complex problems difficult to investigate. In this thesis, the method of surrogate model optimisation is adapted to PWR loading pattern generation. Surrogate models are developed based around three approaches: deep learning methods (convolutional neural networks and multi-layer perceptrons), the fission matrix and simulated quantum annealing. The models are used to predict core parameters of reactors in simplified optimisation scenarios for a microcore, a small modular reactor, and a ‘standard’ PWR. The experiments with deep learning models show that competitive results can be obtained for training sets using a much lower number of simulations than direct optimisation. Fission matrix experiments demonstrate the method to predict core parameters for the first time, with interesting preliminary results. Novel experiments using simulated quantum annealing demonstrate the technique is able to generate loading patterns by following heuristic rules and is suitable for application to custom optimisation hardware. The principal contribution of this work is to show that surrogate model optimisation can be used to augment fuel loading pattern optimisation, generating competitive results and providing enormous computational cost reduction and thus permitting more investigation within a given computational budget. These methods can also make use of new computational hardware such as neural chips and quantum annealers. The promising methods developed in this thesis thus provide candidate implementations that can bring the benefits of these innovations to the sphere of nuclear engineering