Optimal retrofit solutions for residential buildings under different climate types using surrogate model-based optimization

In subsurface flow simulation, data-driven deep learning surrogate models have emerged as a promising alternative to traditional simulation methods. However, a major challenge is the large amount of high-fidelity simulation data required for training, and purely data-driven flow surrogate models often lack theoretical support, limiting their applicability to engineering problems. In this study, we propose an efficient surrogate model construction method that integrates physical constraints and multi-fidelity training data. The process begins with pre-training a deep learning model using a large amount of low-cost, low-fidelity data to effectively initialize network parameters. The pre-trained model is then fine-tuned with a smaller amount of high-fidelity data. Throughout the training, physical losses constrain the model to adhere to physical principles. For the case considered here, this approach reduces the need for high-fidelity data by approximately 83% compared to traditional training methods without compromising predictive accuracy. Additionally, incorporating physical loss during model training e ffectively enhances the surrogate model's predictive accuracy, with physical loss reduced by nearly 70% compared to purely data-driven training. The method employs the Jacobian matrix to aid in model training, significantly reducing the computational cost of physical losses. Overall, this surrogate model construction method addresses the challenges of obtaining training data and the lack of physical support in surrogate models, providing reliable support for complex reservoir development decisions.

In general, static tools and simplified assessment approaches are still dominating the Danish building energy retrofit market. These static tools are generally associated with a large number of assumptions and tend to neglect the overall building dynamics. This leads to major uncertainties and substantial gaps between the predicted performance, promised before retrofitting, and the real building performance after carrying out the retrofit project. To overcome these challenges, this work presents the design, development and demonstration of DanRETRO, a tool for Danish buildings energy retrofit design and evaluation. The tool uses a large database of dynamic performance simulations employing EnergyPlus, for different building types, ages and sizes, allowing a preliminary assessment of the technical, economic and environmental impacts of various retrofit measures. In this regard, the tool provides a large selection of retrofit techniques and measures along with retrofit packages. DanRETRO is intended to be a comprehensive building energy retrofit assessment tool, but at the same time being simple to use with minimal inputs. The demonstration of the tool in an office building, a single-family house and an apartment in Denmark is presented and assessed. DanRETRO evaluation results are aimed to serve as a basis to aid energy retrofit projects decision-making.

An accurate and efficient surrogate model plays a significant role in the optimal design of electrical motors. This paper presents an accurate and efficient surrogate modeling method for a flux reversal permanent magnet arc motor (FR-PMAM) based on machine learning algorithm. Specifically, the motor topology and the optimized design of the FR-PMAM are firstly illustrated. Then, the PM magnetic motive force-permeance model is developed to select structural parameters of FR-PMAM. After that, a finite-element analysis (FEA) model of FR-PMAM is calculated to obtain sample data. A machine learning algorithm random forest is introduced into the field of motor optimal design for the first time to evaluate the contribution of each parameter to the design objectives. Based on the sample data, an efficient machine learning algorithm CatBoost is innovatively employed to establish the accurate and efficient surrogate model of the FR-PMAM that can capture the complex function relationship between output objectives and input parameters. Subsequently, an improved genetic algorithm with the elitist reservation strategy is adopted to obtain the optimal combination of the structural parameters of FR-PMAM. Finally, a prototype of the FR-PMAM is manufactured. The FEA and experimental results validate the effectiveness of the investigated surrogate modeling method.

BackgroundOpioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among patients, due to the complex interplay between biological and clinical factors. Pharmacogenetic testing can be used to match patients’ genetic profiles to individualize opioid therapy, improving pain relief and reducing the risk of adverse effects. Despite its potential, the pharmacogenetic testing uptake (use of pharmacogenetic testing) remains low due to a range of barriers at the patient, health care provider, infrastructure, and financial levels. Since testing typically involves a shared decision between the provider and patient, predicting the likelihood of a patient undergoing pharmacogenetic testing and understanding the factors influencing that decision can help optimize resource use and improve outcomes in pain management.ObjectiveThis study aimed to develop machine learning (ML) models, identifying patients’ likelihood of pharmacogenetic uptake based on their demographics, clinical variables, medication use, and social determinants of health.MethodsWe used electronic health record data from a single center health care system to identify patients prescribed opioids. We extracted patients’ demographics, clinical variables, medication use, and social determinants of health, and developed and validated ML models, including a neural network, logistic regression, random forest, extreme gradient boosting (XGB), naïve Bayes, and support vector machines for pharmacogenetic testing uptake prediction based on procedure codes. We performed 5-fold cross-validation and created an ensemble probability-based classifier using the best-performing ML models for pharmacogenetic testing uptake prediction. Various performance metrics, uptake stratification analysis, and feature importance analysis were used to evaluate the performance of the models.ResultsThe ensemble model using XGB and support vector machine–radial basis function classifiers had the highest C-statistics at 79.61%, followed by XGB (78.94%), and neural network (78.05%). While XGB was the best-performing model, the ensemble model achieved a high accuracy (32,699/48,528, 67.38%), recall (537/702, 76.50%), specificity (32,162/47,826, 67.25%), and negative predictive value (32,162/32,327, 99.49%). The uptake stratification analysis using the ensemble model indicated that it can effectively distinguish across uptake probability deciles, where those in the higher strata are more likely to undergo pharmacogenetic testing in the real world (320/4853, 6.59% in the highest decile compared to 6/4853, 0.12% in the lowest). Furthermore, Shapley Additive Explanations value analysis using the XGB model indicated age, hypertension, and household income as the most influential factors for pharmacogenetic testing uptake prediction.ConclusionsThe proposed ensemble model demonstrated a high performance in pharmacogenetic testing uptake prediction among patients using opioids for pain. This model can be used as a decision support tool, assisting clinicians in identifying patients’ likelihood of pharmacogenetic testing uptake and guiding appropriate decision-making.

Development and validation of an interpretable machine learning model for cerebral small vessel disease risk assessment.

Are machine learning methods superior to traditional statistics in predicting blastocyst ploidy status using morphokinetic and clinical biodata? Mixed effects logistic regression performed better than all machine learning methods for ploidy prediction using our dataset of 8147 embryos. Morphokinetic timings have been demonstrated to be delayed in aneuploid embryos. Machine learning and statistical models are increasingly being built, however, until now they have been limited by data insufficiency. This is a multicentre cohort study. Data were obtained from 8147 biopsied blastocysts from 1725 patients, treated from 2012 to 2020. All embryos were cultured in a time-lapse system at nine IVF clinics in the UK. A total of 3004 euploid embryos and 5023 aneuploid embryos were included in the final verified dataset. We developed a total of 12 models using four different approaches: mixed effects multivariable logistic regression, random forest classifiers, extreme gradient boosting, and deep learning. For each of the four algorithms, two models were created, the first consisting of 22 covariates using 8027 embryos (Dataset 1) and the second, a dataset of 2373 embryos and 26 covariates (Dataset 2). Four final models were created by switching the target outcome from euploid to aneuploid for each algorithm (Dataset 1). Models were validated using internal-external cross-validation and external validation. All morphokinetic variables were significantly delayed in aneuploid embryos. The likelihood of euploidy was significantly increased the more expanded the blastocyst (P < 0.001) and the better the trophectoderm grade (P < 0.01). Univariable analysis showed no association with ploidy status for morula or cleavage stage fragmentation, morula grade, fertilization method, sperm concentration, or progressive motility. Male age did not correlate with the percentage of euploid embryos when stratified for female age. Multinucleation at the two-cell or four-cell stage was not associated with ploidy status. The best-performing model was logistic regression built using the larger dataset with 22 predictors (F1 score 0.59 for predicting euploidy; F1 score 0.77 for predicting aneuploidy; AUC 0.71; 95% CI 0.67-0.73). The best-performing models using the algorithms from random forest, extreme gradient boosting, and deep learning achieved an AUC of 0.68, 0.63, and 0.63, respectively. When using only morphokinetic predictors the AUC was 0.61 for predicting ploidy status, whereas a model incorporating only embryo grading was unable to discriminate aneuploid embryos (AUC = 0.52). The ploidy prediction model's performance improved with increasing age of the egg provider. The models have not been validated in a prospective study design or yet been used to determine whether they improve clinical outcomes. This model may aid decision-making, particularly where pre-implantation genetic testing for aneuploidy is not permitted or for prioritizing embryos for biopsy. No specific funding was sought for this study; university funds supported the first author. A.Ca. is a minor shareholder of participating centres. N/A.

Comparative study of surrogate approaches while optimizing computationally expensive reaction networks

Implementation of online optimization and control of complex processes near impossible in given time frame owing to large computational time of the models. Fast and efficient surrogate models such as ANN offer a credible solution to this problem. Optimization of the complex model can proceed with a data based surrogate model, thereby making it much faster than the conventional run. However, the process of surrogate model construction involves certain number of parameters that are usually fixed based on some heuristic, thereby constructing an inefficient model. This paper aims at presenting a novel parameter free surrogate building algorithm specifically focusing on artificial neural networks (ANN) for emulating the physics based models. The proposed algorithm intelligently designs the configuration of the network along with simultaneous determination of sample size required for training it to predict results with maximum accuracy. The novel sample size determination algorithm is based on a potential concept of hypercube sampling technique, which assures convergence to results in very less computational time. The multi-objective optimization framework ensures parsimonious behavior of the network. The proposed algorithm is utilized to build optimal ANN models for highly nonlinear polyvinyl acetate polymer reaction network model. Optimization of this complicated reaction network with the ANN surrogate in place is achieved nearly 12 times faster than the first principle based model. All this being possible with a surrogate building algorithm, which just takes the simulation model as input.

Efficient surrogate modeling is a key requirement for uncertainty quantification in data-driven scenarios. In this work, a novel approach of using sparse random features for surrogate modeling in combination with self-supervised dimensionality reduction is described. The method is compared to other methods on synthetic and real data obtained from crashworthiness analyses. The results underline the applicability of the here described approach, performing at least as well as polynomial chaos expansions and neural networks.

Sustainable High-rises

The National Regulations for Energy Efficiency of Residential Buildings (RTQ -R ), published in 2012 by INMETRO contains two procedures for evaluating the performance of buildings: a prescriptive method and a method by simulation . The prescriptive method consists of applying equations for calculating degree- hours of discomfort and energy consumption for cooling and heating of the housing unit under analysis. Through the equations, building performance indicators are related to parameters that describe the building design. However, the list of parameters does not take into account the type of glass or the solar factor of the glazing system installed in the facade. The aim of this work is to propose new equations for the RTQ-R, including the solar factor as the parameter that describes the influence of the glass in the energy efficiency level of the building. A set of parametric simulations were carried out with EnergyPlus software and weather files of eight Brazilian cities covering all bioclimatic zones defined in NBR 15220. New equations to describe the performance of residential buildings were obtained by regression analysis replicating the same methodology applied by CB3E (Brazilian Center for Energy Efficiency in Buildings), when obtaining the equations for the current development version of RTQ-R. Three different specifications of glass were adopted to adjust the batch simulations performed in that methodology. As a result, the new equations were generated and the coefficients of determination (R²) obtained were equal to or better than the current model of the RTQ-R. The best model was achieved for Bioclimatic Zone 2, where R² was 0.91 in this proposal, against 0.82 for the current model. This work

Significant progress in the oil and gas industry advances the application of pipeline into an intelligent era, which poses rigorous requirements on pipeline safety, reliability, and maintainability, especially when crossing seismic zones. In general, strike-slip faults are prone to induce large deformation leading to local buckling and global rupture eventually. To evaluate the performance and safety of pipelines in this situation, numerical simulations are proved to be a relatively accurate and reliable technique based on the built-in physical models and advanced grid technology. However, the computational cost is prohibitive, so one has to wait for a long time to attain a calculation result for complex large-scale pipelines. In this manuscript, an efficient and accurate surrogate model based on machine learning is proposed for strain demand prediction of buried X80 pipelines subjected to strike-slip faults. Specifically, the support vector regression model serves as a surrogate model to learn the high-dimensionally nonlinear relationship which maps multiple input variables, including pipe geometries, internal pressures, and strike-slip displacements, to output variables (namely tensile strains and compressive strains). The effectiveness and efficiency of the proposed method are validated by numerical studies considering different effects caused by structural sizes, internal pressure, and strike-slip movements.

Purpose This study aims to develop a hybrid simulation–learning framework that couples an incompressible smoothed particle hydrodynamics solver with data-driven surrogate models for efficient pblackiction of transient coupled heat and mass transfer in a wavy porous annulus filled with nano-encapsulated phase change materials. Design/methodology/approach A Darcy–Forchheimer–Brinkman porous-flow formulation is solved using the incompressible smoothed particle hydrodynamics method to simulate unsteady coupled heat and mass transfer in a wavy annular cavity with a rotating inner body. Time-dependent average heat-and mass-transfer indicators extracted from parametric simulations are used to train compact machine-learning surrogates, including Random Forest regression and extreme gradient boosting, based on interaction-aware dimensionless input features. Findings The numerical results reveal that shortening the cooled boundary segment intensifies early time thermal and solutal gradients, positive buoyancy interactions significantly enhance convective transport and higher permeability levels promote advection-dominated behavior. The trained surrogate models accurately reproduce the high-fidelity simulation trends and enable rapid response-surface evaluation; Random Forest provides stable global pblackictions, while extreme gradient boosting effectively captures localized nonlinear variations. Research limitations/implications The pblackictive capability of the surrogate models is restricted to the parametric ranges coveblack by the present simulation database. Extension to wider operating conditions or additional physical effects requires further numerical data generation and model retraining. Practical implications The proposed hybrid incompressible smoothed particle hydrodynamics and machine-learning workflow offers a fast and reliable design-support tool for thermal systems incorporating nano-encapsulated phase change materials, enabling efficient geometry optimization, operating-condition selection and sensitivity assessment with near-instantaneous pblackictions. Social implications By facilitating improved thermal management and energy-storage design through rapid surrogate-assisted screening, the proposed framework can contribute to enhanced energy efficiency and blackuced environmental impact in advanced heat-management applications. Originality/value This work presents a unified incompressible smoothed particle hydrodynamics and data-driven modeling framework for a wavy porous annulus with nano-encapsulated phase change material filling and rotating-body mixing, combining high-fidelity physics-based simulations with compact and interpretable machine-learning surrogates for fast pblackiction of coupled heat and mass transfer performance.

Automated Vehicles (AVs) need to be thoroughly evaluated in order to ensure their driving capabilities. However, comprehensive evaluations are intractable due to both time and monetary costs. To address this problem, we propose an Adaptive Design of Experiments (ADOE) method to evaluate the safety of AVs. Using this method, a Surrogate Model (SM) is established and updated iteratively. SM in ADOE is used to approximate the results of AV testing and help to select the next concrete scenario to be tested in each iteration. Two different ADOE approaches are proposed in this study for different testing purposes. Since the choice of the surrogate model has a profound impact on the performance of the ADOE method, 6 surrogate models were compared with two logical scenarios at different scales – a car following logical scenario and a cut-in logical scenario. Results show that Extreme Gradient Boosting (XGB) is suitable for both ADOE approaches. And both proposed ADOE approaches achieved desired performance. Scenario-oriented ADOE made full use of each concrete scenario, capturing one collision case for every 1.12 test runs in the car following logical scenario, while SM-oriented ADOE successfully depicted the boundary between safety and danger. Using 0.46% test resources compared to enumeration, the SM-oriented ADOE found 93.9% dangerous scenarios with 90.9% precision. ADOE approaches have great potential in accelerating the evaluation of AV safety.

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model.