Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization

Our work presents a novel approach to shape optimization, that has the twofold objective to improve the efficiency of global optimization algorithms while promoting the generation of high-quality designs during the optimization process free of geometrical anomalies. This is accomplished by reducing the number of the original design variables defining a new reduced subspace where the geometrical variance is maximized and modeling the underlying generative process of the data via probabilistic linear latent variable models such as Factor Analysis and Probabilistic Principal Component Analysis. We show that the data follows approximately a Gaussian distribution when the shape modification method is linear and the design variables are sampled uniformly at random, due to the direct application of the central limit theorem. The model uncertainty is measured in terms of Mahalanobis distance, and the paper demonstrates that anomalous designs tend to exhibit a high value of this metric. This enables the definition of a new optimization model where anomalous geometries are penalized and consequently avoided during the optimization loop. The procedure is demonstrated for hull shape optimization of the DTMB 5415 model, extensively used as an international benchmark for shape optimization problems. The global optimization routine is carried out using Bayesian Optimization and the DIRECT algorithm. From the numerical results, the new framework improves the convergence of global optimization algorithms, while only designs with high-quality geometrical features are generated through the optimization routine thereby avoiding the wastage of precious computationally expensive simulations

An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constants

In this work, we present a new deterministic partition-based Global Optimization (GO) algorithm that uses estimates of the local Lipschitz constants associated with different sub-regions of the domain of the objective function. The estimates of the local Lipschitz constants associated with each partition are the result of adaptively balancing the global and local information obtained so far from the algorithm, given in terms of absolute slopes. We motivate a coupling strategy with local optimization algorithms to accelerate the convergence speed of the proposed approach. In the end, we compare our approach HALO (Hybrid Adaptive Lipschitzian Optimization) with respect to popular GO algorithms using hundreds of test functions. From the numerical results, the performance of HALO is very promising and can extend our arsenal of efficient procedures for attacking challenging real-world GO problems. The Python code of HALO is publicly available on GitHub. https://github.com/dannyzx/HAL

PIV data clustering of a buoyant jet in a stratified environment

Three spatial clustering approaches of a high-Reynolds number transient buoyant jet in a linearly stratified environment are applied along with proper orthogonal decomposition to identify similar/consistent regions in the domain of interest. The velocity fields analyzed are obtained from an experimental test with large scale, time-resolved, particle image velocimetry (PIV) measurements. Clustering is performed by the k-means method considering: (a) crosssection velocity profiles, (b) point-wise energy spectra, and (c) point-wise Reynolds stress tensor components. Three metrics are used for the assessment of clustering approaches, namely: (a) within-cluster sum of squares, (b) average silhouette, and (c) within-cluster number of POD modes required to resolve prescribed levels of total variance/energy. Results are promising and lay the foundation for an in depth analysis of local features of complex flows as well as the formulation of efficient reduced order models

Highly contiguous assemblies of 101 drosophilid genomes

Over 100 years of studies in Drosophila melanogaster and related species in the genus Drosophila have facilitated key discoveries in genetics, genomics, and evolution. While high-quality genome assemblies exist for several species in this group, they only encompass a small fraction of the genus. Recent advances in long-read sequencing allow high-quality genome assemblies for tens or even hundreds of species to be efficiently generated. Here, we utilize Oxford Nanopore sequencing to build an open community resource of genome assemblies for 101 lines of 93 drosophilid species encompassing 14 species groups and 35 sub-groups. The genomes are highly contiguous and complete, with an average contig N50 of 10.5 Mb and greater than 97% BUSCO completeness in 97/101 assemblies. We show that Nanopore-based assemblies are highly accurate in coding regions, particularly with respect to coding insertions and deletions. These assemblies, along with a detailed laboratory protocol and assembly pipelines, are released as a public resource and will serve as a starting point for addressing broad questions of genetics, ecology, and evolution at the scale of hundreds of species

A lipschitzian global optimization algorithm and machine learning for fluid dynamics

The research conducted and resumed in this thesis covers two different topics. In chapter 1, I focused my research on the development of a new Global Optimization algorithm informed with an estimate of the Lipschitz constant of the objective function. Estimation of the Lipschitz constant is obtained using the tools from the Extreme Value Theory. To extract the information of the local behavior of the objective function, I proposed a clustering strategy to enlighten the algorithm of the local Lipschitz constants. In chapters 2, 3, and 4, I show my research by developing and applying Machine Learning methodologies to three Fluid Dynamics phenomena of different nature. Specifically, in chapter 2, I propose a new framework for design space dimensionality reduction for shape optimization based on Probabilistic Linear Latent Variable models. The new framework performs the classical reduction of the number of the design variables, which is crucial to speed up the convergence of the optimization process. Furthermore, It provides the uncertainty of the new geometrical parametrization by introducing a constraint in the optimization problem based on the Mahalanobis distance. In chapter 3, my research is concentrated on the extraction and the interpretation of highly nonlinear turbulent phenomena measured with the Particle Image Velocimetry technique. Data-driven analysis is carried out for two high Reynolds number vortices flows namely for uniform and buoyant jets and 4- and 7-bladed propeller wakes. In chapter 4, I focused on the prediction of the ship motion at a high sea state level. For this application, Deep Learning methods for sequential data such as Recurrent-type Neural Networks have very desirable properties due to the high nonlinearities present inside the system. Besides the model's predictive performance, the uncertainty information is retrieved from a Bayesian perspective through Variational Inference

Data on roof renovation and photovoltaic energy production including energy storage in existing residential buildings

This data article refers to the paper "Optimizing photovoltaic electric generation and roof insulation in existing residential buildings” [1]. The reported data deal with roof retrofit in different types of existing residential buildings (single-family, multi-family and apartment complex) located in Milan (Northern Italy). The study focus on the optimization of envelope insulation and photovoltaic (PV) energy production associated with different building geometries, initial insulation level, roof constructions, and materials. The data linked within this article relate to the modelled building energy consumption, renewable production, potential energy savings, and costs. Data refer to two main scenarios: refurbishment (roof in need of replacement and insulation) and re-roofing (energy intervention for roof improvement). Data allow to visualize energy consumption before and after the optimization, selected insulation level and material, costs and PV renewable production (with and without energy storage). The reduction of energy consumption can be visualized for each building type and scenario. Further data is available on CO(2) emissions, envelope, materials, and systems

Data on cost-optimal Nearly Zero Energy Buildings (NZEBs) across Europe

This data article refers to the research paper A model for the cost-optimal design of Nearly Zero Energy Buildings (NZEBs) in representative climates across Europe [1]. The reported data deal with the design optimization of a residential building prototype located in representative European locations. The study focus on the research of cost-optimal choices and efficiency measures in new buildings depending on the climate. The data linked within this article relate to the modelled building energy consumption, renewable production, potential energy savings, and costs. Data allow to visualize energy consumption before and after the optimization, selected efficiency measures, costs and renewable production. The reduction of electricity and natural gas consumption towards the NZEB target can be visualized together with incremental and cumulative costs in each location. Further data is available about building geometry, costs, CO2 emissions, envelope, materials, lighting, appliances and systems

Impact of appliances and lighting for Nearly Zero Energy Buildings (nZEBs) in Europe

As established by the recast of the European Union (EU) Directive on Energy Performance of Buildings (EPBD), all new buildings should be nearly zero energy buildings (nZEBs) within the EU by the end of 2020. However, reaching this result at the lowest possible cost remains an important challenge. Balancing renewable power generation with energy efficiency will be vital in Europe. We describe results obtained from the use of energy optimization software BEopt developed at the U.S. National Renewable Energy Laboratory. The model performs detailed hourly sequential simulations using the energy performance software EnergyPlus showing how to best achieve very low or zero energy home designs at the lowest possible cost in 36 representative locations across Europe. We have adapted the model to run using European hourly climatic data, using relevant construction methods, cost data and unit energy consumption. A novel aspect is the inclusion of the likelihood of future climate change relative to cooling loads estimated. This anticipates building design changes necessary to address the challenges to be faced in a changing world. A key finding of the research is that energy reductions of 80% and beyond are economically feasible for new construction, although the mix of selected measures varies strongly with climate. Results show that a broad approach to efficiency mixed with renewables performs best, while a narrow focus on building thermal performance can be counterproductive. In particular, we illustrate how exclusion of lighting and appliances results in sub-optimal solutions, especially for electricity use which has a disproportionate impact on greenhouse gas emissions.JRC.F.7-Renewables and Energy Efficienc

Data on cost-optimal Nearly Zero Energy Buildings (NZEBs) across Europe

This data article refers to the research paper A model for the cost-optimal design of Nearly Zero Energy Buildings (NZEBs) in representative climates across Europe [1]. The reported data deal with the design optimization of a residential building prototype located in representative European locations. The study focus on the research of cost-optimal choices and efficiency measures in new buildings depending on the climate. The data linked within this article relate to the modelled building energy consumption, renewable production, potential energy savings, and costs. Data allow to visualize energy consumption before and after the optimization, selected efficiency measures, costs and renewable production. The reduction of electricity and natural gas consumption towards the NZEB target can be visualized together with incremental and cumulative costs in each location. Further data is available about building geometry, costs, CO2 emissions, envelope, materials, lighting, appliances and systems.JRC.C.2-Energy Efficiency and Renewable

A framework for the cost-optimal design of nearly zero energy buildings (NZEBs) in representative climates across Europe

Combining cost-optimal solutions to reach nearly zero energy buildings (NZEBs) in compliance with European policies is an ongoing challenge. Energy consumption can be reduced evaluating different configurations at the design stage and implementing the most appropriate solutions according to the building and the location. This paper develops a simulation-based optimization framework of cost-optimal choices and energy efficiency measures for new buildings. It combines energy and cost simulations using a sequential search technique to find the most effective combination of energy efficiency and renewable energy measures starting from a base configuration. The method is applied to a residential building prototype, taking into consideration hourly climatic data, construction methods, cost data and energy consumption. A cost database and a library of potential measures, related to envelope, appliances and systems, have been established and used within the optimization process. The potential impact of climate change on the estimated cooling loads has been included in the calculations. The paper shows the feasibility of European requirements for new NZEBs located in different cities. It shows how to best achieve the NZEB design at the lowest cost in 14 locations across Europe. Results highlight how the cost-optimal measures vary with climate and how in each location final selected options differ. Insulation and building tightness appear essential in colder climates, while efficient appliances and lighting are key measures in warmer locations. A key finding of the research is that a source energy reduction of 90% and beyond is feasible for new constructions in all locations. Results also show how efficient lighting and appliances considerably impact the building energy performance. The importance of integrating renewables and energy efficiency measures is confirmed as crucial to reach the NZEBs target.JRC.C.2-Energy Efficiency and Renewable