Inefficiencies in the Moscow market for prime office space and the role of the local government

Thesis (M.C.P. and S.M.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1999.Includes bibliographical references (leaves [159]-[162]).The Moscow market for international quality office space has been expanding rapidly since the onset of the market. The growth in office stock has not been sufficient to satisfy demand for the past 7 years. Disequilibrium conditions in the Moscow office market have persisted over the typical period of a real estate cycle. With a yearly average shortfall of 200,000, the Moscow office market has sustained rents of over USD 1,000 per square meter per annum. Demand is driven by foreign firms in the trade and services sectors, looking for class-A office premises in the city center. The purpose of this thesis is to examine the inefficiencies in the office market for quality office space in Moscow during the period of 1991 to June 1998. In doing so, the thesis focuses on the effects of the local government on new office development in Moscow. Although there has been no shortage of eager developers looking to enjoy yields of 25 to 30 percent, the difficulty of setting up operations in Moscow, the lack of properly defined property rights on buildings and land, the inability to secure development financing and to repatriate profits, the instability of the taxation system, and the constant willingness of local officials to intervene at all stages of the development process have all undermined the development of an efficient market for quality office space. The recent crisis in the Russian economy has reversed market conditions in the Moscow office market, resulting to an oversupply of quality office accommodations. Many international tenants are shrinking their operations in an effort to reduce operating expenses. With rents falling by 25- 30 percent and with no indications for a possible economic recovery in Russia expected for the next 18 months, office construction has-at best-slowed down if not stopped. Difficulties associated with office development in Moscow, combined with weak market conditions are likely to hold back plans for new office construction for the period until June 2000. This will give the local government the time to make all necessary reforms and provide the appropriate legal, regulatory and institutional framework for an efficient office market operation in Moscow. In the presence of strong long term real estate market fundamentals and a total stock of prime office space 30 times less the office stock of London or Paris, it is only a matter of time until office demand pick ups again in the Moscow region. And this time the city must be ready.by Maria Koronaki.M.C.P.and S.M

Investigation of the chemical vapor deposition of Cu from copper amidinate through data driven efficient CFD modelling

peer reviewedA chemical reaction model, consisting of two gas-phase and a surface reaction, for the deposition of copper from copper amidinate is investigated, by comparing results of an efficient, reduced order CFD model with experiments. The film deposition rate over a wide range of temperatures, 473K-623K, is accurately captured, focusing specifically on the reported drop of the deposition rate at higher temperatures, i.e above 553K that has not been widely explored in the literature. This investigation is facilitated by an efficient computational tool that merges equation-based analysis with data-driven reduced order modeling and artificial neural networks. The hybrid computer-aided approach is necessary in order to address, in a reasonable time-frame, the complex chemical and physical phenomena developed in a three-dimensional geometry that corresponds to the experimental set-up. It is through this comparison between the experiments and the derived simulation results, enabled by machine-learning algorithms that the prevalent theoretical hypothesis is tested and validated, illuminating the possible underlying dominant phenomena

Physics-agnostic and Physics-infused machine learning for thin films flows: modeling, and predictions from small data

Numerical simulations of multiphase flows are crucial in numerous engineering applications, but are often limited by the computationally demanding solution of the Navier-Stokes (NS) equations. Here, we present a data-driven workflow where a handful of detailed NS simulation data are leveraged into a reduced-order model for a prototypical vertically falling liquid film. We develop a physics-agnostic model for the film thickness, achieving a far better agreement with the NS solutions than the asymptotic Kuramoto-Sivashinsky (KS) equation. We also develop two variants of physics-infused models providing a form of calibration of a low-fidelity model (i.e. the KS) against a few high-fidelity NS data. Finally, predictive models for missing data are developed, for either the amplitude, or the full-field velocity and even the flow parameter from partial information. This is achieved with the so-called "Gappy Diffusion Maps", which we compare favorably to its linear counterpart, Gappy POD

Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era

This study presents a collection of purely data-driven workflows for constructing reduced-order models (ROMs) for distributed dynamical systems. The ROMs we focus on, are data-assisted models inspired by, and templated upon, the theory of Approximate Inertial Manifolds (AIMs); the particular motivation is the so-called post-processing Galerkin method of Garcia-Archilla, Novo and Titi. Its applicability can be extended: the need for accurate truncated Galerkin projections and for deriving closed-formed corrections can be circumvented using machine learning tools. When the right latent variables are not a priori known, we illustrate how autoencoders as well as Diffusion Maps (a manifold learning scheme) can be used to discover good sets of latent variables and test their explainability. The proposed methodology can express the ROMs in terms of (a) theoretical (Fourier coefficients), (b) linear data-driven (POD modes) and/or (c) nonlinear data-driven (Diffusion Maps) coordinates. Both Black-Box and (theoretically-informed and data-corrected) Gray-Box models are described; the necessity for the latter arises when truncated Galerkin projections are so inaccurate as to not be amenable to post-processing. We use the Chafee-Infante reaction-diffusion and the Kuramoto-Sivashinsky dissipative partial differential equations to illustrate and successfully test the overall framework.Comment: 27 pages, 22 figure

Prefabricated engineered timber schools in the united kingdom: Challenges and opportunities

Due to changing demographics, the UK faces a significant shortage of school places. The UK government aims to build large numbers of new schools to meet this demand. However, legally binding carbon emissions mitigation commitments might limit the ability of the government to adequately meet this demand on-time, on-budget, and within sustainability targets. This paper assesses the opportunity for prefabricated engineered timber construction methods to help meet the demand for new primary and secondary school buildings in the UK within these constraints. Building on a study of past government-led school building programmes and the state-of-the-art developments in engineered timber construction, this paper outlines the benefits that an engineered timber school building programme could have on a sustainability and procurement level. A strategy is then proposed for the wider adoption of engineered timber for the construction of school buildings in the UK, including detailed guidelines for designers and policymakers. The study concludes with recommendations for the adaptation of this strategy in different countries, depending on context-specific requirements, therefore promoting a generalised adoption of sustainable and efficient construction processes.</jats:p

Miejski eksperyment klimatyczny w Atenach - rozkład przestrzenny temperatury

This paper presents the main characteristics and the first results of a large scale experiment undertaken in Athens in the frame of the POLIS research program of the European Commission. Researches have been carried out to investigate the temperature distribution in the major Athens urban area. Twenty stations have been installed since June 1996 and the recorded data have been analyzed. A very important temperature increase has been recorded in the central Athens area. Energy analysis has been shown that temperature increase has a very important impact on the energy consumption of buildings for cooling purposes.Praca przedstawia główne charakterystyki i pierwsze wyniki eksperymentu podjętego na dużą skalę w Atenach w ramach POLIS - programu badawczego Komisji Europejskiej. Badania przeprowadzono w celu poznania rozkładu temperatury powietrza na obszarze Aten. Od czerwca 1996 r. uruchomiono 20 stacji pomiarowych i dokonano analizy uzyskanych danych. Zanotowano wyraźny wzrost temperatury w centrum Aten. Analiza wykazała, że wzrost temperatury wywiera istotny wpływ na ilość energii zużywanej na klimatyzację pomieszczeń

From partial data to out-of-sample parameter and observation estimation with diffusion maps and geometric harmonics

peer reviewedA data-driven framework is presented, that enables the prediction of quantities, either observations or parameters, given sufficient partial data. The framework is illustrated via a computational model of the deposition of Cu in a Chemical Vapor Deposition (CVD) reactor, where the reactor pressure, the deposition temperature and feed mass flow rate are important process parameters that determine the outcome of the process. The sampled observations are high-dimensional vectors containing the outputs of a detailed CFD steady-state model of the process, i.e. the values of velocity, pressure, temperature, and species mass fractions at each point in the discretization. A machine learning workflow is presented, able to predict out-of-sample (a) observations (e.g. mass fraction in the reactor), given process parameters (e.g. inlet temperature); (b) process parameters, given observation data; and (c) partial observations (e.g. temperature in the reactor), given other partial observations (e.g. mass fraction in the reactor). The proposed workflow relies on two manifold learning schemes: Diffusion Maps and the associated Geometric Harmonics. Diffusion Maps are used for discovering a reduced representation of the available data, and Geometric Harmonics for extending functions defined on the discovered manifold. In our work a special use case of Geometric Harmonics is formulated and implemented, which we call Double Diffusion Maps, to map from the reduced representation back to (partial) observations and process parameters. A comparison of our manifold learning scheme to the traditional Gappy-POD approach is provided: ours can be thought of as a “Gappy DMAPs” approach. The presented methodology is easily transferable to application domains beyond reactor engineering