81,547 research outputs found
An error indicator-based adaptive reduced order model for nonlinear structural mechanics -- application to high-pressure turbine blades
The industrial application motivating this work is the fatigue computation of
aircraft engines' high-pressure turbine blades. The material model involves
nonlinear elastoviscoplastic behavior laws, for which the parameters depend on
the temperature. For this application, the temperature loading is not
accurately known and can reach values relatively close to the creep
temperature: important nonlinear effects occur and the solution strongly
depends on the used thermal loading. We consider a nonlinear reduced order
model able to compute, in the exploitation phase, the behavior of the blade for
a new temperature field loading. The sensitivity of the solution to the
temperature makes {the classical unenriched proper orthogonal decomposition
method} fail. In this work, we propose a new error indicator, quantifying the
error made by the reduced order model in computational complexity independent
of the size of the high-fidelity reference model. In our framework, when the
{error indicator} becomes larger than a given tolerance, the reduced order
model is updated using one time step solution of the high-fidelity reference
model. The approach is illustrated on a series of academic test cases and
applied on a setting of industrial complexity involving 5 million degrees of
freedom, where the whole procedure is computed in parallel with distributed
memory
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Experimental design with integrated temperature sensors in MEMS: an example of application for rarefied gases
This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute.This paper presents a new MEMS experimental device with integrated temperature sensors.
Conventional silicon planar techniques for the fabrication of microelectronic sensors have been used to realize a particular layout, which does not limit the material of the microstructures it can be used with. The study of rarefied gases has been chosen as case study for the validation of the local measuring system. In this work the attention will be focused on the description of the sensor functioning principles and on the presentation of the preliminary results obtained during the calibration procedures. The tests showed promising results for a future development of the sensor design.The European Communityâs Seventh Framework Program (FP7/2007-20013) under grant agreement no 215504
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 2: Application to TRACE
Inverse Uncertainty Quantification (UQ) is a process to quantify the
uncertainties in random input parameters while achieving consistency between
code simulations and physical observations. In this paper, we performed inverse
UQ using an improved modular Bayesian approach based on Gaussian Process (GP)
for TRACE physical model parameters using the BWR Full-size Fine-Mesh Bundle
Tests (BFBT) benchmark steady-state void fraction data. The model discrepancy
is described with a GP emulator. Numerical tests have demonstrated that such
treatment of model discrepancy can avoid over-fitting. Furthermore, we
constructed a fast-running and accurate GP emulator to replace TRACE full model
during Markov Chain Monte Carlo (MCMC) sampling. The computational cost was
demonstrated to be reduced by several orders of magnitude.
A sequential approach was also developed for efficient test source allocation
(TSA) for inverse UQ and validation. This sequential TSA methodology first
selects experimental tests for validation that has a full coverage of the test
domain to avoid extrapolation of model discrepancy term when evaluated at input
setting of tests for inverse UQ. Then it selects tests that tend to reside in
the unfilled zones of the test domain for inverse UQ, so that one can extract
the most information for posterior probability distributions of calibration
parameters using only a relatively small number of tests. This research
addresses the "lack of input uncertainty information" issue for TRACE physical
input parameters, which was usually ignored or described using expert opinion
or user self-assessment in previous work. The resulting posterior probability
distributions of TRACE parameters can be used in future uncertainty,
sensitivity and validation studies of TRACE code for nuclear reactor system
design and safety analysis
Improving Building Energy Efficiency through Measurement of Building Physics Properties Using Dynamic Heating Tests
© 2019 the author. Licensee MDPI, Basel, Switzerland.Buildings contribute to nearly 30% of global carbon dioxide emissions, making a significant impact on climate change. Despite advanced design methods, such as those based on dynamic simulation tools, a significant discrepancy exists between designed and actual performance. This so-called performance gap occurs as a result of many factors, including the discrepancies between theoretical properties of building materials and properties of the same materials in buildings in use, reflected in the physics properties of the entire building. There are several different ways in which building physics properties and the underlying properties of materials can be established: a co-heating test, which measures the overall heat loss coefficient of the building; a dynamic heating test, which, in addition to the overall heat loss coefficient, also measures the effective thermal capacitance and the time constant of the building; and a simulation of the dynamic heating test with a calibrated simulation model, which establishes the same three properties in a non-disruptive way in comparison with the actual physical tests. This article introduces a method of measuring building physics properties through actual and simulated dynamic heating tests. It gives insights into the properties of building materials in use and it documents significant discrepancies between theoretical and measured properties. It introduces a quality assurance method for building construction and retrofit projects, and it explains the application of results on energy efficiency improvements in building design and control. It calls for re-examination of material properties data and for increased safety margins in order to make significant improvements in building energy efficiency.Peer reviewedFinal Published versio
Carbon Free Boston: Buildings Technical Report
Part of a series of reports that includes:
Carbon Free Boston: Summary Report;
Carbon Free Boston: Social Equity Report;
Carbon Free Boston: Technical Summary;
Carbon Free Boston: Transportation Technical Report;
Carbon Free Boston: Waste Technical Report;
Carbon Free Boston: Energy Technical Report;
Carbon Free Boston: Offsets Technical Report;
Available at http://sites.bu.edu/cfb/OVERVIEW:
Boston is known for its historic iconic buildings, from the Paul Revere House in the North End, to City
Hall in Government Center, to the Old South Meeting House in Downtown Crossing, to the African
Meeting House on Beacon Hill, to 200 Clarendon (the Hancock Tower) in Back Bay, to Abbotsford in
Roxbury. In total, there are over 86,000 buildings that comprise more than 647 million square feet of
area. Most of these buildings will still be in use in 2050.
Floorspace (square footage) is almost evenly split between residential and non-residential uses, but
residential buildings account for nearly 80,000 (93 percent) of the 86,000 buildings. Bostonâs buildings
are used for a diverse range of activities that include homes, offices, hospitals, factories, laboratories,
schools, public service, retail, hotels, restaurants, and convention space. Building type strongly
influences energy use; for example, restaurants, hospitals, and laboratories have high energy demands
compared to other commercial uses.
Bostonâs building stock is characterized by thousands of turn-of-the-20th century homes and a postWorld War II building boom that expanded both residential buildings and commercial space. Boston is in
the midst of another boom in building construction that is transforming neighborhoods across the city. [TRUNCATED]Published versio
Bayesian quantification of thermodynamic uncertainties in dense gas flows
A Bayesian inference methodology is developed for calibrating complex equations of state used in numerical fluid flow solvers. Precisely, the input parameters of three equations of state commonly used for modeling the thermodynamic behavior of so-called dense gas flows, â i.e. flows of gases characterized by high molecular weights and complex molecules, working in thermodynamic conditions close to the liquid-vapor saturation curveâ, are calibrated by means of Bayesian inference from reference aerodynamic data for a dense gas flow over a wing section. Flow thermodynamic conditions are such that the gas thermodynamic behavior strongly deviates from that of a perfect gas. In the aim of assessing the proposed methodology, synthetic calibration data âspecifically, wall pressure dataâ are generated by running the numerical solver with a more complex and accurate thermodynamic model. The statistical model used to build the likelihood function includes a model-form inadequacy term, accounting for the gap between the model output associated to the best-fit parameters, and the rue phenomenon. Results show that, for all of the relatively simple models under investigation, calibrations lead to informative posterior probability density distributions of the input parameters and improve the predictive distribution significantly. Nevertheless, calibrated parameters strongly differ from their expected physical values. The relationship between this behavior and model-form inadequacy is discussed.ANR-11-MONU-008-00
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