scholarly journals Evaluating Model Uncertainty Based on Probabilistic Analysis and Component Output Uncertainty Descriptions

2012 ◽  
Author(s):  
Magnus Carlsson ◽  
Hampus Gavel ◽  
Johan Ölvander
Keyword(s):  
Cooling System ◽  
Measurement Data ◽  
Simulation Models ◽  
System Level ◽  
Upper And Lower Bounds ◽  
Early Model ◽  
Model Uncertainties ◽  
Component Level ◽  
Output Uncertainty ◽  
Model Components

To support early model validation, this paper describes a method utilizing information obtained from the common practice component level validation to assess uncertainties on model top level. Initiated in previous research, a generic output uncertainty description component, intended for power-port based simulation models of physical systems, has been implemented in Modelica. A set of model components has been extended with the generic output uncertainty description, and the concept of using component level output uncertainty to assess model top level uncertainty has been applied on a simulation model of a radar liquid cooling system. The focus of this paper is on investigating the applicability of combining the output uncertainty method with probabilistic techniques, not only to provide upper and lower bounds on model uncertainties but also to accompany the uncertainties with estimated probabilities. It is shown that the method may result in a significant improvement in the conditions for conducting an assessment of model uncertainties. The primary use of the method, in combination with either deterministic or probabilistic techniques, is in the early development phases when system level measurement data are scarce. The method may also be used to point out which model components contribute most to the uncertainty on model top level. Such information can be used to concentrate physical testing activities to areas where it is needed most. In this context, the method supports the concept of Virtual Testing.

Definition and Implementation of a Method for Uncertainty Aggregation in Component-Based System Simulation Models

2017 ◽  
Vol 2 (1) ◽  
Author(s):  
Magnus Eek ◽  
Hampus Gavel ◽  
Johan Ölvander
Keyword(s):  
Cooling System ◽  
System Simulation ◽  
Measurement Data ◽  
Simulation Models ◽  
Bench Test ◽  
Aggregation Method ◽  
Component Level ◽  
Output Uncertainty ◽  
Liquid Cooling System ◽  
And Control

Component-based system simulation models are used throughout all development phases for design and verification of both physical systems and control software, not least in the aeronautical industry. However, the application of structured methods for uncertainty quantification (UQ) of system simulation models is rarely seen. To enable dimensionality reduction of a UQ problem and to thereby make UQ more feasible for industry-grade system simulation models, this paper describes a pragmatic method for uncertainty aggregation. The central idea of the proposed aggregation method is to integrate information obtained during common practice component-level validation directly into the components, and to utilize this information in model-level UQ. A generic component output uncertainty description has been defined and implemented in a Modelica library for modeling and simulation (M&S) of aircraft vehicle systems. An example is provided on how to characterize and quantify a component's aggregated output uncertainty based on the component-level bench test measurement data. Furthermore, the industrial applicability of the uncertainty aggregation method is demonstrated in an approximate UQ of an aircraft liquid cooling system simulation model. For cases when the concept of thorough UQ resulting in probability boxes is not feasible, the demonstrated approximate UQ using aggregated uncertainties is considered to be a pragmatic alternative fairly in reach for the common M&S practitioner within the area of system simulation.

Electronics Cooling System and Component Design According to the Second Law

2014 ◽  
Vol 136 (5) ◽  
Author(s):  
Ruben Gielen ◽  
Martine Baelmans
Keyword(s):  
Cooling System ◽  
Electronics Cooling ◽  
The Other ◽  
System Level ◽  
Second Law ◽  
Minimum Entropy ◽  
Component Level ◽  
Individual Level ◽  
Component Design ◽  
Minimum Entropy Generation

This work focuses on a comparison of second law based component design on one hand and second law based system design on the other within the context of electronics cooling. Typical electronics cooling components such as a heat sink and a heat exchanger are modeled and designed towards minimum entropy generation on individual level and on system level. A comparison of these levels allows us to qualify and quantify the influences among components induced by a system. Simultaneously, this article endeavors to be an illustrated assessment of the usefulness of efficiency criteria on component level. It turns out that the results of this work reveal a substantial influence of system dependencies on the optimal component design. As such a note of caution is raised about second law based component design which does not take into account the system in which a component has to operate.

Thermal Control Strategies for Reliable and Energy-Efficient Data Centers

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Rehan Khalid ◽  
Aaron P. Wemhoff
Keyword(s):  
Data Center ◽  
Energy Efficient ◽  
Cooling System ◽  
System Level ◽  
Component Level ◽  
Simple Method ◽  
Control Schemes ◽  
Data Center Cooling ◽  
Efficient Data ◽  
The Impact

Two self-developed control schemes, ON/OFF and supervisory control and data acquisition (SCADA), were applied on a hybrid evaporative and direct expansion (DX)-based model data center cooling system to assess the impact of controls on reliability and energy efficiency. These control schemes can be applied independently or collectively, thereby saving the energy spent on mechanical refrigeration by using airside economization and/or evaporative cooling. Various combinations of system-level controls and component-level controls are compared to a baseline no-controls case. The results show that reliability is consistently met by employing only sophisticated component-level controls. However, the recommended conditions are met approximately 50% of the simulated time by employing system-level controls only (i.e., SCADA) but with a reduction in data center cooling system power usage effectiveness (PUE) values from 3.76 to 1.42. Moreover, the recommended conditions are met at all averaged times with an even lower cooling system PUE of 1.13 by combining system-level controls only (SCADA and ON/OFF controls). Thus, the study introduces a simple method to compare control schemes for reliable and energy-efficient data center operation. The work also highlights a potential source of capital expenses and operating expenses savings for data center owners by switching from expensive built-in component-based controls to inexpensive, yet effective, system-based controls that can easily be imbedded into existing data center infrastructure systems management.

scholarly journals Probabilistic Health and Mission Readiness Assessment at System-Level

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Leonardo Barbini ◽  
Michael Borth
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Cooling System ◽  
Synthetic Data ◽  
Simulation Models ◽  
Remaining Useful Life ◽  
System Level ◽  
Readiness Assessment ◽  
Physics Based Simulation ◽  
Operational Data

Predictive maintenance strategies which estimate remaining useful life of system components to prevent breakdowns and down-times by timely and well-scheduled maintenance ensures the reliable availability of assets and lowers total costs of ownership. The focus on the components’ life times falls short, however, to infer the system-level capability to achieve upcoming tasks, especially if these tasks vary either in the strain they cause for the system or in the environmental conditions in which the system needs to perform. Such an assessment of the health and mission readiness of a system is crucial for mobile assets like seafaring vessels undertaking long-term operations without the option to easily come in for repairs or for industrial assets that need to complete long production runs in one go under varying circumstances. We propose a multi-step methodology to achieve such assess­ments using both Bayesian reasoning for diagnosis and prognosis and physics-based simulation models. First, we construct an appropriate Bayesian network in an object-oriented way by fitting a pre-compiled library of network fragments to the system’s schematics using generative techniques. We then parameterize the obtained network using a combination of expert knowledge and machine learning to fine tune system-level interactions between components and their link to the system’s performance. The learning step uses past operational data that we augment or complement with synthetic data, created by a physics-based simulation model, where needed. Finally, we use the trained Bayesian network to assess the mission readiness of the system given the probabilistics of its diagnosed state, expected impact of possible maintenance interventions, and the estimated profile of the future use. We illustrate and verify our methodology on a cooling system with an active feedback control loop, but our approach for mission readiness assessment is domain-independent, universally applicable, and typically feasible where operational data and engineering knowledge can be brought together to solve its challenge.

Dynamic Infrared System Level Fault Isolation

2003 ◽  
Author(s):  
John A. Naoum ◽  
Johan Rahardjo ◽  
Yitages Taffese ◽  
Marie Chagny ◽  
Jeff Birdsley ◽  
...  
Keyword(s):  
Fault Isolation ◽  
System Level ◽  
System Component ◽  
Component Level ◽  
Ir Imaging ◽  
Non Destructive

Abstract The use of Dynamic Infrared (IR) Imaging is presented as a novel, valuable and non-destructive approach for the analysis and isolation of failures at a system/component level.

scholarly journals Performance Analysis of Hybrid Desiccant Cooling System with Enhanced Dehumidification Capability Using TRNSYS

2021 ◽  
Vol 11 (7) ◽  
pp. 3236
Author(s):  
Ji Hyeok Kim ◽  
Joon Ahn
Keyword(s):  
Thermal Comfort ◽  
Cooling System ◽  
Operation Mode ◽  
Heat Removal ◽  
Simulation Models ◽  
Coefficient Of Performance ◽  
Dimensional Structure ◽  
Dominant Factor ◽  
Exhaust Heat ◽  
Chp System

In a field test of a hybrid desiccant cooling system (HDCS) linked to a gas engine cogeneration system (the latter system is hereafter referred to as the combined heat and power (CHP) system), in the cooling operation mode, the exhaust heat remained and the latent heat removal was insufficient. In this study, the performance of an HDCS was simulated at a humidity ratio of 10 g/kg in conditioned spaces and for an increasing dehumidification capacity of the desiccant rotor. Simulation models of the HDCS linked to the CHP system were based on a transient system simulation tool (TRNSYS). Furthermore, TRNBuild (the TRNSYS Building Model) was used to simulate the three-dimensional structure of cooling spaces and solar lighting conditions. According to the simulation results, when the desiccant capacity increased, the thermal comfort conditions in all three conditioned spaces were sufficiently good. The higher the ambient temperature, the higher the evaporative cooling performance was. The variation in the regeneration heat with the outdoor conditions was the most dominant factor that determined the coefficient of performance (COP). Therefore, the COP was higher under high temperature and dry conditions, resulting in less regeneration heat being required. According to the prediction results, when the dehumidification capacity is sufficiently increased for using more exhaust heat, the overall efficiency of the CHP can be increased while ensuring suitable thermal comfort conditions in the cooling space.

System Level Bench Test for Trailing Arm Strength Estimation

2006 ◽  
Author(s):  
Jaychandar Muthu ◽  
Kanak Soundrapandian ◽  
Jyoti Mukherjee
Keyword(s):  
Failure Mode ◽  
Real Life ◽  
Element Model ◽  
Suspension System ◽  
System Level ◽  
Bench Test ◽  
Component Level ◽  
Arm Strength ◽  
Bench Testing ◽  
Nonlinear Finite Element Model

For suspension components, bench testing for strength is mostly accomplished at component level. However, replicating loading and boundary conditions at the component level in order to simulate the suspension system environment may be difficult. Because of this, the component's bench test failure mode may not be similar to its real life failure mode in vehicle environment. A suspension system level bench test eliminates most of the discrepancies between simulated component level and real life vehicle level environments resulting in higher quality bench tests yielding realistic test results. Here, a suspension level bench test to estimate the strength of its trailing arm link is presented. A suspension system level nonlinear finite element model was built and analyzed using ABAQUS software. The strength loading was applied at the wheel end. The analysis results along with the hardware test correlations are presented. The reasons why a system level test is superior to a component level one are also highlighted.

Simulation and Experimental Measurement of the Transmission Error of Real Hypoid Gears Under Load

2000 ◽  
Vol 122 (1) ◽  
pp. 109-122 ◽  
Author(s):  
Claude Gosselin ◽  
Thierry Guertin ◽  
Didier Remond ◽  
Yves Jean
Keyword(s):  
Measurement Data ◽  
Simulation Models ◽  
Transmission Error ◽  
Contact Analysis ◽  
Hypoid Gear ◽  
Tooth Contact Analysis ◽  
Tooth Contact ◽  
Hypoid Gears ◽  
Loaded Tooth Contact Analysis ◽  
Analysis Models

The Transmission Error and Bearing Pattern of a gear set are fundamental aspects of its meshing behavior. To assess the validity of gear simulation models, the Transmission Error and Bearing Pattern of a Formate Hypoid gear set are measured under a variety of operating positions and applied loads. Measurement data are compared to simulation results of Tooth Contact Analysis and Loaded Tooth Contact Analysis models, and show excellent agreement for the considered test gear set. [S1050-0472(00)00901-6]

Towards Automated Evaluation of Vehicle Dynamics in System-Level Designs

2012 ◽  
Author(s):  
Zsolt Lattmann ◽  
Adam Nagel ◽  
Jason Scott ◽  
Kevin Smyth ◽  
Chris vanBuskirk ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Test Bench ◽  
Simulation Models ◽  
Mobility Model ◽  
System Level ◽  
Bond Graphs ◽  
Automated Evaluation ◽  
Design Alternatives ◽  
Automatic Composition

We describe the use of the Cyber-Physical Modeling Language (CyPhyML) to support trade studies and integration activities in system-level vehicle designs. CyPhyML captures parameterized component behavior using acausal models (i.e. hybrid bond graphs and Modelica) to enable automatic composition and synthesis of simulation models for significant vehicle subsystems. Generated simulations allow us to compare performance between different design alternatives. System behavior and evaluation are specified independently from specifications for design-space alternatives. Test bench models in CyPhyML are given in terms of generic assemblies over the entire design space, so performance can be evaluated for any selected design instance once automated design space exploration is complete. Generated Simulink models are also integrated into a mobility model for interactive 3-D simulation.

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