Ansys, Inc. is an engineering simulation software developer headquartered south of Pittsburgh in the Southpointe business park in Cecil Township, Pennsylvania, United States. One of its most significant products is Ansys CFD, a proprietary computational fluid dynamics program. Wikipedia.
Davis T.A.,University of Florida |
Natarajan E.P.,ANSYS Inc.
ACM Transactions on Mathematical Software | Year: 2010
KLU is a software package for solving sparse unsymmetric linear systems of equations that arise in circuit simulation applications. It relies on a permutation to Block Triangular Form (BTF), several methods for finding a fill-reducing ordering (variants of approximate minimum degree and nested dissection), and Gilbert/Peierls' sparse left-looking LU factorization algorithm to factorize each block. The package is written in C and includes a MATLAB interface. Performance results comparing KLU with SuperLU, Sparse 1.3, and UMFPACK on circuit simulation matrices are presented. KLU is the default sparse direct solver in the Xyce™circuit simulation package developed by Sandia National Laboratories. © 2010 ACM.
El-Asrag H.A.,ANSYS Inc. |
Combustion and Flame | Year: 2014
Direct numerical simulations (DNSs), for a stratified flow in HCCI engine-like conditions, are performed to investigate the effects of exhaust gas recirculation (EGR) by NOx and temperature/mixture stratification on autoignition of dimethyl ether (DME) in the negative temperature coefficient (NTC) region. Detailed chemistry for a DME/air mixture with NOx addition is employed and solved by a hybrid multi-time scale (HMTS) algorithm. Three ignition stages are observed. The results show that adding (1000ppm) NO enhances both low and intermediate temperature ignition delay times by the rapid OH radical pool formation (one to two orders of magnitude higher OH radicals concentrations are observed). In addition, NO from EGR was found to change the heat release rates differently at each ignition stage, where it mainly increases the low temperature ignition heat release rate with minimal effect on the ignition heat release rates at the second and third ignition stages. Sensitivity analysis is performed and the important reactions pathways for low temperature chemistry and ignition enhancement by NO addition are specified. The DNSs for stratified turbulent ignition show that the scales introduced by the mixture and thermal stratifications have a stronger effect on the second and third stage ignitions. Compared to homogenous ignition, stratified ignition shows a similar first autoignition delay time, but about 19% reduction in the second and third ignition delay times. Stratification, however, results in a lower averaged LTC ignition heat release rate and a higher averaged hot ignition heat release rate compared to homogenous ignition. The results also show that molecular transport plays an important role in stratified low temperature ignition, and that the scalar mixing time scale is strongly affected by local ignition. Two ignition-kernel propagation modes are observed: a wave-like, low-speed, deflagrative mode (the D-mode) and a spontaneous, high-speed, kinetically driven ignition mode (the S-mode). Three criteria are introduced to distinguish the two modes by different characteristic time scales and Damkhöler (Da) number using a progress variable conditioned by a proper ignition kernel indicator (IKI). The results show that the spontaneous ignition S-mode is characterized by low scalar dissipation rate, high displacement speed flame front, and high mixing Damkhöler number, while the D-mode is characterized by high scalar dissipation rate, low displacement speeds in the order of the laminar flame speed and a lower than unity Da number. The proposed criteria are applied at the different ignition stages. © 2013 The Combustion Institute.
Lalgudi S.,ANSYS Inc.
IEEE Transactions on Components, Packaging and Manufacturing Technology | Year: 2013
The existing formulation for the causality testing of tabulated S-parameters can be oversensitive to noncausality, in that it can report causality violations for data that may not result in an inaccurate transient simulation. Existing numerical procedures that implement the present formulation can result in false-positive test outcomes (i.e., causal data being declared as noncausal). A mechanism to reduce the oversensitivity in the formulation and new testing procedures that minimize false positives are proposed. Oversensitivity is reduced significantly if the target fitting error (of the macromodel to tabulated data) is chosen as the minimum amplitude of the noncausality to be detected. Sources of false-positive outcomes are identified, and four procedures that minimize false positives are proposed. These procedures differ in resolution, accuracy, and computational requirements. Numerical results demonstrating the proposed modifications to the formulation and numerical results comparing and contrasting the different procedures are presented. © 2011-2012 IEEE.
Agency: Cordis | Branch: H2020 | Program: MSCA-ITN-ETN | Phase: MSCA-ITN-2014-ETN | Award Amount: 3.70M | Year: 2015
VPH-CaSE is focused on state-of-the-art developments in personalised cardiovascular support, underpinned by simulation and experimentation, building on the foundations of the Virtual Physiological Human (VPH) Initiative. The Individual Research Projects of 14 ESRs provide knowledge exchange across three research clusters (i) Cardiac tissue function and cardiac support (ii) Cardiovascular haemodynamics - pathology and intervention (iii) Image-based diagnosis and imaging quality assurance. The work will be directed by the needs of industrial and clinical Beneficiaries and Partners, providing a truly multi-disciplinary, multi-sectoral environment for the ESRs. This will combine the expertise of nine core Beneficiaries (5 academic, 4 industrial) and 10 Partners (5 clinical, 4 industrial, 1 academic) to provide scientific support, secondments and training. VPH-CaSE will foster the development of ESRs within a collaborative environment. The recruited researchers will find themselves in an enviable position to leverage the expertise of a strategic sector of the European medical devices/simulation industry and engage with the issues faced by clinical experts in the domain of cardiac medicine and cardiovascular support. Their postgraduate studies will be informed by a translational bias that delivers a competitive skill-set, equipping them to address the challenges presented by a career at the cutting edge of technological innovation in healthcare delivery. The inclusion of a technology translation SME within the consortium is designed to promote the delivery of novel, tangible research outputs, providing benefits to a breadth of European sectors (eg. biomedical, clinical, VPH). The ultimate vision is the production of VPH-capable scientists with experience of tight integration of academic/industrial/clinical areas, able to apply their skills to real life scenarios, accelerating the acceptance of innovative and effective healthcare in the clinic.
Agency: Cordis | Branch: H2020 | Program: RIA | Phase: PHC-30-2015 | Award Amount: 5.00M | Year: 2016
Valvular Heart Disease currently affects 2.5% of the population, but is overwhelmingly a disease of the elderly and consequently on the rise. It is dominated by two conditions, Aortic Stenosis and Mitral Regurgitation, both of which are associated with significant morbidity and mortality, yet which pose a truly demanding challenge for treatment optimisation. By combining multiple complex modelling components developed in recent EC-funded research projects, a comprehensive, clinically-compliant decision-support system will be developed to meet this challenge, by quantifying individualised disease severity and patient impairment, predicting disease progression, ranking the effectiveness of alternative candidate procedures, and optimising the patient-specific intervention plan. This algorithmically-driven process will dramatically improve outcomes and consistency across Europe in this fast-growing patient group, maximising individual, societal and economic outcomes.