Anisotropic output-based adaptation with tetrahedral cut cells for compressible flows

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections."September 2008."Includes bibliographical references (leaves 153-164).Anisotropic, adaptive meshing for flows around complex, three-dimensional bodies remains a barrier to increased automation in computational fluid dynamics. Two specific advances are introduced in this thesis. First, a finite-volume discretization for tetrahedral cut-cells is developed that makes possible robust, anisotropic adaptation on complex bodies. Through grid refinement studies on inviscid flows, this cut-cell discretization is shown to produce similar accuracy as boundary-conforming meshes with a small increase in the degrees of freedom. The cut-cell discretization is then combined with output-based error estimation and anisotropic adaptation such that the mesh size and shape are controlled by the output error estimate and the Hessian (i.e. second derivatives) of the Mach number, respectively. Using a parallel implementation, this output-based adaptive method is applied to a series of sonic boom test cases and the automated ability to correctly estimate pressure signatures at several body lengths is demonstrated starting with initial meshes of a few thousand control volumes. Second, a new framework for adaptation is introduced in which error estimates are directly controlled by removing the common intermediate step of specifying a desired mesh size and shape. As a result, output error control can be achieved without the adhoc selection of a specific field (such as Mach number) to control anisotropy, rather anisotropy in the mesh naturally results from both the primal and dual solutions. Furthermore, the direct error control extends naturally to higher-order discretizations for which the use of a Hessian is no longer appropriate to determine mesh shape. The direct error control adaptive method is demonstrated on a series of simple test cases to control interpolation error and discontinuous Galerkin finite element output error. This new direct method produces grids with less elements but the same accuracy as existing metric-based approaches.by Michael Andrew Park.Ph.D

Profile of Xeomin® (incobotulinumtoxinA) for the treatment of blepharospasm

Even though conventional botulinum neurotoxin (BoNT) products have shown successful treatment results in patients with benign blepharospasm (BEB), the main, potential long-term side effect of BoNT use is the development of immunologic resistance due to the production of neutralizing antibody to the neurotoxin after repeated injections. Xeomin® (incobotulinumtoxinA), a unique botulinum neurotoxin type A (BoNT/A) drug free of complexing proteins otherwise contained in all conventional BoNT/A drugs, was recently approved by US Food and Drug Administration for the treatment of cervical dystonia or blepharospasm in adults. The newly approved BoNT/A drug may overcome this limitation of previous conventional products, since it contains pure neurotoxin (150 kDa) through a manufacturing process that separates it from complexing proteins such as hemagglutinins produced by fermentation of Clostridium botulinum. Many studies have also shown that Xeomin® has the same efficacy and safety profile as complexing protein-containing products such as Botox® and is exchangeable with Botox® using a simple 1:1 conversion ratio. Xeomin® represents a new treatment option for the repeated treatment of patients with blepharospasm in that it may reduce antibody-induced therapy failure. But, long-term comparative trials in naïve patients between Xeomin® and conventional BoNT/A drugs are required to confirm the low immunogenicity of Xeomin®

High Energy Electron Confinement in a Magnetic Cusp Configuration

We report experimental results validating the concept that plasma confinement is enhanced in a magnetic cusp configuration when beta (plasma pressure/magnetic field pressure) is order of unity. This enhancement is required for a fusion power reactor based on cusp confinement to be feasible. The magnetic cusp configuration possesses a critical advantage: the plasma is stable to large scale perturbations. However, early work indicated that plasma loss rates in a reactor based on a cusp configuration were too large for net power production. Grad and others theorized that at high beta a sharp boundary would form between the plasma and the magnetic field, leading to substantially smaller loss rates. The current experiment validates this theoretical conjecture for the first time and represents critical progress toward the Polywell fusion concept which combines a high beta cusp configuration with an electrostatic fusion for a compact, economical, power-producing nuclear fusion reactor.Comment: 12 pages, figures included. 5 movies in Ancillary file

Distribution of DDS-cerberus Authenticated Facial Recognition Streams

Successful missions in the field often rely upon communication technologies for tactics and coordination. One middleware used in securing these communication channels is Data Distribution Service (DDS) which employs a publish-subscribe model. However, researchers have found several security vulnerabilities in DDS implementations. DDS-Cerberus (DDS-C) is a security layer implemented into DDS to mitigate impersonation attacks using Kerberos authentication and ticketing. Even with the addition of DDS-C, the real-time message sending of DDS also needs to be upheld. This paper extends our previous work to analyze DDS-C’s impact on performance in a use case implementation. The use case covers an artificial intelligence (AI) scenario that connects edge sensors across a commercial network. Specifically, it characterizes how DDS-C performs between unmanned aerial vehicles (UAV), the cloud, and video streams for facial recognition. The experiments send a set number of video frames over the network using DDS to be processed by AI and displayed on a screen. An evaluation of network traffic using DDS-C revealed that it was not statistically significant compared to DDS for the majority of the configuration runs. The results demonstrate that DDS-C provides security benefits without significantly hindering the overall performance

SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers

We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task aids in supervised training of Euclidean neural networks (E3NNs) and increases manifold smoothness (MS) around point-cloud geometries. We demonstrate this property for multiple drug-activity prediction tasks while maintaining relevant performance metrics, and propose an extension of MS to probabilistic and regression settings. We provide an analysis of representation collapse, finding substantial effects of task-weighting, latent dimension, and regularization. We expect the presented protocol to aid in the development of reliable E3NNs from molecular conformers, even for small-data drug discovery programs.Comment: Submitted to the MLDD workshop, ICLR 202

Applying genome scale metabolic models integrated with OMICs technologies for improvemwent of commercial CHO cell culture process

Although metabolic flux analysis has been established in microbial fermentation, their application in CHO cell culture is sparse. In general CHO cell culture process development is highly rely on empirical experience with limited cell and metabolite data without good mechanism understanding. The purpose of this research is to apply genome scale metabolic modeling for CHO cell culture process improvement. Recently we found that several medium components had significant impact on mAb production by BMSCHO1, a proprietary cell line (Fig. 1). Some of medium components at a low concentration, though within normal ranges for CHO cell culture, caused the BMSCHO1 crashed. Meanwhile some of the other medium components at a low concentration did not cause cell crash, but significantly decreased productivity. The preliminary genetic test results indicated no change in DNA copy number and southern blot integration profile under different medium conditions. Currently we are investigating both supernatant and cell pellets for metabolomics analysis using NMR and LCMS, and assessing epigenetic characteristics. In addition, transcriptomics data have been analyzed by RNA sequence and RT-PCR. Genome-scale modeling integrated with these OMICS datasets have been built and analyzed. In the presentation, we plan to share the investigation details of commercial cell-line and manufacturing process based on the application of genome scale modeling integrated with OMICS technology. Please click Additional Files below to see the full abstract

Quantifying DDS-cerberus Network Control Overhead

Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of subscribers and publisher nodes from three to eighteen. By categorizing the network traffic from these nodes into either data message, security, or discovery+ with Quality of Service (QoS) best effort and reliable, the mean security traffic from DDS-C has minimal impact to Data Distribution Service (DDS) operations compared to other network traffic. The results reveal that applying DDS-C to a representative distributed network robotics operating system network does not impact performance