162 research outputs found
Low noise wing slat system with rigid cove-filled slat
Concepts and technologies described herein provide for a low noise aircraft wing slat system. According to one aspect of the disclosure provided herein, a cove-filled wing slat is used in conjunction with a moveable panel rotatably attached to the wing slat to provide a high lift system. The moveable panel rotates upward against the rear surface of the slat during deployment of the slat, and rotates downward to bridge a gap width between the stowed slat and the lower wing surface, completing the continuous outer mold line shape of the wing, when the cove-filled slat is retracted to the stowed position
Wing Leading Edge Concepts for Noise Reduction
This study focuses on the development of wing leading edge concepts for noise reduction during high-lift operations, without compromising landing stall speeds, stall characteristics or cruise performance. High-lift geometries, which can be obtained by conventional mechanical systems or morphing structures have been considered. A systematic aerodynamic analysis procedure was used to arrive at several promising configurations. The aerodynamic design of new wing leading edge shapes is obtained from a robust Computational Fluid Dynamics procedure. Acoustic benefits are qualitatively established through the evaluation of the computed flow fields
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of C{\alpha} Protein Traces
Coarse-grained molecular models of proteins permit access to length and time
scales unattainable by all-atom models and the simulation of processes that
occur on long-time scales such as aggregation and folding. The reduced
resolution realizes computational accelerations but an atomistic representation
can be vital for a complete understanding of mechanistic details. Backmapping
is the process of restoring all-atom resolution to coarse-grained molecular
models. In this work, we report DiAMoNDBack (Diffusion-denoising Autoregressive
Model for Non-Deterministic Backmapping) as an autoregressive denoising
diffusion probability model to restore all-atom details to coarse-grained
protein representations retaining only C{\alpha} coordinates. The
autoregressive generation process proceeds from the protein N-terminus to
C-terminus in a residue-by-residue fashion conditioned on the C{\alpha} trace
and previously backmapped backbone and side chain atoms within the local
neighborhood. The local and autoregressive nature of our model makes it
transferable between proteins. The stochastic nature of the denoising diffusion
process means that the model generates a realistic ensemble of backbone and
side chain all-atom configurations consistent with the coarse-grained C{\alpha}
trace. We train DiAMoNDBack over 65k+ structures from Protein Data Bank (PDB)
and validate it in applications to a hold-out PDB test set,
intrinsically-disordered protein structures from the Protein Ensemble Database
(PED), molecular dynamics simulations of fast-folding mini-proteins from DE
Shaw Research, and coarse-grained simulation data. We achieve state-of-the-art
reconstruction performance in terms of correct bond formation, avoidance of
side chain clashes, and diversity of the generated side chain configurational
states. We make DiAMoNDBack model publicly available as a free and open source
Python package
Temporally Coherent Backmapping of Molecular Trajectories From Coarse-Grained to Atomistic Resolution
Coarse-graining offers a means to extend the achievable time and length scales of molecular dynamics simulations beyond what is practically possible in the atomistic regime. Sampling molecular configurations of interest can be done efficiently using coarse-grained simulations, from which meaningful physicochemical information can be inferred if the corresponding all-atom configurations are reconstructed. However, this procedure of backmapping to reintroduce the lost atomistic detail into coarse-grain structures has proven a challenging task due to the many feasible atomistic configurations that can be associated with one coarse-grain structure. Existing backmapping methods are strictly frame-based, relying on either heuristics to replace coarse-grain particles with atomic fragments and subsequent relaxation or parametrized models to propose atomic coordinates separately and independently for each coarse-grain structure. These approaches neglect information from previous trajectory frames that is critical to ensuring temporal coherence of the backmapped trajectory, while also offering information potentially helpful to producing higher-fidelity atomic reconstructions. In this work, we present a deep learning-enabled data-driven approach for temporally coherent backmapping that explicitly incorporates information from preceding trajectory structures. Our method trains a conditional variational autoencoder to nondeterministically reconstruct atomistic detail conditioned on both the target coarse-grain configuration and the previously reconstructed atomistic configuration. We demonstrate our backmapping approach on two exemplar biomolecular systems: alanine dipeptide and the miniprotein chignolin. We show that our backmapped trajectories accurately recover the structural, thermodynamic, and kinetic properties of the atomistic trajectory data
Data-driven discovery of cardiolipin-selective small molecules by computational active learning
Subtle variations in the lipid composition of mitochondrial membranes can have a profound impact on mitochondrial function. The inner mitochondrial membrane contains the phospholipid cardiolipin, which has been demonstrated to act as a biomarker for a number of diverse pathologies. Small molecule dyes capable of selectively partitioning into cardiolipin membranes enable visualization and quantification of the cardiolipin content. Here we present a data-driven approach that combines a deep learning-enabled active learning workflow with coarse-grained molecular dynamics simulations and alchemical free energy calculations to discover small organic compounds able to selectively permeate cardiolipin-containing membranes. By employing transferable coarse-grained models we efficiently navigate the all-atom design space corresponding to small organic molecules with molecular weight less than ≈500 Da. After direct simulation of only 0.42% of our coarse-grained search space we identify molecules with considerably increased levels of cardiolipin selectivity compared to a widely used cardiolipin probe 10-N-nonyl acridine orange. Our accumulated simulation data enables us to derive interpretable design rules linking coarse-grained structure to cardiolipin selectivity. The findings are corroborated by fluorescence anisotropy measurements of two compounds conforming to our defined design rules. Our findings highlight the potential of coarse-grained representations and multiscale modelling for materials discovery and design
Orbital Mixer: Using Atomic Orbital Features for Basis Dependent Prediction of Molecular Wavefunctions
Leveraging ab initio data at scale has enabled the development of machine
learning models capable of extremely accurate and fast molecular property
prediction. A central paradigm of many previous works focuses on generating
predictions for only a fixed set of properties. Recent lines of research
instead aim to explicitly learn the electronic structure via molecular
wavefunctions from which other quantum chemical properties can directly be
derived. While previous methods generate predictions as a function of only the
atomic configuration, in this work we present an alternate approach that
directly purposes basis dependent information to predict molecular electronic
structure. The backbone of our model, Orbital Mixer, uses MLP Mixer layers
within a simple, intuitive, and scalable architecture and achieves competitive
Hamiltonian and molecular orbital energy and coefficient prediction accuracies
compared to the state-of-the-art
Lift Recovery for AFC-Enabled High Lift System
This project is a continuation of the NASA AFC-Enabled Simplified High-Lift System Integration Study contract (NNL10AA05B) performed by Boeing under the Fixed Wing Project. This task is motivated by the simplified high-lift system, which is advantageous due to the simpler mechanical system, reduced actuation power and lower maintenance costs. Additionally, the removal of the flap track fairings associated with conventional high-lift systems renders a more efficient aerodynamic configuration. Potentially, these benefits translate to a approx. 2.25% net reduction in fuel burn for a twin-engine, long-range airplane
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