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Due to the fascinating generative performance of text-to-image diffusion models, growing text-to-3D generation works explore distilling the 2D generative priors into 3D, using the score distillation sampling (SDS) loss, to bypass the data scarcity problem. The existing text-to-3D methods have achieved promising results in realism and 3D consistency, but text-to-4D generation still faces challenges, including lack of realism and insufficient dynamic motions. In this paper, we propose a novel method for text-to-4D generation, which ensures the dynamic amplitude and authenticity through direct supervision provided by a video prior. Specifically, we adopt a text-to-video diffusion model to generate a reference video and divide 4D generation into two stages: static generation and dynamic generation. The static 3D generation is achieved under the guidance of the input text and the first frame of the reference video, while in the dynamic generation stage, we introduce a customized SDS loss to ensure multi-view consistency, a video-based SDS loss to improve temporal consistency, and most importantly, direct priors from the reference video to ensure the quality of geometry and texture. Moreover, we design a prior-switching training strategy to avoid conflicts between different priors and fully leverage the benefits of each prior. In addition, to enrich the generated motion, we further introduce a dynamic modeling representation composed of a deformation network and a topology network, which ensures dynamic continuity while modeling topological changes. Our method not only supports text-to-4D generation but also enables 4D generation from monocular videos. The comparison experiments demonstrate the superiority of our method compared to existing methods.

Accurate diagnostics of the combustor region of ramjet engines can improve engine design and create benchmarks for computational fluid dynamics models. Previous works demonstrate that dual frequency comb spectroscopy can provide low uncertainty diagnostics of multiple flow parameters in the non-combusting regions of ramjets. However, the high temperatures present in the combustor present a challenge for broadband spectroscopic absorption models that are used to interpret measurements in these regions. Here, we utilize a new water vapor absorption database created for high temperature water-air mixtures to fit spectra measured in a ground-test ramjet engine with a broadband near-infrared dual comb absorption spectrometer. We extract 2D profiles of pressure, temperature, water mole fraction, and velocity using this new database. We demonstrate that the new database provides the lowest fit residuals compared to other water vapor absorption databases. We compare computational fluid dynamics simulations of the combustor with the measured data to demonstrate that the simulations overpredict heat release and water vapor production., Comment: This work has been cleared by the Air Force under case number AFRL-2023-4205

Supersonic engine development requires accurate and detailed measurements of fluidic and thermodynamic parameters to optimize engine designs and benchmark computational fluid dynamic (CFD) simulations. Here, we demonstrate that dual frequency comb spectroscopy (DCS) with mode-locked frequency combs can provide simultaneous absolute measurements of several flow parameters with low uncertainty across a range of conditions owing to the broadband and ultrastable optical frequency output of the lasers. We perform DCS measurements across a 6800-7200 cm-1 bandwidth covering hundreds of H2O absorption features resolved with a spectral point spacing of 0.0067 cm-1 and point spacing precision of 1.68 x 10-10 cm-1. We demonstrate 2D profiles of velocity, temperature, pressure, water mole fraction, and air mass flux in a ground-test dual-mode ramjet at Wright-Patterson Air Force Base. The narrow angles of the measurement beams offer sufficient spatial resolution to resolve properties across an oblique shock train in the isolator and the thermal throat of the combustor. We determine that the total measurement uncertainties for the various parameters range from 1% for temperature to 9% for water vapor mole fraction, with the absorption database/model that is used to interpret the data typically contributing the most uncertainty (leaving the door open for even lower uncertainty in the future). CFD at the various measurement locations show good agreement, largely falling within the DCS measurement uncertainty for most profiles and parameters.

Providing an accurate, representative sample of mass flux across large open areas for atmospheric studies or the extreme conditions of a hypersonic engine is challenging for traditional intrusive or point-based sensors. Here, we demonstrate that laser absorption spectroscopy with mode-locked frequency combs can simultaneously measure all of the components of mass flux (velocity, temperature, pressure, and species mole fraction) with low uncertainty, spatial resolution corresponding to the laser line of sight, and no supplemental sensor readings. The low uncertainty is provided by the broad spectral bandwidth, high resolution, and extremely well-known and controlled frequency axis of stabilized, mode-locked frequency combs. We demonstrate these capabilities using dual frequency comb spectroscopy (DCS) in the isolator of a ground-test supersonic propulsion engine at Wright-Patterson Air Force Base. The mass flux measurements are consistent within 3.6% of the facility-level engine air supply values. A vertical scan of the laser beams in the isolator measures the spatially resolved mass flux, which is compared with computational fluid dynamics simulations. A rigorous uncertainty analysis demonstrates a instrument uncertainty of ~0.4%, and total uncertainty (including non-instrument sources) of ~7% for mass flux measurements. These measurements demonstrate DCS with mode-locked frequency combs as a low-uncertainty mass flux sensor for a variety of applications.

Highly size-asymmetrical fluid mixtures arise in a variety of physical contexts, notably in suspensions of colloidal particles to which much smaller particles have been added in the form of polymers or nanoparticles. Conventional schemes for simulating models of such systems are hamstrung by the difficulty of relaxing the large species in the presence of the small one. Here we describe how the rejection-free geometrical cluster algorithm (GCA) of Liu and Luijten [Phys. Rev. Lett 92, 035504 (2004)] can be embedded within a restricted Gibbs ensemble to facilitate efficient and accurate studies of fluid phase behavior of highly size-asymmetrical mixtures. After providing a detailed description of the algorithm, we summarize the bespoke analysis techniques of Ashton et al. [J. Chem. Phys. 132, 074111 (2010)] that permit accurate estimates of coexisting densities and critical-point parameters. We apply our methods to study the liquid--vapor phase diagram of a particular mixture of Lennard-Jones particles having a 10:1 size ratio. As the reservoir volume fraction of small particles is increased in the range 0--5%, the critical temperature decreases by approximately 50%, while the critical density drops by some 30%. These trends imply that in our system, adding small particles decreases the net attraction between large particles, a situation that contrasts with hard-sphere mixtures where an attractive depletion force occurs., Comment: 11 pages, 10 figures