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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Non-geometric hazards (i.e., those which cannot be characterized solely by their shape, but instead are related to mechanical properties such as strength and friction) may pose a significant risk to planetary rovers. This paper describes a means for an articulated vehicle to detect sinkage and slippage in such material so as to prevent entrapment and to correct for dead-reckoning errors.
The American National Standard Y14.26M-1981 on Digital Representation for Communication of Product Definition Data includes an introduction, three sections corresponding to IGES (Initial Graphics Exchange Specification) Version 1.0, and Section 5, which is a constructive, relational, language based representation for geometric and topological entitles.
The accuracy of a new nongeometric method for calculating left ventricular myocardial volumes from two-dimensional echocardiographic images was assessed in vitro using 20 formalin-fixed normal human hearts. Serial oblique short-axis images were acquired from one point at 5-deg intervals, for a total of 10-12 cross sections. Echocardiographic myocardial volumes were calculated as the difference between the volumes defined by the epi- and endocardial surfaces. Actual myocardial volumes were determined by water displacement. Volumes ranged from 80 to 174 ml (mean 130.8 ml). Linear regression analysis demonstrated excellent agreement between the echocardiographic and direct measurements.
The sinkage and slippage detection methodologies employed by a MESUR Pathfinder microrover, Rocky 3.2, are discussed. Results from a simulation are presented.
Existing tools for community or urban scale energy system modeling and simulation are often limited in their capabilities and require expert-level modeling proficiency to develop system models. To fill this gap, this paper proposes an integrated sizing and modeling platform for district energy systems with geothermal heat pumps. The proposed platform takes in geometric and non-geometric user inputs related to the buildings, borefield, and district energy loop. Then, the platform sizes the geothermal heat exchanger, generates a corresponding district energy system model, and runs an annual simulation automatically. We validated the simulation performance of the borefield in our tool against EnergyPlus. A case study is provided in this paper to demonstrate the workflow and simulation result plausibility of the proposed platform.
Holographic duals for CFTs compactified on a Riemann surface Σ with a twist are cast in the language of wedge holography. Σ starts as part of the field theory geometry in the UV and becomes part of the internal space in the IR. This allows to associate entanglement entropies with splits of the internal space in the IR geometry. Decomposing the internal space in the IR and geometrizing the corresponding subsystems separately leads to two interacting gravitational systems, similar to the intermediate holographic description in braneworld models. For Σ = T 2 the setups are used to model information transfer from a black hole to a gravitating bath. This leads to Page curves with a phase structure which precisely mirrors that in braneworld models. The transition from geometric to non-geometric entropies is also discussed for Σ = S 2 as a model for more general internal spaces in AdS/CFT.
Urban-scale building energy modeling is vital for urban planning. However, it can be challenging to assimilate reliable non-geometry building data for urban-scale modeling without extensive investment. Here, this study introduces a novel approach to developing modern urban-scale building energy stock data using geographic information systems and machine learning algorithms without necessarily requiring pre-supplied non-geometric metadata. The proposed framework integrates building footprint and height data to estimate gross floor areas, and matches each building to a pool of candidate records from ComStock or ResStock—filtered to the same county and ranked by geometric similarity—demonstrate a proof-of-concept case study in Chicago for predicting energy use intensity (EUI) using scalable datasets. The model achieved a mean bias error (MBE) of 0.08 kWh/m² and root mean square error (RMSE) of 14.84 kWh/m² under full metadata input for EUI prediction. With only location inputs, the model captured 69.2 % of EUI within predicted ranges. These results demonstrate the model’s potential to support early-stage urban planning, identify candidates for energy-efficient retrofits. By removing the dependency on detailed pre-surveys or extensive building metadata, the approach overcomes a key barrier in traditional urban-scale building energy modeling, illustrating a pathway toward broader and more cost-effective application, though further multi-city validation and improved treatment of pre-1925 buildings are needed.
Abstract Generative deep learning methods have recently been proposed for generating 3D molecules using equivariant graph neural networks (GNNs) within a denoising diffusion framework. However, such methods are unable to learn important geometric properties of 3D molecules, as they adopt molecule-agnostic and non-geometric GNNs as their 3D graph denoising networks, which notably hinders their ability to generate valid large 3D molecules. In this work, we address these gaps by introducing the Geometry-Complete Diffusion Model (GCDM) for 3D molecule generation, which outperforms existing 3D molecular diffusion models by significant margins across conditional and unconditional settings for the QM9 dataset and the larger GEOM-Drugs dataset, respectively. Importantly, we demonstrate that GCDM’s generative denoising process enables the model to generate a significant proportion of valid and energetically-stable large molecules at the scale of GEOM-Drugs, whereas previous methods fail to do so with the features they learn. Additionally, we show that extensions of GCDM can not only effectively design 3D molecules for specific protein pockets but can be repurposed to consistently optimize the geometry and chemical composition of existing 3D molecules for molecular stability and property specificity, demonstrating new versatility of molecular diffusion models. Code and data are freely available on GitHub .
Characterizing the entanglement structure of ground states of local Hamiltonians is a fundamental problem in quantum information. In this work we study the computational complexity of this problem, given the Hamiltonian as input. Our main result is that to show it is cryptographically hard to determine if the ground state of a geometrically local, polynomially gapped Hamiltonian on qudits (d=O(1)) has near-area law vs near-volume law entanglement. This improves prior work of Bouland et al. (arXiv:2311.12017) showing this for non-geometrically local Hamiltonians. In particular we show this problem is roughly factoring-hard in 1D, and LWE-hard in 2D. Our proof works by constructing a novel form of public-key pseudo-entanglement which is highly space-efficient, and combining this with a modification of Gottesman and Irani's quantum Turing machine to Hamiltonian construction. Our work suggests that the problem of learning so-called "gapless" quantum phases of matter might be intractable.
A planetary rover will be traversing largely unknown and often unknowable terrain. In addition to geometric obstacles such as cliffs, rocks, and holes, it may also have to deal with non-geometric hazards such as soft soil and surface breakthroughs which often cannot be detected until rover is in imminent danger. Therefore, the rover must monitor its progress throughout a traverse, making sure to stay on course and to detect and act on any previously unseen hazards. Its onboard planning system must decide what sensors to monitor, what landmarks to take position readings from, and what actions to take if something should go wrong. The planning systems being developed for the Pathfinder Planetary Rover to perform these execution monitoring tasks are discussed. This system includes a network of planners to perform path planning, expectation generation, path analysis, sensor and reaction selection, and resource allocation.
Automatic differentiation (AD) is a powerful computational method that provides for computing exact sensitivity derivatives (SD) from existing computer programs for multidisciplinary design optimization (MDO) or in sensitivity analysis. A pre-compiler AD tool for FORTRAN programs called ADIFOR has been developed. The ADIFOR tool has been easily and quickly applied by NASA Langley researchers to assess the feasibility and computational impact of AD in MDO with several different FORTRAN programs. These include a state-of-the-art three dimensional multigrid Navier-Stokes flow solver for wings or aircraft configurations in transonic turbulent flow. With ADIFOR the user specifies sets of independent and dependent variables with an existing computer code. ADIFOR then traces the dependency path throughout the code, applies the chain rule to formulate derivative expressions, and generates new code to compute the required SD matrix. The resulting codes have been verified to compute exact non-geometric and geometric SD for a variety of cases. in less time than is required to compute the SD matrix using centered divided differences.
Burns et al. (1979) use the parameter beta to describe the ratio of radiation pressure to gravity for a particle in the Solar System. The central potential that these particles experience is effectively reduced by a factor of (1- beta ), which in turn lowers the escape velocity. Burns et al. (1979) derived a simple expression for the value of beta at which particles ejected from a comet follow parabolic orbits and thus leave the Solar System; we expand on this to derive an expression for critical beta values that takes ejection velocity into account, assuming geometric optics. We use our expression to compute the critical value and corresponding mass for cometary ejecta leading, trailing, and following the parent comet's nucleus for 10 major meteor showers. Finally, we numerically solve for critical beta values in the case of non-geometric optics. These values determine the mass regimes within which meteoroids are ejected from the Solar System and therefore cannot contribute to meteor showers.
Urban building modeling tools are developing rapidly; these tools use emerging simulation workflows for specific urban environmental design tasks, such as assessing the impacts of energy efficiency technologies at a district scale. However, with the emergence of new environmental design tasks, addressing all possible use cases and tasks is challenging and cannot be covered by a single tool. Urban-scale analysis at this level of complexity often requires linking multiple emerging tools, rather than using a single tool, to adequately evaluate a variety of possible fields in urban environmental design. To achieve this, flexible platforms are needed to support multiple input formats (e.g., geometric and non-geometric building properties), enabling the mapping of such inputs to underlying simulation engines. This paper provides an overview of the open-source URBANopt Software Development Kit (SDK) for modeling high-performance buildings and energy systems at a district scale. URBANopt's flexible SDK is composed of several modules that can be customized to integrate with other tools and generate new workflows to perform urban environmental design tasks, such as capturing interactions between individual buildings, district energy systems, distributed energy resources, and the electric distribution grid. We describe the functionality of the core SDK modules in URBANopt (called Core Gem, GeoJSON Gem, and Scenario Gem) and discuss the flexibility of these modules as a means of integration with a variety of tools. We also document and demonstrate technical details of writing and combining new modules to create customized workflows. Finally, we present a case study that uses the URBANopt SDK to model a hypothetical mixed-use urban project and simulate various scenarios to meet district energy performance goals.