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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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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Inverse design of cellular structures with the targeted nonlinear mechanical response

Advanced additive manufacturing capabilities have enabled a transformational ability to create sophisticated cellular structures using diverse materials. By altering the topology of the unit cell, the mechanical behavior, such as the stress-strain response during compression, can be modulated. Nevertheless, identifying a printable topology within an enormous design space that would precisely deliver the targeted nonlinear material response is challenging. We propose a data-driven generative framework based on a conditional variational autoencoder (cVAE) architecture that can inverse design the cellular structure based on the intended nonlinear stress-strain response. Trained on a dataset of structure-property pairs, the cVAE learns a compact and expressive latent space that enables efficient mapping from targets to feasible geometries. Two inference modes are explored: (1) decoder-only generation, which enables the exploration of diverse designs conditioned solely on the desired mechanical response, and (2) encoder-decoder generation, which further allows for the incorporation of desired topologies, ensuring the generated structure conforms to both mechanical properties and to desired-topology constraints. The results demonstrate that the model can generate structurally plausible and mechanically accurate designs, with the predicted stress-strain curves closely matching the targets. Even under joint conditioning, the model effectively balances geometric fidelity and functional performance.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nuclear Data Adjustment for Nonlinear Applications in the OECD/NEA WPNCS SG14 Benchmark—A Bayesian Inverse UQ-Based Approach for Data Assimilation

The Organisation for Economic Co-operation and Development Working Party on Nuclear Criticality Safety has proposed a benchmark exercise to assess the performance of current nuclear data adjustment techniques applied to nonlinear applications and experiments with low correlation to applications. This work introduces Bayesian inverse uncertainty quantification (IUQ) employing scientific machine learning surrogate models as a method for nuclear data adjustments in this benchmark, and compares IUQ to the more traditional methods of generalized linear least squares (GLLS) and Monte Carlo Bayes (MOCABA). Posterior predictions from IUQ showed agreement with GLLS and MOCABA for linear applications. Here, when comparing GLLS, MOCABA, and IUQ posterior predictions to computed model responses using adjusted parameters, we observe that the GLLS predictions failed to replicate the computed response distributions for nonlinear applications, while MOCABA showed near agreement, and IUQ used the computed model responses directly. We also discuss observations on why experiments with low correlation to applications can be informative to nuclear data adjustments and identify some properties useful in selecting experiments for inclusion in nuclear data adjustment. Performance in this benchmark indicates potential for Bayesian IUQ in nuclear data adjustments.

Bayesian calibration↗
DOE OSTI2024

Development of a Hybrid Single/Two-Phase Capillary-Based Micro-Cooler using Copper Inverse Opals Wick with Silicon 3D Manifold for High-Heat-Flux Cooling Application

Previously, we reported two-phase capillary-based cooling using narrow (200 to 1000 ..mu..m) heater bridge copper inverse opal (CIO) wicks with heat flux level of 1400 Wcm -2 and superheat ~ 10 degrees C. Here, we demonstrate the area scaling of the proposed technology to a large-area micro-cooler for high-heat-flux cooling of microprocessors and power electronics. We developed a hybrid single/two-phase micro-cooler that relies on capillary-wicking in a 25-..mu..m-thick CIO with an open silicon microchannel 3D-manifold for liquid delivery and vapor extraction, achieving a high heat flux ~ 400 Wcm -2 over a heated area of 10 x 10 mm 2 . For a range of inlet water flowrates from 5 to 60 g(min) -1 , we achieved total thermal resistances and vapor qualities of 0.68 cm 2 degrees CW -1 to 0.2 cm 2 degrees CW -1 and 0.55 to 0.12, respectively. The flowrates are 10x smaller than those of conventional single- or two-phase microchannel cooling technology. The corresponding two-phase thermal resistances ranges from 0.05 to 0.02 cm 2 degrees CW -1 with temperature superheat of 8 to 6 degrees C, respectively. While the overall performance of the large-area (10 x 10 mm 2 ) capillary-based micro-cooler degraded compared to the previous demonstration of the technology for a heated area of 5 x 5 mm 2 , however, preliminary CFD modeling indicates that an improved manifold design will be able to achieve comparable performance.

capillary flow↗
DOE OSTI2024

Pool Boiling Reliability Tests and Degradation Mechanisms of Microporous Copper Inverse Opal (CuIOs) Structures

The rising power density in electronic systems requires thermal management solutions that are both high-performing and reliable. Porous materials such as Copper Inverse Opal (CuIOs) have unique structural features, including high permeability and high thermal conductivity, to enhance pool boiling performance. However, there is little understanding of the degradation mechanism of such porous materials under pool boiling conditions. In this study, samples of 10 ..mu..m thick CuIOs with 4.8 ..mu..m diameter, covering silicon substrate of area 11 mm x 11 mm, with various heated areas ranging from 2.5 mm x 2.5 mm to 10 mm x 10 mm, were tested in 100 degrees C deionized water at a constant heat flux of 110 Wcm -2 for 3-to-7 days. The combined effect of erosion and corrosion caused structural degradation of the CuIOs. The directly heated area had the most severe degradation while the edge of the heater and the unheated area showed progressively less degradation, maintaining some CuIOs structure even after the 7-day reliability test. Among all the tested samples with various heater sizes, the 2.5 mm x 2.5 mm heater sample - in which the heater size was designed to be comparable to the water bubble characteristic length - had the largest critical heat flux (CHF) up to 300 Wcm -2 with a superheat ~ 13 degrees C. Additionally, CuIOs with a smaller heated area performed better in terms of reliability. This study offers preliminary insights into CuIOs degradation mechanisms, contributing to the development of more robust thermal management solutions. We expect that electroless plating of CuIOs with gold (Au), nickel (Ni), and atomic layer deposition (ALD) aluminum oxide (Al 2 O 3 ) in combination with appropriate application-specific coolants will further improve the reliability and lifetime of the CuIOs.

boiling-induced degradation↗
DOE OSTI2024

Revealing Decision Conservativeness Through Inverse Distributionally Robust Optimization

This paper introduces Inverse Distributionally Robust Optimization (I-DRO) as a method to infer the conservativeness level of a decision-maker, represented by the size of a Wasserstein metric-based ambiguity set, from the optimal decisions made using Forward Distributionally Robust Optimization (F-DRO). By leveraging the Karush-Kuhn-Tucker (KKT) conditions of the convex F-DRO model, we formulate I-DRO as a bi-linear program, which can be solved using off-the-shelf optimization solvers. Additionally, this formulation exhibits several advantageous properties. We demonstrate that I-DRO not only guarantees the existence and uniqueness of an optimal solution but also establishes the necessary and sufficient conditions for this optimal solution to accurately match the actual conservativeness level in F-DRO. Furthermore, we identify three extreme scenarios that may impact I-DRO effectiveness. Our case study applies F-DRO for power system scheduling under uncertainty and employs I-DRO to recover the conservativeness level of system operators. Numerical experiments based on an IEEE 5-bus system and a realistic NYISO 11-zone system demonstrate I-DRO performance in both normal and extreme scenarios. An extended version of this paper with additional analyses is available at li2024revealing.

distributionally robust optimization↗
DOE OSTI2025

Hierarchical Bayesian Inverse Problems: A High-Dimensional Statistics Viewpoint

This paper analyzes hierarchical Bayesian inverse problems using techniques from highdimensional statistics. Furthermore, our analysis leverages a property of hierarchical Bayesian regularizers that we call approximate decomposability to obtain non-asymptotic bounds on the reconstruction error attained by maximum a posteriori estimators. The new theory explains how hierarchical Bayesian models that exploit sparsity, group sparsity, and sparse representations of the unknown parameter can achieve accurate reconstructions in high-dimensional settings.

MAP estimation↗
DOE OSTI2025

Preliminary Results on Bayesian Inverse UQ for OECD/NEA WPNCS Subgroup 14 Benchmark Exercise for Error Recovery and Experimental Coverage

The Organization for Economic Cooperation and Development (OECD) Working Party on Nucelar Criticality Safety (WPNCS) has proposed a benchmark exercise representative of neutronic behavior in criticality experiments. Here, the goal is to develop confidence in data assimilation techniques used to adjust nuclear data. Participants are given synthetic experimental models with associated measured data and asked to estimate the model parameters given the model and measurements as well as provide predictions for separate application models. In this work, we performed data assimilation using Bayesian inverse Uncertainty Quantification (UQ) with machine learning surrogate models to produce posterior parameter distributions for the requested parameters and posterior predictive distributions for the requested responses. Several experimental models are shown to insufficiently inform the posterior parameter distributions for the applications involved. However, given sufficient experimental data, posterior parameter estimates yielded reduced uncertainty in the response predictions of interest while covering the experimental data.

Bayesian Inference↗
NASA NTRS2002

Short-range inverse-square law experiment in space

Newton's inverse-square law is a cornerstone of General Relativity. Its validity has been demonstrated to better than one part in thousand in ranges greater than 1 cm. The range below 1 mm has been left largely unexplored, due to the difficulties associated with designing sensitive short-range experiments. However, the theoretical rationale for testing Newton's law at ranges below 1 mm has become very strong recently.

inverse square law general relativity short range ↗
NASA NTRS2002

Short-range inverse-square law experiment in space

The objective of ISLES (Inverse-Square Law Experiment in Space) is to perform a null test ofNewton's law on the ISS with a resolution of one part in lo5 at ranges from 100 pm to 1 mm. ISLES will be sensitive enough to detect axions with the strongest allowed coupling and to test the string-theory prediction with R z 5 pm.

Gravity inverse-square law general relativity phys↗
NASA NTRS2018

Inverse Determination of Aeroheating and Charring Ablator Response

The Mars Science Laboratory (MSL) was protected during its Mars atmospheric entry by an instrumented heatshield that used NASA's Phenolic Impregnated Carbon Ablator (PICA). PICA is a lightweight carbon fiber/polymeric resin material that offers excellent performances for protecting probes during planetary entry. The Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of atmospheric entry and material response. MEDLI recorded, among others, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP). These measurements have been widely used in the literature as a validation benchmark for state-of-the-art ablation codes. The objective of this work is to perform an inverse estimate of the MSL heatshield material properties and aerothermal environment during Mars entry from the MISP flight data.

Aeroheating↗
NASA NTRS2016

Low-CNR Inverse Synethetic LADAR Imaging Demonstration with Atmospheric Turbulence

An Inverse Synthetic Aperture LADAR (ISAL) system is capable of providing high resolution surface mapping of near Earth objects which is an ability that has gained significant interest for both exploration and hazard assessment. The use of an ISAL system over these long distances often presents the need to operate the optical system in photon-starved conditions. This leads to a necessity to understand the implications of photon and detector noise in the system. Here a Carrier-to-Noise Ratio is derived which is similar to other optical imaging CNR definitions. The CNR value is compared to the quality of experimentally captured images recovered using the Phase Gradient Autofocus technique both with and without the presence of atmospheric turbulence. A minimum return signal CNR for the PGA to work is observed.

ISAL↗