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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Implementation of Triply Periodic Minimal Surfaces (TPMS) as surface objects in OpenMC

Triply Periodic Minimal Surfaces (TPMS) represent a promising geometry for future fuel designs due to their significant surface-to-volume ratio, which facilitates efficient cooling of nuclear fuel, a crucial factor for safety and efficiency. Demonstrating the remarkable capabilities of TPMS fuel requires initial modeling and simulation. This paper presents an implementation of TPMS in the Monte Carlo code OpenMC, enabling reactor physics modeling of TPMS. Here, the primary advantages over traditional methods using CAD files include reduced memory requirements for computations and high-fidelity implementation. This implementation has been tested against CAD files loaded in Serpent2, yielding promising results with low biases in the $k_{\textrm{eff}}$, comparable to biases in the material balance sheet. The implementation presented in this work will be used in future reactor physics computations related to new reactor designs involving TPMS-based fuels.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

A generalized family of anisotropic compact object in general relativity

Highlights: • We present model for anisotropic compact star under general relativity. • Here a 4-dimensional spacetime is embedded into the 5-dimensional flat metric. • The spherically symmetric metric has class 1 under which solution set is obtained. • Interesting and physically viable results are found out. • Investigations on the physical features of the model are performed. - Abstract: We present model for anisotropic compact star under the general theory of relativity of Einstein. In the study a 4-dimensional spacetime has been considered which is embedded into the 5-dimensional flat metric so that the spherically symmetric metric has class 1. A set of solutions for the field equations are found depending on the index n involved in the physical parameters. The interior solutions have been matched smoothly at the boundary of the spherical distribution to the exterior Schwarzschild solution which necessarily provides values of the unknown constants. We have chosen the values of n as n=2 and n=10 to 20000 for which interesting and physically viable results can be found out. The investigations on the physical features of the model include several astrophysical issues, like (i) regularity behaviour of stars at the centre, (ii) well behaved condition for velocity of sound, (iii) energy conditions, (iv) stability of the system via the following three techniques — adiabatic index, Herrera cracking concept and TOV equation, (v) total mass, effective mass and compactification factor and (vi) surface redshift. Specific numerical values of the compact star candidates LMC X-4 and SMC X-1 are calculated for central and surface densities as well as central pressure to compare the model value with actual observational data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation

Microstructure sensitive design has a critical impact on the performance of engineering materials. The safety and performance requirements of critical components, as well as the cost of material and machining of Titanium components, make dovetailing of the microstructure imperative. This paper addresses the optimization of several microstructure design problems for Titanium components under specific design constraints using a feedback-aware data-driven solution methodology. In this study, the microstructure is modeled with an orientation distribution function (ODF) that measures the volumes of different crystallographic orientations. Two algorithms are used to sample the entire microstructure space followed by machine learning-aided identification of a minimal subset of ODF dimensions which is subsequently explored by targeted sampling. Conventional optimization methods lead to a unique microstructure rather than yielding a comprehensive space of optimal or near-optimal microstructures. Multiple solutions are crucial for the deployment of materials design for manufacturing as traditional manufacturing processes can only generate a limited set of microstructures. Our data sampling-based methodology not only outperforms or is on par with other optimization techniques in terms of the optimal property value, but also provides numerous near-optimal solutions, 3–4 orders of magnitude more than previous methods. Consequently, the proposed framework delivers a spectrum of optimal solutions in the microstructure space which can accelerate materials development and reduce manufacturing costs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A neural-network-enhanced parameter-varying framework for multi-objective model predictive control applied to buildings

Management of the electrical grid is becoming more complex due to the increased penetration of alternative energy generation technologies and a broadening diversity of electric loads. This complexity creates challenges in balancing demand and generation that can increase the potential for grid instabilities. One effective way to address this issue is to leverage previously unexploited demand flexibility through advanced control strategies. In this work, we propose an advanced control method, called adaptive neural parameter-varying model predictive control (ANPV-MPC), to control the temperature and energy consumption of a building via its Heating, Ventilation, and Air Conditioning system. ANPV-MPC combines key ideas in parameter-varying control, adaptive control, and online learning strategies to bridge the gap between computationally efficient linear model predictive control and more accurate nonlinear model predictive control. The novelty in ANPV-MPC is the use of a physics-inspired Bayesian neural network to estimate the coefficients of the parameter-varying linear control model. The Bayesian neural network additionally provides uncertainty estimates, triggering online training to capture evolving building system conditions. We show that ANPV-MPC can approximate the building system dynamics with a 28.39% higher accuracy than traditional linear model predictive control, resulting in 36.23% better control performance without increasing complexity of the optimal control problem. ANPV-MPC also adapts in real time to previously unseen conditions using online learning, further improving its performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

The role of SEA in integrating and balancing high policy objectives in European cohesion funding programmes

Funding programmes for European cohesion policy are a crucial tool to support the sustainability goals of the European Union and national policies of its member states. All these funding programmes require a Strategic Environmental Assessment (SEA) to enhance sustainable development. This article compares five first SEA applications at cohesion policy level to discuss challenges, limitations and benefits of this instrument. In order to support the SEA-process a 'Handbook on SEA for Cohesion Policy 2007-13' (GRDP 2006) was developed. The paper examines the special requirements and challenges at the programme level given the special conditions for stakeholder involvement, integration of SEA in the programme development process and strategies to cope with uncertainties to ensure real compatibility with policy goals. Using action research and in-depth interviews with SEA planners and programme managers enabled us to analyse the suitability of the methodology proposed by the handbook. The results show that some recommendations of the handbook should be changed in order to increase the transparency and to enhance the standard and comparability of the SEA-documents. Overall the SEA proved to be a rather successful tool for the integration of sustainability goals at the EU and national policy levels. Its particular strengths emerged as the process makes uncertainties visible and leads to possible redefinitions while maintaining actual policy goals. - Highlights: Black-Right-Pointing-Pointer Comparing five case studies of first applications of SEA at cohesion policy level. Black-Right-Pointing-Pointer Overall the SEA proved to be a rather successful tool for the integration of sustainability goals. Black-Right-Pointing-Pointer The study makes uncertainties visible and shows how SEA could lead to possible redefinitions.

54 ENVIRONMENTAL SCIENCES↗