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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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NASA NTRS2001

In Vivo Simulator for Microwave Treatment

Method and apparatus are provided for propagating microwave energy into heart tissues to produce a desired temperature profile therein at tissue depths sufficient for thermally ablating arrhythmogenic cardiac tissue to treat ventricular tachycardia and other arrhythmias while preventing excessive heating of surrounding tissues, organs, and blood. A wide bandwidth double-disk antenna is effective for this purpose over a bandwidth of about 6 GHz. A computer simulation provides initial screening capabilities for an antenna such as antenna. frequency, power level, and power application duration. The simulation also allows optimization of techniques for specific patients or conditions. In operation, microwave energy between about 1 GHz and 12 GHz is applied to monopole microwave radiator having a surface wave limiter. A test setup provides physical testing of microwave radiators to determine the temperature profile created in actual heart tissue or ersatz heart tissue. Saline solution pumped over the heart tissue with a peristaltic pump simulates blood flow. Optical temperature sensors disposed at various tissue depths within the heart tissue detect the temperature profile without creating any electromagnetic interference. The method may be used to produce a desired temperature profile in other body tissues reachable by catheter such as tumors and the like.

Arndt, G. Dickey↗

NASA NTRSDate not supplied

Quantifying the Sensitivity of Condition Incidence Parameters in the Evidence Library

One approach to quantifying spaceflight risk at NASA makes use event driven probabilistic techniques. The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) is such a tool that estimates medical risk metrics via simulation and enables optimization of medical resources subject to mission constraints [1]. Previous analyses have informed medical set composition, exercise countermeasures, and water intake, where each analysis quantifies the risk associated with proposed variations in system design. As future mission profiles extend beyond Low-Earth Orbit (LEO) and lengthen in duration, understanding these risks and contributing factors is critical. MEDPRAT employs Monte Carlo sampling techniques to simulate missions and track the occurrence of medical events. These events follow fault-tree-like progressions through levels of severity and mitigation via medical treatment to many possible outcomes and these are reported throughout the mission. Making this possible, are the medical databases that contain evidence gathered by the Human Research Program (HRP). Quantifying the impact of uncertainty or variability in the input data is an important step in evaluating the credibility of modeling and simulation results. In this work, we investigate the sensitivity of medical risk metrics with respect to the condition incidence parameters within the Evidence Library (EL) [2] as the medical database input for MEDPRAT. The medical conditions, contained in the EL, are equipped with incidence rates that describe the likelihood that the condition will occur. These incidence rates reflect historical spaceflight data or when appropriate, terrestrial data. In this presentation, we will explore how uncertainty in these rates propagate to the medical risk described by MEDPRAT. These results identify the conditions and parameters with the largest contribution to medical risks.

Ian Lim↗

DOE OSTI2022

Data–driven and constrained optimization of semi–local exchange and nonlocal correlation functionals for materials and surface chemistry

Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Herein, we present an empirical approach to simultaneously optimize semi-local exchange and nonlocal correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk, surface, and gas-phase chemistry and physical exchange-correlation model constraints leads to the VCML-rVV10 exchange-correlation functional. Owing to the variety of training data, the applicability of VCML-rVV10 extends beyond surface chemistry simulations. It provides optimized gas phase reaction energetics and an accurate description of bulk lattice constants and elastic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2011

The Traverse Planning Process for the Drats 2010 Analog Field Simulations

Traverse planning concentrates on optimizing the science return within the overall objectives of planetary surface missions or their analog field simulations. Such simulations were conducted in the San Francisco Volcanic Field, northern Arizona, from Aug. 26 to Sept 17, 2010 and involved some 200 individuals in the field, with some 40 geoscientists composing the science team. The purpose of these Desert Research and Technology Studies (DRATS) is to exercise and evaluate developmental hardware, software and operational concepts in a mission-like, fully-integrated, setting under the direction of an onsite Mobile Mission Control Center(MMCC). DRATS 2010 focused on the simultaneous operation of 2 rovers, a historic first. Each vehicle was manned by an astronaut-commander and an experienced field geologist. Having 2 rovers and crews in the field mandated substantially more complex science and mission control operations compared to the single rover DRATS tests of 2008 and 2009, or the Apollo lunar missions. For instance, the science support function was distributed over 2 "back rooms", one for each rover, with both "tactical" teams operating independently and simultaneously during the actual traverses. Synthesis and integration of the daily findings and forward planning for the next day(s) was accomplished overnight by yet another "strategic" science team.

Horz, Friedrich↗

DOE OSTI2024

"Designing, simulating, and performing the 100-AV field test for the CIRCLES consortium: Methodology and Implementation of the Largest mobile traffic control experiment to date"

Previous controlled experiments on single-lane ring roads have shown that a single partially autonomous vehicle (AV) can effectively mitigate traffic waves. This naturally prompts the question of how these findings can be generalized to field operational, high-density traffic conditions. To address this question, the Congestion Impacts Reduction via CAV-in-the-loop Lagrangian Energy Smoothing (CIRCLES) Consortium conducted MegaVanderTest (MVT), a live traffic control experiment involving 100 vehicles near Nashville, TN, USA. This article is a tutorial for developing analytical and simulation-based tools essential for designing and executing a live traffic control experiment like the MVT. It presents an overview of the proposed roadmap and various procedures used in designing, monitoring, and conducting the MVT, which is the largest mobile traffic control experiment at the time. The design process is aimed at evaluating the impact of the CIRCLES AVs on surrounding traffic. The article discusses the agent-based traffic simulation framework created for this evaluation. A novel methodological framework is introduced to calibrate this microsimulation, aiming to accurately capture traffic dynamics and assess the impact of adding 100 vehicles to existing traffic. The calibration model's effectiveness is verified using data from a six-mile section of Nashville's I-24 highway. The results indicate that the proposed model establishes an effective feedback loop between the optimizer and the simulator, thereby calibrating flow and speed with different spatiotemporal characteristics to minimize the error between simulated and real-world data. Finally, We simulate AVs in multiple scenarios to assess their effect on traffic congestion. This evaluation validates the AV routes, thereby contributing to the execution of a safe and successful live traffic control experiment via AVs.

Ameli, Mostafa↗

DOE OSTI2019

Accuracy of a Crude Approach to Urban Multi-Scale Building Energy Models Compared to 15-min Electricity Use

ASHRAE has recently named a TC4.7 subcommittee on Multi-Scale Energy Modeling and there have been many technical developments focused on urban-scale energy modeling with the goal of creating building energy models for all buildings in a city to facilitate simulation-informed energy optimization for the built environment. These efforts often rely on non-scalable, local sources of information (e.g. county’s tax assessor’s database) to create more accurate models of those buildings. In partnership with the local utility, a year’s worth of real-world 15-minute electrical use data from 178,377 premise buildings has been assessed. This smart meter data is statistically analyzed and compared to corresponding simulation data for 97 different building prototypes in order to better assess simulation accuracy for various utility-defined use cases. To our knowledge, this is the first time real world and simulated building energy use data have been compared at this scale.

Garrison, Eric↗

DOE OSTI2022

Dynamic Earth Energy Storage: Terawatt-year, Grid-scale Energy Storage Using Planet Earth as a Thermal Battery (GeoTES): Phase I Project (Final Report)

Grid-scale energy storage has been identified by the U.S. Department of Energy’s (DOE) Energy Storage Grand Challenge as a necessary technology to support the continued build-out of intermittent renewable energy resources required to attain a carbon-free energy future. To meet this goal, the 2018 Department of Energy Research and Innovation Act mandated the creation of a comprehensive program to accelerate the development and commercialization of next-generation energy storage technologies. One of numerous energy storage technology options is the storage of excess energy as heated geothermal brine in suitable geologic formations. This concept, known as reservoir thermal energy storage (RTES), geologic thermal energy storage (GeoTES), aquifer thermal energy storage (ATES), etc., relies on the storage of thermal energy in geologic formations for recovery and use in large-scale direct use geothermal (e.g., district heating, industrial processes, etc.) and electrical power generation applications. This thermal energy is derived from excess or waste heat from any high-temperature heat source, such as concentrated solar or from conventional thermal/nuclear generation. As such, RTES can potentially play a significant role in meeting the energy storage shortfall in the coming decades. RTES can provide energy arbitrage through both the storage and production of thermal energy stored in geologic formations for direct use applications and can serve as a source of hot fluids that can be used to generate electricity to support peak demand ramping, thus easing stress on transmission and distribution. This energy storage option has geographic benefits in that energy can be stored locally or regionally depending on the various needs/loads. RTES can also be located across an enormous geographic area, without the need for a traditional hydrothermal resource but where thermal gradients and hydrogeology allow economic exploitation of subsurface heat. The work conducted for this project includes (1) a review of lessons learned from past high-temperature RTES international projects; (2) geochemical experimental investigation and numerical simulations of potential domestic sedimentary reservoirs and (3) development of a thermo-hydrological-mechanical (THM) numerical simulation tool for optimizing formation properties and design parameters to maximize thermal energy storage performance.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗

NASA NTRS1999

NASA's Virtual Product Laboratory: An Overview

The Virtual Product Laboratory (VPL) developed by NASA's Commercial Remote Sensing Program at the John C. Stennis Space Center is a tool that enables simulation, design, and verification of remote sensing systems within a software (virtual) environment. The VPL can serve industry, government, and university communities by providing a means to conduct system trade studies, optimization, visual modeling, and data product simulations entirely in a virtual environment. The VPL can serve as a complete end-to-end simulation tool capable of producing system and subsystem performance characterizations, system optimization, and simulated data products or as a means of evaluating any one component of a remote sensing system. In this paper, we present an overview of the VPL capabilities. The VPL functional areas include Requirements, Design & Analysis, Simulation, Project Management, Knowledge Base, and Help. A description of each function along with the tools and techniques used to accomplish these functions will be provided. When possible, sample VPL displays and products will be used in the presentation.

Gasser, Jerry↗

DOE OSTI2021

Aero-structural design and optimization of 50 MW wind turbine with over 250-m blades

The quest for reduced levelized cost of energy has driven significant growth in wind turbine size; however, larger rotors face significant technical and logistical challenges. The largest published rotor design is 25 MW, and here we consider an even larger 50 MW design with blade length over 250 m. This paper shows that a 50 MW design is indeed possible from a detailed engineering perspective and presents a series of aero-structural blade designs, and critical assessment of technology pathways and challenges for extreme-scale rotors. The 50 MW rotor design begins with Monte Carlo simulations focused on optimizing carbon spar cap and root design. A baseline design resulted in a 250-m blade with mass of 502 tonnes. Subsequently, an aero-structural design and optimization were performed to reduce the blade mass/cost with more than 25% mass reduction and 30% cost reduction by determining optimal blade chord and airfoil thickness for best aero-structural performance.

Yao, Shulong↗

DOE OSTI2024

Bayesian optimization algorithms for accelerator physics

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques toward solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Scalable freeform optimization of wide-aperture 3D metalenses by zoned discrete axisymmetry

We introduce a novel framework for design and optimization of 3D freeform metalenses that attains nearly linear scaling of computational cost with diameter, by breaking the lens into a sequence of radial “zones” with 𝑛-fold discrete axisymmetry, where 𝑛 increases with radius. This allows vastly more design freedom than imposing continuous axisymmetry, while avoiding the compromises of the locally periodic approximation (LPA) or scalar diffraction theory. Using a GPU-accelerated finite-difference time-domain (FDTD) solver in cylindrical coordinates, we perform full-wave simulation and topology optimization within each supra-wavelength zone. We validate our approach by designing millimeter and centimeter-scale, poly-achromatic, 3D freeform metalenses which outperform the state of the art. By demonstrating the scalability and resulting optical performance enabled by our “zoned discrete axisymmetry” (ZDA) and supra-wavelength domain decomposition, we highlight the potential of our framework to advance large-scale meta-optics and next-generation photonic technologies.

Sun, Mengdi [Wesleyan University]↗

NASA NTRS1989

Efficient mapping algorithms for scheduling robot inverse dynamics computation on a multiprocessor system

Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed.

Lee, C. S. G.↗

DOE OSTI2018

Thermomechanical design optimization for low metal footprint in hybrid metal-polymer heat exchanger

Material and maintenance costs of metal-based heat exchangers surpass profits in waste heat recovery (WHR) from ultra-low temperature (<100C) sources [1]. A low-cost alternative like polymer pipes suffer from poor thermal conductivity (~0.2 W/mK), and a low overall heat transfer coefficient. Polymers’ thermal conductivity have previously been enhanced through systematic molecular alignment by stretching [2], chemical vapor deposition of extended polymers [3], surface grating [4], etc. However, these approaches suffer from scalability issues for WHR heat exchangers in terms of cost [3], and thermomechanical considerations [5]. In this work, we propose novel hybrid metal-polymer heat exchangers that are made from polymer-copper strips. Through finite element method (FEM) simulations, we first optimize the placement of copper around polymer strips to enhance the transverse thermal conductivity of the strips. The copper cladded polymer strips are wound helically in a roll-to-roll system to form the pipes for a heat exchanger. We then optimize the design of helically wound pipes to reduce thermomechanical strains to < 0.2 % at 100C and 40 psi working conditions. At 25 % volume fraction of copper in the pipe, we predict up to 20 % enhancement in overall heat transfer from a base polymer with a thermal conductivity of 0.2 W/mK. We also compare and contrast the thermomechanical performance of the optimized metal-polymer strips to random metal matrix composites. Finally, we experimentally verify the thermal conductivity of the optimized strips through bulk thermal conductivity and thermal interface resistance measurement setup. By systematically improving the design through FEM and experimental measurements, we provide an optimized design of hybrid metal-polymer pipes that provides a low metal footprint and cost-effective means for harvesting waste heat from ultra-low temperature sources. Thekdi, Arvind, and Sachin U. Nimbalkar. Oak Ridge National Lab, 2015 Shen, Sheng, et al. Nature nanotechnology 5.4 (2010): 251. Xu, Yanfei, et al. Science advances 4.3 (2018): 3031. Smith, M. K., Singh, V., Kalaitzidou, K., & Cola, B. A. (2015). ACS nano, 9(2), 1080-1088. Aymonier, C., Bortzmeyer, D., Thomann, R., & Mülhaupt, R. (2003). Chemistry of Materials, 15(25), 4874-4878.

C Rajagopal, Manjunath↗

DOE OSTI2021

Acquire and Test IDAES Framework Components

The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2006

Central East Pacific Flight Routing

With the introduction of the Federal Aviation Administration s Advanced Technology and Oceanic Procedures system at the Oakland Oceanic Center, a level of automation now exists in the oceanic environment to potentially begin accommodating increased user preferred routing requests. This paper presents the results of an initial feasibility assessment which examines the potential benefits of transitioning from the fixed Central East Pacific routes to user preferred routes. As a surrogate for the actual user-provided routing requests, a minimum-travel-time, wind-optimal dynamic programming algorithm was developed and utilized in this paper. After first describing the characteristics (e.g., origin airport, destination airport, vertical distribution and temporal distribution) of the westbound flights utilizing the Central East Pacific routes on Dec. 14-16 and 19-20, the results of both a flight-plan-based simulation and a wind-optimal-based simulation are presented. Whereas the lateral and longitudinal distribution of the aircraft trajectories in these two simulations varied dramatically, the number of simulated first-loss-of-separation events remained relatively constant. One area of concern that was uncovered in this initial analysis was a potential workload issue associated with the redistribution of traffic in the oceanic sectors due to thc prevailing wind patterns.

Grabbe, Shon↗

NASA NTRS1990

Modeling and optimization of a regenerative fuel cell system using the ASPEN process simulator

The Hydrogen-Oxygen Regenerative Fuel Cell System was identified as a key component for energy storage in support of future lunar missions. Since the H2-O2 regenerative electrochemical conversion technology has not yet been tested in space applications, it is necessary to implement predictive techniques to develop initial feasible system designs. The ASPEN simulation software furnishes a constructive medium for analyzing and for optimizing such systems. A rudimentary regenerative fuel cell system design was examined using the ASPEN simulator and this modular approach allows for easy addition of supplementary ancillary components and easy integration with life support systems. The modules included in the preliminary analyses may serve as the fundamental structure for more complicated energy storage systems.

Maloney, Thomas M.↗

DOE OSTI2021

Sensitivity Analysis of Tunable Equation of State Material Model In Pulsed Mercury Target Simulation

A pulsed neutron spallation target is subjected to very short but intense loads from repeated proton pulses. Approximately 60% of the energy from each proton pulse is deposited into the mercury target material and the stainless-steel target structure, leading to a high-pressure region in both the stationary target structure and the flowing mercury. The high-pressure region propagates and leads to fluid-structure interaction. The resultant loading on the target structure containing liquid mercury is difficult to predict, although various simulation approaches and material models for the mercury have been tried. To date, the best match of simulation to experimental data is obtained by using an equation of state (EOS) material model with a specified tensile cutoff pressure, which simulates the cavitation threshold. The inclusion of a threshold to represent cavitation is key to the successful predictions of stress waves triggered by the high-energy pulse striking the mercury and vessel. However, recent measurements of target structure strain show that significant discrepancies remain between the measured and simulated strain values in the EOS mercury model. These differences grow when noncondensable helium gas is intentionally injected into the flowing mercury to reduce the loading on the structure. An EOS-based proportional–integral–derivative (PID) mercury model has been proposed to reduce the gap between the measured and simulated vessel strain responses for targets with gas injection. The conceptual and numerical description and initial investigation of the PID model are presented in previous work. Further studies of this PID model — including the sensitivity of the structure’s strain response to model parameters (the tensile cutoff, PID parameters Kp, Ki, and Kd) — are reported in this article. Results show the strain response is more sensitive to changes in the tensile cutoff value than to changes in the model parameters Kp, Ki, and Kd. These results will aid in future work where the model parameters will be optimized to match simulation data to strain measurements.

Lin, Lianshan↗