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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multi-channel optical detection system and method for multi-chamber assays

A multi-channel optical detection system includes a base unit adapted to receive a multi-chamber assay cartridge having a plurality of reaction chambers loaded with a sample and an optical detection reagent, and an optical detection unit having a multi-channel optical block having a plurality of detection channels each with an associated light source, and an optic sensor. The optical detection unit is connectable to the base unit so that interrogation ports of the detection channels are optically aligned with optically transparent windows of the reaction chambers of a loaded cartridge, so that upon initialization, light sources are activated to interrogate reaction products in the reaction chambers and detect the optical responses therefrom.

Dugan, Lawrence C.↗
DOE OSTI2022

Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys

We report Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and exploit materials design spaces. To date, most BO approaches to materials design have focused on the materials discovery problem as if it were a single expensive-to-query ‘black box’ in which the target is to optimize a single objective (i.e., material property or performance metric). Also, such approaches tend to be constraint agnostic. Here, we present a novel multi-information BO framework capable of actively learning materials design as a multiple objectives and constraints problem. We demonstrate this framework by optimally exploring a Refractory Multi-Principal-Element Alloy (MPEA) space, here specifically, the system Mo-Nb-Ti-V-W. The MPEAs are explored to optimize two density-functional theory (DFT) derived ductility indicators (Pugh’s Ratio and Cauchy pressure) while learning design constraints relevant to the manufacturing of high-temperature gas-turbine components. Alloys in the BO Pareto-front are analyzed using DFT to gain an insight into fundamental atomic and electronic underpinning for their superior performance, as evaluated within this framework.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Neutron Spectrum Determination for the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) with a Multi-Sphere Spectrometer

This report presents the work that was performed to determine the neutron energy spectrum (spectrum) of the Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) with a neutron Multi-Sphere Spectrometer (MSS). The spectrometer measurements were performed when the generator is in an INL Materials and Fuels Complex (MFC) laboratory. The measurements were used in a spectrum unfolding software to calculate the neutron spectrum. Based on initial testing of the MSS spectrometer and experience with the unfolding software, an MMRTG spectrum is reported and a justification for the spectrum is provided.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
NASA NTRS2022

Model Evaluation of Short-Lived Climate Forcers for the Arctic Monitoring and Assessment Programme: A Multi-Species, Multi-Model Study

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008-2009 and 2014-2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, 03, BC, and SO(sup 2-)(sub 4)), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for C"4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Arctic Monitoring and Assessment Programme↗
DOE OSTI2025

Multi-site Milling Strategy Reveals Significant Variation in Biomass Composition of Switchgrass ( Panicum virgatum ) Grown at Ten Locations

Cell wall composition influences biomass use as a forage and as a feedstock for biofuel and chemical conversion. To examine the influence of environment on composition of switchgrass (Panicum virgatum L.), we utilized a multi-environment experiment consisting of clones of switchgrass genotypes grown at up to ten locations in the continental US. We tested the influence of different milling treatments on biomass composition trait predictions via near-infrared reflectance spectroscopy (NIRS). We found that most compositional trait predictions (29/34) were significantly different (P < 0.05) when a single lot of biomass was subjected to disparate milling treatments, i.e., knife milling vs. knife milling with an additional cyclone milling. Further, depending on the plant material tested, three to eight compositional trait predictions vary (P < 0.05) when identical biomass was knife milled at different sites followed by cyclone milling at a single site, including for traits such as Klason lignin, nitrogen, and carbon. In some cases, variation due to milling site exceeded environmentally induced compositional variation of a single switchgrass genotype grown at different sites. From these observations, we recommend a protocol with two sequential millings that decouples growth environment from a particular mill. Utilizing this approach, we found that 46/46 biomass composition traits from the warm season herbaceous forage and switchgrass bioethanol NIRS equations vary significantly (P < 0.001) in clones of a switchgrass genotype (WBC) grown at ten sites, with the growth site representing the largest average source of variation (41%). This multi-site milling approach can be used to examine environmental and gene-by-environment influences on composition with the goal of optimizing cell wall composition in different environments for biomass utilization.

09 BIOMASS FUELS↗
DOE OSTI2025

Tailoring mechanical properties of a multi-principal element alloy through a multi-length-scale approach

In this study, a multi-length-scale strengthening approach was used to tailor the microstructure and the mechanical properties of a NiCoCr-based multi-principal element alloy (MPEA). Grain size refinement, severe lattice distortion, and stacking fault energy (SFE) reduction with Mo addition (up to 10 at.%) enhance yield strength by 85% with only 10% reduction in ductility in as-annealed MPEAs. A pronounced increase in the strain hardening rate was observed with the addition of Mo, which is ascribed to the promotion of complex stacking fault (SF) interaction and intersection, accompanied by Lomer-Cottrell (L-C) and Hirth locks inhibiting dislocation motion and substantial increase in the accumulation of back stress. To push the limit of the yield strength further, the Suzuki segregation phenomenon was manipulated by a careful control of SF density by pre-straining and a subsequent 500 °C heat treatment. The stress-strain responses of the pre-strained and heat treated MPEAs showed an obvious SF density and Mo concentration dependence. Furthermore, the yield strength of the pre-strained Mo-added MPEAs with subsequent heat treatment was increased up to true stress of 2.3 GPa with a corresponding fracture elongation of 12% true strain. SFs formed during pre-straining served as Cr segregation sites during subsequent heat treatment, which substantially varies the local SFE within the SF, presenting a roughened landscape and frustrating the dislocation dynamics.

mechanical properties↗
DOE OSTI2020

EVALUATION OF HOT CHANNEL FACTOR FOR SODIUM-COOLED FAST REACTORS WITH MULTI-PHYSICS TOOLKIT

The evaluation of hot channel factor (HCF) is of great significance to the quantification of safety margins for reactor designs. In this paper, HCFs for a sodium-cooled fast reactor (SFR) are evaluated with the Simulation-based High-efficiency Advanced Reactor Prototyping (SHARP) toolkit, which is developed under the Nuclear Energy Advanced Modeling and Simulation (NEAMS) Campaign of DOE for multi-physics reactor performance and safety simulations. The high-fidelity neutronics and thermal hydraulics solvers PROTEUS and Nek5000 in the SHARP toolkit are coupled to perform the multi-physics simulations for HCF evaluation. The HCFs induced by cladding manufacturing tolerance, fissile content mal-distribution, wire orientation and uncertainties on the cladding, coolant, and fuel properties are evaluated for a reference core SFR design (AFR-100). The HCFs calculated with the SHARP toolkit are compared to legacy HCFs for similar reactor types. The comparison demonstrates the reduction or elimination of modeling uncertainties in the calculation of HCFs using high fidelity advanced modeling and simulation tools without the need of expensive experiments. Moreover, the reduction of the uncertainties on HCFs evaluation allows an increase in nominal parameters and safety margin, which in turn improves the economic competitiveness of the SFR.

Hot channel factor (HCF)↗
DOE OSTI2021

Element redistributions during early stages of oxidation in a Ni38Cr22Fe20Mn10Co10 multi-principal element alloy

Element redistributions after initial gaseous oxidation of a Ni38Cr22Fe20Mn10Co10 (at%) multi-principle element alloy at 120°C and 300°C is captured via psuedo in situ atom probe tomography. All cations contribute to the oxide in both conditions, consisting of a Cr-rich inner oxide and Fe, Mn, Co and Ni-rich outer oxide. At the lower temperature, Ni tends to be trapped at the outer/inner oxide interface, while Ni enriches at the oxide/metal interface at higher temperature in tandem with Cr metal depletion. These observations confirm oxidation of complex alloys involves the formation of metastable, multi-layered oxide films, with a distinct tendency for solute trapping.

multi-principal element alloy, oxidation mechanism↗
DOE OSTI2023

Application of multi-method-multi-model inference to radiation related solid cancer excess risks models for astronaut risk assessment

The impact of including model-averaged excess radiation risks (ER) into a measure of radiation attributed decrease of survival (RADS) for the outcome all solid cancer incidence and the impact on the uncertainties is demonstrated. It is shown that RADS applying weighted model averaged ER based on AIC weights result in smaller risk estimates with narrower 95% CI than RADS using ER based on BIC weights. Further a multi-method-multi-model inference approach is introduced that allows calculating one general RADS estimate providing a weighted average risk estimate for a lunar and a Mars mission. For males the general RADS estimate is found to be 0.42% (95% CI: 0.38%; 0.45%) and for females 0.67% (95% CI: 0.59%; 0.75%) for a lunar mission and 2.45% (95% CI: 2.23%; 2.67%) for males and 3.91% (95% CI: 3.44%; 4.39%) for females for a Mars mission considering an age at exposure of 40 years and an attained age of 65 years. It is recommended to include these types of uncertainties and to include model-averaged excess risks in astronaut risk assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Multi-Period Active Distribution Network Planning Using Multi-Stage Stochastic Programming and Nested Decomposition by SDDIP

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Magnetic Field Reconstruction for a Realistic Multi-Point, Multi-Scale Spacecraft Observatory

Future in situ space plasma investigations will likely involve spatially distributed observatories comprised of multiple spacecraft, beyond the four and five spacecraft configurations currently in operation. Inferring the magnetic field structure across the observatory, and not simply at the observation points, is a necessary step towards characterizing fundamental plasma processes using these unique multi-point, multi-scale data sets. We propose improvements upon the classic first-order reconstruction method, as well as a second-order method, utilizing magnetometer measurements from a realistic nine-spacecraft observatory. The improved first-order method, which averages over select ensembles of four spacecraft, reconstructs the magnetic field associated with simple current sheets and numerical simulations of turbulence accurately over larger volumes compared to second-order methods or first-order methods using a single regular tetrahedron. Using this averaging method on data sets with fewer than nine measurement points, the volume of accurate reconstruction compared to a known magnetic vector field improves approximately linearly with the number of measurement points.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Using AI Simulations to Dynamically Model Multi-agent Multi-team Energy Systems

The complexity of energy systems is well known as they are complex and intricate systems. As a result, many extant studies have used many simplifications or generalizations that do not accurately reflect the nature of this complex system. In particular, most HVAC systems are modeled as a single unit, or several large units, rather than as a hierarchical composite (e.g., as a floor rather than as a collection of disparate rooms). The net result of this is that the simulations are too generic to perform meaningful analysis, machine learning, or integrated simulation. We propose using a multi-agent multi-team strategic simulations framework called SiMAMT to better define, model, simulate, and learn the HVAC environment. SiMAMT allows us to create distinct models for each type of room, hierarchically aggregate them into units (like floors, or sections), and then into larger sets (like buildings or a campus), and then perform a simulation that interacts with each sub-element individually, the teams of sub-elements collectively, and the entire set in aggregation. Further, and most importantly, we additionally model another ‘team’ within the simulation framework - the users of the systems. Again, each individual is modeled distinctly, aggregated into sub-sets, then collected into large sets. Each user, or agent, is performing on their own but with respect to the larger team goals. This provides a simulation that has a much higher model fidelity and more applicable results that match the real-world.

Franklin, D. Michael↗
DOE OSTI2021

A multi-dimensional parametric study of variability in multi-phase flow dynamics during geologic CO 2 sequestration accelerated with machine learning

Successful geologic CO 2 storage projects depend on numerical simulations to predict reservoir performance during site selection, injection verification, and post-injection monitoring phases of the project. These numerical simulations solve non-linear sets of coupled partial differential equations, while accounting for multi-phase fluid dynamics on the basis of constitutive equations that are embedded into the solution scheme. As a consequence, individual simulations often require tens to hundreds of hours to complete on high-performance computing clusters. Moreover, laboratory experiments reveal that parametric functions for capillary pressure and relative permeability exhibit substantial variability, even within the same rock type. This combination of computational expense and wide-ranging parametric variability means that there remains substantial uncertainty in the behavior of multi-phase CO 2 -water systems, particularly in the context of feedbacks between relative permeability and capillary pressure. To bridge this knowledge gap, here we develop a novel workflow that utilizes physics-based numerical simulation to train an artificial neural network (ANN) emulator for interrogating the multivariate parameter space that governs both capillary pressure and relative permeability. With this approach, the ANN is trained to emulate both fluid pressure distribution and CO 2 saturation, which are then interrogated quantitatively to generate parametric response surface mappings with high-fidelity resolution. Results from this study initially show that capillary entry pressure is the dominant control on both CO 2 plume geometry and fluid pressure propagation when considering the combined effects of capillary pressure and relative permeability, particularly when phase interference is low and residual CO 2 saturation is high. Moreover, the ANN emulator provides tremendous computational speed-up by computing 2691 individual simulations in several minutes; whereas, the same simulation ensemble would have required ~3 years of simulation time using only physics-based simulation methods (25,000 times speed up).

58 GEOSCIENCES↗
DOE OSTI2022

Multi-scale and multi-physics analysis, design optimization, and experimental validation of heat exchangers utilizing high performance, non-round tubes

Air-to-refrigerant heat exchangers (HXs) are fundamental components in HVAC&R systems, and considerable research has been dedicated designing continually smaller, lighter, and more efficient HX designs. In recent years, researchers have leveraged advancements in Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), and optimization algorithms to consider primary tube shape and topology optimization to design highly compact, high performance HXs for a multitude of applications. In this research, we present a computationally efficient, comprehensive, multi-scale, and multi-physics analysis and optimization method for air-to-refrigerant HXs featuring automated CFD and FEA simulations and approximation-assisted optimization. This methodology was utilized to design HXs with shape-optimized, non-round tubes which outperform current state-of-the-art tube-fin HXs without compromising structural integrity. The optimal HXs were shown to deliver similar thermal performance to the baseline HXs while also achieving more than 20% reductions in airside pressure drop and core envelope volume and more than 30% reduction in internal volume. Comprehensive experimental validation of the optimization methodology was conducted through the testing of two prototypes in a standardized wind tunnel facility under multiple operating conditions. For prototype #1 under dry evaporator conditions, the predicted heat load agreed within ± 10% of measured values and the predicted airside pressure drop agreed within ± 30%, while for dehumidifying conditions, the predicted sensible and latent heat loads agreed within ± 10% and ± 20% of the measured values, respectively. For prototype #2, the predicted condenser heat load agreed within ± 3.0% of measured values, and the predicted airside pressure drop agreed within ± 27%. The acceptable agreement between simulation and experimental results for the present application highlights the flexibility of the novel optimization methodology to design next generation HXs with improved performance and reduced volume, weight, and environmental impact.

42 ENGINEERING↗