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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

An algorithm for physics informed scan path optimization in additive manufacturing

Site specific microstructure control is a critical research area within the field of additive manufacturing due to its potential to revolutionize part performance. One way to achieve site specific microstructure control is through control of the solidification conditions via the construction of intricate scan paths; however, the search space for such a problem is large. Previous attempts only considered the solidification conditions at the top surface while also requiring either lots of manual-fine tuning or large amounts of computational resources. This paper introduces a general method for scan path optimization which considers the solidification conditions in the bulk of the material without an increase in computational expense. This method consists of three core components:1. A heat transfer model for simulating the temperature field at a given time.2. A surrogate model which takes scan pattern information and temperature data and predicts the solidification conditions of the bulk as well as the meltpool depths for a spot melt.3. A decision algorithm to decide which spot melt should be printed next based on the outputs of the surrogate model.Each of these components can be changed without changing the overall method. Within this work, this method is applied in the creation of an algorithm containing a semi-analytic heat transfer model to simulate the temperature field, a fully convolutional neural network (FCNN) as the surrogate model, and a greedy decision algorithm. The resulting algorithm produced complex scan patterns which gave strong results for simulated microstructure control.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Application and evaluation of a pattern-based building energy model calibration method using public building datasets

Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings' monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Idealized Simulations of the Tropical Climate and Variability in the Single Column Atmosphere Model (SCAM): Radiative-Convective Equilibrium

To explore the interactions among column processes in the Community Atmosphere Model (CAM), the single-column version of CAM (SCAM) is integrated for 1000 days in radiative-convective equilibrium (RCE) with tropical values of boundary conditions, spanning a parameter or configuration space of model physics versions (v5 vs. v6), vertical resolution (standard and 60 levels), sea surface temperature (SST), and some interpretation-driven experiments. The simulated time-mean climate is reasonable, near observations and RCE of a cyclic cloud-resolving model. Updraft detrainment in the deep convection scheme produces distinctive grid-scale structures in humidity and cloud, which also interact with radiative transfer processes. These grid artifacts average out in multi-column RCE results reported elsewhere, illustrating the nuts-and-bolts interpretability that SCAM adds to the hierarchy of model configurations. Multi-day oscillations of precipitation arise from descent of warm convection-capping layers starting near the tropopause, eventually reset by a burst of convective deepening. Experiments reveal how these oscillations depend critically on an internal parameter that controls the number of neutral buoyancy levels allowed for determining cloud top and computing dilute convective available potential energy in the deep convection scheme, and merely modified a little by disabling cloud-base radiation (heating of cloud base). This strong dependence of transient behavior in 1D on this parameter will be tested in the second part of this work, in which SCAM is coupled to a parameterized dynamics of two-dimensional, linearized gravity wave, and in the 3D simulations in future study.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Integrating viruses into soil food web biogeochemistry

The soil microbiome is recognized as an essential component of healthy soils. Viruses are also diverse and abundant in soils, but their roles in soil systems remain unclear. Here we argue for the consideration of viruses in soil microbial food webs and describe the impact of viruses on soil biogeochemistry. The soil food web is an intricate series of trophic levels that span from autotrophic microorganisms to plants and animals. Each soil system encompasses contrasting and dynamic physicochemical conditions, with labyrinthine habitats composed of particles. Conditions are prone to shifts in space and time, and this variability can obstruct or facilitate interactions of microorganisms and viruses. Because viruses can infect all domains of life, they must be considered as key regulators of soil food web dynamics and biogeochemical cycling. Finally, we highlight future research avenues that will enable a more robust understanding of the roles of viruses in soil function and health.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

3D theory of microscopic instabilities driven by space-charge forces

Microscopic, or short-wavelength, instabilities are known for a drastic reduction of the beam quality and strong amplification of the noise in a beam. Space charge and coherent synchrotron radiation are known to be the leading causes of such instabilities. In this paper, we present a rigorous 3D theory of such instabilities driven by the space-charge forces. We define the condition when our theory is applicable to an arbitrary accelerator system with 3D coupling. Finally, we derive a linear integral equation describing such instability and identify conditions when it can be reduced to an ordinary second-order differential equation.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Neural conditional reweighting

There is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions. To date, the focus has been on marginal reweighting, where a subset of features are used for reweighting while all other features are integrated over. There are some situations, though, where it is preferable to condition on auxiliary features instead of marginalizing over them. Here, we introduce neural conditional reweighting, which extends neural marginal reweighting to the conditional case. This approach is particularly relevant in high-energy physics experiments for reweighting detector effects conditioned on particle-level truth information. Furthermore we leverage a custom loss function that not only allows us to achieve neural conditional reweighting through a single training procedure, but also yields sensible interpolation even in the presence of phase space holes. As a specific example, we apply neural conditional reweighting to the energy response of high-energy jets, which could be used to improve the modeling of physics objects in parametrized fast simulation packages.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Chaos in inhomogeneous neutrino fast flavor instability

In dense neutrino gases, the neutrino-neutrino coherent forward scattering gives rise to a complex flavor oscillation phenomenon not fully incorporated in simulations of neutron star mergers (NSM) and core collapse supernovae (CCSNe). Moreover, it has been proposed to be chaotic, potentially limiting our ability to predict neutrino flavor transformations in simulations. To address this issue, we explore how small flavor perturbations evolve in the nonlinear regime of the neutrino quantum kinetic equation within a narrow centimeter-scale region inside a NSM and a toy neutrino distribution. Our findings reveal that paths in the flavor state space of solutions with similar initial conditions diverge exponentially, exhibiting chaos. This inherent chaos makes the microscopic scales of neutrino flavor transformations unpredictable. However, the domain-averaged neutrino density matrix remains relatively stable, with chaos minimally affecting it. This particular property suggests that domain-averaged quantities remain reliable despite the exponential amplification of errors. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Adaptation and Plasticity of Nannochloropsis sp. in Response to Seasonal and Geographic Climate Variation

Phytoplankton respond to their environment through genetic adaptation and plasticity to maintain fitness. This poses challenges when growing microalgae for industrial applications because, inherently, outdoor mass cultivation may lead to adaptations that alter desirable phenotypic traits and environmental niches. Here, we used common garden experiments to quantify the plasticity and adaptive responses to seasonal and geographic climate variation of Nannochloropsis, a microalga commonly used in biotechnology. An initially monoclonal strain was grown outdoors across four locations in Hawaii, Texas, California, and New Mexico. Following 17 and 22 months of cultivation outdoors, we collected samples during winter and summer, respectively, and we compared strains' growth from the four sites across temperature and light gradients in the laboratory. Despite hundreds of generations of exposure to divergent climates, with ~20°C and three-fold differences in daily light intensity, strains showed only minor differences in performance. Thermal performance varied more among seasons than sites, whereas light performance varied with both season and site. Our study indicates that Nannochloropsis exhibits broad plasticity in response to light and temperature, which may inhibit genetic adaptation in space or time. Highly variable field conditions, with daily and seasonal climate fluctuations, may favor plasticity and prevent the rapid adaptation often seen in laboratory studies of microorganisms in constant environments.

common garden experiment↗
DOE OSTI2022

Scaled Vecchia Approximation for Fast Computer-Model Emulation

Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a popular tool for the analysis of computer experiments, enabling interpolation between input settings, but direct GP inference is computationally infeasible for large datasets. We adapt and extend a powerful class of GP methods from spatial statistics to enable the scalable analysis and emulation of large computer experiments. Specifically, we apply Vecchia’s ordered conditional approximation in a transformed input space, with each input scaled according to how strongly it relates to the computer-model response. The scaling is learned from the data by estimating parameters in the GP covariance function using Fisher scoring. Our methods are highly scalable, enabling estimation, joint prediction, and simulation in near-linear time in the number of model runs. In several numerical examples, our approach substantially outperformed existing methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Validation of Printed Strain Gauges at Moderate Temperatures (up to 300°C)

Strain gauges in material test reactors serve to generate critical mechanical property data for qualifying the performance of reactor components. Resistance based strain gauge technologies are both well established and commercially available; however, they present limitations in terms of reactor experiment conditions, especially in areas where physical space is a challenge. In this work, an additively manufactured capacitance-based strain gauge was printed on a stainless-steel specimen and tested at up to the prototypic pressurized-water reactor operating temperature of 300°C. In addition, a high-temperature resistive strain gauge (RSG) was used to better understand how the RSGs operate, and to provide baseline measurements for comparison against the printed strain gauges. Future development of printed strain gauges will focus on expanding their temperature limits to 500°C and enabling applications currently beyond the capabilities of commercial RSGs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Magnetostrictive materials for enhanced sensors and electronic components

Magnetostriction is a property of magnetic materials that causes them to change their shape or dimensions proportional to their magnetization. Low-cost, mechanically robust magnetostrictive sensors would be valuable for a wide range of applications across the DOE and national security mission space, such as monitoring the internal conditions of pipelines, rapidly detecting high-impedance faults in power lines (e.g. trees touching power lines), or enhancing implantable systems for the human body. Dilute doping (<1 at.%) of rare earth elements (REE) has been shown to amplify the magnetostriction. Furthermore, alloy processing by rapid cooling from high temperature tends to result in texturing and therefore greater magnetostriction values, which bodes well for developing advanced manufacturing approaches significantly less expensive than single crystal growth. The goal of this effort was to develop robust, low-cost magnetostrictive materials compatible with advanced manufacturing techniques to exploit the rapid cooling of these approaches while retaining the ability to produce fully dense structures. To be useful as a sensor or actuator the coercivity of the material needs to be minimized so that there is minimal magnetic hysteresis. Employing a small scale laser powder bed fusion (L-PBF) system, specimens of Fe-Ga-Ce were built from alloy powder which had a magnetostriction of 289 ppm along the build direction and 197 ppm perpendicular to the build direction. These values are not far from the 310-350 ppm observed in single crystals of Fe-Ga. The results of the series of samples run suggest this is a very promising route for REE doping to higher levels, especially if the powders can be produced using far from equilibrium approaches such as ultrasonic atomization. The promising results from applying additive manufacturing techniques to these materials, particularly the inexpensive Fe-Al system, has potential for inexpensive high-performing magnetostrictive parts producible at large scales for low-cost sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Optimal Decision Making in High-Throughput Virtual Screening Pipelines

ABSTRACT Effective selection of the potential candidates that meet certain conditions in a tremendously large search space has been one of the major concerns in many real-world applications. In addition to the nearly infinitely large search space, rigorous evaluation of a sample based on the reliable experimental or computational platform is often prohibitively expensive, making the screening problem more challenging. In such a case, constructing a high-throughput screening (HTS) pipeline that pre-sifts the samples expected to be potential candidates through the efficient earlier stages, results in a significant amount of savings in resources. However, to the best of our knowledge, despite many successful applications, no one has studied optimal pipeline design or optimal pipeline operations. In this study, we propose two optimization frameworks, applying to most (if not all) screening campaigns involving experimental or/and computational evaluations, for optimally determining the screening thresholds of an HTS pipeline. We validate the proposed frameworks on both analytic and practical scenarios. In particular, we consider the optimal computational campaign for the long non-coding RNA (lncRNA) classification as a practical example. To accomplish this, we built the high-throughput virtual screening (HTVS) pipeline for classifying the lncRNA. The simulation results demonstrate that the proposed frameworks significantly reduce the effective selection cost per potential candidate and make the HTS pipelines less sensitive to their structural variations. In addition to the validation, we provide insights on constructing a better HTS pipeline based on the simulation results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data-Driven Model Reduction of Multiphase Flow in a Single-Hole Automotive Injector

Fuel injector design has a substantial influence on the performance and emissions of direct injection engines. To date, large eddy simulations coupled with a single-fluidmixture modeling approach have shown great success in evaluating the complex interplay among injector design, fuel properties, and operating conditions on the injector performance. However, this simulation approach is too computationally expensive to be used by industry routinely for injector design due to the fine temporal and spatial resolution required to resolve wall-bounded flow within the injector. The work presented in this paper highlights a potential pathway to addressing this issue. To study the influence of injector design, fuel properties, and operating conditions on injector performance, large eddy simulations were performed to model the turbulent multiphase flow development through a side-oriented singlehole diesel injector. Using Latin hypercube sampling, the design space spanning a range of fuel properties, operating conditions, and needle lifts were explored. Two techniques for dimensionality reduction, namely proper orthogonal decomposition and autoencoders, were compared to evaluate their accuracy in representing the flow in a reduced dimensional space. Finally, based on the findings from this work, recommendations are provided in using machine learning approaches within the context of emulation to construct reduced-ordermodels for internal flow development relevant to automotive applications.

42 ENGINEERING↗
DOE OSTI2023

Wave-function-based emulation for nucleon-nucleon scattering in momentum space

Emulators for low-energy nuclear physics can provide fast and accurate predictions of bound-state and scattering observables for applications that require repeated calculations with different parameters, such as Bayesian uncertainty quantification. In this paper, we extend a scattering emulator based on the Kohn variational principle (KVP) to momentum space (including coupled channels) with arbitrary boundary conditions, which enable the mitigation of spurious singularities known as Kohn anomalies. We test it on a modern chiral nucleon-nucleon (N N) interaction, including emulation of the coupled channels. We provide comparisons between a Lippmann-Schwinger equation emulator and our KVP momentum-space emulator for a representative set of neutron-proton (n p) scattering observables, and also introduce a quasi-spline-based approach for the KVP-based emulator. Furthermore, our findings show that while there are some trade-offs between accuracy and speed, all three emulators perform well. Self-contained Jupyter notebooks that generate the results and figures in this paper are publicly available.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Multi-Objective design of interlocking metasurfaces using conditional diffusion models

Unit cell design remains a major challenge for interlocking metasurfaces, a promising joining technology for dissimilar materials, due to the complex, competing, multivariate design space and the need for rapid adaptation to varying performance requirements. This study explores Conditional Diffusion Models as a design optimization tool for interlocking metasurfaces. Given the complex, competing, multivariate design space for interlocking metasurfaces, unit cell design remains a major challenge for this joining technology. We trained a conditional diffusion model on 25,000 finite element analysis-simulated interlocking metasurface unit cells to generate designs with tailored thermo-mechanical properties (tensile strength, shear strength, and thermal conductivity) based on specified performance criteria. The model demonstrated a success rate of approximately 72 % in producing designs that met specified property bounds. The conditional diffusion model generated both thermally resistive and conductive designs, revealing clear trends in design characteristics: taller, dendritic structures were advantageous for tensile loads, while shorter, robust designs excelled in shear applications. Our findings indicate that the model's performance is more influenced by the breadth of the design space than by the quantity of training data, highlighting the importance of expansive design domains for generating innovative solutions. This work establishes conditional diffusion models as a highly efficient and adaptable tool for rapid interlocking metasurface unit cell design, paving the way for advancements in multi-material joining technologies, as well as highlighting the justification to leverage conditional diffusion models as design tools across complex design domains.

Conditional diffusion models↗
DOE OSTI2024

Simulation & Analysis of the Hydronic Shell Retrofit System as a Solution for Deep Energy Retrofits and Electrification of Large Multifamily Housing Communities in Cold Climate

Among 32 million multi-family buildings in the United States, approximately 42% have poor or no insulation. Envelope retrofits of these buildings will provide thermal resiliency and enable a pathway to electrification of space heating systems as a result of improved thermal performance. Hydronic Shell (HS) is a technology which combines an insulated retrofit panel with a heating, ventilation and air-conditioning (HVAC) thus enabling both envelope retrofit and space heating electrification. In this study, we used whole building energy simulation to evaluate energy impact of multi-family building retrofit with hydronic shell system. The simulation was performed for four locations of New York, Syracuse, Chicago and Boston. The results from the simulation showed more than 66% cooling energy reduction and more than 88% heating energy reduction from Hydronic Shell retrofit compared to Baseline building in all four locations. This reduction in energy consumption resulted in up to 219 MT reduction in annual CO2 emission. The cost reduction per floor area achieved from HVAC energy consumption reduction using Hydronic Shell retrofit was 0.66 to 0.99 $\$$/ft 2 . The results also showed higher percentage of heating energy reduction comes from the envelope only retrofit and higher percentage of cooling energy reduction comes from the retrofit HVAC system for the four locations under study.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

A Review of Existing Test Methods for Occupancy Sensors

One of the key new features of connected lighting systems (CLS) is their ability to collect data from various types of integral sensors and share that data with other lighting or building systems. Occupancy and vacancy sensors have been widely adopted as an energy-saving strategy in buildings, yet published test methods for reproducibly characterizing their performance remain few and limited in their sophistication. As a result, it has been difficult to predict the performance of such sensors in a specific application, and in practice, they frequently do not meet energy-savings expectations. Occupants at times remove or otherwise bypass occupancy sensors that hinder their work or otherwise do not perform as expected, thereby compromising the sensors’ potential to reduce energy consumption. Poor performance can result from multiple causes – ranging from fundamental limitations of the sensor technology, to misconfiguration, to poor placement in the room or space. Innovative occupancy sensors, some of them combining multiple sensing technologies (i.e., multimodal), have come on the market over the years, with claims of improved performance compared to their predecessors. However, in practice, their performance has neither differed enough from the performance of previous products to necessitate a test method that facilitated comparison between them, nor has it led to high deployment or high user satisfaction in human-occupied spaces with persistent presence. While the performance of both common and novel occupancy sensors has been the subject of many published research articles, the test methods that have been employed for them typically have been loosely described and have incorporated custom equipment or techniques that render them difficult to reproduce, or have been limited in their ability to fairly characterize devices that utilize varying sensor technology. The lack of a fully described, technology-agnostic test method that yields reproducible results across different implementations has been a barrier to the commercial success of new occupancy-sensor products, as users and specifiers who have been disappointed with previous products are often unwilling to take a chance with new ones. Motivated by a desire to fairly characterize new technologies that continue to enter the market and claim not only improved occupancy detection but, in some cases, additional capabilities (e.g., the ability to measure traffic or discern between different object types), this report presents the results of a literature review of recently published fully described test methods for characterizing occupancy-sensor performance, as well as research articles containing ad-hoc test methods. The review also identifies and consolidates test conditions for characterizing sensor performance in indoor spaces and identifies apparent test method gaps that need to be filled in order to evaluate emerging technologies and products. The identified test-method conditions are intended to enable the development of a future technology-agnostic test method that facilitates occupancy-sensor performance characterization more-accurately representing performance in buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗