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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Building the Next Generation Through 20 Years of Solar Decathlon: From Collegiate Prize to Multifaceted Clean Energy Workforce Program: Preprint

The U.S. Department of Energy Solar Decathlon, initiated in 2002 as a collegiate competition that challenges student teams to design and build high-performance, low-carbon buildings powered by renewables, has always been about developing students to be the next generation of buildings professionals. As we celebrate the 20th anniversary of the Solar Decathlon in 2022, this paper highlights its evolution from a singular prize to a multi-faceted platform preparing the broader buildings workforce to address the challenges of climate change. The original program challenged students to design and construct a residence. To improve the accessibility of the competition, the Design Challenge -- a 1-2 semester design-only competition-- was added to the Solar Decathlon platform. The Solar Decathlon has also scaled globally with events in Europe, the Middle East, China, Latin America and the Caribbean, Africa, and India. In 2021, the Solar Decathlon broadened its reach once again by introducing Solar Decathlon Pro, a zero-energy design practicum for professional architects and engineers. This paper utilizes participation data, surveys, alumni profiles, project submissions, and more to understand how changes to the program have expanded program reach to minority-serving institutions, environmental justice communities, and impacted the building industry's capacity to construct high performance and zero energy buildings. As one of the oldest federal government prizes, the Solar Decathlon offers a roadmap for dramatically increasing prize impact and demonstrates that it continues to push boundaries to tackle decarbonization and environmental justice.

build↗
DOE OSTI2022

WBS 2.1.5.401 - Model Validation and Site Characterization for Early Deployment MHK Sites and Establishment of Wave Classification Scheme

The "Resource Characterization" project delivers the data and tools needed to engineer robust marine renewable energy devices and projects. The project measures resource details at commercially promising sites, runs high resolution models of promising sites and regions, and develops classification schemes that streamline device engineering, project development, and increase investor confidence.

ENGINEERING,TIDAL AND WAVE POWER↗
DOE OSTI2015

Uncertainty in decommissioning, the benefits of 3D simulation - 15124

Any industrial plant whatever the industry is will face its end of life. In the nuclear field decommissioning is inevitable for returning to green. Because of the specificity of each plant, decommissioning projects are never the same. The choice of a specific strategy for each operation during a scenario has cascade consequences on the generated waste, extension of due date, costs and risks. Decommissioning in nuclear industry is a new domain where countries have a limited experience. When starting a decommissioning project engineers have to gather some data needed for planning and realizing the best scenario strategy. Scenario which should consider the costs, waste management, received dose and the planning. The quality of the input data has a strong influence on the risks and should be analyzed with care to reduce all doubts for the study. Nuclear plants running decommissioning programs concerns old plants with poorly documented history resulting in missing or uncertain data. The ambient dose rate is not always well-known as dose measurement or samples have not been taken or updated for many years. This kind of data may vary quite a lot when radiological events happen. An efficient way to reduce contingencies is to go through simulations. It allows determining the best scenario strategy for each set of data while evaluating the influence of all uncertainties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Design optimization of an ethanol heavy-duty engine using design of experiments and bayesian optimization

Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditions of alcohol fueled engines enable the mitigation of NOx emissions in aftertreatment stage. With the promise of retrofitting diesel engines to run on ethanol to reduce emissions, the thermal efficiency of these engines remains the primary optimization target. In order to find the optimal ethanol-fueled engine design that maximizes the thermal efficiency, a large design space needs to be investigated using engineering tools. In this study, previous research by the authors on optimizing the design of a single-cylinder ethanol-fueled engine was extended to explore the design space for a heavy-duty multi-cylinder engine configuration. A heavy-duty engine setup with multiple operating conditions at different engine speeds and loads were considered. A design optimization analysis was performed to identify the potential designs that maximize the indicated thermal efficiency in an ethanol-fueled compression ignition engine. First, a computational fluid dynamics (CFD) model of the engine was validated using experimental data for four drive cycle points. Using a design of experiments (DoE) approach and a parameterized piston bowl geometry, the model was then exercised to explore the relationship among geometric features of the piston bowl and spray targeting angle and indicated thermal efficiency across all tested operating conditions. After evaluating 165~candidate designs, a piston bowl geometry was identified that yielded an increase between 1.3 to 2.2 percentage points in indicated thermal efficiency for all tested conditions, while satisfying the operational design constraints for peak pressure and maximum pressure rise rate. The increased performance was attributed to enhanced mixing that led to the formation of a more homogeneous distribution of in-cylinder temperature and equivalence ratio, higher combustion temperatures, and shorter combustion duration. Finally, a Bayesian optimization (BOpt) analysis was employed to find the optimal piston bowl geometry with a fixed spray injector angle for one of the operating conditions. Using BOpt, a piston candidate was identified that resulted in a 1.9~percentage point increase in thermal efficiency from the baseline design, yet only required 65\% of the design samples investigated using the DoE approach.

Tekgul, Bulut↗
DOE OSTI2020

Advanced Intra-Cycle Detection of Pre-Ignition Events through Phase-Space Transforms of Cylinder Pressure Data

The widespread adoption of boosted, downsized SI engines has brought pre-ignition phenomena into greater focus, as the knock events resulting from pre-ignitions can cause significant hardware damage. Much attention has been given to understanding the causes of pre-ignition and identify lubricant or fuel properties and engine design and calibration considerations that impact its frequency. This helps to shift the pre-ignition limit to higher specific loads and allow further downsizing but does not fundamentally eliminate the problem. Real-time detection and mitigation of pre-ignition would thus be desirable to allow safe engine operation in pre-ignition-prone conditions. This study focuses on advancing the time of detection of pre-ignition in an engine cycle where it occurs. Furthermore, phase space transforms through time-delay embedding of cylinder pressure and principal component analysis were applied to same-cycle detection of pre-ignition and shown to enable detection on the order of a crank degree earlier than deviation in cylinder pressure can be identified through direct statistical observation of the pressure data. Additionally, it appears that the deviation of the trajectory in phase space may offer the opportunity to extend this method to further extend the detection window and allow more time for mitigation actions to occur.

42 ENGINEERING↗
DOE OSTI2020

Development of Saturated Zone Three-Dimensional Initial Condition Plumes for the Composite Analysis and Cumulative Impacts Evaluation Modeling

The purpose of this environmental calculation file (ECF) is to document and present the methodology, input data, and results of extending two-dimensional (2-D) plume depictions documented in DOE/RL- 2017-66, Hanford Site Groundwater Monitoring for 2017, into three dimensions (3-D) for select contaminants of interest (COIs) in the 200-BP-5, 200-UP-1, 200-ZP-1, and 200-PO-1 Groundwater Operable Units at the Hanford Site. Plumes with sufficient available vertical profile data were interpolated in 3-D using the Leapfrog Geo®1 geologic modeling platform, which includes an interpolation engine for analyzing and visualizing 3-D data. Results of these interpolations are intended for use in developing inputs for transport modeling to support forecasts of Central Plateau contaminant plume fate and transport.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Model Assumptions and Data Characteristics: Impacts on Domain Adaptation in Building Segmentation

Studies on domain adaptation (DA) for remote sensing (RS) imagery analysis lack consistency in selection and description of evaluation scenarios. Without properly characterizing datasets, model assumptions, and evaluation scenarios, it is difficult to objectively compare DA methods and reach conclusions about their suitability across different applications. With this motivation, this work seeks to empirically assess to which extent the interaction between data characteristics and model assumptions influences the effectiveness of DA methods. Using the widely explored task of building footprint segmentation as a case study, we perform a large-scale study across over 200 DA scenarios that include variations across view angles, areas observed, and sensors used for data acquisition. Rather than adopting different model architectures or optimization criteria, we contrast the performances of two DA methods based on adversarial learning that differ only in their assumptions about source and target domains. Informed by metadata and data characteristics unveiled using traditional computer vision (CV) techniques as well as pretrained deep models, we provide a detailed meta-analysis of experiments highlighting the importance of accurately considering data assumptions for DA in RS segmentation tasks. As demonstrated by a “cherry-picking” exercise, different claims regarding which model is best could be made by selecting different subsets of evaluation scenarios. While well-calibrated assumptions can be beneficial, mismatching assumptions can lead to negative biases in DA applications. Furthermore, this study intends to motivate the community toward more consistent evaluation protocols while providing recommendations and insights toward creating novel benchmark datasets, documenting data characteristics, application-specific knowledge, and model assumptions.

42 ENGINEERING↗
DOE OSTI2023

Scientific machine learning for modeling and simulating complex fluids

The formulation of rheological constitutive equations—models that relate internal stresses and deformations in complex fluids—is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate empirical models in many engineering disciplines, the development of similar models for complex fluids has lagged. The diversity of techniques for characterizing non-Newtonian fluid dynamics creates a challenge for classical machine learning approaches, which require uniformly structured training data. Consequently, early machine-learning based constitutive equations have not been portable between different deformation protocols or mechanical observables. Here, we present a data-driven framework that resolves such issues, allowing rheologists to construct learnable models that incorporate essential physical information, while remaining agnostic to details regarding particular experimental protocols or flow kinematics. These scientific machine learning models incorporate a universal approximator within a materially objective tensorial constitutive framework. By construction, these models respect physical constraints, such as frame-invariance and tensor symmetry, required by continuum mechanics. We demonstrate that this framework facilitates the rapid discovery of accurate constitutive equations from limited data and that the learned models may be used to describe more kinematically complex flows. This inherent flexibility admits the application of these “digital fluid twins” to a range of material systems and engineering problems. We illustrate this flexibility by deploying a trained model within a multidimensional computational fluid dynamics simulation—a task that is not achievable using any previously developed data-driven rheological equation of state.

Science & Technology - Other Topics↗
DOE OSTI2019

Radiation Tolerance and Mechanical Properties of Nanostructured Amorphous-Ceramic/Metal Composites

The objective of this work was to use a radically non-traditional approach to design amorphous-ceramic/metal composites for service in extreme irradiation environments. The project was built on a strong integration among four research teams for materials synthesis and characterization, ion irradiation, mechanical evaluation, and atomic scale modeling. Rather than try to prevent microstructure changes in polycrystalline aggregates, the research team worked to evolve composite systems where one of the constituents is intentionally synthesized in a non-crystalline or “amorphous” state. Because amorphous materials possess no translational symmetry, such alloys do not contain conventional crystal defects such as vacancies, interstitials, or dislocations. These materials offer the possibility of eliminating the root cause responsible for radiation damage in polycrystalline solids - namely the production of point defects and clusters thereof in collision cascades - and could serve as the basis for developing a new class of structural materials with unprecedented resistance to radiation. The amorphous alloys were used to develop advanced amorphous-ceramic/metal composites with greatly improved radiation tolerance and stability above 500oC, and improved mechanical performance by combining the good properties of amorphous materials (high strength and elastic limit) with those of crystalline materials (high toughness, strain hardening). The ceramic component of the composite consisted of a high crystallization temperature and radiation tolerant amorphous alloy composed of Si-O-C, silicon oxycarbide, while the metal layer was Fe and Fe(Cr), chosen to serve as model materials for steel. A prime hypothesis that drove this work is that the composite interface, together with the composite constituents, would provide significantly enhanced radiation tolerance, similar to or superior to those observed in metallic nanolayered structures, in a more engineering relevant material system. As our experimental data and modeling show, we have successfully developed a new class of ceramic/metal composites that can be adapted for engineering applications, thus significantly impacting improved materials performance for advanced nuclear reactors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A product data network to enable faster, easier, and better planning of building envelopes

The building envelopes contributes significantly to the energy-efficiency of the building. Building performance simulation has made it possible to compare façade technologies regarding energy demand, daylighting, thermal and visual comfort in detail. Planners, such as architects and engineers, need experience to find product data with the right quality and level of detail, and to process the data to fit the calculation and the application. In the available time, planners can compare only a limited number of products, which means that better solutions could go unnoticed. This paper presents a new concept for making product data easily accessible for building façade planning. The concept consists of a network of databases for the efficient exchange and use of optical and calorimetric data of glazing units, shading devices, and combinations of both. The paper presents the research questions, an analysis of the current challenges, six design goals for the product data network and its implementation together with a discussion. Many product data sources can be connected to many planning software applications via the specified application programming interface. When planning software connects to the product data network, the planning of building envelopes can be much faster because planners do not need to spend so much time to search and process product data manually. The planning of building envelopes can also become much easier, especially for planners with limited experience. They do not need to understand all the details about which data fits which calculation if the software company implements this. The planning of building envelopes can become much more reliable when software companies validate their use of the product data network, because the current manual process is prone to errors. The planning of building envelopes can also improve because more products can be compared in the available time, allowing better solutions to be found.

Maurer, Christoph↗
DOE OSTI2019

Numerical Simulation of Gas and Liquid Two-Phase Flow in the RH Process

A mathematical model has been established to simulate the gas–liquid two-phase flow in the RH process. The effects of interphase forces and the bubble-induced turbulence on the fluid flow and motion of gas bubbles were investigated. The interphase forces include the drag force, the virtual mass force, the lift force, and the pressure gradient force, respectively. The prediction results of the liquid velocity and the recirculation rate were compared with the experimental data measured with a Particle Image Velocimetry technique. The results indicated that the drag force and the virtual mass force dominated the gas plume shape in the up-leg snorkel, and significantly influenced the liquid velocity and the distribution of the gas phase. The lift force mainly affected the spreading of gas bubbles in the radial direction of the snorkel. However, the pressure gradient force could be neglected because it had no effect on the liquid velocity and the gas volume fraction. The increase of the bubble-induced turbulence decreased the liquid velocity, and by adjusting the lift force, it widened the shape of the individual gas plumes. To accurately simulate the gas–liquid flow behavior in the RH degasser, these appropriate parameters, such as the drag coefficient, the virtual mass coefficient, the lift coefficient, and the bubble-induced turbulence coefficient, were determined by comparing the predicted results and the measured data.

42 ENGINEERING↗
DOE OSTI2019

Investigating Microtopographic and Soil Controls on a Mountainous Meadow Plant Community Using High-Resolution Remote Sensing and Surface Geophysical Data

Our study aims to explore the microtopographic controls that dictate the heterogeneity of plant communities in a mountainous floodplain-hillslope system, using remote sensing and surface geophysical techniques. Working within a lower montane floodplain-hillslope study site (750 m × 750 m) in the Upper Colorado River Basin, we developed a new data fusion framework, based on machine learning and feature engineering, that exploits remote sensing optical and light detection and ranging (LiDAR) data to estimate the distribution of key plant meadow communities at submeter resolution. We collected surface electrical resistivity tomography data to explore the variability in soil properties along a floodplain-hillslope transect at 0.50-m resolution and extracted LiDAR-derived metrics to model the rapid change in microtopography. Furthermore, we investigated the covariability among the estimated plant community distributions, soil information, and topographic metrics. Results show that our framework estimated the distribution of nine plant communities with higher accuracy (87% versus 80% overall; 85% versus 60% for shrubs) compared to conventional classification approaches. Analysis of the covariabilities reveals a strong correlation between plant community distribution, soil electric conductivity, and slope, indicating that soil moisture is a primary control on heterogeneous spatial distribution. At the same time, microtopography plays an important role in creating particular ecosystem niches for some of the communities. Such relationships could be exploited to provide information about the spatial variability of soil properties. This highly transferable framework can be employed within long-term monitoring to capture community-specific physiological responses to perturbations, offering the possibility of bridging local plot-scale observations with large landscape monitoring.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Thickness and Load-Deflection of Direct Ink Write (DIW) Polymer Parts

Parts were made by direct ink write (DIW)using a developmental 50 A durometer ink. Thickness and load-deflection were measured at four test locations on each part.Original thickness was measured using a digital thickness gauge (Checkline MTG-DX) with a 28.7-mm-diameter foot applying 0.6 kPa stress. Load-deflection was measured using an Instron 5966 equipped with a 10 kN load cell, 28.68-mm-diameter fixed lower platen, and a 50-mm-diameter spherical seat upper platen. Three load-unload cycles to a compressed thickness of 0.85 mm were performed at a test speed of 1.26 mm/min. The compressed thickness was corrected to account for instrument compliance. Original thickness is given in Table 1. Load-deflection curves(engineering stress vs. compressed thickness)are shown in Figure1. Load-deflection data(uncorrected thickness, compliance-corrected thickness, load, and engineering stress) is provided in Tables 2-4.

36 MATERIALS SCIENCE↗
DOE OSTI2020

US Potable Water Reuse System Costs

This submission contains a set of U.S.-specific potable reuse capital and operations and maintenance (O&M) cost data ($2020) found in published presentations and reports from engineering consulting firms, utility and water agency press releases or websites, and literature. For any unbuilt facilities, the reported costs found in technical documents are mostly engineer estimates and may be subject to change as construction proceeds. This data set contains a mix of both facility specific and total capital costs, which include conveyance infrastructure. Note that this dataset does not include detailed cost breakdowns for each of the facilities. This submission also contains the sources used to build this dataset in pdf format.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Optimized self-designing key-value storage engine

Embodiments of the invention utilize an optimized key-value storage engine to strike the optimal balance between cloud-cost and performance and supports queries, including updates, lookups, range queries, inserts, and read-modify-writes. Cloud cost is manifested in purchasing both storage and processing resources. The improved approach has the ability to self-design and instantiate holistic configurations given a workload, a cloud budget, and optionally performance goals and a set of Service Level Agreement (SLA) specifications. A configuration reflects an optimized storage engine design in terms of, for example, the individual data structures design (in-memory and on-disk) in the engine as well as their algorithms and interactions, a cloud provider, and the exact virtual machines to be used.

Idreos, Stratos↗
DOE OSTI2019

Computed tomography object inspection system

A method and apparatus for inspecting an object in a virtual reality environment. Computed tomography scan data for the object is identified. A first group of dimensions for a model of the object and a second group of dimensions for the computed tomography scan data for the object are adjusted such that the first group of dimensions for a first group of features in the model corresponds to the second group of dimensions for a second group of features in the computed tomography scan data. The model and the computed tomography scan data are displayed by a game engine in the virtual reality environment on a display system. Graphical indicators in the virtual reality environment are displayed when a difference is present between features in the model and the features in the computed tomography scan data, enabling identifying the difference between the object and the model of the object.

97 MATHEMATICS AND COMPUTING↗