Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “generalizability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Interpreting AI for fusion: An application to plasma profile analysis for tearing mode stability

Artificial intelligence models have demonstrated strong predictive capabilities for various instabilities in fusion devices such as Tokamaks, including tearing modes (TM), edge localized modes, and disruptive events, but their opaque nature raises concerns about safety and trustworthiness when applied to fusion power plants. Here, we present a physics-based interpretation framework using a TM prediction model as a demonstration that is validated through a dedicated DIII-D TM avoidance experiment. By applying Shapley analysis, we identify how profiles such as rotation, temperature, and density contribute to the model's prediction of TM stability. Our analysis shows that in our experimental scenario, core electron temperature and rotation peaking play the primary role in TM stability, while density changes have smaller effects on stability. We show that off-axis ion temperature stabilizes TMs, suggesting that off-axis neutral beam heating can further stabilize this scenario. This work presents a generalizable ML-based event prediction methodology, from training to physics-driven interpretation, bridging the gap between physics understanding and opaque ML models.

Farre-Kaga, Hiro J. [Princeton Univ., NJ (United S↗
DOE OSTI2021

Mechanisms of transport enhancement for self-propelled nanoswimmers in a porous matrix

Significance As with living microorganisms, many real-world applications of synthetic micro/nanoswimmers are likely to rely on transport in complex environments, a phenomenon that remains poorly understood. Here, we report anomalously rapid transport of self-propelled particles in a porous medium, where nanoswimmers escaped from cavities more than an order of magnitude faster than expected compared to corresponding Brownian particles. This phenomenon was determined to be due to nuanced effects associated with self-propulsion, including enhanced mobility in confinement, efficient surface-mediated searching for holes, and the abolition of barriers at cavity exits. These mechanisms are broadly generalizable, and the previously unknown transport efficiency of nanoswimmers in complex porous environments suggests tremendous opportunities in forward-looking applications, including drug delivery and environmental remediation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Sorting for secreted molecule production using a biosensor-in-microdroplet approach

Significance The design–build–test cycle of metabolic engineering is swiftly becoming test limited owing to advances in DNA synthesis and library design. At the same time, improving extracellular production requires high-throughput studies to detect extracellular (not intracellular) content. Traditional approaches that utilize chromatography-based analysis or plate-based fluorescence assays are relatively throughput limited and require a high degree of liquid handling. These limitations are avoided here by creating a generalizable approach for small molecule production screening. Namely, this paradigm combines “off-the-shelf” biosensors with large library screening from producing cells in a cell-type agnostic manner.

Bowman, Emily K.↗
DOE OSTI2024

Modeling prebiotic chemistries with quantum accuracy at classical costs

Molecular Dynamics (MD) simulations using classical force-fields are commonly employed in numerous scientific investigations. However, many natural processes involve bond breaking and quantum forces. This complexity is compounded by the presence of multiple competing length and timescales. For example, accurately modeling the thermodynamics and dynamics of a chemical reaction requires accounting for the concerted movements of numerous solvent molecules and ions with their own fast or slow timescales. While widely used static Density Functional Theory (DFT) calculations at 0 temperature can be beneficial for such investigations, they do not account for dynamics, and lack precision in describing the molecular environments. They particularly fail at correct, rigorous treatments of finite-temperature fluctuations, and thus generalization to experimentally relevant conditions. In PNAS Benayad et al develop a scalable, generalizable approach for designing Neural Network Potentials (NNPs) that can handle chemical reactivity in solvated systems with quantum accuracy at classical costs. Specifically, they study phosphoester bond formation and rupture, which is fundamentally relevant to the Phosphorus-Oxygen bond formation central to life, and especially for the RNA world hypothesis. The framework developed here has the potential to generalize to different chemical reactions of energy and biological relevance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Simulating regional workforce impacts of decarbonizing integrated steelmaking

Global efforts to mitigate climate change are increasing pressure on heavy manufacturing industries to decarbonize production. The iron and steel industry is responsible for 7% of CO 2 emissions globally (2% in the United States) and is often a major employer in the regions where iron and steel is produced. Understanding the future prospects for workers in regions with high CO 2 emitting industries—including impacts of phasing out or evolving such industries—will be critical for informing regional economic and clean energy strategies. We simulate the impact of an “in-place” transition that replaces today’s integrated production with direct reduced iron (DRI) used in electric arc furnaces (EAFs), using Southwest Pennsylvania as an application of our generalizable approach. Our results suggest that the integrated steelmaking workforce today has the skills, knowledge, and abilities (SKAs) to fill over 95% of all jobs required by DRI/EAF facilities, but the number of jobs is only 25% of those at integrated plants. We also find that some occupational groups have greater general transferability into the broader job market, while other groups, such as production workers, are ill-equipped today based on current SKAs to transition out of the iron and steel industry. Our methodology further suggests factors that limit transitions: Around 85% of occupations are more limited by missing skills, while 15% are more limited by insufficient wages. These results may help to improve the design of social policy and the targeting of retraining programs, while the simulation approach can be readily adapted for other regions and industries.

decarbonization↗
DOE OSTI2025

Feasibility of Recycling Discharged Microreactor Heavy Metal in Light Water and Sodium-Cooled Fast Reactors: A Neutronics Analysis

Nuclear microreactors (MRs) offer unique advantages, such as rapid deployment, potability, low maintenance requirements, and operational flexibility. Their compact size makes them a promising solution for decentralized power generation, particularly in remote areas, military bases, and disaster-stricken regions. However, MRs face challenges, including unutilized fissile material at the end of life, economic inefficiency, increased heavy metal (HM) waste complicating disposal, and the accumulation of plutonium (Pu) with high 239 Pu concentrations raising proliferation risks. Here, this study investigated the neutronics feasibility of a novel three-stage fuel cycle where discharged HM from MRs is recycled and burned in light water reactors and sodium-cooled fast reactors. This approach converts discharged HM into valuable fuel, enhancing the efficiency of MR deployments while improving the safeguardability of their final waste products. Neutronics analysis demonstrated that the safety characteristics of reactor designs in each stage were minimally impacted by the proposed cycle. For two representative MR designs, a fast-spectrum MR with solid pellet fuel and a thermal-spectrum MR with TRISO (TRi-structural-ISOtropic) fuel compacts, the proposed fuel cycle reduced the uranium disposal mass flow rate by ~60%, decreased the 235 U enrichment of the discharge fuel to ~1 wt%, eliminated plutonium disposal, and increased the cumulative fuel burnup to ~580 gigawatt-day per metric ton of initial heavy metal (GWd/t-iHM) or 60% fissions per initial metal atom. Despite the significant differences between the two MR designs, the performance and infrastructure requirements of the developed fuel cycles were remarkably similar, indicating its generalizability to a broader class of MRs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

A physics-constrained neural ordinary differential equations approach for robust learning of stiff chemical kinetics

The high computational cost associated with solving for detailed chemistry poses a significant challenge for predictive computational fluid dynamics (CFD) simulations of turbulent reacting flows. While deep learning techniques have been explored to develop faster surrogate models, they often fail to integrate reliably with CFD solvers. This instability arises because traditional deep learning approaches optimize for training error without ensuring compatibility with ordinary differential equation (ODE) solvers, resulting in accumulation of errors over time. Recently, neuralODE (NODE) based approaches have been shown to be a promising technique to emulate and accelerate detailed chemistry computations. Here, in the present work, we extend this NODE framework for stiff chemical kinetics by incorporating mass conservation constraints directly into the loss function during training. This ensures that the total mass as well as the individual elemental species masses are conserved in an a-posteriori manner. Proof-of-concept studies are performed with the novel physics-constrained NODE (PC-NODE) approach for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is demonstrated that the PC-NODE framework not only improves the physical consistency of the resulting data-driven model with respect to mass conservation criteria, but also improves training efficiency. PC-NODE is shown to achieve 2–100× speedup relative to the hydrogen-air detailed chemical mechanism depending on the type of the ODE solver (implicit or explicit) used during autoregressive inference tests. Lastly, a-posteriori studies are performed wherein the trained PC-NODE model is coupled with a CFD solver. It is shown that higher accuracy is achieved with PC-NODE relative to the purely data-driven NODE approach. Moreover, PC-NODE also exhibits robustness and generalizability to unseen initial conditions from within (interpolative capability) as well as outside (extrapolative capability) the training regime.

computational combustion↗
DOE OSTI2024

A five-year milestone: reflections on advances and limitations in GeoAI research

The Annual Meeting of the American Association of Geographers (AAG) in 2023 marked a five-year milestone since the first Geospatial Artificial Intelligence (GeoAI) Symposium was held at AAG in 2018. In the past five years, progress has been made while open questions remain. In this context, we organized an AAG panel and invited five panellists to discuss the advances and limitations in GeoAI research. The panellists commended the successes, such as the development of spatially explicit models, the production of large-scale geographic datasets, and the use of GeoAI to address real-world problems. The panellists also shared their thoughts on limitations in current GeoAI research, which were considered as opportunities to engage theories in geography, enhance model explainability, quantify uncertainty, and improve model generalizability. This article summarizes the presentations from the panellists and also provides after-panel thoughts from the organizers. We hope that this article can make these thoughts more accessible to interested readers and help stimulate new ideas for future breakthroughs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Dissociation of gas-phase anisole induced by low-energy electron interactions: understanding patterns of aromatic bond cleavage

Abstract In view of elucidating the fragmentation patterns of aromatic systems induced by low-energy electron interactions, dissociative electron attachment (DEA) to gas-phase anisole was performed. Anionic fragments resulting from this DEA process were detected by a quadrupole mass spectrometer, and ion yields of those fragments as a function of incident electron energy were rendered. Our study showed the formation of CH 3 − , HCC − , and OCH 3 − fragments, suggesting that various dissociation channels proceed out of DEA to anisole. We employed density functional theory to compute thermodynamic threshold energies for each potential dissociation channel. Those theoretical calculations supported the prediction that the CH 3 − and OCH 3 − fragments form via mechanisms of single-bond cleavage; the HCC − fragments may form through two-, three-, or four-body dissociation channels that entail hydrogen transfers and the cleavage of multiple aromatic bonds. The experimental resonance energies that form the CH 3 − , HCC − , and OCH 3 − fragments were 6.0 eV, 5.8 and 9.7 eV, and 9.8 eV, respectively. Given the classification of anisole as a monosubstituted aromatic species, our results explain generalizable patterns of electron-mediated dissociation in aromatic systems.

Finley, Jacob (ORCID:0009000606963352)↗
DOE OSTI2020

An analytic implementation of the IR-resummation for the BAO peak

We develop an analytic method for implementing the IR-resummation of [1], which allows one to correctly and consistently describe the imprint of baryon acoustic oscillations (BAO) on statistical observables in large-scale structure. We show that the final IR-resummed correlation function can be computed analytically without relying on numerical integration, thus allowing for an efficient and accurate use of these predictions on real data in cosmological parameter fitting. In this work we focus on the one-loop correlation function and the BAO peak. We show that, compared with the standard numerical integration method of IR-resummation, the new method is accurate to better than 0.2%, and is quite easily improvable. We also give an approximate resummation scheme which is based on using the linear displacements of a fixed fiducial cosmology, which when combined with the method described above, is about six times faster than the standard numerical integration. Finally, we show that this analytic method is generalizable to higher loop computations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Harmonic analysis of discrete tracers of large-scale structure

It is commonplace in cosmology to analyze fields projected onto the celestial sphere, and in particular density fields that are defined by a set of points e.g. galaxies. When performing an harmonic-space analysis of such data (e.g. an angular power spectrum) using a pixelized map one has to deal with aliasing of small-scale power and pixel window functions. We compare and contrast the approaches to this problem taken in the cosmic microwave background and large-scale structure communities, and advocate for a direct approach that avoids pixelization. We describe a method for performing a pseudo-spectrum analysis of a galaxy data set and show that it can be implemented efficiently using well-known algorithms for special functions that are suited to acceleration by graphics processing units (GPUs). The method returns the same spectra as the more traditional map-based approach if in the latter the number of pixels is taken to be sufficiently large and the mask is well sampled. The method is readily generalizable to cross-spectra and higher-order functions. It also provides a convenient route for distributing the information in a galaxy catalog directly in harmonic space, as a complement to releasing the configuration-space positions and weights, and a route to spectral apodization. Finally, we make public a code enabling the application of our method to existing and upcoming datasets.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Combining physics-based and data-driven models for quantitatively accurate plasma profile prediction that extrapolates well; with application to DIII-D, AUG, and ITER tokamaks

For design, scenario planning, and control, ITER and all other envisioned tokamaks rely on a variety of statistical and physics-based models to extrapolate to unseen regimes; most notably from low plasma current to high. A 'meta-learning' methodology for combining the accuracy of data-driven models with the generalizability of physics-based models is described and tested, yielding a 5–10 percent improvement in performance beyond either alone for the task of extrapolating time-dependent plasma profile prediction from low- to high- plasma current DIII-D tokamak discharges. Meanwhile, it is shown that both machine learning models extrapolated far-distribution and state-of-the-art 'physics-based' profile predictors fare worse than merely assuming plasma profiles do not change from their initial values. Finally, a variety of other mechanisms for helping data-driven models generalize—transfer learning, adding contextual information from physics simulators, and adding data from the ASDEX Upgrade tokamak—are attempted for similar extrapolation tasks but, in the methodology used in this paper, yield no significant improvement beyond simple data-driven models. Results are summarized in figures 15 and 16.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Integrated energy-water-land nexus planning to guide national policy: an example from Uruguay

Despite broad consensus on the benefits of a nexus approach to multi-sector planning, actual implementation in government and other decision-making institutions is still rare. This study presents an approach to conducting integrated energy-water-land (EWL) planning, using Uruguay as an example. This stakeholder-driven study focuses on assessing the EWL nexus implications of actual planned policies aimed at strengthening three of Uruguay's key exports (beef, soy, and rice), which account for more than 40% of total national export revenue. Five scenarios are analyzed in the study: a reference scenario, a climate impacts scenario, and three policy scenarios. The three policy scenarios include measures such as increasing the intensity of beef production while simultaneously decreasing emissions, increasing irrigated soybean production, and improving rice yields. This study supplements previous sector-specific planning efforts in Uruguay by conducting the first stakeholder-driven integrated multi-sector assessment of planned policies in Uruguay using a suite of integrated modeling tools. Key insights from the study are: as compared to a reference scenario, improving beef productivity could lead to cropland expansion (+30%) and significant indirect increases in water requirements (+20%); improving rice yields could lead to increases in total emissions (+3%), which may partially offset emissions reductions from other policies; expanding irrigated soy could have the least EWL impacts amongst the policies studied; and climate-driven changes could have significantly less impact on EWL systems as compared to human actions. The generalizable insights derived from this analysis are readily applicable to other countries facing similar multi-sector planning challenges. In particular, the study's results reinforce the fact that policies often have multi-sector consequences, and thus policies can impact one another's efficacy. Thus, policy design and implementation can benefit from coordination across sectors and decision-making institutions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Multi-scale planning model for robust urban drought response

Increasingly severe droughts are straining municipal water resources and jeopardizing urban water security, but uncertainty in their duration, frequency, and intensity challenges drought planning and response. We develop the Drought Resilient Interscale Portfolio Planning model (DRIPP) to generate optimal planning responses to urban drought. DRIPP is a generalizable multi-scale framework for optimizing dynamic planning strategies of long-term infrastructure deployment and short-term drought response. It integrates climate and hydrological variability with high-fidelity representations of urban water distribution, available technology options, and demand reduction measures to yield robust and cost-effective water supply portfolios that are location-specific. We apply DRIPP in Santa Barbara, California to assess how least cost water supply portfolios vary under different drought scenarios and identify portfolios that are robust across drought scenarios. In Santa Barbara, we find that drought intensity, not duration or frequency, drives cost increases, reliability risk, and regret of overbuilding infrastructure. Under uncertain drought conditions, a diversified technology portfolio that includes both rapidly deployable, decentralized technologies alongside larger centralized technologies minimizes water supply cost while maintaining high robustness to climate uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Plan evaluation for heat resilience: complementary methods to comprehensively assess heat planning in Tempe and Tucson, Arizona

Abstract Escalating impacts from climate change and urban heat are increasing the urgency for communities to equitably plan for heat resilience. Cities in the desert Southwest are among the hottest and fastest warming in the U.S., placing them on the front lines of heat planning. Urban heat resilience requires an integrated planning approach that coordinates strategies across the network of plans that shape the built environment and risk patterns. To date, few studies have assessed cities’ progress on heat planning. This research is the first to combine two emerging plan evaluation approaches to examine how networks of plans shape urban heat resilience through case studies of Tempe and Tucson, Arizona. The first methodology, Plan Quality Evaluation for Heat Resilience, adapts existing plan quality assessment approaches to heat. We assess whether plans meet 56 criteria across seven principles of high-quality planning and the types of heat strategies included in the plans. The second methodology, the Plan Integration for Resilience Scorecard™ (PIRS™) for Heat, focuses on plan policies that could influence urban heat hazards. We categorize policies by policy tool and heat mitigation strategy and score them based on their heat impact. Scored policies are then mapped to evaluate their spatial distribution and the net effect of the plan network. The resulting PIRS™ for Heat scorecard is compared with heat vulnerability indicators to assess policy alignment with risks. We find that both cities are proactively planning for heat resilience using similar plan and strategy types, however, there are clear and consistent opportunities for improvement. Combining these complementary plan evaluation methods provides a more comprehensive understanding of how plans address heat and a generalizable approach that communities everywhere could use to identify opportunities for improved heat resilience planning.

Environmental Sciences & Ecology↗
DOE OSTI2021

Watch and learn—a generalized approach for transferrable learning in deep neural networks via physical principles

Transfer learning refers to the use of knowledge gained while solving a machine learning task and applying it to the solution of a closely related problem. Such an approach has enabled scientific breakthroughs in computer vision and natural language processing where the weights learned in state-of-the-art models can be used to initialize models for other tasks which dramatically improve their performance and save computational time. Here we demonstrate an unsupervised learning approach augmented with basic physical principles that achieves fully transferrable learning for problems in statistical physics across different physical regimes. By coupling a sequence model based on a recurrent neural network to an extensive deep neural network, we are able to learn the equilibrium probability distributions and inter-particle interaction models of classical statistical mechanical systems. Our approach, distribution-consistent learning, DCL, is a general strategy that works for a variety of canonical statistical mechanical models (Ising and Potts) as well as disordered interaction potentials. Using data collected from a single set of observation conditions, DCL successfully extrapolates across all temperatures, thermodynamic phases, and can be applied to different length-scales. This constitutes a fully transferrable physics-based learning in a generalizable approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Differentiable physics-enabled closure modeling for Burgers’ turbulence

Abstract Data-driven turbulence modeling is experiencing a surge in interest following algorithmic and hardware developments in the data sciences. We discuss an approach using the differentiable physics paradigm that combines known physics with machine learning to develop closure models for Burgers’ turbulence. We consider the one-dimensional Burgers system as a prototypical test problem for modeling the unresolved terms in advection-dominated turbulence problems. We train a series of models that incorporate varying degrees of physical assumptions on an a posteriori loss function to test the efficacy of models across a range of system parameters, including viscosity, time, and grid resolution. We find that constraining models with inductive biases in the form of partial differential equations that contain known physics or existing closure approaches produces highly data-efficient, accurate, and generalizable models, outperforming state-of-the-art baselines. Addition of structure in the form of physics information also brings a level of interpretability to the models, potentially offering a stepping stone to the future of closure modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning

The study of phonon dynamics is pivotal for understanding material properties, yet it faces challenges due to the irreversible information loss inherent in powder inelastic neutron scattering spectra and the limitations of traditional analysis methods. In this study, we present a machine learning framework designed to reveal obscured phonon dynamics from powder spectra. Using a variational autoencoder, we obtain a disentangled latent representation of spectra and successfully extract force constants for reconstructing phonon dispersions. Notably, our model demonstrates effective applicability to experimental data even when trained exclusively on physics-based simulations. The fine-tuning with experimental spectra further mitigates issues arising from domain shift. Analysis of latent space underscores the model’s versatility and generalizability, affirming its suitability for complex system applications. Furthermore, our framework’s two-stage design is promising for developing a universal pre-trained feature extractor. This approach has the potential to revolutionize neutron measurements of phonon dynamics, offering researchers a potent tool to decipher intricate spectra and gain valuable insights into the intrinsic physics of materials.

domain adaptation↗