Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Model interpretation”

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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Theory, measurements, and models of the upper atmosphere and ionosphere of Saturn

The structure and composition of the thermosphere, exosphere, and ionosphere of saturn have been determined from observations at optical and radio wavelengths mainly by instruments aboard Voyager spacecraft. Techniques for determining the vertical profiles of temperature and density and the atmospheric vertical mixing in the upper Saturn atmosphere are discussed. Radio occultation measurements and theoretical models of Saturn's ionosphere are reviewed, and attempts to interpret the measurements using the models are discussed. Finally, mechanisms of thermospheric heating are examined.

Atreya, S. K.↗
NASA NTRS1988

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

The estimation of the spatially variable surface moisture and heat fluxes of natural, semivegetated landscapes is difficult due to the highly random nature of the vegetation (e.g., plant species, density, and stress) and the soil (e.g., moisture content, and soil hydraulic conductivity). The solution to that problem lies, in part, in the use of satellite remotely sensed data, and in the preparation of those data in terms of the physical properties of the plant and soil. The work was focused on the development and testing of a stochastic geometric canopy-soil reflectance model, which can be applied to the physically-based interpretation of LANDSAT images. The model conceptualizes the landscape as a stochastic surface with bulk plant and soil reflective properties. The model is particularly suited for regional scale investigations where the quantification of the bulk landscape properties, such as fractional vegetation cover, is important on a pixel by pixel basis. A summary of the theoretical analysis and the preliminary testing of the model with actual aerial radiometric data is provided.

Eagleson, Peter S.↗
NASA NTRSDate not supplied

Models, In situ, and Remote sensing of Aerosols (MIRA)

The Models, In situ, and Remote sensing of Aerosols (MIRA) working group connects members of the different aerosol communities through collaborative projects. This abstract, additional details, and a list of current projects can be found at https://science.larc.nasa.gov/mira-wg/. Join our email distribution list of 200+ members at https://espo.nasa.gov/lists/listinfo/mira.In this talk, we will describe some current MIRA projects that are relevant to AeroCom (e.g., the Table of Aerosol Optics, or TAO) and the benefits of presenting your multi-disciplinary work at MIRA sessions and meetings. What is MIRA? MIRA is forum that fosters international collaborations amongst the aerosol Modeling, In situ, and Remote sensing specialties. MIRA advances scientific knowledge about aerosol properties and improves our understanding of air quality, weather, and climate through the pursuit of specific goals. MIRA emphasizes areas of study where the aerosol specialties overlap, and MIRA projects seek additional data and insight from others (observational and modeled). Why? The purpose of MIRA is to contextualize both observations and model results through the encouragement of holistic projects and collaborations. Linking aerosol disciplines removes gaps in understanding that hinder our interpretation of observations and model results. Such linkages improve our understanding of air quality, weather, and climate. How does MIRA differ from other working groups? MIRA focuses on interdisciplinarity to improve measurements and their utility, so MIRA complements the activities of other groups. Other interdisciplinary aerosol groups have different primary foci. For instance, AeroCom mainly focuses on improving global aerosol models, AeroSat focuses on strengthening collaboration amongst satellite aerosol retrieval groups, and ICAP focuses on aerosol forecasting. We hope that many MIRA projects engage these groups.

Gregory Schuster↗
DOE OSTI2025

Validation and Calibration of Energy Models with Real Vehicle Data from Chassis Dynamometer Experiments

Accurate estimation of vehicle fuel consumption typically requires detailed modeling of complex internal powertrain dynamics, often resulting in computationally intensive simulations. However, many transportation applications-such as traffic flow modeling, optimization, and control-require simplified models that are fast, interpretable, and easy to implement, while still maintaining fidelity to physical energy behavior. This work builds upon a recently developed model reduction pipeline that derives physics-like energy models from high-fidelity Autonomie vehicle simulations. These reduced models preserve essential vehicle dynamics, enabling realistic fuel consumption estimation with minimal computational overhead. While the reduced models have demonstrated strong agreement with their Autonomie counterparts, previous validation efforts have been confined to simulation environments. This study extends the validation by comparing the reduced energy model's outputs against real-world vehicle data. Focusing on the MidSUV category, we tune the baseline Autonomie model to closely replicate the characteristics of a Toyota RAV4. We then assess the accuracy of the resulting reduced model in estimating fuel consumption under actual drive conditions. Our findings suggest that, when the reference Autonomie model is properly calibrated, the simplified model produced by the reduction pipeline can provide reliable, semi-principled fuel rate estimates suitable for large-scale transportation applications.

42 ENGINEERING↗
DOE OSTI2017

The low salinity effect at high temperatures

The mechanism(s) of low salinity water flooding (LSWF) must be better understood at high temperatures and pressures if the method is to be applied in high T/P kaolinite-bearing sandstone reservoirs. We measured contact angles between a sandstone and an oil (acid number, AN = 3.98 mg KOH/g, base number, BN = 1.3 mg KOH/g) from a reservoir in the Tarim Field in western China in the presence of various water chemistries. We examined the effect of aqueous ionic solutions (formation brine, 100X diluted formation brine, and softened water), temperature (60, 100 and 140 °C) and pressure (20, 30, 40, and 50 MPa) on the contact angle. We also measured the zeta potential of the oil/water and water/rock interfaces to calculate oil/brine/rock disjoining pressures. A surface complexation model was developed to interpret contact angle measurements and compared with DLVO theory predictions. Contact angles were greatest in formation water, followed by the softened water, and low salinity water at the same pressure and temperature. Contact angles increased slightly with temperature, whereas pressure had little effect. DLVO and surface complexation modelling predicted similar wettability trends and allow reasonably accurate interpretation of core-flood results. Water chemistry has a much larger impact on LSWF than reservoir temperature and pressure. As a result, low salinity water flooding should work in high temperature and high pressure kaolinite-bearing sandstone reservoirs.

02 PETROLEUM↗
DOE OSTI2019

Data for secondary-electron production from ion precipitation at Jupiter III: Target and projectile processes in H{sup +}, H, and H{sup −} + H{sub 2} collisions

To extend the range of data required for modeling the secondary-electron production from ion precipitation into the upper atmosphere of Jupiter, inelastic processes for collisions of 1 keV to 25 MeV H{sup +}, H, and H{sup −} with H{sub 2} are considered. As in other work treating the dominant heavy-ion species of magnetospheric origin, O and S ions (Schultz et al., 2017, 2019; Gharibnejad et al., 2019) the classical trajectory Monte Carlo method is employed to describe the secondary-electron-producing channels (single and double ionization, transfer ionization, and single and double stripping) as well as the other inelastic channels (single and double charge transfer and projectile and target excitation) required to model the energy loss and charge state evolution of the precipitating ions in their passage through the atmospheric gas. Data is described and tabulated both as directly obtained from these calculations and normalized to widely accepted recommended values (Hunter et al., 1990) for channels for which recommendations exist. As in the previous work, the overall accuracy and completeness of the data presented is verified by use of a Monte Carlo ion-transport simulation to obtain the stopping power and ion-fraction populations as a function of impact energy in comparison with accepted values. The addition of the present data to models of secondary-electron production in Jupiter’s atmosphere improves such model’s ability to interpret in situ observations of the precipitating ions’ effect by the spacecraft Juno as well as enhancing the physical reality of models of the coupling of the Jovian magnetosphere, ionosphere, and atmosphere.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Search for gluinos in events with an isolated lepton, jets and missing transverse momentum at $\sqrt{s}$ = 13 Te V with the ATLAS detector

The results of a search for gluinos in final states with an isolated electron or muon, multiple jets and large missing transverse momentum using proton–proton collision data at a centre-of-mass energy of $\sqrt{s}$ = 13 Te V are presented. The dataset used was recorded in 2015 by the ATLAS experiment at the Large Hadron Collider and corresponds to an integrated luminosity of 3.2 fb -1 . Six signal selections are defined that best exploit the signal characteristics. The data agree with the Standard Model background expectation in all six signal selections, and the largest deviation is a 2.1 standard deviation excess. The results are interpreted in a simplified model where pair-produced gluinos decay via the lightest chargino to the lightest neutralino. In this model, gluinos are excluded up to masses of approximately 1.6 Te V depending on the mass spectrum of the simplified model, thus surpassing the limits of previous searches.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Lorentz group equivariant autoencoders

Abstract There has been significant work recently in developing machine learning (ML) models in high energy physics (HEP) for tasks such as classification, simulation, and anomaly detection. Often these models are adapted from those designed for datasets in computer vision or natural language processing, which lack inductive biases suited to HEP data, such as equivariance to its inherent symmetries. Such biases have been shown to make models more performant and interpretable, and reduce the amount of training data needed. To that end, we develop the Lorentz group autoencoder (LGAE), an autoencoder model equivariant with respect to the proper, orthochronous Lorentz group $$\textrm{SO}^+(3,1)$$ SO + ( 3 , 1 ) , with a latent space living in the representations of the group. We present our architecture and several experimental results on jets at the LHC and find it outperforms graph and convolutional neural network baseline models on several compression, reconstruction, and anomaly detection metrics. We also demonstrate the advantage of such an equivariant model in analyzing the latent space of the autoencoder, which can improve the explainability of potential anomalies discovered by such ML models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Exploring CO 2 reduction and crossover in membrane electrode assemblies

Electrochemical CO 2 reduction (CO 2 R) using renewable electricity is a key pathway toward synthesizing fuels and chemicals. In this study, multi-physics modeling is used to interpret experimental data obtained for CO 2 R to CO using Ag catalysts in a membrane electrode assembly. The one-dimensional model is validated using measured CO 2 crossover and product formation rates. The kinetics of CO formation are described by Marcus–Hush–Chidsey kinetics, which enables accurate prediction of the experimental data by accounting for the reorganization of the solvent during CO 2 R. Further, the results show how the performance is dictated by competing phenomena including ion formation and transport, CO 2 solubility, and water management. The model shows that increasing the ion-exchange capacity of the membrane and surface area of the catalyst increases CO formation rates by >100 mA cm –2 without negatively impacting CO 2 utilization. Here we provide insights into how to manage the trade-off between productivity and CO 2 utilization in CO 2 electrolyzers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

Galactic X-ray sources

Data on galactic X-ray sources from rocket and balloon experiments and the UHURU satellite are reported. The outline emphasizes general discrete source models and begins with some a priori considerations about X-ray emission mechanisms and their applicability, after which contemporary data are interpreted according to these models. Topics covered include the production of X-rays by various mechanisms, source regimes, observational parameters, stellar distributions, individual stars, flare stars, supernovae and pulsars, the Crab nebula, old and young remnants, binary systems, and others.

Holt, S. S.↗
NASA NTRS1987

Cloud chemistry on Jupiter

Chemical equilibrium models used currently to interpret observations of Jupiter are reexamined using new data defining thermal profiles, which are substantially different from those used in the previous models. A model is developed for the chemical reactions controlling the composition of the upper troposphere on Jupiter, specifically the cloud-forming region from 10 bar to 0.1 bar, which includes, for the first time, the effects of aqueous chemistry on the composition and the vertical distribution of many measurable species in the atmosphere, identifying the factors influencing their abundances above the H2O cloud. The thermodynamic data for potential condensates on Jupiter, i.e., NH3(s), NH4SH(s), (NH4)2S(s), and H2S(s), are reexamined, recognizing the lack of data on sulfides for the temperature range of interest on Jupiter. Vertical profiles of mixing ratios for CO2, H2S, NH3, and H2, obtained for several assumed bulk abundances with respect to solar, are presented.

Carlson, Barbara E.↗
DOE OSTI2026

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representation for this task, yet no systematic evaluation exists of how well vision-language models (VLMs) interpret them. We introduce QCalEval, the first VLM benchmark for quantum calibration plots: 243 samples across 87 scenario types from 22 experiment families, spanning superconducting qubits and neutral atoms, evaluated on six question types in both zero-shot and in-context learning settings. The best general-purpose zero-shot model reaches a mean score of 72.3, and many open-weight models degrade under multi-image in-context learning, whereas frontier closed models improve substantially. A supervised fine-tuning ablation at the 9-billion-parameter scale shows that SFT improves zero-shot performance but cannot close the multimodal in-context learning gap. As a reference case study, we release NVIDIA Ising Calibration 1, an open-weight model based on Qwen3.5-35B-A3B that reaches 74.7 zero-shot average score.

Cao, Shuxiang↗
NASA NTRS1999

Testing the Pair Reflection Model for AGN

This project involved the interpretation of X-ray observations of AGN, and more specifically, evaluation and interpretation of the spectra in terms of the pair-reflection model. The grant was originally awarded on 3/14/94. We have now completed this work, which led to a number of publications. We found that reflection features are common in Seyfert galaxies, and may also be present in their radio-loud analogs, the broad-emission-line radio galaxies. In the latter case, however, a strong, nonthermal component is also present, and can swamp the thermal reflection spectrum.

Urry, C. Megan↗
DOE OSTI2024

Extracting and Interpreting Electrochemical Impedance Spectra (EIS) from Physics-Based Models of Lithium-Ion Batteries

This paper implements a highly efficient algorithm to extract electrochemical impedance spectra (EIS) from physics-based battery models (e.g., a P2D model). The mathematical approach is different from how EIS is practiced experimentally. Experimentally, the voltage (current) is harmonically perturbed over a wide range of frequencies and the amplitude and phase shift of the corresponding current (voltage) is measured. The experimental approach can be implemented in simulation software, but is computationally expensive. The approach here is to determine locally linear state-space models from the full physical model. The four Jacobian matrices that are the basis of the state-space models can be derived by numerical differentiation of the physical model. The EIS is then extracted from the state-space model using computationally efficient matrix-manipulation techniques. The algorithm can evaluate the full EIS at an instant in time during a transient, independent of whether the battery is in a stationary state. The approach is also able to separate the full-cell impedance to evaluate partial EIS, such as for a battery anode alone. Although such partial EIS is difficult to measure experimentally, the partial EIS provides valuable insights in interpreting the full-cell EIS.

25 ENERGY STORAGE↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗
DOE OSTI2022

Assessing Impacts on Pressure Stabilization and Leasing Acreage for CO 2 Storage Utilizing Oil Migration Concepts

Favorable geological storage for CO 2 has long been pictured as large anticlines with thick sandstones, similar to oil reservoirs in the petroleum system. Unlike oil, however, stored CO 2 does not need to be recoverable, which raises the possibility of using dissolution and residual trapping to augment the capacity of buoyant traps and tap more of the bulk rock volume. The work presented builds on that idea, asking the following question: If we inject CO 2 down to a syncline – analogous to the carrier bed in the petroleum system – how would this injection mechanism impact storage capacity and plume shape, migration, and stabilization? To address this question, we built a reservoir model, based on seismic interpretation of Middle Miocene strata, offshore Galveston, Texas. 3-D seismic and well logs were used to characterize key intervals. Reservoirs chosen for modeling are progradational-aggradational sands with mud intercalation. They have a higher degree of heterogeneity than the more conventional reservoirs commonly targeted for CO 2 storage. Modeling investigated how far the CO 2 plume would migrate under two scenarios: (1) injecting CO 2 at the base of the salt withdrawal basin (syncline scenario) and (2) injecting CO 2 at the base of the structural closure, similar to a common injection well location for EOR purposes (base scenario). For each scenario, we separately simulated injection of 30 MT of CO 2 and 60 MT of CO 2 continuously for 30 years and observe the plume and pressure evolution for 100 years after the injection stops. The simulation shows that injecting the CO 2 into a syncline limits the vertical migration of CO 2 , thus making synclinal injection more secure. In the syncline scenario, the geological layer around the injection point is more heterogeneous than the layer in the base scenario; thus, the CO 2 tends to migrate laterally. Additionally, in the syncline scenario, the plume does not even reach the upper part of the anticline, allowing us to safely store an additional amount of CO 2 into the reservoir. Furthermore, the simulation also shows that in the syncline scenario, the times needed for the reservoir to reach its stabilized pressure after the end of injections are faster. To summarize, CO 2 injection at the base of a syncline could provide additional storage, increase the safety of the project from the limited vertical plume migration, and expedite plume stabilization, which could result in the decrease of monitoring frequency as the project runs, thus lowering the operating cost of the project in the long run.

58 GEOSCIENCES↗
DOE OSTI2016

DEPENDENCE OF X-RAY BURST MODELS ON NUCLEAR REACTION RATES

X-ray bursts are thermonuclear flashes on the surface of accreting neutron stars, and reliable burst models are needed to interpret observations in terms of properties of the neutron star and the binary system. We investigate the dependence of X-ray burst models on uncertainties in (p, γ ), ( α , γ ), and ( α , p) nuclear reaction rates using fully self-consistent burst models that account for the feedbacks between changes in nuclear energy generation and changes in astrophysical conditions. A two-step approach first identified sensitive nuclear reaction rates in a single-zone model with ignition conditions chosen to match calculations with a state-of-the-art 1D multi-zone model based on the Kepler stellar evolution code. All relevant reaction rates on neutron-deficient isotopes up to mass 106 were individually varied by a factor of 100 up and down. Calculations of the 84 changes in reaction rate with the highest impact were then repeated in the 1D multi-zone model. We find a number of uncertain reaction rates that affect predictions of light curves and burst ashes significantly. The results provide insights into the nuclear processes that shape observables from X-ray bursts, and guidance for future nuclear physics work to reduce nuclear uncertainties in X-ray burst models.

79 ASTRONOMY AND ASTROPHYSICS↗