Search NASA⌕ Search

SEARCH · Search NASA

Results for “Compiler”

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 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Application of community data to surface complexation modeling framework development: Iron oxide protolysis

This study presents a comprehensive community data-driven surface complexation modeling framework for simulating potentiometric titration of mineral surfaces. Compiled community data for ferrihydrite, goethite, hematite, and magnetite are fit to produce representative protolysis constants that can reproduce potentiometric titration data collected from multiple literature sources. Using this framework, the impact of surface complexation model type and surface site density (SSD) on the fit quality and protolysis constants can be readily evaluated. For example, the non-electrostatic model yielded a poor data fit compared to diffuse double layer model and constant capacitance models due to the absence of known surface charge effects. Regardless of the choice of iron oxide mineral, pK a1 decreased with increasing SSD while the opposite tendency was observed for pK a2 . This newly developed framework demonstrates a method to reconcile community data-wide potentiometric titration data using Findable, Accessible, Interoperable, Reusable data principles to produce mineral protolysis constants that improve robustness of surface complexation models for applications in metal sorption and reactive transport modeling. The framework is readily expandable (as community data increase) and extensible (as the number of minerals increase). The framework provides a path forward for developing self-consistent, comprehensive, and updateable surface complexation databases for surface complexation and reactive transport modeling.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Life cycle assessment of fuel ethanol produced from soluble sugar in sweet sorghum stalks in North China

This paper describes the results of a life cycle assessment of sweet sorghum stalk (SSS)-based ethanol in North China. We determined the environmental performance of SSS-based ethanol and examined its advantages and disadvantages, as compared to gasoline, focusing on the life cycle of feedstock production, transportation, ethanol production and distribution, and use. The GREET transportation model and the method developed by the Centre of Environmental Sciences at Leiden University (CML method) were used to compile a life cycle inventory and to assess environmental impacts. Results indicate that SSS-based ethanol has advantages in terms of energy consumption, with a well to wheel decrease of 85% fossil energy and 44% global warming potential, as compared with gasoline. Abiotic depletion potential, acidification potential, and photochemical ozone creation potential were also 50–90% lower than in the case of gasoline, while human health toxic potential was 36% lower. However, SSS-based sorghum did not have advantages over gasoline in terms of life cycle cost, land use, and water consumption. Results indicate that such an evaluation cannot just consider a few types of environmental impacts, researchers should promote systematic and comprehensive life cycle assessment of ethanol to guide the development of an energy strategy for China.

Environment impact↗
DOE OSTI2022

A multi-product landscape life-cycle assessment approach for evaluating local climate mitigation potential

Increasing demand for land-based climate mitigation requires more efficient management of agricultural landscapes for competing objectives. Here we develop methods for assessing trade-offs and synergies between intensification and carbon-sequestering conservation measures in annual crop production landscapes using the DayCent ecosystem model and the Greenhouse Gases, Regulated Emissions, and Energy Use in Technologies (GREET) life-cycle assessment (LCA) model. We compiled county-scaled crop yields, fertilizer application rates, and tillage intensity for a corn–soybean farming case study landscape in the US state of Iowa. Using DayCent, we estimated a baseline soil organic carbon (SOC) accrual rate of 0.29 Mg C ha -1 y -1 driven by historical increases in crop productivity and reductions in tillage intensity. We then simulated the effects of management interventions targeted toward intensification (stover removal) and SOC sequestration (tillage intensity reduction and winter cover crop addition) individually and in combination. We propose a new multi-product landscape–LCA approach that analyzes marginal changes in corn grain, corn stover, and soybean production from the landscape in terms of their value for biofuel production (corn ethanol, soy biodiesel, and cellulosic ethanol from stover) and associated net displacement of conventional fossil-derived fuel use. This enables us to evaluate both intensification and sequestration effects in common CO 2 -equivalent mitigation units. We also used DayCent-simulated yields under the different land management scenarios to estimate farm-level costs and revenues. Our results show that intensification via collecting 30% of corn stover for biofuel production would increase the total greenhouse gas (GHG) mitigation potential of this landscape by 0.93 Mg CO 2 e ha -1 y -1 and provide $49 ha -1 y -1 of additional net revenue from biomass sales, but would reduce the baseline SOC accumulation rate by approximately 40%. In contrast, integrated approaches that include co-adoption of winter cover cropping and/or tillage intensity reduction would result in increased rates of SOC accumulation above the baseline, achieving simultaneous improvements in both farm profits and the overall GHG mitigation potential of the landscape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Meta-analysis of biogas upgrading to renewable natural gas through biological CO 2 conversion

Biogas upgrading through CO 2 conversion by hydrogenotrophic methanogenesis is receiving an increasing attention worldwide because of the demand for renewable natural gas. Herein, a holistic and statistical study of the operation conditions, driving forces, performances, and potential implementation of biogas upgrading via biological CO 2 conversion was conducted. Based on a systematic review and meta-analysis of 46 existing publications that were selected from 1475 papers, we have compiled a global dataset of CO 2 bioconversion biogas upgrading, encompassing 308 study cases. Subsequently, we employed a rigorous analytical framework incorporating data processing and mixed effects linear regression analysis to examine the dataset. This analysis revealed a significant positive relationship between the H 2 :CO 2 ratio and the methane percentage in the upgraded biogas when using the study as a random effect. Furthermore, we performed meta-analysis on observations taken when the ratio was close to 4:1 and found that ex situ reactors (91.93% [88.11%, 95.75%]) can perform better than in situ reactors (84.74% [80.69%, 88.80%]). No evidence of differential performance was found based on the present dataset between different temperature regimes or operation modes. Furthermore, those findings establish a database that will contribute to a deeper understanding of the biogas upgrading via biological CO 2 conversion.

Biogas upgrading↗
DOE OSTI2025

A time-parallel multiple-shooting method for large-scale quantum optimal control

Quantum optimal control plays a crucial role in quantum computing by providing the interface between compiler and hardware. Solving the optimal control problem is particularly challenging for multi-qubit gates, due to the exponential growth in computational complexity with the system's dimensionality and the deterioration of optimization convergence. To ameliorate the computational complexity of time-integration, this paper introduces a multiple-shooting approach in which the time domain is divided into multiple windows and the intermediate states at window boundaries are treated as additional optimization variables. Further, this enables parallel computation of state evolution across time-windows, significantly accelerating objective function and gradient evaluations. Since the initial state matrix in each window is only guaranteed to be unitary upon convergence of the optimization algorithm, the conventional gate trace infidelity is replaced by a generalized infidelity that is convex for non-unitary state matrices. Continuity of the state across window boundaries is enforced by equality constraints. A quadratic penalty optimization method is used to solve the constrained optimal control problem, and an efficient adjoint technique is employed to calculate the gradients in each iteration. We demonstrate the effectiveness of the proposed method through numerical experiments on quantum Fourier transform gates in systems with 2, 3, and 4 qubits, noting a speedup of 80x for evaluating the gradient in the 4-qubit case, highlighting the method's potential for optimizing control pulses in multi-qubit quantum systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Data driven investigation to understand the influence of total solids on biological biogas upgrading

In situ biogas upgrading achieves CO 2 conversion to CH 4 via hydrogenotrophic methanogenesis; however, gas-liquid mass transfer constraints limit the upgrading performance. Recognizing that optimization studies often underrepresent the effects of total solids (TS) and organic loading rate (OLR), this study undertook a holistic, statistics driven assessment of operating conditions for in situ H 2 assisted biogas upgrading, centering the analysis on TS and OLR. A dataset of 31 studies was compiled and comprised 99 observations. A rigorous analytical framework was employed, combining data standardization, fixed- and random-effects (REML) weighted regressions with cluster-robust errors, stratified analyses, and machine learning. Mixed-effects meta regression indicated that TS was the main factor explaining differences of methane fraction (CH 4 %) when considering the between studies heterogeneity. Focusing on a near-stoichiometric subset (H 2 /CO 2 ≈ 4:1), TS remained significant. Stratified results showed a stronger negative relationship between TS and CH 4 % in UASB reactors than in CSTRs, with a negative effect under mesophilic conditions and no significant effect under thermophilic conditions. A Random Forest model corroborated the statistical findings, consistently ranking H 2 /CO 2 ratio, OLR, TS, and hydrogen injection rate (HIR) as the most influential predictors. These findings delineate trends across increasing TS levels, particularly between 1% and 10%, and provide preliminary insights for TS above 15% in in situ biogas upgrading. They further provide insights for the influence of TS by reactor type and temperature, thereby advancing the evidence base for implementing biological CO 2 conversion to CH 4 in practice.

In situ biogas upgrading↗
DOE OSTI2023

Review of the adsorbents/catalysts for the removal of sulfur compounds from natural gas

Here this paper aims to provide a compilation of studies on adsorptive removal of H2S and organic sulfur compounds from natural gas at relatively low temperatures and pressures. The manuscript compares sulfur uptakes among different materials, including zeolites, activated carbons, metallic organic frameworks (MOFs), and metal oxides, and the impact of modifications to these materials. Best sulfur uptakes were achieved by adding metallic or nitrogen species to the adsorbent surfaces. This contribution to the peer-reviewed literature could provide a better understanding of the improvements necessary to achieve greater sulfur capacities and the mechanisms of sulfur interactions with adsorbents' surfaces.

03 NATURAL GAS↗
DOE OSTI2025

Impact of inland boundary conditions on seawater intrusion modeling in stratified coastal aquifers under sea-level rise

This study systematically examines the impact of inland boundary conditions on seawater intrusion modeling in general stratified coastal aquifers under sea-level rise (SLR). We derived analytical solutions for seawater wedge toe positions in stratified aquifers with general head boundary (GHB) conditions and compiled solutions for constant head boundary (CHB), constant flux boundary (CFB), and GHB conditions in both homogeneous and stratified aquifers. Additionally, we introduced the concept of equivalent boundary conditions and developed equations to ensure consistent steady-state toe positions across different boundary types. Unlike previous studies focusing on specific layering configurations, we conducted a comprehensive sensitivity analysis of seawater intrusion in general stratified aquifers using transmissivity centroid elevation (TCE). Our results show that SLR generally exacerbates seawater intrusion, with unconfined aquifers exhibiting higher sensitivity across all boundary conditions. Among boundary types, CHB conditions were the most sensitive to SLR, CFB the least, and GHB showed an intermediate response depending on its distance from the model boundary. Furthermore, seawater intrusion increases with the TCE of the stratified aquifer under SLR, suggesting that high-permeability or preferential flow layers near the surface amplify the impact of SLR. These findings emphasize the importance of selecting appropriate boundary conditions when evaluating SLR-driven seawater intrusion in stratified coastal aquifers.

Boundary condition↗
DOE OSTI2022

BIPSPI+: Mining Type-Specific Datasets of Protein Complexes to Improve Protein Binding Site Prediction

Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating the specific residues that compose the interface of protein complexes. In recent years, new machine learning and other algorithmic approaches have been proposed to solve this problem. However, little effort has been made in finding better training datasets to improve the performance of these methods. With the aim of vindicating the importance of the training set compilation procedure, in this work we present BIPSPI+, a new version of our original server trained on carefully curated datasets that outperforms our original predictor. We show how prediction performance can be improved by selecting specific datasets that better describe particular types of protein interactions and interfaces (e.g. homo/hetero). In addition, our upgraded web server offers a new set of functionalities such as the sequence-structure prediction mode, hetero- or homo-complex specialization and the guided docking tool that allows to compute 3D quaternary structure poses using the predicted interfaces. BIPSPI+ is freely available at https://bipspi.cnb.csic.es.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

An overview of the first principles studies of doped RE-TM5 systems for the development of hard magnetic properties

In this study, we compiled all the first principles calculations performed on RE-TM 5 (RE: rare earth, TM: transition metal) system to address the following question: Has everything been tried to improve the hard magnetic properties of this special intermetallic compound, and are there any venues that are worth pursuing? As it is the case with the first principles studies in any field, they are driven mainly by experimental work: (i) to validate their results, and (ii) to extend our understanding of the underlying physical phenomena so that the theory in hand can be used as a predictive tool to shed light on certain what-if scenarios. Our analysis indicates that three major compounds are investigated more than others: (1) YCo 5 , because it can potentially be an intermediate-performance hard magnet, and the presence of yttrium with no localized 4f electrons simplifies calculations significantly, (2) SmCo 5 , because it has the largest magnetic anisotropy energy among all RE hard magnets as well as very impressive high temperature performance, (3) CeCo 5 , because Ce is the most abundant rare earth element with a curious 4f electron behavior that gives rise to pronounced deviations in its structural and magnetic properties across the lanthanides series. This is followed by a brief analysis of several ab initio approaches that were developed to treat these rather complicated systems both at 0 K and at finite temperatures. Towards the end, we elucidate the role of exchange-correlation approximations such as local density approximations (LDA) and generalized gradient approximation (GGA) formulation in determining the MAE and M s by analyzing their corresponding density of states (DOS), and providing our results on a rather overlooked hard-magnetic material: LaCo 5 .

36 MATERIALS SCIENCE↗
DOE OSTI2025

A snapshot of high-entropy alloy processing techniques and their effects on resulting mechanical properties

High-entropy alloys (HEAs) exhibit exceptional strength, corrosion resistance, and thermal stability, making them promising candidates for nuclear, aerospace, and other extreme applications. While most prior work has focused on compositional design, manufacturing techniques themselves can alter microstructure and mechanical properties as dramatically as alloy chemistry. This review compiles and compares the effects of processing routes—including arc melting, induction melting, mechanical alloying with spark plasma sintering, and additive manufacturing—on the structure and properties of HEAs. Quantitative comparisons highlight, for example, that SPS-processed alloys can achieve ∼20–45 % higher yield and tensile strength than arc-melted counterparts, while Bridgman solidification produces nearly single-crystal structures with elongation to failure exceeding 80 %. Additive manufacturing routes such as selective laser melting offer fine microstructures but also introduce anisotropy and porosity, leading to yield strengths spanning 100–600 MPa for the same composition. By synthesizing such results, this review provides actionable insight into how processing routes interact with HEA core effects (high entropy, lattice distortion, sluggish diffusion, and cocktail effect) to determine performance, thereby offering a practical guide for optimizing manufacturing strategies.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Thermophysical properties of U 3 Si 2 to 1773K

Use of U 3 Si 2 in nuclear reactors requires accurate thermophysical property data to capture heat transfer within the core. Compilation of the limited previous research efforts focused on the most critical property, thermal conductivity, reveals extensive disagreement. Assessment of this data is challenged by the fact that the critical structural and chemical details of the material used to provide historic data is either absent or confirms the presence of significant impurity phases. This study was initiated to fabricate high purity U 3 Si 2 to quantify the coefficient of thermal expansion, heat capacity, thermal diffusivity, and thermal conductivity from room temperature to 1773 K. Here, the datasets provided in this manuscript will facilitate more detailed fuel performance modeling to assess both current and proposed reactor designs that incorporate U 3 Si 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

Predicting zeolites’ stability during the corrosion of nuclear waste immobilization glasses: Comparison with glass corrosion experiments

During the long-term corrosion of nuclear waste glasses under nuclear waste disposal conditions, the precipitation of zeolitic phases has been linked to a delayed acceleration in glass corrosion (known as “Stage III”). Hence, predicting the thermodynamic propensity for zeolites to form upon the dissolution of nuclear waste glasses is key to ensure their long-term performance. Here, we compile a unified, internally-consistent thermodynamic database “clay20” to estimate the stability of clay and feldspar phases relevant to nuclear waste immobilization glasses, including beidellite(Mg, Ca, Na, K), kaolinite, montmorillonite(Mg, Ca, Na, K), nontronite(Mg, Ca, Na, K), saponite(Ca, Na, K), and albite. Based on this, we report a geochemical modeling method allowing us to predict the stability of secondary phases (including zeolites, calcium–silicate–hydrate gels, and clays) upon the dissolution of nuclear waste immobilization glasses. We show that this approach offers a realistic description of the stability of the secondary phases forming during the dissolution of two archetypical model nuclear glasses (namely, the International Simple Glass, ISG, and WVUTh-203) under conditions relevant to nuclear waste disposal (T = 90°C, p = 1 bar) as a function of pH. We find that the formation of silica and clay secondary phases is thermodynamically favored at low pH (pH < 10), whereas, in contrast, zeolite (analcime) and calcium–silicate–hydrate phases are favored at high pH (pH > 10.5). This suggests that thermodynamics (i.e., not solely kinetics) plays a key role in determining the range of solution pH wherein stage III corrosion may occur, i.e., when zeolite formation is favored.

Zhen-Wu, Bi Yun↗
DOE OSTI2021

Kinetics and transport of hydrogen in graphite at high temperature and the effects of oxidation, irradiation and isotopics

The kinetics of uptake and desorption impact the performance of graphite as a vector for tritium in high-temperature fission reactors and in the blanket of fusion reactors. Graphite components in these reactors are exposed to temperatures > 500 °C and H 2 partial pressures of few Pa and desorption temperatures are limited to < 1600 °C; limited data is available at these conditions. Here we review the mechanisms for uptake in, transport and desorption of hydrogen from graphite at high temperature, compiling data on uptake rates, diffusion coefficients and activation energies and providing a discussion of the impact of irradiation, pre-oxidation and isotope. At FHR conditions, trapping impacts uptake rates, leading to a reduction in apparent diffusivity by 35 to 80% compared to higher partial-pressure uptake. Timelines for desorption are not clearly defined; extrapolating from available data, at 1150 °C desorbing 80% of tritium uptaken at FHR conditions may take from 100 to 10,000 h.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Uranium carbide properties for advanced fuel modeling – A review

Uranium carbides are receiving renewed interest as a preferred nuclear fuel composition for advanced reactors due to their numerous favorable properties. Like many other refractory and transition metal carbides these compounds exist in both hypo- and hyper-stoichiometric compositions which results in significant variations in physical, thermal, and mechanical properties. This manuscript surveys both historic and recent literature to compile important properties data mainly as a function of temperature and carbon content to support fuel performance modeling. Expressions and fits for such properties are also suggested when allowed by complete data sets. Finally, the manuscript also attempts to highlight gaps and discrepancies in the reported data to assist the nuclear fuels community identify key areas that may require further attention in terms of future research and development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

A case study of CUDA FORTRAN and OpenACC for an atmospheric climate kernel

The porting of a key kernel in the tracer advection routines of the Community Atmosphere Model – Spectral Element (CAM-SE) to use Graphics Processing Units (GPUs) using OpenACC is considered in comparison to an existing CUDA FORTRAN port. The development of the OpenACC kernel for GPUs was substantially simpler than that of the CUDA port. Also, OpenACC performance was about 1.5× slower than the optimized CUDA version. Particular focus is given to compiler maturity regarding OpenACC implementation for modern FORTRAN, and it is found that the Cray implementation is currently more mature than the PGI implementation. Still, for the case that ran successfully on PGI, the PGI OpenACC runtime was slightly faster than Cray. The results show encouraging performance for OpenACC implementation compared to CUDA while also exposing some issues that may be necessary before the implementations are suitable for porting all of CAM-SE. Furthermore, most notable are that GPU shared memory should be used by future OpenACC implementations and that derived type support should be expanded.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Deep learning of experimental electrochemistry for battery cathodes across diverse compositions

Artificial intelligence (AI) has emerged as a tool for discovering and optimizing novel battery materials. However, the adoption of AI in battery cathode representation and discovery is still limited due to the complexity of optimizing multiple performance properties and the scarcity of high-fidelity data. Here, we present a machine learning model (DRXNet) for battery informatics and demonstrate the application in the discovery and optimization of disordered rocksalt (DRX) cathode materials. We have compiled the electrochemistry data of DRX cathodes over the past 5 years, resulting in a dataset of more than 19,000 discharge voltage profiles on diverse chemistries spanning 14 different metal species. Learning from this extensive dataset, our DRXNet model can capture critical features in the cycling curves of DRX cathodes under various conditions. Our approach offers a data-driven solution to facilitate the rapid identification of novel cathode materials, accelerating the development of next-generation batteries for carbon neutralization.

25 ENERGY STORAGE↗